{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "colab": { "name": "SackOverflow_Tag_Prediction.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "machine_shape": "hm" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "metadata": { "id": "1NlEndv2CPWo", "colab_type": "code", "outputId": "3ac0b55f-c36c-4637-aff0-6a3b4185fad1", "colab": { "base_uri": "https://localhost:8080/", "height": 122 } }, "source": [ "!pip3 install scikit-multilearn" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Collecting scikit-multilearn\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/bb/1f/e6ff649c72a1cdf2c7a1d31eb21705110ce1c5d3e7e26b2cc300e1637272/scikit_multilearn-0.2.0-py3-none-any.whl (89kB)\n", "\u001b[K |████████████████████████████████| 92kB 9.8MB/s \n", "\u001b[?25hInstalling collected packages: scikit-multilearn\n", "Successfully installed scikit-multilearn-0.2.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Ihs1Hb3RU93S", "colab_type": "code", "colab": {} }, "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import pandas as pd\n", "import sqlite3\n", "import csv\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "from wordcloud import WordCloud\n", "import re\n", "import os\n", "from sqlalchemy import create_engine # database connection\n", "import datetime as dt\n", "from nltk.corpus import stopwords\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem.snowball import SnowballStemmer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.multiclass import OneVsRestClassifier\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn import metrics\n", "from sklearn.metrics import f1_score,precision_score,recall_score\n", "from sklearn import svm\n", "from sklearn.linear_model import LogisticRegression\n", "from skmultilearn.adapt import mlknn\n", "from skmultilearn.problem_transform import ClassifierChain\n", "from skmultilearn.problem_transform import BinaryRelevance\n", "from skmultilearn.problem_transform import LabelPowerset\n", "from sklearn.naive_bayes import GaussianNB\n", "from datetime import datetime" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rAZDkLNPU93Y", "colab_type": "text" }, "source": [ "# Stack Overflow: Tag Prediction" ] }, { "cell_type": "code", "metadata": { "id": "zjmPP4ZVC3gd", "colab_type": "code", "outputId": "0a133e92-377b-44b9-9550-52bb782cbec2", "colab": { "base_uri": "https://localhost:8080/", "height": 122 } }, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n", "\n", "Enter your authorization code:\n", "··········\n", "Mounted at /content/drive\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "TpVk_usWU93a", "colab_type": "text" }, "source": [ "

1. Business Problem

" ] }, { "cell_type": "markdown", "metadata": { "id": "5-1WlUboU93c", "colab_type": "text" }, "source": [ "

1.1 Description

" ] }, { "cell_type": "markdown", "metadata": { "id": "37SdsKLdU93e", "colab_type": "text" }, "source": [ "

Description

\n", "

\n", "Stack Overflow is the largest, most trusted online community for developers to learn, share their programming knowledge, and build their careers.
\n", "
\n", "Stack Overflow is something which every programmer use one way or another. Each month, over 50 million developers come to Stack Overflow to learn, share their knowledge, and build their careers. It features questions and answers on a wide range of topics in computer programming. The website serves as a platform for users to ask and answer questions, and, through membership and active participation, to vote questions and answers up or down and edit questions and answers in a fashion similar to a wiki or Digg. As of April 2014 Stack Overflow has over 4,000,000 registered users, and it exceeded 10,000,000 questions in late August 2015. Based on the type of tags assigned to questions, the top eight most discussed topics on the site are: Java, JavaScript, C#, PHP, Android, jQuery, Python and HTML.
\n", "
\n", "

" ] }, { "cell_type": "markdown", "metadata": { "id": "9brAMjUsU93f", "colab_type": "text" }, "source": [ "

Problem Statemtent

\n", "Suggest the tags based on the content that was there in the question posted on Stackoverflow." ] }, { "cell_type": "markdown", "metadata": { "id": "URJINSY2U93h", "colab_type": "text" }, "source": [ "

Source: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/

\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ggC_3T9XU93j", "colab_type": "text" }, "source": [ "

1.2 Source / useful links

" ] }, { "cell_type": "markdown", "metadata": { "id": "Nr7T9iknU93l", "colab_type": "text" }, "source": [ "Data Source : https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data
\n", "Youtube : https://youtu.be/nNDqbUhtIRg
\n", "Research paper : https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tagging-1.pdf
\n", "Research paper : https://dl.acm.org/citation.cfm?id=2660970&dl=ACM&coll=DL" ] }, { "cell_type": "markdown", "metadata": { "id": "hbXm8lAuU93p", "colab_type": "text" }, "source": [ "

1.3 Real World / Business Objectives and Constraints

" ] }, { "cell_type": "markdown", "metadata": { "id": "88TwKPItU93q", "colab_type": "text" }, "source": [ "1. Predict as many tags as possible with high precision and recall.\n", "2. Incorrect tags could impact customer experience on StackOverflow.\n", "3. No strict latency constraints." ] }, { "cell_type": "markdown", "metadata": { "id": "lE0wX1roU93s", "colab_type": "text" }, "source": [ "

2. Machine Learning problem

" ] }, { "cell_type": "markdown", "metadata": { "id": "ynAszp_uU93u", "colab_type": "text" }, "source": [ "

2.1 Data

" ] }, { "cell_type": "markdown", "metadata": { "id": "elwijxMGU93w", "colab_type": "text" }, "source": [ "

2.1.1 Data Overview

" ] }, { "cell_type": "markdown", "metadata": { "id": "mdFiIj7_U93x", "colab_type": "text" }, "source": [ "Refer: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data\n", "
\n", "All of the data is in 2 files: Train and Test.
\n", "
\n",
        "Train.csv contains 4 columns: Id,Title,Body,Tags.
\n",
        "Test.csv contains the same columns but without the Tags, which you are to predict.
\n",
        "Size of Train.csv - 6.75GB
\n",
        "Size of Test.csv - 2GB
\n",
        "Number of rows in Train.csv = 6034195
\n",
        "
\n", "The questions are randomized and contains a mix of verbose text sites as well as sites related to math and programming. The number of questions from each site may vary, and no filtering has been performed on the questions (such as closed questions).
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Ji0A66hWU93z", "colab_type": "text" }, "source": [ "__Data Field Explaination__\n", "\n", "Dataset contains 6,034,195 rows. The columns in the table are:
\n", "
\n",
        "Id - Unique identifier for each question
\n",
        "Title - The question's title
\n",
        "Body - The body of the question
\n",
        "Tags - The tags associated with the question in a space-seperated format (all lowercase, should not contain tabs '\\t' or ampersands '&')
\n",
        "
\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "WNDiy42GU931", "colab_type": "text" }, "source": [ "

2.1.2 Example Data point

" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "id": "D5IcxRbYU932", "colab_type": "text" }, "source": [ "
\n",
        "Title:  Implementing Boundary Value Analysis of Software Testing in a C++ program?\n",
        "Body : 
\n",
        "        #include<\n",
        "        iostream>\\n\n",
        "        #include<\n",
        "        stdlib.h>\\n\\n\n",
        "        using namespace std;\\n\\n\n",
        "        int main()\\n\n",
        "        {\\n\n",
        "                 int n,a[n],x,c,u[n],m[n],e[n][4];\\n         \n",
        "                 cout<<\"Enter the number of variables\";\\n         cin>>n;\\n\\n         \n",
        "                 cout<<\"Enter the Lower, and Upper Limits of the variables\";\\n         \n",
        "                 for(int y=1; y<n+1; y++)\\n         \n",
        "                 {\\n                 \n",
        "                    cin>>m[y];\\n                 \n",
        "                    cin>>u[y];\\n         \n",
        "                 }\\n         \n",
        "                 for(x=1; x<n+1; x++)\\n         \n",
        "                 {\\n                 \n",
        "                    a[x] = (m[x] + u[x])/2;\\n         \n",
        "                 }\\n         \n",
        "                 c=(n*4)-4;\\n         \n",
        "                 for(int a1=1; a1<n+1; a1++)\\n         \n",
        "                 {\\n\\n             \n",
        "                    e[a1][0] = m[a1];\\n             \n",
        "                    e[a1][1] = m[a1]+1;\\n             \n",
        "                    e[a1][2] = u[a1]-1;\\n             \n",
        "                    e[a1][3] = u[a1];\\n         \n",
        "                 }\\n         \n",
        "                 for(int i=1; i<n+1; i++)\\n         \n",
        "                 {\\n            \n",
        "                    for(int l=1; l<=i; l++)\\n            \n",
        "                    {\\n                 \n",
        "                        if(l!=1)\\n                 \n",
        "                        {\\n                    \n",
        "                            cout<<a[l]<<\"\\\\t\";\\n                 \n",
        "                        }\\n            \n",
        "                    }\\n            \n",
        "                    for(int j=0; j<4; j++)\\n            \n",
        "                    {\\n                \n",
        "                        cout<<e[i][j];\\n                \n",
        "                        for(int k=0; k<n-(i+1); k++)\\n                \n",
        "                        {\\n                    \n",
        "                            cout<<a[k]<<\"\\\\t\";\\n               \n",
        "                        }\\n                \n",
        "                        cout<<\"\\\\n\";\\n            \n",
        "                    }\\n        \n",
        "                 }    \\n\\n        \n",
        "                 system(\"PAUSE\");\\n        \n",
        "                 return 0;    \\n\n",
        "        }\\n\n",
        "        
\\n\\n\n",
        "        
The answer should come in the form of a table like
\\n\\n\n",
        "        
       \n",
        "        1            50              50\\n       \n",
        "        2            50              50\\n       \n",
        "        99           50              50\\n       \n",
        "        100          50              50\\n       \n",
        "        50           1               50\\n       \n",
        "        50           2               50\\n       \n",
        "        50           99              50\\n       \n",
        "        50           100             50\\n       \n",
        "        50           50              1\\n       \n",
        "        50           50              2\\n       \n",
        "        50           50              99\\n       \n",
        "        50           50              100\\n\n",
        "        
\\n\\n\n",
        "        
if the no of inputs is 3 and their ranges are\\n\n",
        "        1,100\\n\n",
        "        1,100\\n\n",
        "        1,100\\n\n",
        "        (could be varied too)
\\n\\n\n",
        "        
The output is not coming,can anyone correct the code or tell me what\\'s wrong?
\\n'\n",
        "Tags : 'c++ c'\n",
        "
" ] }, { "cell_type": "markdown", "metadata": { "id": "MomJjFPnU934", "colab_type": "text" }, "source": [ "

2.2 Mapping the real-world problem to a Machine Learning Problem

" ] }, { "cell_type": "markdown", "metadata": { "id": "sJ5lyxIUU936", "colab_type": "text" }, "source": [ "

2.2.1 Type of Machine Learning Problem

" ] }, { "cell_type": "markdown", "metadata": { "id": "5Jb9u-38U938", "colab_type": "text" }, "source": [ "

It is a multi-label classification problem
\n", "Multi-label Classification: Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A question on Stackoverflow might be about any of C, Pointers, FileIO and/or memory-management at the same time or none of these.
\n", "__Credit__: http://scikit-learn.org/stable/modules/multiclass.html\n", "

" ] }, { "cell_type": "markdown", "metadata": { "id": "QIOwycKkU93_", "colab_type": "text" }, "source": [ "

2.2.2 Performance metric

" ] }, { "cell_type": "markdown", "metadata": { "id": "DDQXZ4k5U94A", "colab_type": "text" }, "source": [ "Micro-Averaged F1-Score (Mean F Score) : \n", "The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:\n", "\n", "F1 = 2 * (precision * recall) / (precision + recall)
\n", "\n", "In the multi-class and multi-label case, this is the weighted average of the F1 score of each class.
\n", "\n", "'Micro f1 score':
\n", "Calculate metrics globally by counting the total true positives, false negatives and false positives. This is a better metric when we have class imbalance.\n", "
\n", "\n", "'Macro f1 score':
\n", "Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n", "
\n", "\n", "https://www.kaggle.com/wiki/MeanFScore
\n", "http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
\n", "
\n", " Hamming loss : The Hamming loss is the fraction of labels that are incorrectly predicted.
\n", "https://www.kaggle.com/wiki/HammingLoss
" ] }, { "cell_type": "markdown", "metadata": { "id": "xqsVRjSmU94C", "colab_type": "text" }, "source": [ "

3. Exploratory Data Analysis

" ] }, { "cell_type": "markdown", "metadata": { "id": "azda-BmRU94H", "colab_type": "text" }, "source": [ "

3.1 Data Loading and Cleaning

" ] }, { "cell_type": "markdown", "metadata": { "id": "62GDC_VjU94J", "colab_type": "text" }, "source": [ "

3.1.1 Using Pandas with SQLite to Load the data

" ] }, { "cell_type": "code", "metadata": { "id": "289tS71cU94L", "colab_type": "code", "colab": {} }, "source": [ "#Creating db file from csv\n", "#Learn SQL: https://www.w3schools.com/sql/default.asp\n", "if not os.path.isfile('train.db'):\n", " start = datetime.now()\n", " disk_engine = create_engine('sqlite:///train.db')\n", " start = dt.datetime.now()\n", " chunksize = 180000\n", " j = 0\n", " index_start = 1\n", " for df in pd.read_csv('Train.csv', names=['Id', 'Title', 'Body', 'Tags'], chunksize=chunksize, iterator=True, encoding='utf-8', ):\n", " df.index += index_start\n", " j+=1\n", " print('{} rows'.format(j*chunksize))\n", " df.to_sql('data', disk_engine, if_exists='append')\n", " index_start = df.index[-1] + 1\n", " print(\"Time taken to run this cell :\", datetime.now() - start)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "d5yUhXVNU94Q", "colab_type": "text" }, "source": [ "

3.1.2 Counting the number of rows

" ] }, { "cell_type": "code", "metadata": { "id": "3ORCclXYU94R", "colab_type": "code", "outputId": "9625d8e0-bf34-413e-8246-932fa1cb21b7", "colab": {} }, "source": [ "if os.path.isfile('train.db'):\n", " start = datetime.now()\n", " con = sqlite3.connect('train.db')\n", " num_rows = pd.read_sql_query(\"\"\"SELECT count(*) FROM data\"\"\", con)\n", " #Always remember to close the database\n", " print(\"Number of rows in the database :\",\"\\n\",num_rows['count(*)'].values[0])\n", " con.close()\n", " print(\"Time taken to count the number of rows :\", datetime.now() - start)\n", "else:\n", " print(\"Please download the train.db file from drive or run the above cell to genarate train.db file\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Number of rows in the database : \n", " 6034196\n", "Time taken to count the number of rows : 0:01:15.750352\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Xso2eOEvU94Z", "colab_type": "text" }, "source": [ "

3.1.3 Checking for duplicates

" ] }, { "cell_type": "code", "metadata": { "id": "iBHCcr3DU94b", "colab_type": "code", "outputId": "2340d414-8570-4d7e-fda6-a69c39f13780", "colab": {} }, "source": [ "#Learn SQl: https://www.w3schools.com/sql/default.asp\n", "if os.path.isfile('train.db'):\n", " start = datetime.now()\n", " con = sqlite3.connect('train.db')\n", " df_no_dup = pd.read_sql_query('SELECT Title, Body, Tags, COUNT(*) as cnt_dup FROM data GROUP BY Title, Body, Tags', con)\n", " con.close()\n", " print(\"Time taken to run this cell :\", datetime.now() - start)\n", "else:\n", " print(\"Please download the train.db file from drive or run the first to genarate train.db file\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Time taken to run this cell : 0:04:33.560122\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Gap4NRPWU94h", "colab_type": "code", "outputId": "e0af72d7-4faf-4232-849b-d27bc1963221", "colab": {} }, "source": [ "df_no_dup.head()\n", "# we can observe that there are duplicates" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TitleBodyTagscnt_dup
0Implementing Boundary Value Analysis of S...<pre><code>#include&lt;iostream&gt;\\n#include&...c++ c1
1Dynamic Datagrid Binding in Silverlight?<p>I should do binding for datagrid dynamicall...c# silverlight data-binding1
2Dynamic Datagrid Binding in Silverlight?<p>I should do binding for datagrid dynamicall...c# silverlight data-binding columns1
3java.lang.NoClassDefFoundError: javax/serv...<p>I followed the guide in <a href=\"http://sta...jsp jstl1
4java.sql.SQLException:[Microsoft][ODBC Dri...<p>I use the following code</p>\\n\\n<pre><code>...java jdbc2
\n", "
" ], "text/plain": [ " Title \\\n", "0 Implementing Boundary Value Analysis of S... \n", "1 Dynamic Datagrid Binding in Silverlight? \n", "2 Dynamic Datagrid Binding in Silverlight? \n", "3 java.lang.NoClassDefFoundError: javax/serv... \n", "4 java.sql.SQLException:[Microsoft][ODBC Dri... \n", "\n", " Body \\\n", "0 
#include<iostream>\\n#include&...   \n",
              "1  
I should do binding for datagrid dynamicall...   \n",
              "2  
I should do binding for datagrid dynamicall...   \n",
              "3  
I followed the guide in I use the following code
\\n\\n
...   \n",
              "\n",
              "                                  Tags  cnt_dup  \n",
              "0                                c++ c        1  \n",
              "1          c# silverlight data-binding        1  \n",
              "2  c# silverlight data-binding columns        1  \n",
              "3                             jsp jstl        1  \n",
              "4                            java jdbc        2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzFO4EeDU94n",
        "colab_type": "code",
        "outputId": "198980b2-6480-4a49-ab0a-8074199f5edb",
        "colab": {}
      },
      "source": [
        "print(\"number of duplicate questions :\", num_rows['count(*)'].values[0]- df_no_dup.shape[0], \"(\",(1-((df_no_dup.shape[0])/(num_rows['count(*)'].values[0])))*100,\"% )\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of duplicate questions : 1827881 ( 30.2920389063 % )\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gd2VdpN6U94t",
        "colab_type": "code",
        "outputId": "05af061e-31e0-4a93-d41a-d738c5b9e890",
        "colab": {}
      },
      "source": [
        "# number of times each question appeared in our database\n",
        "df_no_dup.cnt_dup.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2656284\n",
              "2    1272336\n",
              "3     277575\n",
              "4         90\n",
              "5         25\n",
              "6          5\n",
              "Name: cnt_dup, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EogeNAhCU94z",
        "colab_type": "code",
        "outputId": "661010f0-0d38-4a33-d20f-e94ef51344ab",
        "colab": {}
      },
      "source": [
        "start = datetime.now()\n",
        "df_no_dup[\"tag_count\"] = df_no_dup[\"Tags\"].apply(lambda text: len(text.split(\" \")))\n",
        "# adding a new feature number of tags per question\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)\n",
        "df_no_dup.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 0:00:03.169523\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "
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              "\n",
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              "
TitleBodyTagscnt_duptag_count
0Implementing Boundary Value Analysis of S...<pre><code>#include&lt;iostream&gt;\\n#include&...c++ c12
1Dynamic Datagrid Binding in Silverlight?<p>I should do binding for datagrid dynamicall...c# silverlight data-binding13
2Dynamic Datagrid Binding in Silverlight?<p>I should do binding for datagrid dynamicall...c# silverlight data-binding columns14
3java.lang.NoClassDefFoundError: javax/serv...<p>I followed the guide in <a href=\"http://sta...jsp jstl12
4java.sql.SQLException:[Microsoft][ODBC Dri...<p>I use the following code</p>\\n\\n<pre><code>...java jdbc22
\n",
              "
"
            ],
            "text/plain": [
              "                                               Title  \\\n",
              "0       Implementing Boundary Value Analysis of S...   \n",
              "1           Dynamic Datagrid Binding in Silverlight?   \n",
              "2           Dynamic Datagrid Binding in Silverlight?   \n",
              "3      java.lang.NoClassDefFoundError: javax/serv...   \n",
              "4      java.sql.SQLException:[Microsoft][ODBC Dri...   \n",
              "\n",
              "                                                Body  \\\n",
              "0  
#include<iostream>\\n#include&...   \n",
              "1  
I should do binding for datagrid dynamicall...   \n",
              "2  
I should do binding for datagrid dynamicall...   \n",
              "3  
I followed the guide in I use the following code
\\n\\n
...   \n",
              "\n",
              "                                  Tags  cnt_dup  tag_count  \n",
              "0                                c++ c        1          2  \n",
              "1          c# silverlight data-binding        1          3  \n",
              "2  c# silverlight data-binding columns        1          4  \n",
              "3                             jsp jstl        1          2  \n",
              "4                            java jdbc        2          2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iItMHo6MU948",
        "colab_type": "code",
        "outputId": "824508df-c0c0-4d33-a667-83af84360bfc",
        "colab": {}
      },
      "source": [
        "# distribution of number of tags per question\n",
        "df_no_dup.tag_count.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    1206157\n",
              "2    1111706\n",
              "4     814996\n",
              "1     568298\n",
              "5     505158\n",
              "Name: tag_count, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xMgCGUKU95C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Creating a new database with no duplicates\n",
        "if not os.path.isfile('train_no_dup.db'):\n",
        "    disk_dup = create_engine(\"sqlite:///train_no_dup.db\")\n",
        "    no_dup = pd.DataFrame(df_no_dup, columns=['Title', 'Body', 'Tags'])\n",
        "    no_dup.to_sql('no_dup_train',disk_dup)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ou53MzeU95H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This method seems more appropriate to work with this much data.\n",
        "#creating the connection with database file.\n",
        "if os.path.isfile('drive/My Drive/Stackoverflow/data/train_no_dup.db'):\n",
        "    start = datetime.now()\n",
        "    con = sqlite3.connect('train_no_dup.db')\n",
        "    tag_data = pd.read_sql_query(\"\"\"SELECT Tags FROM no_dup_train\"\"\", con)\n",
        "    #Always remember to close the database\n",
        "    con.close()\n",
        "\n",
        "    # Let's now drop unwanted column.\n",
        "    tag_data.drop(tag_data.index[0], inplace=True)\n",
        "    #Printing first 5 columns from our data frame\n",
        "    tag_data.head()\n",
        "    print(\"Time taken to run this cell :\", datetime.now() - start)\n",
        "else:\n",
        "    print(\"Please download the train.db file from drive or run the above cells to genarate train.db file\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwZVL3doU95O",
        "colab_type": "text"
      },
      "source": [
        "
 3.2 Analysis of Tags 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYs5peW8U95P",
        "colab_type": "text"
      },
      "source": [
        "
 3.2.1 Total number of unique tags 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROdC95M_U95Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing & Initializing the \"CountVectorizer\" object, which \n",
        "#is scikit-learn's bag of words tool.\n",
        "\n",
        "#by default 'split()' will tokenize each tag using space.\n",
        "vectorizer = CountVectorizer(tokenizer = lambda x: x.split())\n",
        "# fit_transform() does two functions: First, it fits the model\n",
        "# and learns the vocabulary; second, it transforms our training data\n",
        "# into feature vectors. The input to fit_transform should be a list of strings.\n",
        "tag_dtm = vectorizer.fit_transform(tag_data['Tags'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz5N0GH0U95V",
        "colab_type": "code",
        "outputId": "a65cd48f-6df2-44f5-e77c-99b38bb7ae2a",
        "colab": {}
      },
      "source": [
        "print(\"Number of data points :\", tag_dtm.shape[0])\n",
        "print(\"Number of unique tags :\", tag_dtm.shape[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of data points : 4206314\n",
            "Number of unique tags : 42048\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Otn6CUuQU95b",
        "colab_type": "code",
        "outputId": "91ff8093-ebae-4218-a786-a117a9eadc0e",
        "colab": {}
      },
      "source": [
        "#'get_feature_name()' gives us the vocabulary.\n",
        "tags = vectorizer.get_feature_names()\n",
        "#Lets look at the tags we have.\n",
        "print(\"Some of the tags we have :\", tags[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some of the tages we have : ['.a', '.app', '.asp.net-mvc', '.aspxauth', '.bash-profile', '.class-file', '.cs-file', '.doc', '.drv', '.ds-store']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQa3ETSeU95g",
        "colab_type": "text"
      },
      "source": [
        "
 3.2.3 Number of times a tag appeared 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beThyuyqU95h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://stackoverflow.com/questions/15115765/how-to-access-sparse-matrix-elements\n",
        "#Lets now store the document term matrix in a dictionary.\n",
        "freqs = tag_dtm.sum(axis=0).A1\n",
        "result = dict(zip(tags, freqs))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ALwSSx9U95m",
        "colab_type": "code",
        "outputId": "452a449b-5605-4c32-e571-961eee563161",
        "colab": {}
      },
      "source": [
        "#Saving this dictionary to csv files.\n",
        "if not os.path.isfile('tag_counts_dict_dtm.csv'):\n",
        "    with open('tag_counts_dict_dtm.csv', 'w') as csv_file:\n",
        "        writer = csv.writer(csv_file)\n",
        "        for key, value in result.items():\n",
        "            writer.writerow([key, value])\n",
        "tag_df = pd.read_csv(\"tag_counts_dict_dtm.csv\", names=['Tags', 'Counts'])\n",
        "tag_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "
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              "
TagsCounts
0.a18
1.app37
2.asp.net-mvc1
3.aspxauth21
4.bash-profile138
\n",
              "
"
            ],
            "text/plain": [
              "            Tags  Counts\n",
              "0             .a      18\n",
              "1           .app      37\n",
              "2   .asp.net-mvc       1\n",
              "3      .aspxauth      21\n",
              "4  .bash-profile     138"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzmS8yiNU95t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tag_df_sorted = tag_df.sort_values(['Counts'], ascending=False)\n",
        "tag_counts = tag_df_sorted['Counts'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mcj55FIU95z",
        "colab_type": "code",
        "outputId": "d6c9d567-a9a4-48e6-9d44-6f099f198339",
        "colab": {}
      },
      "source": [
        "plt.plot(tag_counts)\n",
        "plt.title(\"Distribution of number of times tag appeared questions\")\n",
        "plt.grid()\n",
        "plt.xlabel(\"Tag number\")\n",
        "plt.ylabel(\"Number of times tag appeared\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bgVuAybnn2XBSJ4I5OU+jbZiZWR9o5WW0ccB5ud1mCDA7Ii6RdB8wS9K3gTuAs/PyZwMX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\nBxszM2s5BxszM2s5BxszM2u5/w8lGoFeJHe/AAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzTqln6XU955",
        "colab_type": "code",
        "outputId": "07f08ef0-d26b-4797-eb3c-6586011e2588",
        "colab": {}
      },
      "source": [
        "plt.plot(tag_counts[0:10000])\n",
        "plt.title('first 10k tags: Distribution of number of times tag appeared questions')\n",
        "plt.grid()\n",
        "plt.xlabel(\"Tag number\")\n",
        "plt.ylabel(\"Number of times tag appeared\")\n",
        "plt.show()\n",
        "print(len(tag_counts[0:10000:25]), tag_counts[0:10000:25])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S9BLSqZOfD3e+7PnDdzlov31JTSdjSRe6HhrNL2Mws/YZSzpVshOpyW8uqRndDHCTqZmZ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\nd6aj9e8rrgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "400 [331505  44829  22429  17728  13364  11162  10029   9148   8054   7151\n",
            "   6466   5865   5370   4983   4526   4281   4144   3929   3750   3593\n",
            "   3453   3299   3123   2989   2891   2738   2647   2527   2431   2331\n",
            "   2259   2186   2097   2020   1959   1900   1828   1770   1723   1673\n",
            "   1631   1574   1532   1479   1448   1406   1365   1328   1300   1266\n",
            "   1245   1222   1197   1181   1158   1139   1121   1101   1076   1056\n",
            "   1038   1023   1006    983    966    952    938    926    911    891\n",
            "    882    869    856    841    830    816    804    789    779    770\n",
            "    752    743    733    725    712    702    688    678    671    658\n",
            "    650    643    634    627    616    607    598    589    583    577\n",
            "    568    559    552    545    540    533    526    518    512    506\n",
            "    500    495    490    485    480    477    469    465    457    450\n",
            "    447    442    437    432    426    422    418    413    408    403\n",
            "    398    393    388    385    381    378    374    370    367    365\n",
            "    361    357    354    350    347    344    342    339    336    332\n",
            "    330    326    323    319    315    312    309    307    304    301\n",
            "    299    296    293    291    289    286    284    281    278    276\n",
            "    275    272    270    268    265    262    260    258    256    254\n",
            "    252    250    249    247    245    243    241    239    238    236\n",
            "    234    233    232    230    228    226    224    222    220    219\n",
            "    217    215    214    212    210    209    207    205    204    203\n",
            "    201    200    199    198    196    194    193    192    191    189\n",
            "    188    186    185    183    182    181    180    179    178    177\n",
            "    175    174    172    171    170    169    168    167    166    165\n",
            "    164    162    161    160    159    158    157    156    156    155\n",
            "    154    153    152    151    150    149    149    148    147    146\n",
            "    145    144    143    142    142    141    140    139    138    137\n",
            "    137    136    135    134    134    133    132    131    130    130\n",
            "    129    128    128    127    126    126    125    124    124    123\n",
            "    123    122    122    121    120    120    119    118    118    117\n",
            "    117    116    116    115    115    114    113    113    112    111\n",
            "    111    110    109    109    108    108    107    106    106    106\n",
            "    105    105    104    104    103    103    102    102    101    101\n",
            "    100    100     99     99     98     98     97     97     96     96\n",
            "     95     95     94     94     93     93     93     92     92     91\n",
            "     91     90     90     89     89     88     88     87     87     86\n",
            "     86     86     85     85     84     84     83     83     83     82\n",
            "     82     82     81     81     80     80     80     79     79     78\n",
            "     78     78     78     77     77     76     76     76     75     75\n",
            "     75     74     74     74     73     73     73     73     72     72]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ntm8E_K9U95-",
        "colab_type": "code",
        "outputId": "750edd11-e52b-46bd-caba-1835994a0e05",
        "colab": {}
      },
      "source": [
        "plt.plot(tag_counts[0:1000])\n",
        "plt.title('first 1k tags: Distribution of number of times tag appeared questions')\n",
        "plt.grid()\n",
        "plt.xlabel(\"Tag number\")\n",
        "plt.ylabel(\"Number of times tag appeared\")\n",
        "plt.show()\n",
        "print(len(tag_counts[0:1000:5]), tag_counts[0:1000:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZCjN7t5n9E7BntQuWNTn5OUHnnMuaNEHw/JRpNc2vGOOcc9lT9pygpBMIozVn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\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "200 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
            "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703\n",
            "  13364  13157  12407  11658  11228  11162  10863  10600  10350  10224\n",
            "  10029   9884   9719   9411   9252   9148   9040   8617   8361   8163\n",
            "   8054   7867   7702   7564   7274   7151   7052   6847   6656   6553\n",
            "   6466   6291   6183   6093   5971   5865   5760   5577   5490   5411\n",
            "   5370   5283   5207   5107   5066   4983   4891   4785   4658   4549\n",
            "   4526   4487   4429   4335   4310   4281   4239   4228   4195   4159\n",
            "   4144   4088   4050   4002   3957   3929   3874   3849   3818   3797\n",
            "   3750   3703   3685   3658   3615   3593   3564   3521   3505   3483\n",
            "   3453   3427   3396   3363   3326   3299   3272   3232   3196   3168\n",
            "   3123   3094   3073   3050   3012   2989   2984   2953   2934   2903\n",
            "   2891   2844   2819   2784   2754   2738   2726   2708   2681   2669\n",
            "   2647   2621   2604   2594   2556   2527   2510   2482   2460   2444\n",
            "   2431   2409   2395   2380   2363   2331   2312   2297   2290   2281\n",
            "   2259   2246   2222   2211   2198   2186   2162   2142   2132   2107\n",
            "   2097   2078   2057   2045   2036   2020   2011   1994   1971   1965\n",
            "   1959   1952   1940   1932   1912   1900   1879   1865   1855   1841\n",
            "   1828   1821   1813   1801   1782   1770   1760   1747   1741   1734\n",
            "   1723   1707   1697   1688   1683   1673   1665   1656   1646   1639]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SRUeKuWU96I",
        "colab_type": "code",
        "outputId": "f20a242c-2f7d-4e7d-f3f2-291d2ea38caa",
        "colab": {}
      },
      "source": [
        "plt.plot(tag_counts[0:500])\n",
        "plt.title('first 500 tags: Distribution of number of times tag appeared questions')\n",
        "plt.grid()\n",
        "plt.xlabel(\"Tag number\")\n",
        "plt.ylabel(\"Number of times tag appeared\")\n",
        "plt.show()\n",
        "print(len(tag_counts[0:500:5]), tag_counts[0:500:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CcYQ2/R2E5pdvEJpVnHN73yn03shhDqEJz5vL3G7efOqcc85FXlN0zjnnIr+ZbDRlyhSb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\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
            "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703\n",
            "  13364  13157  12407  11658  11228  11162  10863  10600  10350  10224\n",
            "  10029   9884   9719   9411   9252   9148   9040   8617   8361   8163\n",
            "   8054   7867   7702   7564   7274   7151   7052   6847   6656   6553\n",
            "   6466   6291   6183   6093   5971   5865   5760   5577   5490   5411\n",
            "   5370   5283   5207   5107   5066   4983   4891   4785   4658   4549\n",
            "   4526   4487   4429   4335   4310   4281   4239   4228   4195   4159\n",
            "   4144   4088   4050   4002   3957   3929   3874   3849   3818   3797\n",
            "   3750   3703   3685   3658   3615   3593   3564   3521   3505   3483]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCOE0Nu4U96N",
        "colab_type": "code",
        "outputId": "1f8df41e-b0dc-4d7f-a3b2-0a0c472ef448",
        "colab": {}
      },
      "source": [
        "plt.plot(tag_counts[0:100], c='b')\n",
        "plt.scatter(x=list(range(0,100,5)), y=tag_counts[0:100:5], c='orange', label=\"quantiles with 0.05 intervals\")\n",
        "# quantiles with 0.25 difference\n",
        "plt.scatter(x=list(range(0,100,25)), y=tag_counts[0:100:25], c='m', label = \"quantiles with 0.25 intervals\")\n",
        "\n",
        "for x,y in zip(list(range(0,100,25)), tag_counts[0:100:25]):\n",
        "    plt.annotate(s=\"({} , {})\".format(x,y), xy=(x,y), xytext=(x-0.05, y+500))\n",
        "\n",
        "plt.title('first 100 tags: Distribution of number of times tag appeared questions')\n",
        "plt.grid()\n",
        "plt.xlabel(\"Tag number\")\n",
        "plt.ylabel(\"Number of times tag appeared\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "print(len(tag_counts[0:100:5]), tag_counts[0:100:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v55xzzuH+++9n0aJFB6374Y71FAuJujddxNk3QvWETHYNSqLuTUmxVsk4HGk7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\nxcEqRYvFYrFYHKxStFgsFovF4f8BQbXpJZqft+gAAAAASUVORK5CYII=\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "20 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
            "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnwXytypU96R",
        "colab_type": "code",
        "outputId": "3427f2fd-4b27-435e-d1a8-f1bcf4515d9c",
        "colab": {}
      },
      "source": [
        "# Store tags greater than 10K in one list\n",
        "lst_tags_gt_10k = tag_df[tag_df.Counts>10000].Tags\n",
        "#Print the length of the list\n",
        "print ('{} Tags are used more than 10000 times'.format(len(lst_tags_gt_10k)))\n",
        "# Store tags greater than 100K in one list\n",
        "lst_tags_gt_100k = tag_df[tag_df.Counts>100000].Tags\n",
        "#Print the length of the list.\n",
        "print ('{} Tags are used more than 100000 times'.format(len(lst_tags_gt_100k)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "153 Tags are used more than 10000 times\n",
            "14 Tags are used more than 100000 times\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOXPF7q1U96W",
        "colab_type": "text"
      },
      "source": [
        "Observations:
\n",
        "1. There are total 153 tags which are used more than 10000 times.\n",
        "2. 14 tags are used more than 100000 times.\n",
        "3. Most frequent tag (i.e. c#) is used 331505 times.\n",
        "4. Since some tags occur much more frequenctly than others, Micro-averaged F1-score is the appropriate metric for this probelm."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zb-Tg_sgU96a",
        "colab_type": "text"
      },
      "source": [
        "
 3.2.4 Tags Per Question 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsPStbjGU96g",
        "colab_type": "code",
        "outputId": "2e57d47f-1ae9-4776-ab17-d4315b9d0c14",
        "colab": {}
      },
      "source": [
        "#Storing the count of tag in each question in list 'tag_count'\n",
        "tag_quest_count = tag_dtm.sum(axis=1).tolist()\n",
        "#Converting list of lists into single list, we will get [[3], [4], [2], [2], [3]] and we are converting this to [3, 4, 2, 2, 3]\n",
        "tag_quest_count=[int(j) for i in tag_quest_count for j in i]\n",
        "print ('We have total {} datapoints.'.format(len(tag_quest_count)))\n",
        "\n",
        "print(tag_quest_count[:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have total 4206314 datapoints.\n",
            "[3, 4, 2, 2, 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLbm7crfU96n",
        "colab_type": "code",
        "outputId": "f71ff030-9937-4081-9d4a-fb2c62386e11",
        "colab": {}
      },
      "source": [
        "print( \"Maximum number of tags per question: %d\"%max(tag_quest_count))\n",
        "print( \"Minimum number of tags per question: %d\"%min(tag_quest_count))\n",
        "print( \"Avg. number of tags per question: %f\"% ((sum(tag_quest_count)*1.0)/len(tag_quest_count)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum number of tags per question: 5\n",
            "Minimum number of tags per question: 1\n",
            "Avg. number of tags per question: 2.899440\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mb1vdd8KU96x",
        "colab_type": "code",
        "outputId": "4579b7c7-ff27-4fb3-f3b9-29401610a3a0",
        "colab": {}
      },
      "source": [
        "sns.countplot(tag_quest_count, palette='gist_rainbow')\n",
        "plt.title(\"Number of tags in the questions \")\n",
        "plt.xlabel(\"Number of Tags\")\n",
        "plt.ylabel(\"Number of questions\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EBBARf8az5czMrBdU6QlNl3QV8NP0/nhgRuNCMjOzZlElCZ0MnAp8juKe0F3ADxoZlJmZ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\nRK5CYII=\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RsUcQkNU963",
        "colab_type": "text"
      },
      "source": [
        "Observations:
\n",
        "1. Maximum number of tags per question: 5\n",
        "2. Minimum number of tags per question: 1\n",
        "3. Avg. number of tags per question: 2.899\n",
        "4. Most of the questions are having 2 or 3 tags"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-M5-A-MU963",
        "colab_type": "text"
      },
      "source": [
        "
3.2.5 Most Frequent Tags 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "Brokf0gSU965",
        "colab_type": "code",
        "outputId": "b9794ec6-8e67-49ad-f641-069638dbd951",
        "colab": {}
      },
      "source": [
        "# Ploting word cloud\n",
        "start = datetime.now()\n",
        "\n",
        "# Lets first convert the 'result' dictionary to 'list of tuples'\n",
        "tup = dict(result.items())\n",
        "#Initializing WordCloud using frequencies of tags.\n",
        "wordcloud = WordCloud(    background_color='black',\n",
        "                          width=1600,\n",
        "                          height=800,\n",
        "                    ).generate_from_frequencies(tup)\n",
        "\n",
        "fig = plt.figure(figsize=(30,20))\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis('off')\n",
        "plt.tight_layout(pad=0)\n",
        "fig.savefig(\"tag.png\")\n",
        "plt.show()\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n+rVjcOaqNUVHto1LfrmQRz6hxtZRpY6umHn9OJbcEWvE3XBEGY2Xra2gudwN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UcafkwRc0vFRIXVVILL24PXLyZAgmE/Jl0uuZYKJeyRztcmNS97W55hrS4HgKrCNZTm1uHwiMm+\nYMiHUZeD1SjkibUaA28e+F2C4UdZ3uLoeSM6jQGbsUw5ZzEWMad0vRJfMOTD7hYLbkO+voR4YnEA\nSjwWo1gYmlO6XokDwO5uS4hDIpHMXFoDhzno3Tp2OL+QxM1VFypGIKMRcyNxPM7wANvcmxQ/36nw\nRYQc9B7PW6yzXDVqWACT2gYIlxSxazMhtljeFWikXDd3zPD5mmhfqM5lKJz5hHm2mZVGnmMEI366\nAk1jBxwnKlRUpdFGYkSIsN+7RWlj2cYR6sMR6qOB3QBoVFpKtNXKfS7SzkKFmtbAwUlJPx0WGNcm\ndYk0GjFXPTs9r+AMjd81QIQIPcEWeoJiMXa2fgkLjWtRSdE5iURyMiItqiWnAA5fF4XmGgDMuny8\nQScOn3AHVWCuwht04PCO391OzA1HjAgRZWyvVetRqdQYo/MZLn/83IA36FDccgCYdLloVFplYV6J\nMzoJ7vL3YtMXK8YLheYapUyirN1KmQClXO1OYdBQbJ3DebX/rBi+NA68h907fgPjdudeJU4QhifH\nx6lWaTDr8wFYUX51Utc5Jl1yw/bYfUqZvmMfZ1TfgkYtXBmGwoGo+xJhCBKJhCc1/fGgQsXycvE9\n5/T10GLPTLL++I0J6YSNuTqqylvF3q5NBKOGP0tKLmFWzlJa7bsyyoNEIpGcysTctgzZO9iz+U9p\nXdPT9kHc30yx9woXX7ve+n3S8+88E2/4EXPVMnfZtbQ3vEPTgRdSxp1XPJ+cwtq4Y4M9YjPY2099\nK+O81u96gvpdT6Q87xpsTRmv3+fk4Hv3Z5zmdBIJhwl5hsYOKAEg11DG7Fxh2GPU2hRj22S82/bg\nVGVLIkkbnVHN3FU5052Nkw5p+DFFNHRvBmBWwQpsxhKstuEdr/6Qmx67mCho6t1Kv2vyFswAAiEP\nW+v/AgjDj7L8peRZqgAxieALuBgYEgs03faD+ALOhDiWV18HgEqVOLtpMRQyv+yCuGOHO14Fhuth\nZDyp4gCSxnN8HBKJZOYRM3Q47H0vo+sO+7YzSz8vLVWMkcQm4fZ7No9p9DESe6iH/mBH2ioDOZpC\neoKZG37EaPYfTMvwI0alfgGHMqzDbGNV55GjKRw7YJT2wFHChCYtPzmaoozUPo76Ppg0o49khCJB\nOgLH6AiIiQS9ykiproauQPOU5WEkVnUes3TzMrrGE3aybWgjAN5xGDqNRpN/H0NhO6vMFwGglgYg\nEolEIpGcUDh83VTniQnmHGMpdl8nzuiCfk3+Wtz+wTgViEwJhTPwa59kzT48YmfjWIv6MUW7mEFC\ndd5qpUwATl+3UiYRrisujzvaHifHWMbsaH2sq7qFI31vcax/2Je6Rq1nw7w7kqZ/qOc1GgaGFY1C\n4YASJ8DsvNVKnEA0XpWS7+1tj9LnThxjxny9H084MvoY3eHrwhuwU2IRY8dO1yFKrPPZ3vroiFCT\nl36qujrUIzZHjayrGItLN2DUisnibS1/SzjvD7kVBZfj0WmMBEKeMcPqNOJYLOzikkuARHWa99se\n5ZzazymKIONRvJFIJBLJzEMdNfxQa7SEgr6U4bQ6EyZLIQM9k6uCK5HEWF3+Yf4/e/cd39Z1Hnz8\nhz0I7j0kkhoUtSVry7ZkK95LdpzZTDdJm+3Mvk13mvG2Te00aZK3Sdw0y01ix7ITx45XvCVZe8uS\nSEqkuPfGHu8fhwBNiSIAAiAA8vl+Pv7IJM6994DABe495znP0zGixjy7h85f8RpMiFRVsz4Lg0nG\nhuNN/qJCCCGEEEIIIYQQQgghhBBCCCGEEGlKMn7MkPaBUxP+jVbPsFpF8Oyxr4Vte6Frd9isGMGV\nFk09+2nqib6m+HPHvx71NoncjxAitTSPZVjwBNxRbecJuOj2NFNsqIpqu66xbAoDvu4wLSfZ1tsc\nccYPmzaHbpqjPkbQgK+LYX8/mdrciNqXGRZR5zyEP4kR29Fmi2j11CWoJ0q2riB8I1TJGYBG98lE\ndicsd8BJszt5ZV6qTatCqzIj4cfHEfuf4p7p4616vC286VD115dbrk7YcYQQQggRf6OuHky6DAAy\njYUMONtCpVEMOgtWY05MGT/CCQT8OD2q9EqGKZ8e+4XQY0ZdBnqtMfSzwzOIz+8my1QU+hlAo1Fr\noDKM+bQOnWDUpcrLmHQZoecE4PQOh54TMOnzGnJ2cKJDZUrrGb3AipJbJ2T88PndvNb40KTPxe2b\nPJX5kFNlHDnR8XRon6AyfvgDXuzufgAyTUV0j56/wl9qetqGTlNsqxnrnwOvz8mAszX0eCKPf6W/\n1ZX+TovyryHfUsUbzSp1ebDkylsNujrIMhUD0D5WsiZYqkWnMUwoPxNsG2wXbKvTqNI3wbbBUjf2\ngYEJx3J6h/H7vVgN6nHJ+CGEELODz6vGl/q7zlGxaBsBn8r8NTrUjkarx2IrBKCkciNavZG2hteT\n1tdUZszOJ2vRCkx56ntZazTjdztx9anrq6H6k7gHe6fahbiEwzNE+4gacwxevwqRTpZuyUl2F2Yl\nCfwQYg766Q9H+OkPR5LdDTGLdXmnX7Kq29sSdeBHLMEGg1EEi1i1mdM+TlCL+wxLzVsiamvUmCky\nVNLhuRC+cZwFgwVKDQsiaj/s6wNgyJfYmzRLhK9BMAjI95Y673OJUaNSUpcYqsO0nKjedZRhf38i\nujRBi0elPi02VFKgr0j48YQQQggRHwECuH0qQDTbXMLFwcOhx9xeO5mmIlqHprfgJVItQ8cBqMrZ\nQL+9GddYYEBNwfYJ5V0CAT/n+/exuGA7oAbHXb4RqnM3ASqIoX34zdA2bp/9is8JCD2vIpsKjPb6\nXIy4e2DsujnHUo7DPTEYAGDUHdn1cZFt0Vv2CaCZdJ/1fWqhz9LCGxhx9dDvbAHAoDWTb62ibVj1\nM6qyOWPahk9xdd59gPp7BPc1U8eP5G8VLDU0L3s1+1t+FQr4CNa1D4wtdAoQoHngKKtL7wSg234e\nh2eQpUU3qJ9HG3B5x8dFgm277SqYJdg2WM4l2LbfoZ5vZe46hl2doePPy1kLwNBYqSAhhBCzy9mD\nD1NR8zZKqjYDYDRngUaD26kCUod6L3DmwC8YHZLvgXEaSrbdAUDBuutAc+WFSSXb7qLn4Et0vPbU\n2G+mLtkn4GzvK6wvvRcAl28Uj895xVKH+1p/NZNdEyIiy66ObHGsiI4EfgghhIgrb8Ad0+R/tFk7\n/Pjo9U4/qnnEf/ng7JWYtNZpHyeozdNAjWk9QGj12FQqDEuSEviRq1e1xc3ajIjaByfyE00fwd8M\nVKaNuSyYqUUbYVU/p19NmDS5EjtRc6lzzoMU2CTwQwiRvp56qJ2nHmpPdjeEmFHBzBf51ipc3tEJ\nv5+fs3bC5P3SohsozVwKgF5rQqvRceOizwPg8bs41flsaGI9Uhf69gFgNeSwad778I1lemvo3UuG\nYeLg4fnevaFr7vUV70KvNYYm7g+2PhLKhhrs/5WeE4wHJRh16p6gtnAHJn1mKNBg0NnO0fbfR/Vc\n3sqos4b2CSqAYbJ9to0FoOg0BpYUXo/VoFbKefwO+h0ttA1NP+OdwzMYCjwpz1rB3os/v6xNIo8f\niUX51wBg1Fm4tupjlz1+pG0XAJ0jdXSPNlDfq1Zery65E73OTO+ourc62fnMhO2CbVeXqECRYNtL\n253ueh5Qr//m+R9ANxZwMujq5GDrI3h8c/s+RAghIlV39LfUHf1tsrsRMa/HSeOpp2g89VT4xgKA\ngnXbKVh/PQDugR76ju/F2aPunXyOUbRGE+bCMgDyVm2lYMMOvHaVSa7n0CvJ6XQaWVtyF11j19G9\njqYJ17VCpLrMPAMVtZHNO4joRDYbIIQQQgghhBBCCCGEEEIIIYQQQgghUo4mEEh+yiSNRpP8Tggh\nhLiiVZZtAJQaFoZt2+/rYP/oH2M63k1ZHwJAE0F84oCvi32jsUXbvy3zfQDoNcYp2w36enhj9MmY\njgWw3LwVgArjkojavzbyW+z+4ZiPG40VFrWSrtywOGxbPz5eHv4NAJ7A5bW142mpeTPzjUvDtuvx\nqlrkh+zPJbQ/qWpThkqlmaMrjKj9OedBAC64TySsT1ey3noz+fqyqLZJt9d3S8ZdAGTp8sO2tfuH\neW0kfVY9CSGEEEIIIYQQQlyq5r6vgEaN7TY8/CA+l+OKbbV6Iwvf/3k0Wh0A537yzRnpYzrbWPZu\n3ux5EYBhd3QZtIVItg23F/IXD0Y2N/JWQz2qnOQXr94X7y6lvEAgcOV6WW8hpV6EEELE1ahvKOZ9\nOP2qbrhFawvbNpayMkHBsiDhAj/CPR6pi54zQOSBHxWGGs65DsXl2JHQoaNYXxVx+y7PxYQHfARF\nWsIlV6dqses0enwBbyK7lHJMGkvEAR+g6p+3euoS2KOptXkaog78EEIIIYQQQgghhBCpy5CVR+9R\nVXJtqqAPAL/XzVDdcQrW75iJrs0Ko54+NpW/FwC7px+v38WVVtgfaHtk5jomRASWXZ2T7C7MWhL4\nIYQQIq7sgdgzU7gCY4EfhA/8GPUPxnw871hd8HD0Y/XBYzXs6wNg0NdNdgQT9OXGxdS5jgAQwB+X\nPkylyFAZ1XNtmcGgAXuEr3ewlnu1cSX1Y3+7uSJXXxJV+35vR8QBNYnQ7W1O2rGFEEIIIYQQQggh\nRPx5RgbQ6nQRt9eZM/CMxj7OO1cMONsZcLYnuxtCTMuyLRL4kSjhc+gLIYQQQgghhBBCCCGEEEII\nIYQQQoiUNGcyfiz7yHr175+rf+0dakX60/c+HNfjrPn8NVTdvgRXv0pdtf9rL9J7vCOuxxBCiFTm\nGivTEgtfwBNxW4d/JPbjEVkpEG2c4yWb3WfJtoTP+GHUWCgyzAeg09MY1z5MpsywMOK2Tv8ofd62\nBPZmol5vdN+pC0yrGPUP0e5pSFCPUk+urjiq9r2+mXv9JuMJuEJZcDJ1eUntixBCCCGEEEIIIYSI\nXf/xveStuQYA3Z5n8DmvPGasz8gka9HKUGkYEV7r8Mlkd0GIqBVXWQDIKzMluSez15wJ/Eg0a5Eq\nR7DoHSsA0FtUivkl71vDnuPPJK1fQggx01yBqWs2RsKHL4rjxR5o4g9cqQLiRJo4B350eM6zxLwR\ng8YYtm2FoQZIfOCHUWMhX18ecftWTx2BK1aQjD93wMGAr4scXVFE7TVoWWXZRs5YSZ1612E8EZb2\nSVfZuoKo2vdFGUyTCIO+HkACP4QQQggRHb1RS+kCNXhYssBCyQIrhfPMAGQVGMkpMmDLVeMzBpMW\ng0mL3qiu6X0eP15PAK9HXcu67D5G+j0M96kg9OFeD93NTrqa1P1NZ6OD1nN2PK7El14UIlXJOTd7\n5JeZqKjNCE3AFM23kFdmwparpgsy8wxYMvXoDRpAvfZanQafR70eXk8Ar9uPY1iN34wMeBgd9DLY\npe63e9tc9La5Qq9nW50d+1Bki27E9OWVqom0yuU2iirNoZ9zS03klZqwZqnX12hW56fRospwaLXg\ncvhxO9Tr6Xb4cTl8uOzq9R4d9NDT4qKnRZWJ7Wl20tPiDP3sGIl8HE/EJjNPfcbOX2ajbLGV8sVW\nQH0mW7P0mDPUa2q26TFZtHjc6jV0jfpx2X04RtR52Nuqzs+ui+o17Gp00HhyBOdo/F7LvhP7sFUv\nBWDh+7/A4JuHcPV1ARDwedEaTJjy1eKlnKXr8NqHcPaoxUlZi1eBRnPZPofOHYtb/2aj6pwNALQM\nncDjT15Z51SVXajG4Bevz6JssZXSher8Ka60YM3SYcpQn5HmDB0EAjjtwfPHy3C/l65G9Z3WccFB\n67lRzu5TpYlGB+X7LRyTVUfFEitb74luwaKIngR+JNrMzYUJIURK8MZhUt0fiPwmwxNwxXy8AJEN\nJF1+uxEbHz7aPHVUGpeHbZuvLwPAorXFJcvJlZQaFqCJ4pm2euoS1pcraXSfZI1lR1TbzDeqG80y\nw0Ia3ae46H4TiM/7J9VkaLOjaj/i709QTyI37O9LdheEEEKkqW+9uhGAnOLwgRTfYAkAACAASURB\nVLTpwjs2QP+JlXuS3JPUYs3Ss2xrDovWZQGwYE0m85baQpOS0dIbtejf8rax5ejJD7PyzOcN0Pzm\nKAANR4Y48UofZ94YDD2WDpZsVNeKX/rFypj284cfNPO77zTFo0tpY9V1eXzmh8ti2scff9QCwK4H\nGuPQo8SSc252CK6oXbk9lxXX5rJgtXo9swoM09pfMJBHbwQydKFAn8L55rDbDnS6aT4z/nrWHxri\nwgmVldvtkACfaJUssLBmRz4ANRuzqVphIzN/eq8rgMWmw2LTTWvb3jYX548M0XBUvZ4NR4ZofnM0\nLc/T4Hv6229simq7gB8+vmI3fl98n3NRpTq31t5YwNob8liwRp3Dk8RETMo0Ftyj/h1/f8xfZrus\nrd8XoPWcWmBXd2iIuoODnN49ADCtwK2ln/zahJ8LN904ZXt9RhaVOz8yZZuTD35hysfv/VIVALd8\nrCJ8B5Po85v3MdIfecbrSC3J3w5A12hDSgR+rLg2F4D7Hwo/9v1WjSdG+MY7jsZ8/OB309Z7ilm5\nPTf0vo/s/NFgG/vOs+XoyS+HqhUTz5vA2FdX0+kRjr/Yx+5dnfS1z76x5kgFg0oB5tVmMK82g4pa\n9TcrnG+O+HNrKsHrlx+fvSb2nSXAvie7AXjoS2eT1of4Ll0WQgghhBBCCCGEEEIIIYQQQgghhBAz\nRjJ+xIm9S62+rnvkBNV3LcXVr1L+nH049qg0IWYzk1VHaY0NnV7FobXXjWAfvHK067wVWez4aBXV\na3IAMFp1jPS5aTyiVnzseaSFxiMDie+4uKJ4ZPyIxsyW7Yh3zg9odp+NKONHMAtHuaGGetfhuPcj\nqMywMKJ2vd52gIRmH7mSLs9FBowqNWSkJV+C9Boji0xrqTatAqDdXc9Fz5sM+5Kf9SIeTBoL+ghK\nBwU5/CN4A/FfYRAth3842V0QQgghRIopWWBh3S0FrNymSsFVr7Kh1cX/ejwaOr2GqpVq1VrVShtv\n+2AZjmG1AvbI87289HA7jSdn/vo4GucOqHvnnhYnBRXhV+hfyea7Cvn9d5uIsGrmrLB5Z3T3HpPZ\ns6szDj1JDDnnZodgxoaNdxRy9b3FVK/KTHKPxuUUG0MZulZuV6vAg6V/6g8NcvK1AU68orJBttXF\nXtZ3timptnDNO0sAWHtDfigTRCrILzORX1bIhtsLQ7/zuPxcOK7u9Y+92Mfh53pDpWFmI41WlZHo\n74jPav+Fa7O445PzWLEtNy77i4RWp2He0rHV+ksz2PH+0lDWljf3DnD4uV6OvtALECrTNZWOV59M\nXGfFrFa2yIpGO55RI1o1G7O56b5yVl2nrmk0CUqBENxv1QobVSts3Pnp+Zx4VX2PPftQa+i6e7Yw\nmLWUL1LlcSpCGT3GM3xYMiXkIBXIqxBnx76zm2Pf2Z3sbgiR0rQ6Dbd+Rk0ub/tgJQbz+Dev3xfg\nwBNtPP5NlQopWFty9U2q9tf7/30lOv3EgQezTU/BfPWFs35nKa/8tInff+scwJwahEoVPma2pp0v\nkN419Eb9g/SNBVHk6UvDtq8wLqbBdYRAnGuJ2bQqmCpLlx9R+1bPubgePxoBAhx3vArA1oyd6DXR\npzLVoQbDKoxLqDAuYdCn0rC1uM/R4b2QEsEQ02HWXp6qcyrOQGoMkiYjgEgIIYQQqSWv1MTmnUVs\nuK0AgIolGUnuUWSCA5xb317M1rcXc34szf2uBxo5uz/1BnuD98h7n+jizk/Pn/Z+CirMLF6fPesG\ntK/EYtOxZkdeTPtoODJMxwVHnHoUOznnZhdbjp4b7yvn+vePlYmdZsmOmRYsHVS7OYfazTm848tV\nALTV2znwVDf7n+oBoKspdc6dmaLRwurr1RjN9e8rZenWnLikyZ8pBpOWmg2qvFjNhmze+X+quXha\n3fsffr6XI8/10lY/uwJ8cktiC/yYV5vBu/5mAQC1m6Ir45sowXH4FdeqclHB74wHP3wy7LY9B19K\naN/E7GW0aCmcZ6arKfJgsbxSE+/5W3X+rL0xsvHteNNoCQWbrLouj6N/6uWxbzUCpNQ14FSC5a6q\nVtpC5VpABXoUV1mSHhQswpPADyHEjHv739Wy9d2T19nT6jRsurec3DILAD/86CEyco285xuqjm7w\nYtM54g39m5lvRGcYDx7Z/uFKRseyhrzwwwsJex5icv6Ab8aOFSBAgPSvA9vsOQNEFvhh0lgp1M+j\ny3sxrn2INNMHqKwunZ7k1vMOZog46niRtdYbQoEc05WtU6tSsi2F1AY20eVVz6/N00Cvty3ugTaJ\nYtJYomrv9I8mqCfRcQXS4+ZHiKTQaNBlqZWa+vw89FlZaMd+1mVlorNa0ZhV7XatyYzWbAKd+kzU\n6HRodNrxZShyfy5mUNu/PJjsLogUp9VpWLEtl+3vVquXV2zLnRUDiQvWqM/oL/1iJUde6OXhf2oA\nYLB7ZjMjhrN7Vxd3fGp+TJOIm3cWzpnAj3W3FExYtDIdex5PbrYPOedmH40WrnuvGke45wtVaRPs\nEYmyRVZ23l/JzvsrATi3f5BXH+ng0LMq24DXnf5jQVNZuiWHd/6f6lD2hdli/jJb6N/1txTw1buO\nJLlH8ZVTbAKiy2ga/By+41PzuP3j81L+c/nEy7MjY65IfeU1GREHflz/vlLu/XIVJktqfQ+ueVs+\nK8Yyqe3690Ze+Flryi9UvuOT8wB42wfLktwTMV0JSnAjhBBCCCGEEEIIIYQQQgghhBBCCCESTTJ+\nCCFmVOXq7AnZPvy+ACdf7GaoW6XBm7c8i8rV2dRsUZGQa28rIbfcgilDfVwN97r59d+e4szrKsI/\n4A9gMGtZc4tasXLP3yzBbNNz8ydVWq8DT7Qx2Bmf2ooiMn5mNuPHbNDpUdk7XGZHRBkbKow1cc/4\nURpFxo82z/kZfZ2n0uttY//oU6yx7ADAEmWpk8noNPrQ36PUsBBnwE6bux6AVk8ddv9QzMdIFKMm\nuhq/7kBqfD56A7N/NZ4QU9HnqnJbxvkVGMvKMJap6xpDaQn6vDw0BrltE0LMDkaLlmvuVWU8b7yv\nnIKK6K5d0s3aG/JZtDYLgIe+fJbTuweS3KNxva1Ozh0YZMnG6aeSX39LAb/62nkAPK7Zvfp+y86i\nmLb3OP0ceLonTr2JnJxzqXPOxVteqYm/+HYtC9dmJrsrM6JmYzY1G7PpaTkGqNJJs03hfDPv/Ts1\nFrFye26Se5N4rz2a3CxIiZBbbIyqvS3XwCe+Wwuo93g6OP5y3/Q31mjQRJhqLOCf3dcVifL8he8A\n4POnZwnrt6qoyeDI871XfFxv0PC+ry4CCF3rpKJgSbN3faWaZdfk8N9fOgvAyEB6l68XqUtGEIUQ\nM2rzO8qB8brCP/qLw5zbO/GCccu7KnjnPy0FYP3OMoyW8eREP/vccc4fmphSzuP0c+CJNgAG2p18\n4n/WhUq/bLi7TMq9zLCZDcaYHYEfwXI1re5zLDCtDtu+QF+BWavSfcajVEeevjS0v0i0es7FfMx4\nGvL1smf0CQBqTBuoMNagiWM9A7PGygLTKgAWmFbR622lyf0mAN3e5rgdJx70mugGGVIl4CL4ueEL\neNBpDEnujRCJpS/Ix7J0CQDmmkWYqyrR5aTHIJ8QQsRi+TW5fPSBJdhy5tZQVGa+ura5/8fL+e+/\nOsf+P3QnuUfjdj/WGVPghyVTz5q3qUUbyQhqmAl5Zaqc2uINsX1XH36+F8fwzA7wyzmXeudcPATP\n2Y9/txZb7ty6d2o9NzorAz4Att5TxHv/fiHmjNQqU5AoHpefN37XlexuxF00gR9FlWbu//EKiirT\nJyCvq8lBZ2MUpXo1Goo23wRA7opNGDKzibT+6MkHvzCNHorZEPARVL7EesXHzBk67n9oOYuuyprB\nHsVuxbW5fPlhNcb87T8/yUBnaozLitklLa/8tQYtlbeqwdKKHQvJqs7FlK1WSLsGHQw3DdDyoqrn\n2PT0WXxuH37P9CMEK65fwOav3zStbYcvDvDse3897WMDaLQa8paplQWl11SRV1tIZpWK+jVmmdBo\ntXjt6gPC3jFCf10PrS+p59+5v4WAP/KJUZ1Rxz0vfSz0c9/pLl782K7Qzwabkcpbaii/TmVTsJVn\nY8q14HOpLxR75yg9x9o5/7vTAAzWXzkiLxJ6s575t9RQulXVc8xenI8py4zOHN1b19E9ylN3/yKq\nbYJ/83k3LKJoXTnmQjUparAacQ85GWlVK7479jTR9Mw5HN2xT77OBVVr1YrW0y+rG+9Lgz4A9j7S\nwqKN6j2+7LpCAJqOq7rBlwZ9XKpuXx8NB/tZuF5tv2hjngR+zLRUL1SXwpo9Z6k2rQobtKBBQ7lh\nMQANrqMxH7csimwfw74+hnyxfbYngjegvodOO/fQ4jnLEtMGQAW1xFu+vpx8vQpiG/UP0ug6SatH\nZQQJBvEki1YTXRU/H6kVXe7Hz9wY5hJzhkaDqXIeGWtVUJ911Qr0BflJ7pQQQiRHe4Mda+bc/abX\n6jR85N9q8HvV/dLBZ5IfKHHo2R7+7B/UvcB0Jxs3j2XCmK2BH5vvUs8vwgXKV7Tn8Zlf2S7nXOqd\nc7FasS2XT35PLZQymOZeBfdXft2R7C7Elcmi40PfVGM7G24rSHJvZtbh53qxD6XWeEQ85BSbwrYp\nqVbzWF/8+UpyiqJbvJNsx1+eelz+UgVXbadoy80AeB0jjFysxzZfvecdnc0E/D5Muep7Vme2Mtp6\nnoGT++Lb6VlsVdFttA6fBKDXEd/M0KmgvObyRYoGs/ru+8wPl6Vd0EdQ2SIV0PLXv1rFAx8+SfdF\nZ5J7JGabuXeFKIQQQgghhBBCCCGEEEIIIYQQQggxS6Rdxo+s6jw2f+0GsqrzJn3cUpCBpSCDonVq\nJe7id69i3z+8gHsoPaOmat67moX3riCjdOp6jcZsc+jfnCUFVN+hasP1nujgjX94HkfX9LJRZFaq\n7AwFq9WK6Y3/9DasRbbL2mkNKpo122Yie2EeC+9ZDsDZ/z3Kyf/aF1XWEYDCq9Trt/Efd2ApiLz8\nQDyYci1c9eVtlG+vvmIbc74Vc76KzCtYVULth9dx5qeHADjzyyOzpfpEQgQjnxsOTh0hvO+xVgDW\n3qZq3NfvizyiuH5fXyjjR8nCmX3/CHn7x8LpH6XH20Khfl7YthWGGgDOu47FVF5Hh45ifVXE7Vs9\nddM+1kwZ8vVywP4MoDJ+LDSuTkjmD4AMbTbLLVeHSsHUu47Q5mlIyLEioY0yX0YgkFo1U/1Jzpgi\nRDzoc7KxbdkEgG3zBvR5s78+txBCRKKv3cX+p7pDGRTmIq1Ow4f/Ra10bau301ZvT2p/3A4/h8ay\nIFw9zdroK65V33OZ+QaGe2dPevGgLTtje7/2tbsAeHPvQDy6E/Wx5ZxLrXMuFgvWZPKp7y9Fb5yb\n6zjdjtlVGsSWa+CzP1pG9aqpx/xnq9cemV3ZW4JyS6bO4JFfbuZLv1gJQHZhemX7ADj+0uWZu6eS\ns2w97gF1nVH/ywfxu50sv//fAOh47Q+MXqxDo1WfaQUbdlC4YQftnS3x7fQst7bkbgA8fgetw6do\nHT4FgMMzmMxuxUXRfHMou5XH5Uer04SyXtXEWIIvFeSXm/ncQyv4v+9S2bxHBmZfFiSRHGkT+GGr\nUCfytu/eiTnPMuGxgM/PaMcIAH6PD0thBoYM9cWZOT+Hbd+5g/pHT0z72D3H2tnz12oCSW81orfo\nMViNYz8b1H9jx8tfUUxWVfwGd4s3VEwa9OG1q5tpR68dv8uLuUAFIZhyJv5t8leWcO2Dt/PCfY8B\n6u8TDUOGkeINFWz5pkrJpbeq2pE+p/oQsneNEPAHQn3UmcbeUmMpMJe8bw2eETdnfn444mPmLSvi\nmgduU/sz6iY839ZXzjPcNH6znFmVS/n2avSWiTUtO/ZeDP070jrESHP4G+zgc7j2P+4Ivd+CAv4A\nji71HvOMejDlWia8D/VmPSs+rgb4sxbkceDrLxHwyeTVZPQm9ZqO9k89INT65sSanX2tkdcP7G8b\nD/SyZM2teqci/TW7z0QU+GHWqqCmAn053d7p3xQVGeaj14Q/T/yo749kBjVMR5+3nT5vO1k6lTa1\nyricEkMVmjgnPbNo1XfISss25hmXctqxG4Bhf3RpMGdaqgVqxRLEJEQymarHyhLecD3WFctAOzcH\n5IVIhv/5yjkAMvMMmDN0mKzqfsOcoQv9F/zZZJ34s9k2sb3Roou5nIOY2jM/bp3Tk9CgUvsD/OV3\navnnnUfweZN7/bN7lypBMt3AD61OnTSb7ijkhZ+1xa1fqaBqpY2SBZbwDaew9wk1UZ2seGs551Lv\nnItWVoG6X//k9+Zu0AfA/qe6cYxEN66divLL1eLNz/9kOcVVsX2+pKOuJjW+e3Z/+k9KTyZ3ilIv\nGdl6Pv+T5WkZ8AHgHPVx7kB0r5sxt5C+o68D4Her8Xq/xw2AzqjOhYBffUF273sBa1k1xdfcDkDT\nEw9Nq597Hlffu211dmy5Bmy5ar5K/f/EnzNzDWTkqJ+D1zPp5HjX02g16juuwFpFScYStlZ8EIBh\nVyctwyfpHFEL+HyB9AvO1eo0lI6VRbl4aoS7P1cZCjieLYoqzXzy+8sAePC+k3jdMqcoYpcegR8a\nWP+31wNcFvRR95vjnPn5EVwD45PCGq2GwrGMH2s+u5WsBXks++iGaR/e2eeg7bXGiNqu/MSmuAZ+\nnH34KMWb5tF7QkXBtrzYQMcbzQwHAxkuuVfJXpTPmvu3hjJmgMqSUn2XioRreOxk1H24+lu3oTWo\nG4vR9mGO/+ce2vc0AeD3qA+i4OOVN9ew+vPXoDePv7WW3reOC79/E2DC6zQpDVz1pW2hgA+AwYY+\nXvvck4B6LS518v9lcO137lDPdexvb7CpC6j6CJ+v1qBjy/9VwS3BoI9gkMyZnx2m4fHTl/U9eKyl\n961j3g2LQr+ff9Ni7J0jnPwvqUc3Gfugh8x8I7a8qS9yL63z6LJHHvH41pt4nSHxF20Llt1BxYJt\nCT9ONE688WMA+ntSPzuDmKjH24rDrwLNLNrLMyxdqsK4JKbAj1LDovCNgC6PCqjzBFzTPlYyDfnU\nCoPjjlc469xPhXEJAOWGxRH9naORoytks+0uAOqch2h0R//dO13RZszQStU/IWJiXrSAnDtuxbzw\nypnihBCJdXp3/FbRazRMCA4J/n8oWMQ2SfDIJMEmpQvVAOW8pZJ98FKt50Y5+ZoKjE3EwK3X7aez\nUd27t9U76G1zMtilJhgGezy4HT48LnW9FAiA0azFmqnGL7KLjJRUW5hXq163itqMhE4ClC2ysuP9\nZTz/09aEHSMSdQeHAOi+6KRwvnna+9mys2jWBX7Emu0DYM/jnXHoyfTJOTcuVc65aL3nbxcCicsO\n0N2sJmM7zjvoaXGGfh4d8OB2+HGPvX5uhx+dXoPRrO4hzTYd2QVGsotUv4rmmylZYKGgQn2OxPu1\nfOXX7XHd36VMOWZcA4nNGJ5VYODLv1gBjAeAJMvIgBeX3Yfboca/ve4ARsv4+WnJ1CUk0Oi1R5P7\nmZhoOcWXn6fBc+Evv1ObkGCfYJYA54gXt9MfGhe3ZuqwZOqx2NRrqonx5Tz9en/0gXOBQCiwI8jn\nVN8ZBtvlGRtGm+sp3HTDtPsI0N5gn/BvOMGgb0umHluuHluuCrYLBopkXhI4EgwIXXRVVkz9jBd/\nQJ3DXaMNdI02hAJB8i2VzM9ezbIC9fdsHzlD48BBRj3RZW1JtooadV9ly9Fzy8cqYtrX6KCXC8fU\nYuP2Bju9ba7QNYvb5cfj9KM3qjeEyaojr9QUer2rVmaGrlfibfF69V5691eqefiryV9w2Vqnzp3j\nLyfmvRK8TigbC+qJVjA45vSemc+mF4mLp0eS3QUZ7RdCCCGEEEIIIYQQQgghhBBCCCGESFdpkfGj\nfFs1BatKJvwuWDrk5A/3X9Y+4A/QdUCtfn7pE79jx4/vIXN+TuI7mgBdh1p59r2/ZvhiZNFLg/W9\nvPaFp7nhp+8AxrNSlG9TqxCnk/FDa9Ay0qpWgLz0l4/j6r8860Yw88eFP5zBM+pm89dvCj2mM+qY\nd4OKTq//7dTHz1lUQM6Sggm/O/Svr0ya6SPI0TPKkX9/DYDt31MrrPNXqvdLZmXOhNIwV1L7wbXk\nLB4/bsAfYM9XngXGy8ZcaqhRrZjY948vMNo+TO0H1oYeW/K+NbS9egGAvtOzp/5kPPQ02cnMN1K5\neuo6bAF/4JKfIz+GJXP8oy0YuS5EuggQoMVzFoDFpnVh2xfqKzBprLgC0dUqNmpUdG2Bviyi9q2e\n2ZM9xhVw0OBS9RMbXEfJ15dRblB1n4sNlWjRTbV5RIKZNJaYN5Cpy+OkQ31PJbqUSTDSP1LaWJd8\nxJlkIBHpwFBSTO5OlX7WumJZknsjhIinQEClsXaOxnYPce071f3oB78eWWa1uebZH6vxmlizDwx2\nuzm9Z4DzR9TKvYajQ7Ses+P3xed6y5qlZ+X2XK55hyqBUrs5/uNKd356Hq//VmV4TXYJgz2Pd7Lz\n/sppbz9/uY2yxVba6qK7L0lVWp2GDbcXxrSP+kNDdDUlNoNAJOScG5dK51wkajZks+G2gvANIxTw\nw4lX1AreQ8/2cOaNQfra45vV0zCWEWRebQaVy20sXq/G/2o2ZpFdEH3Wkoun1MrZxhORraDd+MXN\nAOx/4I2I2ucsVOfFjn+/gV33PBp1/yJlsuj47I+Wz1imD/uQl7qDQ1w4oc7XphMjdF100t+hXu9g\nJp6p6I1aiirHVmYvtFK2WP0HMH+ZjcJ50T0Xvy+Q9CxIiWYwabGNlQ4JZuJ4+xerAFi6ZXqfacEx\n8fPHhqk7NEjjcXUutJ4bpbfdhcc59WsZzGhRUGGmvCaDiiVjmelqM1iyOYeM7MimCI+9HH0pY89Q\nP6bcid+lrn41V2KrWkLvWBmYIK3egEYX+5hcNAJjX2H2IS/2IW/Y7+3gd+n9Dy1PdNeiZjVkU2ZT\n/SrNrEWnMdA0eAgAjUbH5or3cbr7BQDaR95MWj+j8bYPqHHr3BJT1CU5e9tcvDFWcu/gMz20nB2N\nqS/ZhUY2jl0bXvdnpaHPx3jZ/t5SjrzQG9eMltPx2iMdE/6Ntxs+pF7Td//Ngmltbx9S12//+Zen\n49an2SYtAj+qbq+d8LOjZ5TTPzkU0baeERcn/t8+to6V8UhHkQZ9BPk9vlBpldWf3QpATk1sNwlH\nHlATVpMFfVyq5aXzjLapQJGMMpWmKH+FGvgKF/iRt3xiGk33oJO+U+EvCHuOqXR/XrsHvdUwvr9l\nRWEDP3RGHQvvXTHhd41PnbliwMdkTv34AGXXVgEq2Eaj1bDk/SoQZO/fPBvxfuaChgP9VF+Vw9Jr\n1XsyI8fA6MCVa8wd+oN6bftaw7/3ggqrx9NuDXWlZ1kKMbe1uFWt+kWmtWjCTIRr0FJuXMR51/Go\njlFqWBDaPhynf5Re7+xK2/xWvd620PPTO42UGhZQYagBIEuXH/P+ywwL8QXUDf9p556Y9zcVH9HV\n7NRrUqu2rC49Lk3FHKQxjKUkv/kGsm+4fsYHo4QQiVNVo8YKBvvOh8okzluoSs1WLnobDnsvbx75\nXwDsI7N7smKmnNmn6sM3nhihamXkJfeaTo1w6JkeTryiJh5iHbwNxz7kZd+T3ex7shuA6tWZvPsr\nC1i4NjNux7Bk6tn6djXJ/aefJ/d6e88TXdz1mcqYUsFvubuIx77VGLc+JdOKbblk5hnCN5zC7l2p\n8Zkh59y4VDrnInHzR8vDN4rQ3t918YfvX0x4MFJwEvr80WHOHx3mpYfHS7SULLCwcnseAKuuz2Px\nuix0+qln8l75TXQTT7XvGg/MDhf8MW/bfK795+1R7T9awYnKv/j2EiqXx7fM7Fu5HD72/b6bg8+o\nMrfn9g9GX5bjEl63PxTM11Znh2cmPp5fbmbpFhXYs3RLDrWbc8gquPLn5rEX+xjqiW7MIh3lFJsA\nFfix4tpcbvrz6Z3HbfV2Xnq4ncPPqdd0un+7YGBDd7Mq5XT0T72hxzRaWLBafcYuvzaX1dflMf+S\n92lw+2DQWDRGWxvIqVWL2rR6I36vm5ELat6qdMfbKdm+k6FzxwAwZOaQt/YaXL2p8d2ZDvRaE6U2\nVcq6LHM5OaZSuuznATjT8zI99gsTFqD12htZWXQrkD6BH5e+H8MJBjM+8R9N7HuyO27BqaACYIPl\n4l74eSvb3lXC3Z+vAggFfMVCo4EPf3Mx/3jHEQAcw96Y9ynmppQfXdcatBRvnDfhdy1/asDviTwq\nu2NPE55RVavJkJFaExyJMtI8OOFng009b41Wc1kmhXBG24fp3Ncc1Ta9J9UXdDDww1IUWf0rU+7E\nOnfOvshWigSfk2vAMSHww5QdPuquePP8y9o1PHYqouOGju/zc+F3KsJs9f1XA4QCQYxZJtxDEnwQ\ndOCJNt72sarQCoDr7qviqW9fOZPAw38VfZaa6jXj2UQ6GhI7MCFEIrgDaiCm09NEiaE6bPsKQ03U\ngR9lhshXoLZ66hKeqSJVeANumt1naHafASBbV8B841JKxgJlppuRYp5R3YgN+Lpo89THp7OTCL53\nImXQmBLUk+hoUKNhwVqkQqQSY0U5hfe9HwBDUWwrf4UQqae4/CoA+rrU4GdmdgVVNTcCcObYb8jK\nqWThsjsBOLH/oeR0cpZ69qEW/vI7tVd8vOOCgz27Otn/lJrw6G1NbuaEC8eG+dc/O8btH1djVHd9\ntjLqlYeT2fF+teot2ZPQfW0uzuwbmPaKZIDNdxax64FGILqsnaloy91F4RtNwe3wc/CPPXHqTXzI\nOaekyjkXTn6ZKRQkMV0uh4+f/Y0aczvwdPLfjx3nHXScV5Nmz/9PK9YsPWtvVIst1t9awNItORMC\nQVx2XygQKFJv/kaNzy599zICATjw4OTBH6v+fA1r/uIqRjpU9oSXvvh89eI2SgAAIABJREFU1M8n\nEtePvd9WXR/bazkZ+5CXP/5IZfR59Tcd2IdmdpKwt9XJ679VnxOv/7YTjQYWrFFzARvvKGT9LQUT\nAkFeezQxq8dTTW6JmocZ6HRx37/URPW51VZn59F/U1nET74afYaNaAX80BDM4nRkmN9/92Ioq8vW\ne4rZvLOQvjY1rzHcG33gSf/J/ejNal5Ia1SBH/0n9wGQt3orBeu2U7BuPPgqEPDT+syvYnpOc8mO\nqk/g8KrXr3XoBEc6fofbd+X5tEFXB0ad5YqPp7vXf9vJb76pAl9izeAYTsAPr/y6g6MvqICoj3+3\nlkXrsmLeb26Jids/XgHAb2dJMLWYeZJPWwghhBBCCCGEEEIIIYQQQgghhBAiTaV8xo+s6jy0honx\nKd1HoovI9nv9DJxTUc2Fa8vi1rdU5rskI4pGO7aSVq/F544u2q3rUGvUx7+0JEykmVY8l2TG0Fui\nS6upv+Q4ntHwkagFq0sn/OzqdzBQF30UfMdYVpTVYz8H/+YFq0ppe70x6v3NVt1Ndo4928maW1X5\nnw13l/LsDxrwRlBbMhK5pWbKl41HV9bviz4NnRCpotlzJqKMHxZtJvl69f0WSUmWDG12VCVMWhOY\noSLVDfp6OOF4jXNOVWKuyrSceYZadJrpXUItNW+iy3sRb8Adz26GuP3RrcozaawJ6Ue0DBqVeSuY\n+UOIVJF1/bXk7rxDSrsIMYsZTCp9sH1UrSiuqN5GZ6tKr9vddoyBngY2XPflpPVvNjv0XA/dF50U\nzlfXAV63n/1P9YRWBNcfGkpm9yYV8MMffqDu/fs73XzoG4tjzkAQrA8+rzaD5jPJzVi5+7HOmDJ+\n5BQbqd2stn9zT3Lrk0+XJVNd56+OcXX+oed6Er7aNFpyzimpdM5NZfWOvJieayAAP/mrcxx+rjd8\n4ySxD3nZ/ZjKGr37sU5suQY23aky7F399mLOHx3GZY/uPApm+Ah4/Sx73woYy1564MF96Ew6rv77\nbQBU3VhN55EOXv7rFwFwDcQ/w01xlYV7v1gV9/0CvPZIB7sebGKkP3VKpwQC0HBEfY40HBniN988\nz5JNKivzquvyOPla4jNYpIJgqZc/+8fiKUvfBHk96j2664FG/vTztriWppiOYHmf3/7bBXY90Ehe\n6fQzxTo6LnLxDz+b8Du/V71nz//6P8lfey2mfDVH4BkZZODNgzi7op+LmqsOtj9GnyPyTP3+gI/D\nHb9LYI9mXrAU0aP/eoHn/2fm3zuDPWp8998/eIKPPbiEdTcXxLzPHR9QY/wv/rI9VLpGiGikfOCH\nrSL7st9dWsYkEiMtapu0DfwYu9DPW1ZMweoSchapCTtLYQbGbAuGDHURoTPr0ZnUf/EyfCH6ifNL\ny8lodJHdqQxemHgBaC3JJKM0k9H24Sm3y6rKBS4v7TJQH/7mJntB7oSfh6bxfGH8fen3+CcEK2Uv\nzpfAj0vs+sZZtDr1N3r8/56JW9AHwNV/Ng+NZrym6JGn50YaQTE79Xk7GPUPkKENP/haYagBIgv8\nKDUsjKIP7Tj8U38GzwWugLrxPes8QKPrFDVmVaM0mpI5AHqNkUrjMhpcR+PeRwBHYCSq9hZt4moM\nR8Oknb2pJkX60ej15L/3HQDYNq5Pcm+EEInmdavveIMxA63OQH7Jco7u/cFbWgSkFFmCBPzwu+82\nUTBP3ce/+usOhvtSZwIrnN2PdZJfbuLOT82Py/5W7chL+iT04ed7cYyoSVaLbXrv+2CJlHQN/Fh/\nixqwN5hiS5K8Z1dXPLoTV3LOTZQK59xUlm6dfhAWwOuPdqR00MdkRvo9oRI8f/p5G3rD9CNfDn53\nPz6vj5UfUkv0dCY9BUsLyFuixtTrnjjLvm/txe9NXF2q+/6lBqMlfgnXHcNe/ucrqnTPkedT/7X1\n+wKh74J0/U6Yjg98VY0TaSJ46XtbnfzgM6rU8MVT0Y3nzAS/L0BPS2LKfvlcDrreeC4h+54rRt3R\nzWP5Az567BcS1JvkeOLbjQBJCfp4K583wI+/cBbjD3Ss3J4bfoMpBK9Bd352fugzX4hopHzgh8F2\neaYI91D0Xzae4cSsrE00jVZD9Z1Lqf2QqjtsLZ75CRr3DP7teo62MdIyOCHgZ80XrmHv36iLAL/n\n8ihvnUnPms9dPeF3weCN/jPhb7SNWRODRVyD04uiCwa7eEZcmHLHJ7AuDUYRMNLn5qefO5aQfT/9\nH/U8/R/j2QmSHSUtRKya3WepNW8K267IoAa/DE4TnsDUn2NlhgURH7/FIxeYl3IF7JxwvAZAp6eJ\nVdbr0BH5wHiFcUnCAj+c/lF8AW/EGUms2ky0aPGT3CLsVm3sdTCFiAet1ULxxz+Cqboq2V0RQsyQ\nztbDAKzc8BEA+nvqGBkcHzi02opxuVJvFfxsse/J7mR3ISZ/+H4zK7erzBBVK2Ibr1m6JYenfhD5\nqs1E8Dj9HPyjek2ufWfJtPZx1U1qUvXhf9ThcqRWxotIbN5ZGNP2vW3qXuzsvtSc5JRzblwqnHNT\nKa/JiGn7P/2iPU49SZ5gJoTpOvKDQwS8ah+rPrIGgP0PqIwgZx45HVvnwlh3cwEL12bGbX8DnW4e\n+NAJOi44wjcWSRVJwAdAe4OdB+87yUBnes5bieTbUvE+TnQ9C0CvoynJvZl5B5/p4ekftiS7GyE+\nb4CHvnSWv39cfd8UVMQ2L7jpriJ2PdAUyioiRKTiF3IqhBBCCCGEEEIIIYQQQgghhBBCCCFmVMpn\n/NBbLq+D5nNFv2LA6/LGozszRmdWL82Wr99EyZbLUxgGS58MnOthtH0Y94CK9vXYPXjtHjIrVTrA\n2g+sjbkvvhn82wX8AY7+x26u/rdbAZXxpHRrJTf98l0AXHzmHMNvKfVjm5dN5a1LsJWPrxT2e3wc\n+rdXx3YY/piXvsdifb5ep5e3Vr7TZ1yetUYkjmT4ELNNq6eexaZ1YTM4aMcyTpQaqrnoPnPFdjm6\nIiza8KtOvAEVTdzlaYy8s3NQl/ciR+1/4irrjQBoCJ+K1qyxYhsr3zPij/9KQLt/iExdZDXJNWix\n6XIZ8iU3TaxNe3lpPyFmks6mVowWf/ovMJanaWlIIcS0XDj3DAD2kU60OiNdYxlAgvQGM831LyWj\nayIN+H0BHv1XlTL7y79YGdO+5i+zodGM1ypPlt2Pqcyp0834YbKo+5Krbspn7+9Sr9zJVPLLzSxe\nH9t16Z7HO4Hkv46z1Ww85y6l06t7yoLy6a0UDmadaT2XumVsZtLRH6nvddegk/Wf25TwzNbasXLn\nd3+uMi776+9Qr+e3PnCC7ouJKbkhZlZvq3odv/WBEwz3pk+5rVhZS6sw5haiM0dW6rf38KsJ7lH6\nM+oyGHJ1JrsbSTHS7+F/v9qQ7G5cxj7k5aEvnQPg//xqFZrpVyxDp9ew7T0lPPm9i3HqnZgrUj7w\nw+e8fBJea4i+zqhGG8MZlgSrP7MV4LKgj443mjn5X/sYqOuZcvvSq+NzcZkMHXsvcvAbamBtzeev\nwWAzhkq/LPvohim3dfba2f+1F+k93hHx8bz2iRf8enNsp8Wl23tHJRWTEGL6vAE3Hd4LlBsWR9S+\nzLBoysCP0gjLvLR7zgPgI/3SM8+0Hm8rF91vAlBpXBbRNjk6Vfs8EYEfg/6eiAM/gn1JduBHlq4g\nqccXc5vWaqXksx8HwFA6vUkuIUQaG5vx67wk4COotzOxqeBF+ju3Xy1OaTw5ElPpCYtNR+F8C11N\nyU3j33BElTbqbHRQXBXZ5MxkttxdlHaBH5t3FsY0QB8IwN7H0+s5p6PZds5dypKpxhUjLRdxqeCk\n8lxwz2PvDNMigNeh5hbcI25cgy62fEWVC1/90csXSz5+76Mx92nL3epev2TB9D8/gzwuP9/7pBpr\nkKCP2cFl9/Gff6muLedC0IfOkkHl3aqcorW0KqptJfAjvEFXBxnGXAAGnOlf3isaT/9XC8N9qXkO\nBa+l9z3Zxea7imLa1/b3lPD0f6mydD5vikWqipSV8oEfnhHXZb8zZhpxDUR3UW7MNIVvlCLM+Vaq\n71o64Xcde1VU1+6/+iMBf/gTXKtP7yo+Tc+oqDg0Gjb83fWh33vtHnRmPQGfH1DR2oP1vbTvbgpt\n57VH94HvGpx44WzKmd6FeTC4yGCb+F5zD8qFuRAiNs3uMxEHfmTrCrFqs7D7J69FX6S/PIvUZFo8\n5yLun4CLbnXjHmngh0kb+yDQlfR7O6kw1ETcvkBfHgpcSQYNmlAgjBAzTWM0UvyJj0rAhxACjUZL\nTv4iLNa8UOJIp72Xgd4GAgF/Uvsm0sPuxzpjmoQGKJpvTplJ6D2Pd3HP56e/qKh2cw45xUYGOtNn\nMUysg/N1BwbpbpYxoJky2865IJM1+gWPb+UcnTuLNwzW8FMbBqvK9GzJt0AggGdsgV4k207HjveX\nxm1fv/ynBi6eGonb/kTyPfqvF2itsye7GzOm5JrbQwEfI41nGGo4hc85d55/op3seoYl+dcB0DR4\niCF3N/7A5Nnsff7UDJKIVjBg6uVfp36gy+PfbmLj7YWhTFDTkV1opHazyhp96vX+eHVNzHLpHR0g\nhBBCCCGEEEIIIYQQQgghhBBCCDGHpXzGj5GWy1cs2+bnMNw8GNV+rCWxRYDPpOKNFZeVpnnzp4cA\nIsr2AWDKTdxK4pmwYKdaMX3Vl7cBUPeb4wAc//4boWwf8TJwroeSzeMr4LOqc6e1n8z5KvJOa5gY\nTzXQ0Df9zgkhBDDo6wmV4sjS5YdtX2qopsF1bMLvcnSFAJi1GWG3H/b1J730R7qx+4cBcAecGDXh\nazEbImgzXf2+yMudAeTry9BrjIAqLTTTsnQFEf3NhIirsTzuhR9+H6aqyDIhCSFmJ1t2OQDLrvoA\nJlMWLtf4GITJnIXLMcjpI78EYGSwNSl9FOnhxCuxr8LLLU2dbLV7n+ji7vsrp11uQqOFzXcW8cxD\nLfHtWIJUr8qkpDq2sbTdUuZlRs22cy7I74stlXtuceo9p0R55NZfJbsLE1SttDF/WXzmIE69PsCe\nXZ1x2ZdIDef2D/Lqb6Ibr0l3GfNrGG1pAKBx14+S3JvZZ13pvZj16jOnKGPhlG2fafj3mehSwu0e\n+1z0OFM/I2Nfm4uDf+xh4x2FMe3nqpvUXIBk/BCRSvnAj8Hzffi9/gmlSwrWlIZKe0REA7lLYju5\nZpKl4PJJucEogwfylhfHqzszzlpkY83nr1E/aKD3RAfHvrsnYcfrPtpO7QfHfzZmm8ldUkj/2e6o\n9lO8ad7EX4zdp/Uen1sXdEKIxGh2nwFgueXqsG2L9VWXBX4UG6oiPlarlHmZNl/AAxEEMQRIXF1G\nh3+EYZ+6bsjU5YVtr0VHqWEBMP4+m0llhqlvToVIhJxbbgTAunJ5knsihEi2mpXvAKC/+xznzzyN\nzztepkGvN1Nde1uozeHXv5OUPor00NvqZLDbTXahcdr7yC2e/rbx1t/h4vSeAZZfkzPtfWy5O30C\nP7bcHVuZF5fdx6FneuLUGxGJ2XbOBTlHJk/TH6niKhXAZLLocDnmTtmXVLDt3bGXj/R61FjBw1+t\nj3lfIrX89luNBBI3FJSSDLYsBs8cSnY3Zq2jnb9Pdhdm3O7H0isg7sVftscc+LH2BhX48ct/qkcq\nkIpIpHzgh9/jo+tgKyWbxyfV592wiFM/2j/2ePh3esGqUsz51oT1Md78nssvyg2ZYytxHeFrcZmy\nzVTsSN9JlKL15ROyZgzUJ3bVedfBVuydI1iLxyOyF967nIPffDnifWh0WqrvWjrhdx37LgLgGkit\nWqHpbtl16ouy96KqB9h5fjSZ3RFixrR7zwOwJLAhlJ3hSjJ1eVi0mQA4xjJRFOojW9Hux0+bpyGG\nns5tJm1k1xuJzqzR6VUBspEEfgBUGdXkd4v7HAFm7i5CrzFQKoEfYoaZlywm59Ybk90NIUSKsNrU\nZO/JAz+ZEPQB4PU6aap7nk3XfyUZXRNpqPnMaEyT0GabLo69id3uXZ0xBX6ULbYyf7kaa7l4aiRe\n3YornV5lAdtwW0FM+zn0TA8uu0yyz7TZds4BOEbU+8gx7MWSGf3QvdGixlSvujmfvU9IFpqZotNr\nWH9r7AtP9z6uJjW7LzrDtBTpIpid6MLx4ST3ZOa5h/oxZE7/OkJMbcg1tz7jO8476LiQXnNt548O\n0dvmAiC/bHoZuTLzDYDKTnf+6Nz7HBHRm2bCRiGEEEIIIYQQQgghhBBCCCGEEEIIkWwpn/EDoPGp\nMxMyfliLbNR+aB0Apx86MOW2WoOOVZ/anND+xdtw8+BlvytcUwbAxefqptxWq9ey4R92oDenxUs7\nKb934krjeTcsoudoOwBdB1pwD7sI+OOXFy3g83Puf4+Ol5cBqm6rpe3VRgDaXm8Mu4/lH9tAVlXu\nhN+dffjYFVqLWHz0B2sAePl/1Gr2339LSlKIucEXUOle2zwNzDcuDdMaCvUVAFx0v4lFm0mGNiui\n43R5LuIJuKbf0TkqR6dW9miJbMWY3X/5d308tXrU9cJC0xo0aMK2t469P+Yba2lyn05o396qyrgC\nQ5gMNkLEk9ZioeD97wFN+PNCCDE3jA6pe02zNQ+36/IVVJaMAkaG22e6WyJN9bXFdh1tNKVW9oGj\nL/RiH1L3Idas6Y0zbdmpsuqkasaPFdvUWI4t1xDTfnY/PrdW3aaK2XbOvVVbvZ2FayO7j5/MXZ+Z\nz+HneudcJprsKpVdwN41iscePnN2vCy6KgtLjBlkAn7SpjyWiNzL/zt3ryP7j++haOutAJgPvYKz\nuy3JPZp9jDpV3qvQugCLPouGgX0ABAJ+dFoDwfpCwXHldHbilb5kdyFqgQAc/KMqBXjzR8pj2tfi\ndVmS8UNEJC2iA1peaqDv9GoA8papG8Zl96nAD73FwNlfHJlYTkMDeUtVu1Wf3kLe8mICPhVMoNGl\nfpKT7iNteEZVCnhDhpoMWfXpLQCMtAzSd/rym8ng32X1/VeTv6I4VC5Ga0jdG5gr6dh7EdegSmdn\nyjZjzDSx6as3TLlN8Pm6+p0M1PXQ/IKqg9j8Qn1EQSINu05Rtq0agKJ15aCBzV+/CYAzvzhMw65T\nuPonppHKnK9uJJbet475Ny2euL/HT9F9uDXscYUQIlrN7jMRBX4U6NXF5EX3m6EgkEi0elIzmEo7\nlqTMP4NlSKIx37gsqvaDvsSWMXP6VRmsbm8zRRGW+QFYbLqKHm8Lo/6hRHUNgEydGmCvNq1K6HGE\nuFTeO3aiz8lOdjeEmNU8Xd3J7kJUms+/AkDtmvfS2XIQx2gPGq26j7ZYCyiZt4G2xj0AFJSsnLBt\nT8eJme2sSHmDPbGV8zOYU2vMyuPyc+BpNVi9/T0l09rHxttVgPSj/3oBvy9+i3jiZcvdRTFt392s\nxq/qDiQ2sFtMbradc29Vf3g4psCPggozH/r6Ih76srrHT8XzLxGu+adtAJhzzTx29yMwQ0975fbc\n8I3COL2nn64mKfEymwx2uzn5Wn+yu5E0A28eInOBKi288M8+x+DZozi6VHCT1z4SCkqYzODZIzPS\nx3SWZSpmQ+k71A8aDQatmfMDaqF8AD9ltmUUWCsBONLx+2R1M27O7k/Pa60396jPgFgDPxZelQX/\nLXOOIry0CPwgAAe+/iIA1/2/uzFlm0MP1bxnFYvfuYLRdhXp5Hf7MBdkYMwar5dk7xrh1I/2A7Dh\n73bMYMenx2v3cOZnhwFY+UmVrcScbwVgx4/ezmBDL6Md6vlq9VqyqnKxlmSGtve5vLz+xacB2Pov\nN2OwTa92VLK4h13s+as/AnDNg7eHgl+mEgxwsRRlYCnKoPRq9YW26J0r2f3lP04MDJpEwB9g3z+8\nMHbM28hdUojWoG7+lv35epZ+eB32TrU6xTvqxpRrCb0ml2rf08Tx7+6J4JkKIUT0RvwD9Ps6ydUV\nT9ku+LgGDfn6srD7DQYK9HpTM/q+zLAIgHnGJbR46mj3nAfAG4htoC8eygyLKDUsiLi93T+EPcGB\nFUHnXceiCvzQaQystd7I/lF1HeEOxL92plmbwVWWG4HxgB4hEs28aCEAto3rk9wTIWYXT2cXzroG\nAJx1DTjrG/ANpdcqpMUr3h76/7LKqydtU1597aS/l8APcSm3I7YgZZ0+9TJS7d7VCUw/8COrQGXS\nWHFtLsdfTq2VmtYsPauvz4tpH3vHMn1MMXclEmg2nnNBx1/ui3mSaMPthZisasz0R184Oyeyf2RX\nqSDv8880zFjQB8CK7bF9lgDsezK9gmdFeEf/1Ddngq4mU/vxf57wc86y9eQsi+yeXAI/wltacD0X\nBg8CcL5/H7cs/NKEx3sdTSzOm/z+Jh01Hk/N7HHh1B1SY8BeTwC9YfrXHYuumn4wqJhbZLRdCCGE\nEEIIIYQQQgghhBBCCCGEECJNpUfGD2C4aQCAVz/7JJu/dmOozAao8i22islTNg/W9/LG3z+PdwZr\n+sXD2f89CoC5IIPF73pLOlkNZC/KJ3tR/qTbOXpGeePvn6f3eAcAfae7KN44L+H9jRe9Wc+yj21k\n4d0qZb7OPPYWHQuM9bm8lwVra/VatPrJY5jylhWx5Rs38fKnfhf22MGsIK98+ves+dzVVN1Wqx7Q\ngEarIaM084rb+j0+zv36OACnfrQ/ovIyQggxXc3uM+Raps74odeobElWbTY5uvDpi1s9dQAEZnJJ\nzDRk6QpYpitgiXkjAJ2eRjo85+nzqe+9mapZqUFFaC8wrWahaU1U27bMYDmdQV8PHZ5GSgxVEW+T\noc1iU8ZtAByxv8iIP35pSXN1xay2XodJM3nWLCESQaPVkv+ue5LdDSHSnqejM5TVA1SGD99weq66\nequ9L3w12V0Qs4jXnZplCWNx4ZjK4tPeYKd04fSv4TbvLEq5jB/rby1Ab5z+mrhAAPY83hnHHolo\nzcZzLqju4CB9bS7yymLL5LxqLKvNV/+wll9/4wJH/5TYsqPJFhh7Szj7Z6ZkiiVTjV2XLYrtHtfr\nCXDk+dn92sxFp16fu2VeANpffiLZXZjVsozFHO648ryXx+fEoDVf8fF0MtDpjrm8W7IEs5O1nBml\naqVt2vvJzDOQU2xkoDM9/w5i5qRN4EfQYH0vz3/wUapuXwJAxY6FZFXlYsxSH2DuISdDF/ppfl5N\nYDU9ew6/Z/wmwDPixmALXzok6cbm3Y59ZzetLzWw4B5VCy1/ZYkqMTKWQ9I16GT4Qj/te5oAaHz6\n7IQgl96TnWkR+KG3qtSf131/Jzk1BaHft73eyJmfH2HgrEp15/dOfkMXDBDJrMxh0b0rqLq9NvRY\nwZpSCq8qp/twZPWvvHYPB7/5MnWPqLS9lTfXULyxAkuR+lDWWwy4h5yMtqoUTR37mml65hz2jvRK\naSyESF+dnkbc5k0AGDVTX8AXGyrDtgFo9dTHpW8zRYdKV1tmWEiZYSF+VMrafm8nPd5Wer3qM3/E\nP0iA+A0GGjQmSgzVVBnV97JVG12aPU/ARbP7bNz6E4mzrv0UjJX7CQYEhRN8Xltsd9LkOkWj+xQA\n7kD0g2eZulyqjCqItcywMOrthYiVbfMGDKXTS08vxJwTCODuUJOYrrqGsUAPVV7NN5L+QR5T0Wh1\nFJWuxpo5/nlhH+6ku/0Yfv/MBJYKkcr2PN7FvV+qmvb2a96WhyVTj2M4dc6nLTvDB8hP5ey+AXrb\nXHHqjRATBfzw0sPt3PvlqrjsL7/czKd+sJT6w2o888nvXeT07oG47DuV9JxSY8i5C3Nn5HjzlmYA\noImxatCFY8M4R2d/KZ655tz+wWR3Ial6D7+a7C7Mah6/E4teLVb2+C4v1ZxrKcfunR3vwfbz9mR3\nIWbNZ0ZiCvwAKF1glcAPEVbaBX6Ayq5w/onTAKF/I/W7m3+SiC4lVM/xDnrGMnhE6/R/H+T0fx+M\nuL3P7eO3V//XtI71Vse/t3fCv+Es+8gGgFDQR8feiwDs+etnIqrH6HOqgYOBsz0c/ObL5C4pBAhl\nRilaH3ngR9BgvYqyPl6/F74f1aZCCJFQfvy0ulWAY7Vp5ZRtFxinfhygz9uOw5/ewWvasUCQfH0Z\n+foyQH2v+PEz6vv/7N13fBvXlejxHzpAgr1LJCWqWL1Zki3Zlosc95rYaU7d2PEmTtskL7vZkpe3\nu2+z2WzaphcneXaKkziWe+w4tmxZlmRJVm9Uoyixg50E0YF5f1wCFM0OgGg8389HH4rEzJ0LDAaY\nuXPuOb04Q2p2YX+oB29ogICmgiQDmo8AfkKaGmAx6IwYMF6UMSUHmz6XfIP6XskxFEayfUTjpGcv\nAS2xJ+ie0AAnPLsBWGHbNKV19RiosaxkjkUFunQGmukKtOAMqQFCn+YhpAXR69Trb9SZsens5BrU\n92+hsQK7Pn/0xi/SE1S10Vv8dSyxbphSH6ebDj2mwfeDUWfCqDNH3h9G1O9Dj5sjy1z8ePYUAoSs\n+izWZd0EDL0/w+8Zv+YjoA39Hn5s2PtZ8xNAPZ6oDDipTGcyknfLjcnuhhCpSdPwtbTiOT2YweNM\nHd4zZwk6B5LcscSyZanvrOWX3Y/BYMblbIPB7/qy2Zcy55IbOLLnFwC4B9qT1U0hkm7XUw7e+fk5\n6A3RnQubLHrW3VzM9sejG9+Kt+JKK/NjrJW+c4sjTr0RYnSv/raFd/yNCuLPK47PRMYFg+/7z/9y\nOeePOXnpl2q8dN+LHQQDqZ0BdDL2/1CNg9/0k1spX1tB676Wad1e9WDgR6xqd2deEM5M19XsZaBX\nrsnF9Dnfu5+VpbcBcLZb3YcrsqlJ4DnmUmoK1nOqc3vS+hdPjvOJyeI0nRpOxH6dXT7Pxold8n0h\nxhd9PkMhhBBCCCGEEEIIIYQQQgghhBBCCJFUaZnxI62kf6B0Qsy5eeGw30/9/pD6T5SvX+dRlZ44\nnPHDVhRbnUUhhEg1DX5VLmSijB8GnWnCthr9p+PSp1SkR0+OoYDHIwXZAAAgAElEQVQcg0rzWpGk\nfjT71WzqpiS91s2DpXzyDSVUmRdPsPRI4YwqJcYqSozxLSHn0zwccr8GQIVxXlzbniqTzsLl2bcN\nZezQmSNlhRJFj2Ewa03sNELDMoSc953g/GDZnpki58qNGPPzkt0NIZJrsEyor7kFz5k6PKfVd4Ln\nTB2hgfRPmRurBcvuBqCr7QR1tc+jaUMl4nQ6PfMW38qCZXcBcGTPw0npoxCpoLfdx/EdPSy/Ovry\nCRvuKkmZjB8b7iyJqTSDZyDIvpc64tchIUbhdQf5w9fOAfDgtxfFvf05y+x8/Fuq3fd8uYZtv2+N\nHKPpmkq+f7A095v/tZPrv3Mj57fWA9BZ24G3d/wZ43UvnJ3y9qqWxJa2P+zs/vTOwipGajw5s7Lo\nxYPJnkfBCpUF1rHrL0nuTeo717MXf0h9ri0ovBINjbUV9wDg8ndzomMrzf1Tq5iQqjoa0j/jR/uF\n2J9DeY0tDj0RmU4CP+JMbx7+kgbc/iT1JH2Y86xY8od/YPWfjy1dkcEyfD/4XbIfhBCZJVyapSPQ\nRLFxdtTtBDQfDn99nHolRtMeaOCo+41kdwOAE57dWPXZcQ/eiFZA87Pf9Vc8ITUgYtZbk9ofPXqy\n9ZkTJKDK1Fgw6SwAWHQz6AJRrxIb5l53dZI7IkTy+Fta6X7uRTxn6gAIuSTIYzR5RSro8OSRx4cF\nfQBoWoiGum1cdt2Xk9E1IVLOji1tMQV+LFyXR9Fsdb7X2ZTcAfyNd5fGtP5bL3Tgc4cmXlCIGO19\nXpUZW3VdIZffUTJt28krMXPnZ6q5/SF1rXhkWzfbH2/lyLZuAELB9Jjd+L6XPzjs93m3zB/2czzR\nBH6UzY3PNVbTaQkSyDTtGXCjOtHMBSWUblSlbyXwY3Ia+45EfqoyzCqqNZRhpX97O9IzGPFiHXE4\n9y0ot8ShJyLTSeBHnOUvKBz2u7tdTtomoo1y4WCwRv/W1Ol1lFw6fKbsQHNf1O0JIUQqa/DVxhT4\n0eKvI0gwjj0SF7vgq6XWsxuN1BgU1ghxwLWVlTZ1M7zcVJOUfvg1LwD7XS/TGxyaKWnWJTfwQ2SO\n7DUrATAWRn9zSoh0F+jqxnX4aLK7kfICfjUAZzbb8XlGXjeaLTkEAzJwn670Bh25RSoDXl6JmbwS\nM7Yclc3LajdgyzZizVa/m216TFY9ZosKHjRZDZgsOkyWwcetekwWtQyA2TLyd6MlsysqH3yli4Fe\ndSMhO2/q4zY6HWy8S924fu5HDXHt22TNW50DQOmc2G7W7tjSFo/uZBw55qbPr79yhtJqKzWrcqZ1\nO3qDumm4anMhqzYXRm62vfmUg11POWg6ndqBpHu++WZCt1dQZo5pfXe/+kxN1wwr02HW9e8GoHDl\nRo5+5wtJ7k30ehyyT6fKYJYxoakoy14w6WXbBs5MY0+mX39n+k/s7mz2xtxGTtHEmb2FmDlnx0II\nIYQQQgghhBBCCCGEEEIIIYQQGUYyfsRJ8eoKAErWDJ913X6gORndSSt+pxdvtxsAS4GacVG1WaXf\nO/HI/im3t+KhDWSVXVRfUYOWN+pj7qdIDUe3qhSXLWecSe6JEKmhPdCAJzSAVZ8d1fqN/tNx7tH0\n8JM+MyW8movj7l0AOAIXktybkTRCHHZvA6A/1MUCyxp0CYwF7g91c8D1CjBUsijMJBk/RJzkXn1V\nsruQOKEQ/jYHAL6WVoI9vQR6VdaCYE8vwb4+Ql71Gar5/WiBAJpvcLZMKLqMT5X/8VUAdPrp/ezw\n1tXj3Lsvrm3q9Hp0FjUzU2cyoTOb0ZvUrBmd2YwhPy+SKcZYkI/OlL4zamzLllBw5610P/uC+oOW\nHinaE83RpK45F69+H+dOvshAX4tKSwBk51RQs+hmWhv2JrOLYhy2HCNzV6jr/8pF2ZTX2CgbrH1d\nNtdGXrEZnUx5ipuALxQpO3HtfRVRtREusZKsjB8b74qtxIvjvMoAdGbfzMwsK8dc8njdQb77wDE+\n+7OlAMxfk5uQ7eYVq/Ommx6o5KYHKmk4obJb73rawe5nHfR1pNYs7NrHjydsWzod5JXGlvHDcUGy\nimWq3vb0GceKB2N2LmgaAdfQOI/BMrXsWsbs6c1olGlWlN4y4m96nXHwpwFNC9HrbQXSP+OHszu1\nvmui4feE8LlDmG3RnyjlFsX2nSNmBgn8GEPR8jIAzLlWumsdeLrcoy5nMBuovukSVn56o/qDGh/C\n71Rf7OdfPDXtfc0EF15SNx4Xvlel5V7ysXUABL0Bzj55nKB37JpkepM+EnCz6AOrKV03PPim7pnj\nDLT0j7aqSEO//PTBZHdBiJSiodHoP8UCy5oprdcfUnV6+y4qs5HK2vz1AOwIPsVcyzLKjHMBMOpS\n44acV1PnCed9x7jgO0EwxWtpaqibf3Xew3QEmlhsvQyAAkP5tGwvXE7onPcQ57xHCY1RXsikk1qV\nInam8jIs8+YmuxvTIuT24Dl5CnetOnf2NTbha25G8yf2M0fzq0EXnWV6j9lAVxf9b+ya1m1MxJCT\nEwkEMVWUY6mZg2VONQDmijKY5uCXWOXdsBlTSTEA7Y8+Ftl3Ysi5ky8CoGkhlqy+D71h6NwiGPTR\ncPY1Gs6+mqzuiYtk5RpZekU+y65Wx+SCNbmU1djCcToiQXY8oUqcRBv4ES6xMm91DnUHEztWYzDq\nWHdrSUxt7Hxy5pR4kWMu9bj6AnzzQ0cAuO9/z2fTe6bn+m08VUuyB3/WcO+X5nJkmxpb2PFEG4df\n6yIYmDmBpjlFJgzG2A6ImRYcMJN43alR8jdRFn74S4T8Pk4+/O+Rvy351H8ksUeZ7+Vz3x/zMZsp\njyVFm+l0n09gj6ZPphxPA71+zLbox1Gk1IuYDAn8GEPZ5VUALB0MQAi41ACZr9+Lv9+LzqgG+Oyz\nc9GbDMNX1uDAt7ar5Xslancyjv/yLQAqrpyDvTIP/eDru/IzV7Ds45fRc6YTAE+nCy2kYbSqt66t\nJBt7VR4Gy+hvZcfeRg5/P7mDxUIIMd0afaeYb1kFMOnMDU2+9AxMdIa6Oep+gxM6Vbe32DCbYmMl\nhUY14JWlT8ysJ7+mBmc6Ao20+etxBNSMRY30uxDpC3ayZ0DNBi8yzqbavJgSozoP0hHbIJZP89Dg\nO0mD7wQwFCAzFlOKBPKI9JZzxeXJ7kLchAZcOPe8hevwUUBlwNBCyf+ciQQPTHPgh86c/Nkswf5+\ngv3qxqT3/AWcb+6JPKYzm7FUV2FbugiArFUrMJXGdkNxOmStVsH15YUFOH76S4J9EhR/MU0bDE48\n+QL1p1/CaiuIPOZxdaFpyT/mZipbjpF1txSz4U51XC24NBe9Qe44J1v9UZV9s+m0i9kLs6JuZ+Pd\npQkP/Fh5bSH2/OiHQrUQ7HrKEccepRY55tJDwK8CKx79yhmObOvmg/+6AIDc4sRfS+kNOlZtLgRg\n1eZC+jr8bH9czS5//Q+tdLV4E96nRMopjP1cta9TgnIzVcA7s84hnQ1nRh1B8nS0ABBw9k7YhtGe\nh7U4usBSMZzb38uJjq1sqLwPgPO9U8+sn0p87uiylaaagd4ABeXRj6NYsw0TLyRmvNSeniSEEEII\nIYQQQgghhBBCCCGEEEIIIcYkGT8myZhlGvpZZh9zOV+/l/3feJ3GrWcT1bWMEC6N89pDT7Pun66j\nfENV5DGD1RgpvTO5trzU/lqVAzn9+0OEAjMrulaI6XDY/fqwn9PtkPu1YT+n2z7XSwnZznTxai4c\n/gsAlJnmTrh8iBDN/vT+ngqXUmkLnKctMJS20KQzk2MoJFufD6gMIFZdFha9mo1o1lkw6MwYUd/r\nep0eHfpIZosQIUJakMBgRg+f5sGruXCFVA1vZ7CH3lA7zmAPMFQyJVN0BproDDRFSq4UGSsoMJRf\n9HrmYNKZMQzWDNXQCGh+/JrKcOYM9tIf6qIz0AxAb7B9Sq+RgeRm/PBqbv7S96uk9kHESKcje+3U\nSl+lGm9dPX3bdwLgOngYLZB6paMSVVomFTJ+jEfz+fCcOYvnjPpO7X7mz5gryiMZNrLWrMRckfgU\n7GOxVFdR8flP0/rDnwEQ6OhMco9Si05voLRiFVk5Q/vM1d9Ge8shQqHUOw4z1ayFWdz8QCUA624p\nxmSR+UqpateTbdz79zVRr7/+1hL+8B91kewFibDhrtKY1j+xqyfjMhjIMZfeDrzcyam9ahb93X83\nh6vfW57ULC25xSZu+6Qa073lwUoOvdrFS79oAuDM/r6E96dgfgEL71aZ2YqWFmO2W/A51THcVdvJ\n6WdO0XUy+vMhsy3248XZlR4ZPwy2bMo23oS9ZikApuxcgl4Pvu52ALqP76H76O5R1wMi65qyVZbW\n8Lrdx1U2vdHW1bQQlsIyKq69C4CsWfPQAn5cLfUAtGx7Bl9Pe1yfZzz5fTPrnkTDc4+M+vf2N9WY\na++pQxO2kbdoDVW3fSiu/ZrZNIy61L6mniyfJzOOJ3+Mz0OnA6NJfc8n8hxapBcJ/BhDyw51E8mU\nbSb/kmKyynMiv5uyTZFgAm+vh97TnbTtVinez//lVCSIQUydp9PFG198PhLoUbl5PoVLS7FX5gFg\nzDajN+oJuNVJsX/Ax0BTH311XQC07W2kbU8jQa8MzAkhZprJD+44/Bfwa5k1YBnm13x0BVrpojXZ\nXUlr4fdHq7+eVn994jYsBcNFjKwL5mHIzUl2N6bM16AGxLufeR53beqX4oqUeplmekv6DVL5Wlrx\ntajvoJ4XXsJ6yQLyNl8DgG3p4qR/zhmLi6j4/KcAaPvhz/E1tyS1P6nAllUEwPLL7sdgMONythE+\nryqbfSlzLrmBI3t+AYB7IHVvLqSz8nk2AO75X3NZtbko2YeJmKRdTzt41xfnRn2TOTvPyIprCznw\n18QEoWXlGll5bcHEC45j55NtcepNcskxl1kGetUY6G//9Syv/a6Fd31xLgArrytMYq9UGZg17yhi\nzTvU9+zZA/288LMGDm3tSsj2l35gOWs/vR6dfugNroW0yO8ly0tZdM8SDv5clT84/IuDU96GOQ6B\nUv40KQdSfcdHsRSU0bHvNQACzh6M2blkV6lSQ6bsvDHXAyLrBpxqEk143bHWAyCkUXPPJ3E2nAag\n5dUtmHLyKV57HQBz3/kApx/5BlooM0pAZKqAe2DSy4Z8nmnsSeapsC8e8zGD3kRlzkq6PU0J7NH0\n0TIkxiEewRrhIN2AXz77xOgk8GMM3bXtw36KxOo82jbspxBCiLFZdFmUmqomXnBQkz/1byoKIUQ0\nsi9dnewuTEnQ6aTrT08xsH9w9lOajGZovsQEfqR6xo/J8Jw6g+fUGQBMZaXkbr4G++XrANAZklOf\n15CrZlqWf+4h2n7yMN5z5ydYI7MtWHY3AF1tJ6irfR5NG7oBo9Ppmbf4VhYsUzNNj+x5OCl9zFQW\nm4HbP13FDR+dDYDBKHef00lfh5+jr3fHdHN5492lCQv8WH9rMUZz9Ddp3f0B9ieor9NFjrnM13Ta\nxfc/cRyAmpU53PW5apZdFVvAU7zMX5PDp3+8lAvHnAA8/f0LHH41/kEglZuqAVj32ctwHGrj8C9V\nQEdnbSfeXg+WPCsAxUuKWfE3q1j94KUA9J7r4fzW+iltKx4ZclI98ENnVLePsmfPo333y3S8tXXY\n4+FAkLHWzZ49D2DK64I6V+49fZCW154a9vfgYHBAxTV3k1Uxh4Gmusk8FZEEDc//Gk/75AMPghL4\nMSVLijeP+Vgg5KPX28LJzm0J7JGYSMAf+2f+0PmsBH7EwrqkhpL776LhS/+j/jDJ8bg5P/9nADoe\nfpqB3Uenq3sxkfx9QgghhBBCCCGEEEIIIYQQQgghhBBpSjJ+CCGEEGmu0nwJuknGcnpCA3QGmqe5\nR0IIkRxZy5cmuwuT4j52AoCO3/6RYH9/knszdVpAMn5Ew9/moPOxx+nbqmZdFd57N7bFlyStP/os\nG2WfepC2H/wMAG/9zMz8kVekZqKePPL4sGwfoGrLN9Rt47LrvpyMrmWsWQuyAPjE9xZTMT8ryb0R\nsdjxpCOmjB8rrinEnq+GJp0901uyd+PdpTGtv/fPHTHXZU8WOeZmpnOH+/nu/ceoXmoH4Kb7Z7Pu\nluKoyzPFS/Uy1Z/P/GQptbt7+cN/qGwNjScnXw5iPMs/uAKAvoY+Xv7cXwi4h3+2eHtURoGmXY20\nHWjl9l+rrF5L3rcsKRk/Ar7UzvinBdTr5+1qp2D55bgHszf0nzk64rxptHW9XSqbenjd/jNqdvRE\n64Z1Hdk14m/u1obI/025hSAZP1JW78kDU1re3XKeEz/+yjT1JvNsrf9RsrsgpmiSH33jSvb3eCbR\ngsG0ybw7FRL4IYQQQqQxHToqzYsmvXyT/wwamXdCI4SY2cyzZwFgyB+nRnQq0DS6tjxD32vbk92T\nmGj+xAR+6C2ZFfgR5m9zAND2w5+RtWo5he+6EwBjYfQ3T6Olt1goe+jjqj8/+CneCw0TrJF5An51\nA8hstuPz9I143GzJIRiQtNPxsvr6Ih74lgp4stiSU+5oIl5XcPBnCK87iM+tfve51e9eV2jw9yBe\nd2hoeXeIJRvzqFmZk5yOJ8HhrZ04u9V3gr3ANOX1jSYd624tAeC137XEtW9hJVWqrMP8NbkxtbPz\nyfQsRSzHnLhwXJVW+fkXT/LEt+q57r4KAK66tyyq4zaeFl+ex1eeVKUaX/l1M09+53zMAVaFi4oA\nOP3MqRFBH28X8ARo2tUIwMI7Jz+uEhaPe0X6NCm5dOG5X1F50/upvv2jAAQG+ug+tpfOg9sjv4+1\nHhBZN7xceN2x1gvz93aP+JsWHNqvOoPc3kptOpjCGKQWChF0xycIbCYot0c/iaDVmV5lwDOlPJ3B\nFPvzCMahXIwAz4lzNP7D95PdjWkh34xCiJRw+xcWRv6/7ZHz9Hf6om6r5tJ8qparQZ2m4/2cfWvk\nRYIQmaLUWI1VN/lZW03+09PYGyGESA7b0sXJ7sK4NL8anGx/5Le4Dh1Jcm9ip/kk40e8uA4dxX38\nJKAygORccXnC+6C3qZuiZZ96kNbv/xhf48zKDOZo2g/A4tXv49zJFxnoawGdGpDLzqmgZtHNtDbs\nTWYXM8a6m4v5+LcXJXSWWsCv0XLWBYDjvBvHeQ8djSqQp7fdR1+Hn75O9Znm7g/gGQgSCkZ/J8/2\nv+fPqJvQAb/GnufUbO7NH5oVVRtXDGbimK7Ajw13xZbpo/WcG4CzB9IvQ5ccc+Ltupq9PPHNegCe\n/t4F1t9azDXvU4Eg89ck53UMvz9v+OhsVl1XxM+/UAtA/VFnjC1P8n0VXiyKw8Tvjf3mm9GcHjcz\nvZ1tnP3dd8muWgBA4YoNFK+9lqI1mwBo+POj9NcdH3U9ILJu4YoNAJF1G/78KMCo6wKEAtGPD4vk\nW/Kp/8DjUMFV7rYG3G2NuNtUoLmvp5OpBIWIkebmrSPPqj7DdegIaQFCgykljHozmhbCF1TfyXrd\n8OBPCfxIDmMcAj/8KZ4pKpFsy+dT/qUPAVD/4NfQvMO/M0o/9e7Itb3jB3/EWJzP7H/7WwD09iw0\nf4D6+/991LZ1RgPFH7uT7I0rAdC8PnqefX3M8bD8OzaRe9NGDHYbAN5zzXQ++me855qG2jQYKHzf\nDQDYN61Bn2XFc+IcAB2/fAZ/W1dUr8PbxZ6PTAghhBBCCCGEEEIIIYQQQgghhBBCJIVk/BDjqsld\ny6KCTZzpfROAMz1vTvs2c80lXFZ2LwA93lbecjw57ducTuHn0+NtBUj75zNdNj8wN/L/fc+2xJbx\nY00+t39RZRA59mq7ZPwQGa3KPLlZ7l0BNYPOHUq/mWpCCDER66KFEy+UJCGvF8ePHwbAc/ZcknsT\nHwkr9TIDMn7A0OvZ+djjeM/WUfTee4DEZzzRZ9koe+hBWr71PQACnfGZbZLqzp18EVC15pesvg+9\nYSjtfTDoo+HsazScfTVZ3csIS6/MB5j2zAOuvgC1u3qo3d0LwJn9fTSfdhEMyKy46bRjiypfFW3G\nj5pVKstA2VwbbfXuuPUrbMOdsWX82Lkl/Uq8yDEnJiPgC7HrKQe7nlLHcPk8G1fdW87GwSw5ucWJ\nLwNTOsfKPzymZtb+5v+cZccTUz/+us+o85fZGyrZb3mLoDc45rJGq5HZGysB6DrZOeVtxSPjh8mU\nXvNyBxrORH6acp+n5p5PAlBx3bvGzNpx8brh9cPrVlz3LmDsjB8ivfWePIC1RJ0fFK66Er1p6Pom\n6PXgcQxlAAlnBFGZQECygUys2XkCT0CN89Z2bsMTGCqdZDZksajoGvq86nP0fO/+pPQxXjIl44fB\nGPtnfjy+ezKF+1gdoQF1/ZB96SKcu4Yy7OqMBrIuXYzj+3+M/C3Q0cP5h/4LgKxLF6uMIGPIv+Nq\nbCsW0PJvPwcg2DtA0YdvxVgwvHxkznXr1M9r1tL2zd8Q6OhRv1+/nvJ/+iiNX/iOWr/fRcG7r8e2\nWpWWa/36IwR6neTfoTJnlX/5ozR+6X/QAmOft0yWBH6IlBT+Wg9qiRlUnm4asT0XqzEHf1ClxsyU\n12Q69bV7I/8vX2BPYk+EmD5ZenWSUWSc3ABrg//kdHZHCCGSRqfXY62Zk+xujC4Uov1Xv8mYgI+w\nUIICP9Dr0RmNaIHx67NnEueefXgvqHTIpfd/GFN5WUK3b8ixU/bQxwFo+fb3CQ24Err9ZNA0NbBy\n7uQL1J9+CautIPKYx9WFpsnAWiyKZln4+LdVoHK8b0CHghr7/tLJrqfUgPbxHT1ywzkJLhxX5Rga\nageoWpwddTsb7yrlqf85H69uATB/TS6lc6xRr6+FYNfTjjj2aPrJMSei1Vrn5k/fOMeWb9UDsOKa\nAq54ZxkrrysE4pOefjKMZnVT7KNfW4i9wMRfHm6c0von/3QCgKv+9Rre8d2bOPTwAQA6azvwD/gx\n29WN56Ilxay8fzW5c/IA2PfDt6bc13jcfLPlGCZeKKnC+33kse7v68bVrK5z8hatGWPd0T8jwuuO\nvp7IFM0vP37RbzrMBcWRQBBrcQXWonJyapYAULj6SvRGM0Gvugdy4of/lOjupp35BRvY1fgbgEgA\nSJgv6OJU13aurPwwkP6BH2Zbqn9WTk6sn/laiJhK5GUcTcO5UwV7ZG9YMSzww7ZyIVowhPtIdGXv\nc65dS++fd+A9N1QKt/M3L5B9+fJhy4UDN7r/9Are+qFle57eRv7tm8hao87LnTsPkXfLFbR97w8A\nkWW7fvsCAPaNK8jeuBLn9gNR9fdiEvghUk6fr51XGn6c7G7ETazPR68zsGnWRzjcoT4A2lxn49W1\njOVxDt0csBcmfoaCEIkwz7Jy0st6NTdt/vgOogohRKowV1cmPDPCZHU98TTuYyeS3Y240/yJC8TQ\nmc0zKvADwN+qbqi1fOcHlD30cSxzqhO6fVNpCQBlD36M1h/8JKH7O9m0UBD3QMeIv+cXzQOgp7Mu\n0V1Kex/9+iXY8+M39BQKapEZ4M/84AI9bdFnihTxtetJB1X/WBP1+hvuKuHp751Hi+NYdjhzQbSO\n7ehOu/eYHHMiVuEbSoe2dnFoaxfZeer9tOGuUq66t4zKRdEHeE3VvV+ai7tfnYe8/ofWSa1T96Ia\nNy1YWMiyD67gxh/dEnksFAihv2i2tRbS2P8jFfDRsG3qYyaegdhn5eaWpOZ1TJitXJ2Hzr7hPfTX\nHcfXq86TtGAQW1kleUvWAtBbO/Kmsq28mtk3vAcgsq4WVK9ZeN3R1hOZSsPX3Y6vux0Aj6MRW1kV\ntrIqQAWCZM2eh8ESfcDmTGPUmTEbsoCRgR8AFkM2Bl1m3BvJys2MwI+s3NjO0Qb6Zs61+WQ5dxwE\nYNb/eRCdxYzmVeeq2ZcvY2DXYbTg1II0dQZ1nmAszsPX1D7ssUBHz7DxEZ3RgKm8CIDSz7yX0s+8\nd0R7xpL8wZ8F6MwmfBeGn8+E++drdGCuiu3aJSy9cokJIYQQQgghhBBCCCGEEEIIIYQQQogIyfgh\nRIrLt8zCoJNDdSr0F9V8M1kzIxpUiIvZ9DnMMs2f9PIXfCfQkDTlQojMZJkX/ezi6eLcrWYO9r2+\nI8k9mR5aokq9AHqLmZAr88uNjCbkctP2/Z9S+skHALDOT+x73TJvLkXvezcdv34sodtNRYtWvQ+A\n3Vu/luSepJf1txaz+PK8uLXXfsHDzz5fS/1RZ9zaFPHz5jMO7vnS3KhrsBfNtrJwfR6n9vTGpT9G\nk451txbH1MbOLW1x6UuiyDEnpsNAr5rZ+sqjzbzyaDM1q3IAuO6+CtbfWhwpzTJdPvBVNfbRdGqA\nswdGzmgfy77v7+XCtvMsvOMSAAoXFWHKNuFzqpnAHcc6OP30SbpOdkbdtx5H7BlwcotSeza+v78L\nAF+3g/yl6zBmqf2vBQP4+7pw7FAZqjv2bxt1XV+3KpcVXlcLqvdTeN3R1hOZw2DNJqtCZY2xlVdj\nK6+O/G6wZqNpIbwdava7q+U8vaeexNUiGYsnq8VZy6XldwFQ37MPp3/o8yzbVMjc/HW0DmRG6e9Y\nM2WkilifR3+nZF57u3AplkBHD9mXLmJgz3EAstctpfXrj0TfsG70axotEBx1mdb/egT3sVEyhIbU\nPRljyWBJ2bEulcbYXjQy42gRIoMV21K0Zn0Kq14xNNARTgkpRCZZYr0c3SSTdgU0Pw2+2mnukRBC\nJI+lqjLZXRgm2NNL1xNPJbsb0yqRgR+pWsYnUUJeL20/+hkApQ9+DNuihQndvv2ytXjPXwCgP0MD\nmSbDaJSU01MRHrO6++/icy0bvtH3vQeP4ZL0ximrv8vPkW1drL6+KOo2Nt5VGrfAjxXXFEZKVETD\n1Rfg4CtdcelLIuh0csyJxDh3qD/y849fP8e17y8H4NoPVJBXHP/zNr1Bfanc/9+L+Ort+/F7JpjU\nEr5vokH7YQfthx1x71OYuz+A16VuAFmyopt4VjTLEs8uxdn7BWEAACAASURBVF1gQO3vC89N/eZZ\nYKA/qvWaX3l82M+3c7c1AHD0O1+YctsisZY89O+AKh/l7e7A09ZA++5XALUf3W0NhPxyIztaxzte\npiZ/PQBz8i7FasqNPOYJ9NHYd4RzPXuS1b24ys5P7SC5ybAXmKIOkA7r70zcWEy6cb5xiOzLlhN0\nqolDIacbz+kLU24nXHol0NGDeXYJ7sOnI48Z8uzobUPf25o/gL9NXS+Y51TgOnhqzHYD7d2EPD4s\n1RXqd0c3MFRaxjy7BOfr8Sl/JoEfSVJgmQ1Adc4qCqyzMOttAIS0AJ7gAN1eFaV0rncvrsDIi16b\nUX2Iz8+7jGLrnEgtr0DIS6enkbO9uwGGRfmNJtdcwsL8KyiwzAJAp9PT53Nwtld9IQS1iS/wjHoz\nNbnrACjPWoDNmBdZr9fbyrm+t+j0NIy5vllvY3PV3476WLe3md2tf5ywDxc/n/l5l1NgnR1peyyv\nNv4cAG9wgGLbXNaV3g3Akc6X6PY0sTD/SgCKrFWY9BY8QTXDoc11htM9uwhqo3/IjvV8wvt0oudT\nYpvLnJzV5JhVbW2LQdXOXFNyx5jr/OX898aczR/eP+VZCwAi+6fXq6JpJ9o/2aZCNs36MKd61EBv\nXe9equwrqM5ZCUCWqYCQFsTpVzUej3VunfB9Nx3CX5orbyxj0weG6qA3nZj8bAAh0kGFaT4lxqpJ\nL3/Bdxy/5p3GHgkhRHKZq1Mr8KPjsccJuT3J7sa0ksCPxNJ86vV2/PQXlH/2k1jmJjYwvPBddwLg\na2jEey79Z+BdvvmfpryOwZjaN2VSzfKr1Wym0jljX49PVtNpF9/52FGAyM21VKbXx2+mVjraucUR\nU+DH2puK+N2/nQXA740tY+G6W2LL9rHn+faY+5BIy68ukGNOJJyz289zP1Jjii8+3MRV95Zxy8fV\nuXlhnAMaSqqs3PSx2ZHtjeV9L38QgK6TnXTWdtB5Qo1Rdp7ooL+pL3wPOm6629RN6/Ka6I6/wlkq\nuNRs0+Nzp89njhCToWkhCKmDLuR1E/L7CAXUtU0o4EcLpv73TCoLaUHOdr8JwNnuN9HphiYJalpm\nfZ4Ulqf/uEA8Av36JPBjTP07DlJ5x2cI9jojv8fU3mv7yLv1Sjy19QAEepwUvu/GSAaPsO4trwJQ\n/OHb8DW04TmpxkwMdhu25Qvof0P1Q/P66H32ddUGKrAk0NNP/h2b1OP+AM5dR2Lqc9j05mITQggh\nhBBCCCGEEEIIIYQQQgghhBDTRjJ+JMGC/A0syNsQ+b3P56Db0wSAUW8h11zC7OwlAJzqfmPE+vmW\nikh2CqPeQq+3NZJNwmKwU569kLIsVf/wYPvzONwj6wrlW1QavvVl92LQGen1qbqlLn8PWaZ81pW+\nE4B297lxn4vFkM1lZfeSbVIzelyBHtrd5zANZtooslZRbJvDsS6Vwquhf2TEUkDzcbjjRQBMeht2\nUyFVOSvG3e7b5ZpLAdhQ/h40oNGpZie4A/0UWCooG8x2AXCqZyf9vnZ8IfeobZXYalhUsAk0FY3a\n5W3EoDNROJhFZG7upeSYS9jb9sSo64efT/g1mOrzCWkh+vwd9A1m0Ci11WA3FdHmOgPAQKBnlLVG\nhquHM4WE949rcL3w/imyqowB4f0z2r4Zrb3FBddQnbMy8p5zus6SZconfzBrjC84fg32De+ezS2f\nXTDuMg89so5QcGoh+OF0X+EUkGF7n2yeUjtCpKpcg5o5t8x6xaSWD2f5qPcdnbY+CSFEsuktFkwl\nsc3ojSfX4WO4j2d+ea1EZvzQW9J/Zk+8aP4Ajp/9Pyq+9DkAjAX5CdmuzqBSl5f8zYdo/s9vpn1G\nG7PZDsDJI6OnDx/NopXvnq7uZKRN7y6PSzt+b4gffepEWmQdCDPbZvb8qsOvdUVSUOcUTT0lty3H\nyMprCwHY95eOqPpgsqh9sPK6wqjWD9u5ZfrKQ0yHeBx3csyJWAR8IV77XQtv/EmNMW/+YAW3P1SF\nLSd+tx9uvL+Slx9R43yegdHfp/V/VePgBQsLWfSuJRhtQ9v3OX10nRzMAFLbQeeJoYwg0WYD6WpW\nYy/RZvwIl0ermJfF+WPOqNoQIlWd+ME/Y6tQmbmzKuaQNauGsoUqi7jBmoUWCOB2qCw+ruZ69a+l\nHhgqMyQmL9OyfFws3lmkkqFoduzPobMpva/Fp1PA0Y2vsQ37NZcC0PQvPxqxTNFHbsd+hfoM0mdZ\n0RkNzP3VVwEIuTx0PPwUrgMnAeh5djvGkgJmffXj6nGPj54nX8NTPjy7oXP7AdWe2UTRB2/BWKru\nlYecbjwnz9M/+DhA91Pb0JnVNVL5P34Uvc2Cp1ZlCGn5+v9D88enxKIEfiRYqW0eC/I24A+pk8ID\n7c/QNRj0EaZDh92k3jz+0PAD2aAzsrrkNox69SFxsP15Wl2nhy2Tb6lgfdm7AFhZfDNvND8aKVMS\ntqzoHZH2TnZv51zfvmGPz7YvA2BF0Q3jPp/lRe8g21RA3WBpmNM9u9AuOkvONZdyefl7WFpwHQCd\n7gsjSteEtCDNA7UXrVMy5cCPhfkbAdDrjBxofy4SJAFQDywrup4qu2ozEPKOG9BSnrUQh+ssBzte\nGOyfOtjC5XWurPgARdYq8i2qFlOPtyWuz6fTc4FOz1DtKavBjt1URPPACQDaXGcn1c7ywX0c3j+n\ne3YBRPZPOFgmvH863Wqbo5UWAvW6AARCPrY3P4I70Dfs8XBgyFgBNWGuXj9Gkw5b7tiDQPGo2bb3\nKXUhuO+5lgmWFCL1FRjKWJN1PaA+tyfjjFedVPg1qZUphMhcpoqyodHSZBoMGO7581+S3JHEiNfF\n6GRIqZfhgv39OH72SwAqPv/phL4+xoJ8Cu99Jx2/fixh25wOfr8KVHc0HZhgySHzl4xddlMMZzDq\nWHplfIKSXvpFE47z419fphrLDL8JHQxo7H6uHYB3fGRWVG1suEuVvY028GP5JjXYas02RLV+8xn1\nGXHucHrccAqXvY3HcSfHnIiHgE/deHzpl03sfradD/2bmvy1anNswVgANruBK+8pA+CVR0ef6PXm\n13cO/aKD3Eo1nluwsJCCBYXkz1PHyuyNlSy6ZwlGqxpj8Tl9/P7630y5T02nBoDYj8G5K+wS+CEy\nTsjvZeCCuncV/hlmKSjFWlaJtUSdL9hKZlG46kr0JnV9c/TbX0hsZ0VKKxosi5XOyudlxdxGa116\nnaclWvP//um4j3c+8hydjzw3qbY0f4D2n26h/adbhv299y+7Rl2+75U99L2yZ/xGQyG6fv8SQOTn\ndJDAjwSryVsLwMnu1wFGBH2AujHf7x/9Arc8+xKsBnsk2OPtQR+gAhHq+/YDMD/vcubkrubkRZlD\n8sxl5JjUzEh3oC+y7MWanMcAmJOzKhIgcDG7SZ2sl9hqcAV6RgQVhPX5HDQ5j1GdswpQASWne3YS\nb+EgDA1t1AwnDlddJPBjtOdzMY0QR7teiQR8hIUDHVpcp6iyr4i08/bAj1RgNxVSYqsBiOyf0fYN\nENk/4WCfsfZPOLDjUMcLI4I+ALzBgUn17fBLDo683E7VMnXhdckVhSy+qoh5awsiy/S0egj6Jx9m\nr2kaPreK9Hecc3Hg+VaOvJJes3OEGI1ZZ6XGspI55iXoplCdrS/YQYMv82ecCyGEqbws2V0AYODg\nYQB8TTMj01hiM36k/8yeePM1qvdZ+6O/o/SBjyZ02/bL1uI6rDIFug6lZ1axw7vHHwwaTafjxDT0\nJDPNvzQXS1Z0N9zD/F510/DlMW7qpbL8MvnM2vGEmu0fbeDH8qvVeFNWrhFX39QDDdfeHFsmsJ1P\nptdYwvxL1dhKLMedHHNiuvS2+/jBJ48D8K4vzuWWBytjbnP9reoYHyvwYxgN+hrUGGZfQx9dpzop\nWqLWL1pcTP78AspWq2w5Znt0wbQXjk9uPHQiiy7LY9vvW+PSlhCpSKfTYy4oxlqs7uNYiiuw5Bdj\nLlABn+b8YvQmMyG/N5ndTGkrSm+J/P+I4wU2VX9s0usGQwF6PM2c6toOqMnR6WTWwtiDJpKtclF2\nzG20npPADzExCYsWQgghhBBCCCGEEEIIIYQQQgghhEhTkvEjQfSDqfnzzRVoaLQMnIqqnUKLiozu\ncNePu1y4lMn8vMspslYPeyzPMlT3s8vTMCITxMW6vU2jZsi4uM1OT+O4bfT7OyP/zzWXjNvvaEVm\nwmtaJNX2xTSG6ptNVCahz9eOL+ga83FPQKX7NOlTd1ZBNPtnon0TCKlyEV2expj7p4U0LhxRJWUu\nHOll68P1/OfezQCYrHp+/rcHaDkt6Q1F+rPrC6gyL8IdUu9nt+bEr3kjGYWCBNA0MOpUeSOzzord\nUECBQc1gLzJWTCnTB0CIIEfcb4x73AshRKZIlYwfvX99NdldSKhEZvyQUi9jcx06Sv/O3eRccXlC\nt1v03nsB8Jw6S8idfjOOXM72Ka9z6vDj09CTzFS9JPaZbEe2dQPg7E7cZ028xKN2d7prPKlmv184\n7qR6qX3K6xtNqnTJ2puK2f741Ga/G816Vl4XfTmJUFDjzafTK+OHHHNyzKWLLd+qx2jWccNHZ8fU\nzrzVKsvNWFmBLHnqPVG8rET9W1oS+d2SZ0ELDZZoPNtN+7F29nzrTQA6jk39/ADgwon4jF9ecnke\nOt2ow9pCpK3Km+8byvBRVIbOMHRfJuTz4nY04mpU2ds797+Ou60Bb3d0x+JM4PR1Dvs9y5jP8Y5X\nJrWuXmdgds4ylhSrezBHHC/EvX/TKb/UjD3fiLMncWVn461qcRwyftSNfd9SiDAJ/EgQs8EGqJRW\n3uAAQS26iymrUV00uwPj1xq9+HGrIedtfRlKizRReQ5vcPSBPKtxqM0q+3Kq7MvHbSfMpJ+eWly9\nPpVKtMhaRaG1kk5Pw7DHLw6ECC87Fm9g/BP2dLiZGs3+mWjfjFbeJV5CQY2W0+o9W70ib9q2I0Si\nWfQ2qs1LErrNk549OEPdCd2mEEIki6ls/BJ+ieBrasbXEHtgbDrRfAkM/LBI4Md4up98FtuSRQAY\nC2KrLT9Zhhx1TZp/+810Pf5kQrYp0kc8alcfeyM9z2Wz84zklchnVtjOJx1RBX6EXXZHyZQDP5Zd\nlY/NHn3Jk6Pbu+lt90W9fjLIMSfHXDr50zfqWbRejftVL4vu80GnYsOoWZkz6nv3vS99QP1Hg76G\nXjpr1Y3SI48covN4B521qrx6wB2fm4etdWrs3OsKxlRyKa/YzLzVuZw9MH3jr0IkWu6C5bgdTQB0\nHtyBp60Bd5u6dlYBHql/nyWVnOvZM+z3gOanoe/QpNdvd51jw+z74t2thKlaaufEzp5kd2PK7AVq\nwmdZjS2mdjqbvWkd+CISRwI/EkSHLr7t6cZvb7Lbm+irVdNCo/794tb7fA76fR2T2p4r0Dup5abq\nTI+Kzi4sn83qkts433cAAE/QSZ65nMqc5bgC6kuh0Tl+Lep0COyYSDT7Z6J9c3HWlOnQXKsCbiTw\nQ4joNPpVJqkLvtok90QIIRLHWBT9rN54cb65N9ldSLhQAjN+6CXjx7hCHg+dv/sjAGWfejCh2869\naiPOnW/ia2pJ6HZFaiuujH32fcOJ8SeopKqaVTkTLzSD7H7Gwb1/XxPJ4DFVi9bnkVdinlIgxtqb\niqPaVtjOLemV7QPkmBPpJRTUeOz/qtn9//DYypjaKp9nGzXwI5zRQwtq+Jx+Ai513hr0Bgn6goT8\n8R3fDAXV9mrf7GXV5tiuTS67vUQCP0RGOf6Df0aCO6ZPMDS1YNVAyJPSWewncsn6vLQM/Fh0mbrf\nNcEt3Qmd2SffD2JyppY/XgghhBBCCCGEEEIIIYQQQgghhBBCpAzJ+JEgvqCqvaShYTZkYdCplz6o\nTS01T7iEi9Uwfjq8cEkYAE9weFmYcF8ALIbx60qFS9S8nSc4VA6l19vKsa6t47Yz3bq9KmXYofYX\nWV1yK/PzButc63R4gwM09h/hdO8uAAJTjIRMR6m2fyaj/qCK1lxydTHBgEQCCzEVbYHzHHfvTHY3\nhBAi4YyFBUndvhYK4dy7L6l9SAYtkLj0ojrJ+DEhd63K+uV8cw/2DZclbsN6PYXvuovW7/8kcdsU\nKc+aHfswU/uF0UvOprrFGxJTbildOHsCHH61i0tvLIpqfZ0e1t5czNZfN09qeYNRF9Ns+4HeAIe2\ndka9frLIMSfSzZn9asay47yH0jnRlwQvmj36rPXHNv8GgOJlxZSsKKV0ZRkA1ZvnYsm1EPQFAeg8\n0UH7EQftR1Smn/YjDtyd0R8LR7Z1xZ7x47Zi/vTf5wDwe6Y387IQiSFj/NPpUNvzU1reZswbdt8o\n3Sy+PI+nk92JKCzbFJ/zlUzJ+KHF+LEQa+aUmUACPxIkHODR520jz1JOWdYCAJoHppaSv9NzgUr7\nMkpsNQA0Oo+Nulz4cbVOw7DHen1tkf8XWmePu718S8Wof+9wX1D/KYAiWzU6nUoeM1ZpmOlmNqia\npksKr8HhruNg+58BCE0xsCYVhZ+DXjf5w7XDfQEG74OE90+y9s1k7XmyedhPIcTkNPpPcdy9KyPK\nVCVLyQN3Yr9yBQCN//Iz/E3tSe6REGIiBrsKXtZbkpum1Fd/gdCAa+IFM4zmS2CpF4sEfkxW93Mv\nkr12DTqTKWHbtF6yAOuCeQB4ztQlbLsidVmzY08s6+oPxqEniRdtgEMm2/lkW0yvy7opBH4s3pBP\nVm70w5y7n20n4E+/ayo55kS6Orm7h9I55VGvb802jPr3gFudp7a+1ULrW8PL0eXOyaNokXrfFCws\npGBhIYvuWQKA0Wbk0ct/GXV/jmwbWXZmquwFJjbdqwJVtv5GSukJIcbX7Wmc0vJ9Pgc7Gh6Zpt5M\nv3mrc7Dnq3M9Z0963PfTG3SsuSG2UoRhp9/qjUs7yRbrpO+xvv/FEAn8SLBzfftZXXIriwuuAWDA\n3z0sECMs26Tu2vuCLvwhb+Tvra7TLPBviASOVGRfQsvAqWHr5lnKmZOzBlABJxf6Dw17vNfbitPf\nBYDdVMjc3Eup79s/bJlZ2YsBKLDMGvV5OP1qFoTDdZbSrPksKbgWgJPdr4/IYqLXGSi2zQXUl9HF\nzydewv20GLJxB/rQkTlhXy6/yoRRMvgatgycnHAdp78Th+ssQGT/nOx+HRiZZSa8f8InCtOxf4QQ\n8eXTPNR69gDQ4j+b5N5kgMz5yhBixjDkp8YMT3ftxOdlmUjzJy7wQzJ+TF6wt4++bW+Q947rErrd\n/FtvAqD1ez9O6HZFiopxCpamgRbrNLAEW7A2F4CSquhnrmeqI9u66e1QWVfziqf+eb7g0lzyStR6\nve3jZ29de1NsQQA7t4wcm0sLcsyJNNXVEtv4o8Ew8Xtfp9eRW63eL/nzCymYX0BOlfo9tyqXnMpc\njDZ1eyQcMBKtrhYv5w73U7MyJ6Z2bry/EoDtj7fh96b2JD4hRHrRtBABLX3v/egNOtbeooIotj3W\nmuTeTM7SK/MjwSrR6mj0ANB0OjMmHcX63WayqqBno1lPwCffk6OJPSxcCCGEEEIIIYQQQgghhBBC\nCCGEEEIkhWT8SLBW1ynq+8qZm3spABsr3k+PtxV3QKXpMerNZBnzIxk/drb8Fr9vKOW8poU42P48\n68veBcCq4luZm7sWl1+lk7MYsimwVkYKJR3qeBF3YGTtp6OdfwVgfdk9LC64moqsSwBwBfrINuWT\nYy4BoMl5jNn2ZWM+nyOdf2Wd0U51zkoAyrMW0OdrJ6CpmRhWgx27qRCjXqXh3tb0yxEZJUpt8yKl\nWox6C9mmoRmcNmMu8/MuIxBS7QU0H31eB/3+jmFthMvZ9PkczMlZzZyc1cMeD2p+XH71Gjc6j3K+\n/+CYzylW4ecTfs7h52Mzqojy8PMJv0ajPZ+LNQ2cYF7eemZlq9SDdlMRTn8XhsHSL0a9hb1tT4xY\n78jgPg7vn/LBLDHh/WM12AfbU/tnW5NKZygZP4RITZ7QAAAN/lou+E4Q0BI32zrTtf/8Gdp//kyy\nuyGEmAJDbmwz6eLFXXtq4oUykGT8SF29f32VnCs3AqC3JWYmtHXhfAAs8+biratPyDZF6vK5YysZ\nodNBdm56pXC+8W/GL6E7k4WCGrufUWNaN35s6q+TTg9rblCZPF773ehlDwYrD7P6+ugyfjSdUtdZ\n54+lZ817OeZEuoo11bvHNfos3yu/ejUABfMLyKvJx2AeSgnvd/npOqmyWLcdbOPE74/RWavGZPsu\njBw/n6rtf2yLOeNH0Sw1nnzL31byzPcuxNwnIYTIJFfcrcphpUvGj+s/NHpFhak48NfOOPQkdXhd\n8SkxaM830uMYPyPgTCWBH0lQ2/06HZ7zAFTbV5JvqSDPXApAQPPjDTojpVdGC9ro93ewo+W3gAoi\nKLXNIzdLBWr4Qx7aXGeo690LqECI0fR41QXz7tY/sjD/ikipFLu5mD5fG2+1bQHAExwYN/DDH/Kw\nu/WPVNlXAFCRvYh8SwV6nTqp9gYH6PY20zZYdsQbHBjRxtKizZEghLezGuwszL9i2N/q+/ZR2709\n8rtBZ2JxwSYAsk2FdHma8ATV66ZpGuh0GHWmyHNcUngtRr2Zs717xnxesRjr+YT/NtHzeTtvcIDd\nbX/iksH1cs2l5JiK8YdUiqfeMfZx+PHw/qnIXgQQ2T/hfRHeP6PtGyFEbHqD7RxwvUK2Pg+AbEMe\n2fo8TDp1IW/QmTBiigRyaWgENT9eTaVuGwj10RfspDPQTG+wffSNCCHEDGTIzU3q9jWfurj0nm9I\naj+SJZGBH3qLBH5MRcjlom/rNgDyb7spodvOveYq2iXwY8bzDMQ+kGcvMAHpcRN67go7q98RW4mR\nTLdjsIRKNIEfAJdOEPixcK261sopMkXV/o4to4+ppAs55kS6yo2i/NPFxnrvV18zB4Cuk52c/NMJ\nOmvVDbPO2g76LvTCNFY22vNcO+/5xxoArNmGCZYe3y0fr+StP3fQfCYzUvsLIUQ8zFutgusWrM3l\nzL7YA/amU8X8LJZtKoi5nf0vZVbgR19HfMaTqpfa6XF0xaWtTCOBH0nS4T4/7OdUhW/SH+96leO8\nGnU/+nwO9jmeGneZF89/d9zHQ1owkkEjmkwarzU+POV1LraoYBOV9uUAHGh/jjbXmVGXsxiyAbi2\n8gFmZS8eFvjR4a6f8HmG1fXujQTWjCbW5zOafl87+xxPR7VueP9Em+VkwN816ddmOhiMOmYvyWXW\nIhU4k11oxmIz4HWpAQlnp5+mk/001/YDakaREKkioPlxBGSGRiopuu9G8u/aNObjbf/zR5w7j4zb\nRs6mVeTecBkAplnF6K1mgj1qhqD3fCvO7Qdxvnls2Dr5d1yltv/Bm6h/4D/J2bwWgNzr12MsyiXQ\n3gNA38t76Xl+ZyRz10yzrf8Pye6CSBPJzvjhax6cXRKamfVENX/ibgzpLJaEbStT9L+xC4C8m9+B\nzhDbTYepyFq1AkNODsH+/oRtU6Se7pbYM0iWz1MZQVvPuWNuazrp9HDfV+aj0yW7J6mtebAeef1R\nJ3OXjz7pZzyLLh8Mos8zMtA78vsnnBEkGqGgxpvPpHfghxxzIl1VL8mOaf2ORs+of3/s+l+r/yTh\nktrrDvLm0+oz5dr7KmJqy2jW88kfLOFr96rxXLczPjOkhRAiE9z+UDXfvf9osrsxrnv+19yYz1kc\n5z2cPZDaAS5T1d0an4oD89fkcPg1CfwYjT7ZHRBCCCGEEEIIIYQQQgghhBBCCCGEENGRjB8i7RXb\nqtE0NdvSMVhSZjS+oJplEtICmPSJqXctomO1G7nhEyo14mXvnE12wcQpW/s7Vcr13U808crPzsWt\nVlimW/SZf6flpT/Rd/JQ9I3odHHLThCX/qSRRD7frKr5AFTceC9nf/ENkjL9JQX0vvgmA/tPorep\nGeR6m4XsdYuxX7lyUuvn37mJog/ciLeuGVAZOtDAVF4IgG3FfPxN7SMyflys7Ivvx1Sqlh946wSa\nP0D2+iUAFH3oZgx5djp/+5eon6MQM4HBHtsMwVj5mpqTuv1kS2ypF8n4MVVBp8pC5Tp4hOy1qxO2\nXZ3BgH3jenpf2pqwbYrU09kc+wyuJRtVhoeDr6R2WuO7PjOHmlXJzUCVTnZuaYsq44feoKZKLr+6\ngN3Pjix/ufr6wqj7dGRbN/2diftOmw5yzIl0k1OoxvgWrI2tdGPL2TFKoISHOnRQtamayiurALAW\n2aj78xnOb62PLGowG7AV2QBwd7kJemMfS3z+J6oU5JX3lGGyxDbntrzGxoPfWQzADx86TsA/M8dx\nhBDi7ZZdlc+6m4t568WOZHdlhGVX5QOwanP056hhr/2uJeOSQncNZqsL+DWMpuhToiy9qoAnvxNd\nRY1MJ4EfIu0FQj50RnUinW+poNs7ciBeh475eZcDYNCZaPfWJ7KLYgrK5mfziYfXklc2tUH+nCJV\nG/QdD9aw9vYKfvLAPgDa66UW5nSb/7F/oO5X3wBAm6Ep79OJFgoyU4M+AAJdfQS6hqfIM+TnTDrw\nI2fTSoL9Lpr+5acAaMHh73mdQY/OOP7plak4n4a//wEAIadKp9y95TUAKr/2SfLvuJK+l1U5Mn9b\n96T6JcRMo7fZkrp9CfxI3E0ynVUCP6LVv2NXQgM/AOzr10rgxwzXeHIg5jaWX61qYccxvjyuLr1R\nlRa59RNVSe5Jetn9bDvv+XINRnN0N0JXXFM4IvCjclE2xZXRT+zZsaUt6nVThRxzIt3c8DezgaGg\nrqkKBtSbtOHE6O99nV61e81/bqb62jnDHms/Mry0k8lu5p1b3g3AgZ/s4+gjh6Pq08V62tTEtK2/\naeGm+2fH3F74+PzkD5bw48/UEvDJuJsQQgC8/yvzOXtQlRmNV/mQWNkLTHz0Py+JS1s+dygjzlXf\nLvw93nx6gOqlUw8KD5u73M78NbkZVwonHiTwQ6S9aBE97QAAIABJREFUut69rC65DYD1ZffQ4TmP\ny987+KiGxZBNgWUWVqOaFeAO9HGye3uSeivGYs1RH0ef/MVackuHBvi9riC1b3TQeEx9gPe1+/B7\nQ5isg8E+ZVYql+aw6Co1EGC2GiiYZeWTv1wLwDfu3IXHmbg69DON0Z6Hpag0vo2m4khTBnA1qIxI\ndb/6ZpJ7kt6CPQOYZpdgWagGHT21wyOLtWAILegbt43+7QcjAR9hoQFVn7jv5b0UffAmstcvBaDn\nuR3x6roQGUWfldzAD39L5l18T4UWHJwNGQqBfnqrh+ot5mltP5N5Tp/F72jHVFqSsG2aysswlZcB\n4G+d2cfJTFU3OPgai9I56jN+7c3FvPVCas3iW3FNAR//tpp9rZPiyVPi6gtwcGsX624ujmr95VcX\noNODdtE9z1iyfTi7/Rx+Nf3rgssxl1mql9m5eTBY4LXHWjm1t3eCNdLLnGV2bvxYbMEQp/ep8cGx\nMv0uef8yAKqvnUPdi2epf6kOgM3fvmHEsp4uN237WwGouqo6LoEfYS/8tIFN7y4jKzc+t19WXlvI\n3/9mBT/5u1oAuuKQ7UcIIdJZbrGJT/9IZVD+xgeO4HUnNwO80aznwe8sIr80PmMY2x9vxdWXufe1\nzh91xhT4AXDr31by/U8cj1OPMscMOGUWQgghhBBCCCGEEEIIIYQQQgghhMhMkvFDpL1W12n2tP0J\ngLk5a8izlFNim6se1MAf8uD0d3G+/yAADc4jBELjz8YWiXftR1T6xXC2j/3Pq4j7Lf+3FlfvxOnE\nswtUjdB7/mUxq28pJ79cpXu95iNz+MsPz05Hl9OGzmCg4oZ7AMhdvIaQ30fnHpWCOxQY/traKuZQ\nsulmbOUqm4FOb8DjaKb15S0AeBxN6IxGaj7wWQAsRWpG55IvfmNYO8e/+ffqP1oo0iaArbwq0iZA\n68tb8Diahq1rLixh3oe/oNovqcDb2Urzi39Q229tjPQTiPRVpzcM9q95ZJs6HaVXq6xA+cvWYbBl\nERhQs6J6j+3Dsf3Pw7ZfdNl1FK7dBIDBmoWnrZHWrU8N2/5UTOX1H2/7w7Y9hedkyi2g5oOfw2DL\nAkALBKj9n38a2c/B8iTl178T+7wlGKxqeb3JTMjnoefoXgBaX36SSz71r5H3ROG6a7CVV+LvVzOR\nHK8/T1/twSm/Tumi64+vUPHPH2H2vz4AgPvYOfpf24/zzWMAaL6JP6/8zWPPovM1qNnRptmJm50t\nRDpKdsaPYG9mzb6MVsjvR2+Z3lIs+sHvLxGdgT37yL/95oRuM3vNKgB6XngpodsVqaHH4aP1nMps\nVl4T22f1XZ+dw6FXu/B7UiOt/bXvr+D9X5kXdXkCATu3tEWd8SM7z8jc5TmcOzyU4WLV9UVR92X3\ns+2RVNPprMehxrdaz7nlmMsARpOO9bepa8H1t5XQfNrFG0+oa8Q9z7XT256+45kV87P4zM+WYjDG\ntj8PvDR+VpoFty0EwHGojTe+um3C9nrrewCYs3luTP16u4HeAI/9ex33/3d8Uv4D1KzK4StPrgHg\nj1+r481nHGmVNHfuCjsb7lRZg205Rn715VNJ7pEQIt1VL1MZI7746HK+9/FjOHuSkyHDaNLx4HcW\nsWRjfsxtuZ0qc8lzP2qIua1UduyNHja9pzymNlZeV8i191UA8NrvWuLRrYwggR8iI3R5Gof9FOln\nxTuGyoVcONrHb//hKABaaHJXMAPd6mbrr790lILZNuaszANg5Q2lMz7wo+iyzWTPXQRA/WM/JOhy\nUrb5LgBM9rxhywY9LvqO76flBRVooQUDlF57B7Nufi8AdY9+Gy0QoO6RbwNgmzWHmg9+jhPfUoEe\nWmjkAFG4TYCWF/4QaRNg1s3vpe7Rb/9/9s4zMK7ibNvX9qZd9S5ZsmTLvWMMbmA6piT0ToCEACEh\nvCQfLaQAIfAGSAIJSYCQkPDSi+mYmG6DbWzcuy1btorVtZK21+/H7K5YW12rXZW5/khn9+w8c87u\nOTNn5p77ido/ddYCqt/+DwAeaxOZC8+g8DvXALDv6d8RDATwuxwAkboG/aJTF67rt8tMnjwHywQx\nAVHx0hP47Ta0ofQ0Sk30ZFXK9HmkTDuWyjeeAcDbZiV1xnEUXXSDiP+Ph/A7+5ZDuS/nv7v4+/7x\nkDifTnufjsnb1sKev/4Gc6lIHZJ/9pWd1/OYEwAwZBdS/o+HCAZEJ7Pwgh/gbWmi9qNlUfvnni7y\n4Na89wKOmoOkTJ8nyl96GfZD+/A7bH06T8MF195KKv/nMVK+I8Q55hNmYZhyARnfWwpAy1srsb6z\nqtuURUFv19aD/jbx21bqZWoDiaQ7lDp9QuP7WmUOUYCg1weDLfzQ60ARmiAYTiPbQwTH9p1xF34Y\nZ0wFpPBjNLPhQzEpt/TGwgGVk1Ni4PpHJ/C3n+wEolN8xAtTsprLflkKwLxzpDB3oGxfZY0IFfpj\ngz15QUpE+JGcoaVoSv/toUdazvQNHzbKa24EkjfeyMV3jgXgwv9XzK41rax7rwGAzZ82097c88KD\nRBMWslx1bykG88CmItxOP6vfrO92H3OhBYAdL2zrVZnOZiFW1Fpi36dd83Y9s04VArXZp/VfqPZt\nklLEObzu92WccFkObz12CICdq60xKT9WqNQKxs+xMPMUcdwzT04jPb/jGW77qpZEVU0ikYxAxk43\nc/drM/nn7UJQtm9DfMZskjNEf/amv0ykdJYlJmUuf1rMcdpahn4bPxC2r2rB5w2i1gxMEHrJ3SUA\nNFa52PaFbFtACj8kEskQIa2gY2XK129U91rwcSTBQJCv36iJCD/SCxO7IngokDp9Hk3rxSoHV53o\nONR9+jZARDwQxtPSgKelIeo16+bVFF12c2hLAfTtu+mqTCBUbnSZ1i1rcVRXRLbrPn2HlJ8cC4Bp\nzHhsFbsj5XVd13CHIYhS0zGgGPC48budOGsOdlrXjGNPouHLD3HVdTiGNK75mPRjlwBgLp0ccb7o\nLX05/93FDws3rNvW9emYeoshdwwA9sp9BLwdq4jsFXswj5ty1P7h89BeLvLoNX39KQBZi85En5mL\n/eDeAdVnKONraafxWeGq0vTCCpKOnxoRgqRfcRqqZBNNzy3v8vOKbkQdYcFHwDV8V3JJJPFAodUk\nLHbA5SLoGT7XaPrMPKb//EQAPr3qhb42490S9MZhIEKhQGkQg8QBh3Pw440wPFXV+ENCJVVybAai\nekKbJ1bcqMxJ+NtHphBU0j2r3xKTcmfeUBjRbfWXWaekc/2jEwF47lf7cLYP/iq+8Gr0hRdlc+6P\ni7BkJK7NGWkE/EHWvC1+H2f8oKDPny+bm8x7fxOrH6eekNqv31flTnvU35HC6rfq5TU3wlGqFExe\nkMLkBWI1cTAA5Zva2PxJMwC71lg5tMNOwD80hLITj0vh7JsLmXBscs8795IvXq6NrITuCr9HvK/S\n9W7aw5RlAsBtdQ2scl3w3K/2AVAyw0xKdmwXeJTOsnDbs0JwW73Hzhcv17LxI/F7aKl1xzRWV2j0\nSgCKJidROsvMxNBq9/HHWNAZVHGpg0QikQBkFuq5/fnpAKx8tZb3/lZJ8+HBuRcqFLDwwhzO/3kx\n0CHKGyh1FU5WPFvd844jAJfdz6aPm/rtBhgmLBy55ckpvPX4Qd5/UjwrJELAPFRQJroCEolEIpFI\nJBKJRCKRSCQSiUQikUgkEolEIpFIJJL+IR0/JBLJkODbDh+tdQNTYrY1dHx+tDuCK5RKNJYUPE3R\nVpjeNmF7FfRFr95RG5PIOP5UTEUiJ6pSp0ehUKBQqkLlKfrsxhIuE8BUND5SpihPdVSZHmtT1OcD\nHhc+WysAmtR0qBBlApG6hi3/w3VVKEX5wUCQ1u3rSSqZBMD4G+6hfc/WiAOH87CwxFSoxPFpUzPI\nP+dK8s/pPB2KxpLap2Pv7fnvTfxvx+7NMfUVd6iOxsJSFCp1JG2PsaAEV33N0fs31Ea/ELrYgl4v\nSm1iUzDEk6DHS/vnG7GtFjayY/5wC5aT5nTr+KEt6NqyWFsscht6qxu63EciGapc+r0k7rwvOp9p\nwA+zS2Kfik+hTVw6JH9be8Ji95egL7TUIcb9oqA7PisJlUYjIB0/+otju7DsN8+fF5+AoX6evmwc\n9m82xSemZEhRu19cq9tXtjB1cd/6z50xd6lYBVY608zLD+5n08diNXOsV7UnZ2iZf34WJ1wq+mPf\ntqTvLZU77SSliqG21JzBTYU1XPnydZFipT+OHyUzzShV4h4zZWH/flsjLcVLmNr9TnnNjbJrTqGE\ncbMtjJvd4ejltPnZu16Mn1RssVGxrZ2D24X7Vlvj4Di1aQ1iXWnxNDPTFqcy+zTx+8kqiu24gK3F\ny7tPVPa4X+M28SxdML+Ab/68jqC/6yW/SXlmik8VFvFVq/o3ltITYav+x3+4ndtfmI7eNDguGPll\nJi77ZSmX3iNSJR3aYaN8QxsVW8X3X7XHTnONG3tr7118FEpIShEOPGm5OtLzdWQXC2flvPFG8seb\nyC8T/fTwvVkikUgSiSJkdbD4khzmn5/Npo/EPMOat+vZ8ZUVr6v/NhDmdA1zzxRt3MlX55FVFFun\n+YA/yD/v2DOgOg43Pn/h8IAdP8IolPDdW4uYf14WAP/9ZzWr36rH4xyc86lQgCVDS1qe6H+m5+lI\nz9ez/n3RD2mqic94WWdI4YdEIhkSNFWKwcH8SWZMKQOz9TSnd0wGNR5yDKiskYGiSwVMMBBtkVlw\n3rUE3C4OvvIkAD5bK8b8YoqvuKXf0cNlAhx85clImUCn5So686YNvxbsKBOI1DUsDOmsrgGvh8o3\nngFAn1NA2qyFFF/xEwAaVi2ncc3HHeUr4NBrT2M/1EWakpAYQqkVDfrEWx88ape6z96JpD2JFNrT\n+e9N/EBHJ6VXx9RHGtd8BEBR0XjKbv4Nfpe4Jl21lTSs/ODo6viGT5qDmBD6jtQZyfgaOsmdG/p+\ngoFAj4oz88IZWN/9EgC/VQzCKHTivpd8ylwIBrGv2xGrmkskceO/7zo4sM9Lcqp40r71rmRycgfn\ncUOhSdxjTLzEDrGiaVMNn13z0qCUHYhTyhuV2QyAr7Gphz0lneHcLtqUuAk/QhgmlEnhxyjnnScO\nMWVR/9JxdEZano6b/jwJa52493y1rI4961ojk1q9ncwyp4t+V9HkJMZON0cmysdON0cGi/tDzT4H\nj1y9lbN/VAjAqdfm97+wEUxYGHRgcztjZ5j79Fm9SUVuiRhkn3hc39NH+H1B1r4zcgXW8pqT15wh\nScX0E9MAIn/DONp81B90UX9QXIMtdR7amzy0NQlhgsvux+sO4PN0PM+q1KDRCaGCwawiKUVDcpYY\nc8so0JFbYiQndE0O9sT/Sw/sx9HW829u67ObATjtr2dy6l9OZ+dLHc/W+hQ9WTOyAciencPky6ai\n1Igf4bZ/bxmEWndQucvO32/ZxS1PiTS+g3W+wtd/0ZQkiqYkHfW+2ynGoewtPjzuQNQEo0qjQKMV\n58NgUWOyqAd0jUokQxmdUYXBrMJoFmMLBrMKvUmNwRy+56kxmlUYkjreN5jVMRO1XfvgeNqbvbjs\n4pp0tPtw2fw428W20+bD2e7HZRf3PWe7P7IPgNvhHxULXl958AAg0kfNOjW9T59VaxQcExJqHHNm\nBn5fkIPbRB+mcqedhipXJC2W2+HH4wxE0s/pjCqSs7Tkloo2rniamaIpSTHrY3XGB09VsX/T8Fto\nNBB2rW2lfKNITVs6KzapacOCnCvvHcel95RGvvN937TSWOXG3ir6PY42PwF/EFUoVYxao0SjU2C0\nhK95NUaLOpLGx5KhJTlTG0lJaMnQRtLMfJvw8Ujhh0QiGfVs/EC4B+RPMjNxUQZfLzvaYaC3TD2p\nYzX95uUjczVPbwkGAnjbWtCmiwdbKnYDoDaJAbawgEGhFs2BMb+Ygy93CCkAtKlduxNExAiRJ8Fo\nBaVCrY6UCUTK7a7MI99T6vSok0TD77E2RsoE+lZXwFVbRc0HL2ELnYe8My6hcc3HEecNT0sj+qw8\nbPt3dltOeKKr/JmHjnrPZ+/IZd/b89/X+L05pr6iSRaDQhpzCvuefhC/c2TlvB4oYReZor/8DHd5\nNe4D4h7la2lHadBhnClccjTZaTS/+km3ZflabRT+780A2L/egd/mwDRvivh8fibWt1firWsZrEOR\nSAaN5qYAa7/seLC55kYzObmDE0uhSVzu96A3trnulVoV0366CICs44rQWnSo9OL4fHYPlct3sfVP\nKwE4/a1r2fb4Kqo/jhYInrn8egA2PvAxtSv3Y8gW7cyiv1+ANlkfyXX+wRlPx7TuwbgJP0xxiTNS\ncZUfSEhcXenYhMSVDB32b2pn9bI65p+fHdNyU7LFpOPSGwtZemNh5HVrnQd7qzcyYO6y+1FpFGj1\n4lnFlKIhNUeLzhDb1dbWenEvfOwH23G0+dizTgz2yUno7vlyWV2fhR8AS67MA8Cc1ve+wJZPmyOr\n70ci8pqT11x3GC1qiqclUTztaDHAUOaLl8V4YW9FW3Ubxf6rfvMFx981nyW/Pzny3uTLpzL58qmR\nba/dyxd3i4U7LeWD/wy+fVULT98mxm6+/8iETieMBpvw9Rjr61IiiTels8wUTDR9S5ghhBr6pJBw\nI0kdEWsAQsRhVkdcdxLtUjN9SVrPO3VDMAAuu69DKGL342zv2HbZfDja/bhsIeGIzY/T5scREm2u\nebu+84KHGI52Ud9nbt/DXa/MIH+8sd9lqdQKSmaKvmf471Bg6+ei/Xn7z4PjPDXUeeUhMV5x18sz\nYl62WqOgdJb4rsN/RwNSsymRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQyTJGOHxKJZEiw8v+E\nonHud/KYcVoWM88QK1Q29dGxY8FlhUw+MTOSOuaL50anUvLbWLd+TfrcEwBwVO3HZ28j+4SzxZvB\nUHqKkOOEz27DVDQOR1U5APrMPDKOO6XLsj3WJoIBP5aJMwFo37sVlc6At90aKTdcpohf3mOZKdOO\nxXZglyi/pYHMhWfgaxeuHvaD+yAYiLhqhOuqzxSrvjor1zxuCv5Qqhl3Yy0KhQJjXjEA3tZoy/iG\nr1aQc/J3cTfWRs6XSm/EVFwGQOv2bwh4PYRzzribelZH9+b89yZ+6/ZvAJHmpS/H1FuCXrFqSqHR\nMOEn90deD3jc2Cp2U/P+i5Ht0UgwIL5z69urMM4YR9JCoUJWatX42x14qhsBqPvTy9hWb+u2rJZX\nP0E7RuSxtpw0B1WqGV+j+I03Pbcc63tfDdZhSCQjBkUCPYcD3tiuFC69ZCbJE0QO0k+ueIGgP8C8\nh84CwF7TGnH76AvOOmEP+t/zniV7fjGzf3Vq7Cr8LYKe+KyaViUNr5WpQ42AXaQ+9DW3oE5LjVtc\nTWYGSp2OwDBLjySJLS8/eIAJ80RKjvT82Fhjd0VKtjbiTBAvbFYfj/1gOwDNh8Vvfe960a8LBhlU\nO+jhzrr3GrnkrhI0ur616SdcmtPvmF++MfIdQeU1F9fqSAaZbV+08OL95f367IEPy6lZW03xKcKB\nLGNSBlqzFo9d9F+bdjZy4MNy3K3x7aesXy7GDuxtPm5+YhI6o3TekEj6w+JLcpl/Xlaiq5EwFErh\nchJ2NOktPo8Yix4ujh+WULo4t8PPEz/awd2vijmIcPqN4U7lLjtP3irmQQL+UZC7pxPC6W0+/k8N\nJ1+dl+DajAxGxtUhkUiGPeGckn+9dj3X/30WV/9hOgDHXdjElo/qqdsn0k7YWjwEfEE0IevQpHQd\n+ROSmH66EIoUTU+mqdLJMzeLfOLGZA3mDC2KHp7+Gw85BuW4hgKNaz+JpPEovvzHBDxuGld/BIAm\nJSNq35r3XyTnlPNIn7sEAHfjYWqWv0TRJTd1Wrbf5eDwf18ja/FSAHJPuxCPtZH9/3rkqDIB0ucu\niZQJdFpuzfKXyV5yLgD6rHzcTbVUvvmseDMklAiLEMJ1dTceDn326LqqDElkL/mOOF5zMkG/H+dh\nIQiqevu5qH1bt69HqdGQfeK5ofOTht/pwFEtLMes29Z3eh66oy/nv7v4347dl2PKOfk8LJNmodKJ\n/HYKlYqJtz5IwCOEIzUfvoqjspziy38MwOEPX8VWvoNg6FyrDUkUnHctabMXiuPpRxqZEUEocWbT\n8x/S9PyHAytLpaLljc8AIn8lo5ubbhPprG74qYWn/9zGE4+0dbnfDT+18Pc/ivf//qfo/abO1PKd\ni0zMmScG4HPz1SiVcPCAEPe9+7qD/3umPZKlCyAjU8VH60U+lusuauDiq0yceKq4X6xZ6eKOHzdz\n5Q/EpPt1PzJTW+Pnl7cJG8odW+OT5qNTlIkb1Q/GWPiRMjGLpo3VAPhdouyG9ZUAZC8ojmmsWBNw\nueISR5WcHJc4Ix1PZVVchR8oFGjHFODa278JG8nIwNHm4++3iMHM25+fHnmOGwlY6z388dpt1OyL\nfpa0hyy0a/Y5BmRHPdJxtPnY9FETc8/qPl1nrGhv8kastEcy8pqT19xIYfuqFv764534vP2fCHNb\nXex+TaTS3R2risWInV9ZefjKrdz0l0kApOfpElwjiUQiGXpYMjoEpg2HXDx69VYAbnt2ar/S/g0l\nDpc7ePz67bgd/kRXZUjw2sMVjJ9jYcwUufBnoAxL4YdKpcAfY/XTxFniwWDXxpE7+SuRDGWu+4tQ\na+aMM5FeYIi8XjY/nbL56X0qK63AwO1vH9+nz9w2eUWf9h9OBP0+aj4QQovw3zDNG6JXEdsO7GLf\n0w8eVcbOR2/vsnzrlrVYt6zt8v2uyuys3N1//qX4zP6dXZYXLhPoVV2tW9di3dp1/Y6kZdNqWjat\n7vX+PdGX89/b+H05ptqPl1H78bJu9zGNGYdCKVaZtO3aFPWet92Kp7kBlb5jAG3PE7/usqxdj93d\nq3oNJZTaju5Q0Df4nW25Ck1yJE8+JgQcxy3U8/0fWfjiYzGZvnWjEFZMnSkedH9ws4VN6z089efO\nhSHfu8HM/BP0rP5CfP7T/7pQa+Ck00S7ets9yRiTFBHhyJH8/JfJHDzgY91qsertxNMM3PdoKmWT\nxMP0a8/bueTqJH71v2Li+NKliVs1q1AmcBLDH+h5nz5gO2QlfaZY1aDUqAj6A6TNENtt+/rn4hQv\nAk5nXOKoki1xiTPScVdWY5wxLa4xtYVS+CGBim3Cre+Jm3fw479NRq0d3hPR9QdFO/uHa7fRVN21\nAG7vulY5Cd0DX75RHzfhx5q360fNSkp5zUmGM5/8n1jY8/Lv9g/4mlXpVOgsQlChVPd8HdgO2wYU\nr68c3G7jvu9sBOCq+8ZxzJkZPXxCIpFIRhdHijuqdovFwY9ctZXb/j2V5Iz4Oo/FgnA/7bEfbMfW\nEh8X1eGAzxPgT9dv584XxYLwrCJDD5+QdMXw7vlLJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKR\njGKGpePHz/5YyCP/I+yPY6HWP+n8VG5+IB+ACyZtG3B5Eomk70w9KXarfORqeomkb3haGlGGUsGY\nx03Btn8nCrU2tD0Z8/ipVL7+j0RWcfBQKDDOmRDZ9FQOjxyXkpFFIGQ0c9ctzbyyPIvf/kGkh7pk\naR3BIPz2j2Lb4Qhw1y1Nkf2P5MF7WnA4gric0f3jpx4T+TLf/SKH8y4xden44fWKOmi1oiH9bFMe\np59j5KyFtQBUV/owW5Scd6kJAJ1egduVoJWzCWzsFZrYPkLtfW49GXNE+q7T3rwGn81Dy07hprLr\nH71zd1JpE5MbPOCIT6oXdYpM9RILPJVVcY+pzc+Ne0zJ0GX7KiuP37CDGx+bCIDRMvyGpLZ82sy/\n7toL0OMKvT3rWjnxcnkNdMeOr1poqRVOY6k5g5vm4Ms3Rl8/X15zkuGErcXLC/eVs+79xgGVkz5R\nuGYcf/cC0srSoQ+PDf+Z988Bxe4PjjaRqujJW3ex5bMsLvx/YwGwZAzvFAYSiUQSC7pK51Kzz8ED\nF2zmhj+JMd3SWcPDJfSbDxt5NtSvcdllipcjaW/y8vBVIp3PLU9OoXCSKcE1Gp4Mvx4/sPCsZIJB\nMcj86G1V/RJ/KFWi13fNHTl89/vSRk0iSTRrX69OdBUkklGLt91K9bv/B0DWoqUUnHs1AZ8YVHM3\n1VPz3gvYD+1LZBUHTPoVpwOgTDLgb2kHpegHGGeVoSvOxfaV6FR6Dw9skEkiGQg1VT4e+IWVBx8X\nQo//uVtMdheXiC777Tc3cbi66wfD5qbOU5DYbeL1Pbu8HDtfRzhLSuCI3bdvEallPB7Rtw4LPaor\nfZF9Dlf7I5qL5BQl9bWJeVAN+kVchTr+jzMKTWwHYY05FgyZIofpJ5c/j6e1azGFz+FFbYiOb8y1\noNQkSPgRr1QvKSlxiTPS8dY3xD2mJisr7jElQ5udX1n53UWbAbjx8YkUTBj6g3k+r2gX33ikghXP\n9v65dc+6zoWWkg6CAVj9lrg3Lb2hYFBiHNwu7LSr99gHpfyhjrzmhhf1B12s/0A8k845PQPFCPfq\nDviDrHlLiLJe/X1FTCzvF/x6EQBJeWb2vrUbe5249v3uoT/BtvrNejauEKkez/xhAadek49GPzJ/\nBD7P6Ei9JZFIBkZXwg+Allo3v79CjOee/7NiTrs2f0i2m16XGHx7+cH9fP5SbYJrM/Sx1omxyf+9\nbAtX/3Ycx54dn7SQI4lhKfwAWHS2GPzz++GPP68k2IdU20azitsfLwRg9mIzAK3Nvu4+IpFIBpmX\nf7kj0VWQSEY1bbs3R/0daQTcotOYNH8aqmQTQZ8Y9PHWNdP4r/doW/F1IqsnkUT44C0HC07UA3Dx\nVUIM8NYrYrDyv+92P8mu0ys47xITC08Sny8aq8aSrMRgFEqNsJNH5EH4iP5zW2v0Cy5nkMAR6hCP\nu2OATq1OnOtG0CsGhUeC8MPv9qHSi+M4490B8z+5AAAgAElEQVTvA+BziuNr+LqSjQ98FNlu2VnH\nmHMmU79euB8qgKm3LCTo78PDUAwJOOIj/FCnp8YlzkjHb22FYDCujjmaLLnIQnI0dRXi3vHb8zex\n9MZCzrxeTPgPxcmtjR818cYjFQDUHujbPa+1wUP9QRdZRfpBqNnI4as3hMvVYAk/wuWPZuQ1N3yw\ntXh58tZdAOSMNbDkyjzmnSMmPEzJw3YYP4rwAs5vljfy9l8OUbs/tv1JU7Z4htr92k6++fO6mJYd\nD8IrwJf98SCfvnCYJVfkAbDoouxuJ0CHA+Ub2/n8pcMAA3Z2kUgkowNTSvdtX7hNee33B1j7Tj2X\n/qIEgLK5Q8M1dP0Hjbwe6tc0VsXHMXWk4Hb6efpnuyPtxaX3lJCeN7gOgSOFodfDl0gkEolEIpFI\nJBKJRCKRSCQSiUQikUgkEolEIpH0imEpFf7y/VYWLBWKrRO/k0IgEOSx20W+4p6cP/KKdfzy6SLy\nSzqUQRW7XPz2hxWDVV2JRCKRSCQJpuW1T6P+xhvrO6ui/kok3fHis8KS/OzzjVHbXWG2CC33s69n\nUlqm4fOPxCqC55+xUVfrx94uOsi33pXM5OnaLsvxd2KA5+9HSsV4EPSGKmuIf+xYOn6ojRoW/Pk8\ntjzyOQC1X1YQDATQpYgDm/vAmYy9YBp7/28DADv+9hUz7zyJJf+5DBCpX/Y+9w3alOgTMfWnwuI6\n/+TxaJK0kVQwS//7Q3w2D5sfFvfCutUHB1R/v63732asUOrFyl2l0UjA4YhLzJFI0OfDb7OhMpvj\nFlNpNKJKEmkF/LbRmWZB0jV+X5B3/nKIL14WlsdLbyxkwQVZ6AyJSV8VrtPWz5sBWP50NeUbB5Y6\nYs/61mHtPhAPwm4U5RvbYp6f3ecNsvad+Ke5GqrIa254UXvAyYv3l/PqQ/sBmHZiGjNPTmfqIuGE\nZskYXu4PtfudrH6rni9fEy48rY2eQYmz7d9bAChZWsru13dhq2kflDjxwFrnYdkfKgB45y+HmLs0\ng3nniDR6E+Ylo9YkzoGxJ4IBqNjWzqaPROqade830lApV7tLJJK+0Re3q8qddh6+UqR+mXFSGqdc\nk8/EefF1/gj4g2z+VPRrPniqigObh28bNFTY9LFoR7Z+3syCC7I57bp8ALKLEzAg2ANedyCS2ieR\nDEvhx8O3VkYGoRefk8JJ56USCKXp+/OdVQQ7GZ+etUjYvN3++BhMlo4HmjUr2vjDbZW4HIn/MiQS\niUQikUgkoxuNRsEvHxSDuQ676NT+6iGxffV5Dfh8R3d0L79W9HNLyzQ893Q7j/62tdOyfSMos2HQ\nN/D83/1FoY3dIHvKxCyUGiXVH++Net1ZLwQVtkorGkvH5IWrwc6an71zVDkHXt8Stb3tsZVRfweL\nQJyEH2E0Gem4D0nhx0DwW1vjKvwAUGeki9hS+CHpgtYGMfn34v3lLPtDBXOXirQGc85Ip2xuMhrd\n4JnVuux+9m8SA7KbP23m63cbsLXEro3Zu66VhRdkx6y8kcyXr9fHXPix5ZMm7K0jqAMUI+Q1N7zw\neUX/f+OKJjauaIpkbCuYaKJ0poWx08WzQPE0M1nFhoSLAdoaxfd5YEs7u9ZY2fKZmACrPxifSf8d\nL24DIHV8Gucvuwh7neh/uFvdBAPdj/+/9723B71+/cXnCbD6zXpWv1kPgCFJxdTFqUw7IQ2AsTPM\nZBcb4pnRDyAywXVop50DW9op3yDEWztXW+X9VyKRDBidUczlKlWKSFqX3rD5k2Y2f9JMbqlYULXg\n/CymnZBG3nhjTOsXNiI4uN3Gpk+a+PK1Oqz1gyNsHO34fUG+eLk2ImAunWXm2LMymRISxMZLCOIP\njcvWH3RyaIed/SFxz/5NbVTutEfeTyTDUvgR8Ad59DaR19rnC3LSeamccqH4coMB+Mvd0eKPc67J\n4Pt35wDiBgHwyhOik/T8H+s6FYpIJBKJRCKRSCTx5pY7LEycIoQF997egkLZIfz40c8sPP6/R4s6\nSsZ3dOlXvH90jmxV6O2ikmHZ9e+UoCdxwo+w+0QssFe1ojZpyVlQDEDdmkOo9Gpy5ovt3EVjWXvH\nezGLF2v87fEVfqizMnEfqoxrzJGGr8WKtrAgrjFVyUMjv7JkeOCy+1n5qhjMW/lqLRq9kpLpQqw0\nZkoSBWVG0vLFfTg1W4spRYM2NEmt0Snx+4N4XWJlkMcdwOMM0N4k2oymaheN1e7IiuOKre1U7bb3\n6Bw7EL5aVs9Xy+oHL8AIYuWrtZHvXhI/5DU3/AiPY1futFO5085nL3a8p1QpyCwU31dOiYH0PB3J\nWcL1OiVLiyVDgyFJPBMYzCqMZjVag/g+VRoFKrUSVWjsHAX4vQG8HhHQ5wngbPfR3iy+3/ZmL9Y6\nT8S1p/6gi+q9DpqqE+vqsPiBJQAULh6Dz+nFYxMTcAGvP5HVijlOm5917zey7v3GyGuGJBVFU4QQ\nKG+8kbQ8PWm54vtPy9VhSddEvm+NTolWr0SlEdvBQBC/N4jPKy5Ql92Po80XEW+0NXlpqXXTUivO\nZ/1BJ4fLHZHrezCv63gTFqNdP0E6tg5l/nXnHv51555EV0MSJ3QGJU5b3+/jh8vFwpHXHq7gtYcr\nSMkWLrgTjk0mb5yRnBIhBMkq0mO0qNGbhNAkLDhx2UVMt92PrcUbafMO73dSvcfOrjVijM7RJoVu\n8aZ8YzvlGzscVVJzdBRPTSKvTHynuSVGUrO1JGeJ79yUrEajV6LRdgicfZ4gXo9owLzuAC67n7aQ\nQLq10UtrgwdrnRuAugoXtfsd1B8S7V5fhEjxZvAk3BKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKR\nSCSSQWXYLvsLq0gfu72KgJ+I48epF6cSCAR56t4aAG66L59TLkqNfM7jCvDYHdWsfNca9zpLYodG\nK1RbBlMGemM6eqOwtdMbUtFoTWi0ptB+JlQaPUqlGoUyZAulVKNAQSAo1HrBgI9AwI/fJ5RbXq8D\nn8eB1xOyAnS14nI043a0AOC0N+J0NANDV9ElgbGzUwChYN+zujnBtZEMDAU6g1gpqjemoTekojeK\n+7pOn4Jaa0SjEfcEtdaIWmNAqRTNm1KpQqFUo1SI6z9IgIDfR8Av1Pv+gJeg34c/ILYDfh8+nxOP\nU6h13a5WPK423K5vb7fi9UibeYkkVmi0RgymDIBIm643pIbeM0W16yO9TT9+sZ4rf2DmsxViBcGy\nl0W9TzxV2BVec6OZrz53sX6NO+pz9bUdqx5y8lRs2RBd7rU3Cdv05JSRo/kOJjBvjSrJhEIZXhk3\nsKVtznobG+7/iIk/PA6AOfeejt/tw3ZIPKts+O1HNG6oHliFB5F4O35osjLjGm8k4m+Pf45hdXJs\nUzdIRhdeV4DdX4u+ePivZOSgUCoIBnrXD8uZncPxdx7PskuWiReGRvdtxCGvueFNwB+MrEYO/x1t\n5B6bB0DVl5V8fucn+D0jy+mjO5w2P7vWius2/FcikUhGAip1bPJYWeuEm8PadxpiUt5oZM6kawBI\nSy7B7mzkq82PJ7ZCIYQrlZuNHzUluioJZ9gKP8IEA/DnO6sieXNOvzSN0y9N49iTxeBSaqY4xKY6\nMan3wA0H2bd1dHZ8hyMqlRZLWjGW1CIAkpLzSUrOR6cfuF2wUhH6+SvVqCAyqaQnrcfP+v0eHO3C\nCtPWdpi2loO0NR0AwOmQN5ahwLWPzwAgKU3LbZNXJLg2kh4JJSE1mXNITivGZMkLbediMuegUmtj\nEwYVKrUKlVpYXWr6WY7XLSa6bG012FqrsbUJsaGttRqnvQk5CimRRKNSiWs43KYnJecDyDY9RGq6\nEBHc/4dUWpoD3HdHS9T7994utl9fkc0Df0rjotPrAGhrFaKD95YJMdoV15m5675UikvE3c3tCjJ3\nvo5Zc8X53/C1m9nH6mJef51O3MOnzNBiSlKQZBbHk5qmAgWcdZ4Ru03cF23tAfbv9dLcNDDBRNCb\nQBtNhQKVRVig+6wDH1Ct+XQfNZ/uG3A5iSDgdBL0+lBo4vNYqcnOikuckUwi0iTJVC8SiaQrvvvS\nd3nrsrcACPh77hsEfAH5qCWRSLqlauUhQKTEGU2iD4lEIhnRKGIj/JBI1CoxLpqfNYfM1AmYDGKc\nSaPW4/W5aLOLxVfV9Ruob96RsHoOhGEv/ADRkfvrPeLL8PuCLL0yPSL4ANiz2cEDNx4EoKVe5loa\nypgsuWTkTCUlYzwAltRCFKGV+kMJlUqLOWUMAOaUMeSOmRd5z+Nqo6VxH8114qbQ3LAHvy+x+S1H\nIwbziLi9jVgMJrFiNz17EikZpVhSiwFQawwJrFXv0ehEztTUzDJSM8ui3vP7PLS1iDanuWEXzfW7\ncNqkilgyujBZcgEibboltRBAtuldcO/DQqCSkanip99vPEoU0dQoBizvu7OFPzyVzq/+Vzii/PxG\nIUzZtV1M5N5yXSM/+pmF634kRAleb5BN6z1cc4G4B02aph0U4Udegfhe//lq524MD/wpWoDz0K+s\nvPTvgTlFBByJdV4KT2THQvgx3PG3tqLOSI9LLG1eTlzijGSC3kQIP8xxjymRSIY+xkwjycW9F4bV\nbqjlrcvfGsQaSYY6N96dyUXXd7g6/+T8Q+zYKMfbJNHsem0nAHNvnceZT59N485GADztboL+7pVj\nW/65adDrJ5FIJBKJJDHkZc6krOgMADRq41HvazUmMlLEXE9GShl1TdvYuu81AILBgS1giycjx+9Z\nIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJZJQxYpbEB0OC3b//ugZ7m5+LftRhA7zuk/aEOX3M\nPekODMb4rIAL01S3g+3rno1rzP5iTMoiu2AOGbnTADCYMhJco4Gj1VvILphNdsFsAIJBP9bGcuqq\nvgGgsXYbAX/8V9qNJtRaJSrN6Na1BYeY/204rUNm3gzSsydjTBq5Vu0qtZbUTOFalJo5ntLJ5+By\nNAPQXC8cQFoa9gLi/iAZHsw96Q4A2aZ3QfiaDrfpI6E9h/i16bdc19ir/T750MnMoqou31/1mYtV\nn3W96nHvLi9vv2qPeq2xwd9lmVd9t/6o1/79VDv/fqo96rUD5aKf3V3dYo2/rb3nnQYR6WDQgc9q\njZvjhzorE4VaTdAnXRz7SyJSvSiNR6+kkUgkiUOhFFbZs380m9IzS9ElCzcwV7OL8g/K2fC3DZF9\nlWols38k+kGlS0vRJmmp3SBS5K35/Rraqzra47SyNJY8tIQVt4pUqwt/uZD0Sem4mkXf5N3r3sXT\n5mHpP5YCkDI2BYCrvrwqqn7/mf8fAIKBIKYcE2c9cxYAumQdfo+fF056odPjumT5Jax9eC2TL5sM\nQPrEdBz1Dr75q+i3VXxUAYBKK5zK5v1sHvnz89FZxPGrDWq8di/73hPp19Y+srZX51MikQwtznjy\nrKjtzOm9H3+Sjh8SiUQikYxMMlMnMKX0/Mi2z+eiumEDza3lYtvvxmTIpCT/BAD0uhSy06did4kx\n2/LKT+Jf6X4yYoQf3+a5R+uoqxIDWjfdn8fFN2fx1XJhw1y5z53IqsWFJEteoqvQJUqlmozc6eQW\nHQdAclpxYisUBxQKVVQ6CL/PQ8PhLdRUfAmArbU6kdUbkeiTRuStrU/4fZ6ExtdoTQBk5c8ip3Bu\nJO3DaEVvFGkO8ornk1c8H49bpDioq1zH4UNrI8IQieRIhmqbrlSK+2y4TR8N7TnINn0o4W9rS2j8\ncKoXCfjjmO5GoVSiyc3GUymvtf4SSECqF6VeH/eYEomka0rOKAGg+ORilt+0HFeLEGYkFyWjMWqi\n9p11wywKFhQAsOKnK3A1u5h65VQATn3sVN689E0C3g7bY2OWkbk/nQvAusfW0XaojfSJQhzobHQC\n8M7V7wCQOTWTs/55Fs8teA6AgP9o+2R7rZ1XznoFgMKFhSy6b1G3x3b8Xcez6jerAKjfWs/4c8ez\n6NfiM7Xf1OJqcTHl8ikApE9KZ9nFywj4RNxT/nAKbVVtUvAhkQxzXl36YqKrIJFIJBKJZIjR0LKH\n2qathNdL76p4F6/PGbWPtf0Q9c0i3ff8GT9Bq0miKHc+AAeqvyAQGB6LkIbc7Oibe6b2+TNBwOsW\n35bTFsDe7sdhE6uobVY/yelq/vKBGKAPBrtfBf/dsm19jj/U0BlSUGsMAPi8zh72HnxUal1E6FFQ\nshitbnSvkFSpteQUHkNO4TEAtDbvp2r/Sppqd4T2GFpODbFApVaQli9+k01VTgI95NQcKDqTalDL\nHw74fYnJc2swZVBQspjs0O87PDksiUarSwKgcNwSCsedGHEAqTm4mua6ncMqZ5xkcAm36UOhPYeO\nNr2gZDGAbNNHYZs+VPAl2PFDnZqS0PhDCV+LNa7xdGPGSOHHAEiEW4rSIIUfEslQQq3veEbzOX14\n2sWigYZtDVH7KTVKJl86mc9+8RkAzbuFWH394+sBGHvaWMaeOpby98sjn1FpVex4cUdUeTVf1wzO\ngXTCvnf3UbmqMrK9/fntzL5JOJaklqZyeP1hMiYLZ7rab2rxOTvuiTVf11C4qDBudZVIJIODs2lo\nPDuPRvJ/fA6pp86Keu3Qg0K817ZmVyKqJJFIJBJJiCDb9r3e47xLWAxSWfs1pYUnoVJqAbCY8rC2\nHxr0WsaC0Z0LQSKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiWQYM+SWYitVin59TmUUn9MblaRm\nHX1YipDERUH/yh9umMwirUJr8/6ExFeqhD1oQcliCkoWRxxIJEeTnFZCcloJtjaxCqZi94c01+1M\ncK1iyw1Pz2bcPJHqYvunDTxz89E5M3+3dknM4imkpA2/L35prYzmbIonnA5ARvYUUIyO+2zsUETS\nRqRmluG0N1GxezkADTVbkI4BEpM5N2HtOYg2PezwIdv07hkNbfpQwZ9gxw9NTnZC4w8lfI1NcY2n\nHVMAX8Y1pGSAyFQvfUeav0kGk/IPhENHwYICLlh2AYc+EyvXtr+wncYdjZH9knKTUOlUtOxtifp8\nOCWLdb+V1NLUo8pv3pu4NJbW/dEuVMFAEJ9LuHpoTGKcqvWgSFGWPSsblVYVSfWSPTOb5j0yBadE\nMtJQKENjVL0Yqwp2knJKIpFIJMMJca9PsxSTlTaZZLNwczPq0lCptPgDIvWp092Cta2CyrqvAbA7\nGzsvLsTcKT8gxTyGHfvfAqC6/htSzEWRNCAp5kLUKj0er52m1n0AHKhZidPVm77l4NS5M9IsY8nJ\nmBGq8xh02iSUStFH9noduL02Wm3CPa+5tZz65u7HNIN0tJvJSQWMyTmeFEsRAFqNiUDAi81RD0Bt\n41aq6tf1yencZMhkTM48UffkUnRaC+G5Epe7labWfRw6vBoAp7tnN9q+xLa7os+vRj18xsOHnPBj\n21p7oqswIkiyJEr4oSArfyZjJ54JCIt6Se9IsuQBMHXutbS1HKR8m2hE2lurElmtmFA8s+N3MGF+\neqf76M1D7nY0rPENcqoXrd4CQHHZaWQXHoNCqm1ihsGUzqTZVwBQUHoCB3a+j7VxX4JrJUkkSZZE\nCD/EQ0e4TZfted8YyW36UMHf1pbQ+FL40YE3zsIPXfGYuMYbaSjU8e9zK3S6uMdMNAPNduhxy4kn\nyeARTm/y8c8+Jn1iOhMvmgjA0n8sZdNTm9jy7BaxY1h/3sVcaWQy9QgC3sT9fsMij+7Y8i9xfKcf\nczoXv39xJNVN445GNj65cVDrJ5FIBp/wvWn692cy/twyjJmm0Bs9f/Y/8/45iDWTSCQSyWAzZ/L3\nAEizlHT6vlolnk3NxhzMxhzys0Tq5q37Xu1R5ACQZMgCYEzOcZQVn3mU0YBel0x+1hwAcjOms2nP\nizRZux/XH+w6hwULU8ddQEZKWZf76bRmdFozFlNuJF5P5fv97sjxThp7zlFzREqVihSzGMNJMY8h\nK30SG3Y+B0Aw6O+27KLcBYwfc2qX804mQyYmQyYFWXMB2LH/TQ43bum2zL5wpNDD7bXFrOzBZsjN\ntN59eeJWtI4kTCHhR7wwJGUCMGH6RVjSiuMaeyRiSS1i1sKfAHC48msqdn2A1+NIcK36z8b3a5lz\nbm7k/56o32/HN4DBIrVGSVaJqd+fH/YEg/h9nkEpWqFQkl+yiOKy04AOdx/J4GBOLmD6cT+kpWEP\n5dvfBsBhq09wrSTxJhFt+oTpFwHINj0GjLQ2faiQcMePdOFkplCrCfp6nmQaycTd8SM3B6VRPIAH\nHDKHe19JiPBDrYp7zESjMwzsmD3O7gfBJJJY0bSriS/vFzZKNWtrWHDPgojww1Zjw+vwkjY+LbIN\noFSJwdfk4mT2vrO337GDfqEsUYSdf+P0s0/KSwLAmGXkjQvewN0aP7dMycCQXpiS3jDpsikAzPjB\nLFwtLg6vF26MuXPzaNrZGHH5sRQlo7PoqN9UB8Ded/YkpsISiUQiiRlhkUWKuYgm614araKvanPU\n4fU50WnMAGSkllGYfSzKkGJ/csl3aW7dj8/ffb8wO30qAFptEq3tlRG3CburAaVSQ0ZKGWPzFgGg\nVGqYUXYZX23+MwCuLhwpBrPOSqWGOZOuAcAcEnSEnTEON2yk3VEbcRTRacyYTTlkpgpReHX9N92e\nCwCDLo1JY88GwOtzUlGzkpa2g6F3AyQZcygtEE7/el0KaZaSiEtKRc3KTssszD4WgLIi4TDf0nYA\ngMq6dThdTRGHErMxl7H5i9FpxfmZMu4CPF5HxHFloITPVyAgxvwc/XBYSRRyibZEIpFIJBKJRCKR\nSCQSiUQikUgkEolEIpFIJBLJMGXIOX5IYkO8VgdHVv9PEOor5UA9bSUdhHJP5o6ZR0b2FPZufQOA\nxtptiaxVv3jxF9t56Z7tAAR7sUTjyR9uoKWm/6lK0goM3PPfhf3+/HDH73cT67UwScn5AJRNvzDy\nvyR+pGaWMXvRTwHYv/NdaipWJ7hGkngSjzY9bJsXbtNlex5jRlCbPlTwt7aKf4LBXuXrjjlKcc1o\nsjPxVB+Of/whhK/FStAvlmkrVHFwdlAo0JeMBcCxbcfgxxthKDQJcPyIx+9iiJGSrR3Q51126fgh\nGTwKF4u84V6bF+t+ayT9Qda0LNqrOxy1Av4A257bxpybhX2zrdaGs9HJ1KvESke/x0/FRxX9rkd7\ndTsBX4DiU4oBOPTZIbRJWuz1g5sCOpwORq1Xc9mKyyKvex1eatbWsOreVZHtzgi7Tb+7dRx6o5Jn\nHhGr/1544ugc7tPmGvjTK4WR7aZ6HxfP69rp+JkPiykuE/eP1/7Rwt8eaOhy37RMNd+5OoW5JxgB\nyCvUoDcqsTb72blROGKteKOdrz7quy31xwc6bMDvvKaadZ+L70ShhJPOtXDGhSL1a9F4LcmpKqxN\n4p5Vud/D15/ZeeXpll7HmjBdzwXXirSS048zkpquos0qytu5ycU7/9fKui9EfK+7d+McP39IpOQ7\n85Jkvlxh41c/FI4POr2C865JZcnZYmVoXpEGhRJqK8V3vepDG6/+owV7e98caON5vtRqBaeHylu8\n1EzJRC2WVNHO2tsDVOx288UH4jt/76VWvJ6+jw3lFYmVtGdflszM+UbyxohtY5ISnwdamsQ1VFnu\nYcs6J6uWi3iHyvvmPJtXpInEAMgbo4nEABEnHANg1XJbr2KULh0HQHtVG+9e/RZeu/h+r1j5Pb75\nyzpq14u+u0KlZOrV05h61XQAmncOn5W8EolEIumcytqvAahp2IjHe3Sf0u4Ufavmtv243FYmFC8F\nRFqPtORS6pu7H2MIu0u0tFXwzc5nCQaj+wxttmrabNUAzJp4JSqlhnGFJwOwbd/rca9zacGJEecK\ngMONW9hRvgyAQGepVhpgd8UHoY2ex9q0GlOkzmu3/R2XuzXq/Tb74YiDycJZt6JSasnLnAV07vih\n01oiTh8AVXXr2HngnU5jW9sPUdu0lQUzbwFAozYyoXgpq0MOK8F+zo+FU+vkpE8DoKFlN0CPbjBD\nCTmqP0IxmXPEPwpF72ba+4hWLx4yJs2+guS0sTEvXxKNRpfE5GOuBqC+egP7tr6Jz9d/YUQi6MvP\n0N7S+QBLb3HbRrnlujeWvw0FhaUnUDzxDLHVRU41yeATTqszbup5pGVNZPfmVwHwuodPfjlJ/zCZ\nczomtgepTZ80+woA2abHgZHQpg8Fgl7R1vuaW1CH0q4kAk1O9qgXfhAM4msQA+WanOy4hNRPGA9I\n4Ud/UJkSkA5ROfr6j9nFhp536oammuEzqCQZfuhT9ADM/elcTFkm/F4x6Nu4vZHPf/F51L5b/rUF\nlU5MKp/2+GloTBrqQmkRVtyyAr+n/yIld5ub1Q+tZvaPZgNw/J3H017VzluXvxXZZ97P5jH2NNE/\n1SZpUWqUXPGZ6Ld67V6++t1XVH1Z1euYGqOGM588E4DVD62mclVlJOWMPlXPkt8vYeJFwt5667+3\ndlpGeHx/3w43U48xUDJB12W8KXOi7wXpWWqy8sRQbH1N9LiFRqugsLQjleqebZ33D0/5rhiPu/WB\nLAzGo++vmTlqMs8UkxKLzzSzfqWD+24Wwoe+ChpEnVWROPc9lcfsBcaj9snIUUf+GpOUvRYyXHR9\nKjfcmcmRwwxpmaK8BacmseDUJN78T8iS/FDfx4qKy3SR8h55voCi8UcL88aGvsOxE3QsvSSZ/3eV\n+E0d3Nv3FLqDeb4Kxmr57T/yKCzpXFyYnKpixnFGZhwnYl5wXSp3X1dN5f7eH8fZlyfzk3uzACEy\nORKVAXIKxO80p0DD3BNMXHdbBgCXHL+fpvqex+POvjwZgJ/cm9VljHD54RgA192W0asYlkJR/q7X\ndkREHyBEXxpTx7kL+gNs/ddmsqaL/uusm4/hk9tW9Fh/iUQikQxd/AFP1N/uqG3aFhFRAJgMGb2O\ns7/qs6NEH2EarSJ1WEtbBamWYrLSJlvMgtgAACAASURBVAOgUr4dSasSjzqrlBoKQmlTAByuJnbs\nf7NzwUen9G4Men/1ZwBHiT7CeLxi7qKuaQd5mTMjdVaptPj90cdcmD03ksrF7/ew5+DybmN7fQ4q\na9cCUFKwBJMhA0uSWLTcauv9M8K3KQ0JdYQAJEhFzap+lZNIpPBjhBKeIDSYMnDaul4h0B9SM8Yz\ncZZYFaHRJcW0bEnPZOXPxpxaxM71zwFga6tJcI1ii88dGHBOa2f76BZ++H0DHyjWaMWD9YSZl5CW\nNXHA5UliS1rWJI5ZfBsAuze/QnP9rgTXSDKYKFUaDCbRKR6sNl2254lhpLfp8cBbV59Q4Yc2N4fB\nXZc8PPDUiknAeAk/DBPLet5J0ikqiyXuMUeb44dCAaWzBnaeG6ukIFAyeOx9e2/U3+4IBoJs+OsG\ngMjfrmje08yzxz7b57p0V4+1j65l7aNre1XWy2e83OnrL5z0QuT/nDk5KNViUv7AigNR+9nr7LQd\nakNn6VrI8W32bBPCj7HdCj/0kf8dtgDGJCWTZopZ7fqa9qh9i8u0qFQdk+B7th79XH/qeRbueFQs\n9FIowOMO8uFrbQBsXefA5QiSna/mpO+EFmvN1HPMIiOPviBcR2658BCeXrpmhEnPVvOLx8VK0dkL\njBw+5GX1x2IAv7bKh96ooKBYTKYfu8TEmk967hmdsFQIU268OxMAf0h888ErbWz8yoHfJ7aLxmk5\n67IUvnu1cATpi4AhTF6hht/9U0wAjCnV8tl77RFHjtZmP5m5Gs64SJyvCdP1pGer+f1/CgC49tQK\nHLa+iWUG43xl54sx3sdeKyQlTRURH32xvJ2vP7PT2izG0FLS1Sw4LYnjTxbjOXlFGv7wUgE3nHUI\ngOaG7sfKJs3Uc+v92REhjq0twIevtbJ3m/gttrf6MZmVFJaK+s88zsjk2XrWf+EA6JXoIxwDhCNK\nOAbA3m3uSAyAwlJtJAbA+i8cvYoRDIjfT8AX/d152jyYso4WwIYdQKZdO6PHsiUSiUQycvB4bRHx\nhkKhjDg9dEcwJJqwth/scd9G6x5SLcWoQkIGsykXa/uhAdS4b3VONo+Jer+qbh2BQOznzeqbercg\nx+lqitrWqI1HCT/SUzrGeqy2yk6FMkdic0aPlQ9E+JGVNokxOcdFtg8eXk2bvbrP5SSa0bf0RiKR\nSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiWSEIB0/RjhJlryYrg7OL1lE6aSzE5NLXRLBYExn5oKb\nAdi3bRm1lesTXKOB01IjVrW1NQ7crSLgD+JxCfWlVj+6VhgCA04ZYLLkMnXutQDoDCmxqJJkEAg7\nNEydey17ty3j8ME1Ca6RZDBJsuQBsXP8yC9ZBCDb9CHASGzT44m3tg7D5MQ5U+mKixIWeyjhPSwc\nP5gZn3hhZxF1agq+Fmt8go4QVBbp8DTYjD8mGVPywIZaavc7Y1SboU3W3AIW/+nsbvf54MLnsR9u\n73af3pI+NZv5D50RWQW+8rb3aNvfHJOyJcOD9qp2tEnCraBwUSHVX1WjCo0ZFC4sZMziMXx020e9\nKmvvVvHcHU7PotYo8Hmj3TQmzzJEMjWuWNbGd65KYdJM4V7w+fvRv+vSSWJFpsMufp9VFdErILPy\n1Nz626xI1721xc9tl1ZSsedoF4xl/xZt4/f/XwaX3ZTG+Kmi7OvvyOCJ+/r2PPHdq1MiqVKe/WMT\nzz/RRKALk1alCnS67tcYqjUKbv51ZmQ7GIBffF+43oWdOMKsBF7/p5VHXxQOHBOm6+krCiWR47//\nx4f57L2j7yfvvCDO191/zOWkc82RVCxX/iSdpx5M7PkCuOMR0e9JSVMR8MOvbxTn66uPjk77+sEr\nrVz4/VQAbronk7RMNTf/Spzv+3/SfXrCU86zRKXduf2qKnZv6W58qYmUdBXGpN6vK+1vDKDXcey1\n4rxYxiRHvd52qJW8eXnsejV6ZXL4HqDUyPWxg8IgpKuVSCSSnlAq1WSklJGWLFIGJhmy0WqS0Kj1\nofc1qJTqPqe1d3mE01pv0qXYj3CiMOrTu3X8iHWdzcacqG1re2WvPtcXvD4Hbm/vntV8R6SyUR5x\nHAoUmI0dLrLpyaWcetx9fa6TRn10mr3ekGTMZmrpBZHtVlsl+w4NzxRwUvgxwjGZc2hg88AKUSgo\nnXIuAPnFC2JQK0ksCKfzKZtxMYakLA7s/CD0zvDsUN9/ysqYlue2jV7hx0BSvaRmljF5zlWo1L2z\nt5UMARQKxk87H21ICHJwT+8GKiXDC5NZdNZj1abL9nxoMdLa9HjiratPaHxt8RghnhrlA5reUKqX\neGOcPpW2z4dfvtVEokqJv6g36BtdaRgXXDDwlEflG9tiUJOhj6fNRcvuBnTJYjBTm2JArR+8Yaqc\n48egSzVEtrNm5w1p4YdSo8KUZ8bZICbDfY6erY4l3WOvs/PFr74AYPZNsznxdyfic4t7VGtFKyt/\ns5Lab2p7VdaebWKyOpyepWiclvKdHc/i+UUaUtJVVB0Qg9yb1ziF8GNW5+KFsPBj33ZRxpFp4y+4\nNhW9sWOA/O8PNHQq+oCObskzjzQyd7GJcVNE2edckcKLf2vpMeXHt0nLVPPByyIVx3OPN3W7b8AP\nTkf3qVHmn2IiPavjOv/47bajBB/fxukI8Kd7RH/vb2+P6W21o/hmlUhF0pnoAzrO9eO/ruf4U0wY\nQuf5jIss/PPhRny+3vfzYn2+ph5jYMZxHRMY77xg7VTw8W1ee6YFEPUfO0HH4jNFap2MnAYaa7v+\n7s3JYvwsfD4q9vQ8tmRt8mNt6n265nCMcJzexvj2356o2yiu4bFnlKLWq/G5xDFXf1XFsT8/jmN+\neiwABz+pwJRtYuJFkwFoPSDFxINBOPWORCKRxIPM1AkATBp7LjqtOeq9QNCP1yv6HG5PO/6AG4sp\nv0/lH5mapDuOXJyrVnfeBxysOmvUhqhtTy8FGn1hoAuQv41are+zEKczjhSU9IReK1L+zZp4FSqV\nFpdH9OM27X6xVwKfociwFH6cd30GC5eKAav1n7ax7B+NuHroKI9WwiuE+4tCqWLy7CtJz5kSoxpJ\nBoPC0hPR6cU1sWfzK4OSq2u4ce+SLxJdhYTRnwY3u2AOAGUzLopJAyuJP0VlpwGg0ZrZt/3NUT8J\nOdKIRXsOyDZ9GCDb9L6RaOGHUqdDm5uDp6b7FZQjnUQdv3GGFH70BaVOhzo1AcIP7+iZrM4q0nPc\nuZk979gNHmeAyp1dT4KOJKy7G/n4utejXpt41Sym3jhvUOLVrqmk5DuTI5OAtWtiv+otluQtKua4\n+09lzT3/BaDq0/0JrtHIoOLjiqi//eVQuQe3M4jOIIQfYyfoooQfU+aIwfZ9O8Rru0MOIeOnikF/\nlUqB39/xzBYWfuzZ2vnz/AlniYmAsCPIp+/0PHgfDMB7L7Xy0/uzANBoFSw6M4m3/tO3Ce7n/xo7\ngdScRaao7Y/f6vk4wufk8CEvuWM0fY656sPuhRJh2q1+1n1ujwglklNVjJuiY9fmvo2xxPJ8LTkn\negLow1d7Lwz8+jM7YyfoCD0KMut4IyuWdf35Q/vcgDniyHHdzzN48sGGLh1L+kM4Bgg3lnAMIGZx\n9r27FwBdih61oUP4sfft3ZSdP4HJl08FiPwNCxO+vK9/44iW+UI4MuaOCwHwWUUbvut7jx61r64g\ng9RThEWeaVox2tw0lHrhQuS3OfG12HBXNQJg21BO+4Z9+Fq6//3m/uB0ANLPiW47Kx95ndaV2/t1\nTACTXrwDlbFjUVjQ62f7hQ/0uZygL/qLNZSJCcuUE6djmlqEJk38HpQ6DV6rDU9VI21rdgFg/WIb\nAWfvJ1lVSXom/Os2lNqOKa+A08Oua8R3EXANvE9a8D/nkXLitKjXav76HgDNH34z4PIlEkn/SU8u\nZcaEywHhHuHx2qmoEeMFDS27cLqaCX5rgZVCoeSUeb/pU4w+zZsc6bDcyVD9YNZZcUT84CAsLgsc\nqVQeENH1rW3cSnnVx30uxevrvXumRm1g1qSrASEA8fqcbNz5HAAeb+/6j0MRObsnkUgkEolEIpFI\nJBKJRCKRSCQSiUQikUgkEolEMkwZlo4fc04wM366UM5nFWh4+YnY5LsfiZgsuf36nFIpfhqTj7ma\ntKzE5U2X9J6sfKEY1+hMbF/3LAH/6FlZ1xkB/+h1O/B7+7YaJafwGMqmXyQ2jlSiSoYdecXHo9bo\n2LXxpURXRRJD+tueg2jTJx8j1MuyTR8eyDa99yTa8QNAN7Zo1Dt+hL+HoMeDQquNW1z9uFJUFgv+\nttGRFmOgaHKyEhI36B35zkXhLvSV945DqRpYf3r7ly34+5BWQNJ7mrbW8s7Z/050NXpN9tyCRFdB\n0g0BP5TvdDN5tnDwKJkY3f5FHD9CqVtqK720W/2YU4T9wtiJ2sh74vMhx49t0akv0rPF+Fxmrvi7\nN/S+19O7+8T2b6JXPU6dY+iT40ddtZfDh2LXFw07m4TpyuGkM/bvcvfL8aM36UQ66uOOOH6A+F76\n4vgR6/M1+VupgcK/ud5SXxPd/haUdN9He/eFVi7+YRoms1gneuH3Uzn+lCTefFakjlnxZjvt1oHZ\ncoRjAJjMykgMgDefbYlJjMbtYp7g87s+iXrd7/az/IfvMfFi4dCRMjYVR4Od/cvLAWje3X1qnt6i\nThapeRQqJUF/AIVKnM+ca04RrhxdjLupk02ok03oi0XKuOSFU2he/g01f3svJvVKFEG3uB5URh25\nPzyTlCXTu9xXm5WCNiuFpNnjAMi85ARq/vYe7ev29CqW3+aideV2Uk+eEXlNadCSvEi4u7Ss2Njf\nwxBl6TVYjoseUwl6fLSu6r+zykDJmDSfMQsvjGzvfO1hnC3xfTY155cBMH7pjd3ut+2l3+JpT3yK\nvfFLbwLAnD8eV2s9O155KC5xTdnFlJ56HcGQvdG+D56K+3c10hk35hQUIdeIwP9n77zD4zrLRP+b\nXiWNerOabdmWe4/jJE4jvUIaZAPZLCFhgUtZYC+dvSywd2/o3JRLDQmEEEJwQqrTbMdJHPdeVGz1\n3kYaTS/3j08z8lhtJI10ZqTv9zx6Zs6Z73zfe47OOe9X3hL0sff4b3C6R3+3q1SaUX8bDY3GMH6h\nQc5PteIPDI9EMZ0ynx/5Qq+14vbYYz5+pvEH3IQIRa6HWq3F6Z6+d4ZGrWPN4nuwmsQ8STDo49Dp\np3C4lJ9nnCpJafhROH/o4dq/vX9OL/COh8FkQ6sz4ffFHt5GLhAlN+lZ5Szf+C8c2/M7ALlYNAeZ\nSKqXvKINLFp1O+eH0pIkNzmFa3ENiI5RXeU2haWRxAODSYTmn6xOl/o8OZE6fXwCjgECDgcaq1Ux\nGQxlJfS/u1ux9hOCwfRi3qZmDGWlM9euSoVl/Rr63toxc20mMbr8PEXanQupXm7+fAkAFRdOPZXO\n3pekY4tEkCMNPxKeymPuiOFH2eLohYDw/nONO04f9bD+ErEoXLHaSPVxT8SgI2wQUnWeIURmTvTU\nbUfLxN6p5y/+Z+VNbCq4qy2+uc3TM4cWLAKBEL1dsdff3TE5Wew9sR/X1R59vWyZE1sUivf1yikY\nMnRRa+C1qvJJ15WSOnbg797uAP/+8Ua+/lPRX5hXpqewRMdnvysWRB78RjbvvTHAS38WhkP73nFO\nWIZwGwBf/2lepA2Az343J9IGwEt/7p1UG2Ph7fdy5LeH4lrnMAYNOzRpFgL2AYq/eRcAKesm/r/r\n31cVV9GUIJxKp/R792AqL5zQsbrMFEq+eReNP38egN63j4x7TPer+6IMPwAyrhHpradq+JF6YQVq\nY7TxWd+e0wQGJp5yezYR8Ijn1NnZiNZoQWsUKb3U2plzCEgG0ooq0JqG5i2sBQul4UccUau1pFqG\n3jFd9poxDShApPaYKEa9MA7VqHUEgmP3ySym6BSgA65oeaZbZocz2oAhzTqPvoGmmI+faUKhIAPO\ndqzmQQNIayFizSq+6/9h45lViz5KWkoRwZDoux2qfJre/rq4tqUUSWn4kZYxJHZzbex53uYqltR8\n7F0x5oJVqViy9m65QJTk2DIXsGLjJwE4uue3cqFojhHwj+8Bkpm3DIBFK6XRx2ylZNGHAHANdNLe\ndEBhaSTxImadPjjZJHV68iN1+vh46xsxLVXuPjeUlijWdqLhqW+cWcMPIOXCjdLwI0aMC+Yr0u5s\nN/y49v553PiZorjUNWD3c/ht5T0hJcqSWpoOgCU/ZZySEqU5N1pF2WDEDrNFLK6XLTIQCsGpI0Nl\nTh92n2P4YeIff7JHRcBwOYM0nI2e5zSZo8frHvfEJr/drujc62brxLJ++3zxnWw3mIban+i5eFyT\nyyPvn4Aa8nqiZTKalL1eE/1/jYVGO/7cz6nDbj55jVj0uOa2VG77l3RKysXirVanYst1VrZcJxYu\na056ePT7HRx8b2LGGeEIKp+8pi7SBkBJuT7SBsCW66yRNoAJt6M0uowUcj92aZTBR8gXwHmyHgBn\nVTOBfmdk7K7PtmFaVIBpQX6k7MCRszMveJzJf+BagMh5eRo7AbC/ewJPXXvEaEKTasayvATblhWo\nTecYDKhUFH7+FgC8zd04TzeO2Z6rsglXTUtUm6byAgCMZXm4z7ZO+lxslw2PVtL71uFJ1zdbcHaK\n/8mpv/8kan/e6isp2HCDEiIlJPaGk2QtuZCgX+j5voaTCks0u9Coo42y/IHx10dyMiom0ZJ4Z2ek\nLaCj59SYJbNsIhpOICj+5/3O6PfPdMvc219PMOhDPdjOvNz1NLbvJRSaXH9qJujoOR0x/DDoU8lO\nX0RHz+k4tqBi+YIPA5BpKycUCnKs6lkAunqT39gyTPx6jxKJRCKRSCQSiUQikUgkEolEIpFIJBKJ\nRCKRSCSSGSUpI3709wRIHwx16HYmrnVSomBNiT3ix8Jlt5CVt3yaJZLMBGmZwqOvYu0/cWLfEwlt\nySeJL+OlekmxFVGx5m6xMUpuUcnsYfGqO/C4erB3J7+niCR2nb5wmfCIkTp9diB1+th4ausVjfih\ny81BY0sDINCbuPlSZwJvfcOMt6nLy8W4UDwj7uoYoxzOUYzlCxRpN+idfVE6rTYxH3HP9xay7pqs\nuNX71pPNeCfp0S4ZwjpPvJOv/cvHxiy3/V+30nlkEp6/g0OoeZcvoPjqcmyLxT1gTDeBSoWvX3jr\nORrtdB1ro/mdWgA6j7QMi1SsUqtYcu9aAGwLM0krz8JaEB3CedP3rx5TnNN/FKHrjz76wcTPRTIl\nKo8OeWZm52kxW9SULxcRPFRqaDjjpb93KPXHyUNDKRsXrxSpYOYvGYr4UX3cw/ndPOdA9A6jcWJj\n+PMjVjgdyr5jPO4gIEJs6/UTOxeNbnLzF4YJXLPzy7oUnnd2O4PoDeJ69dsD/OALk49W0N4cW+gT\n/2DUkpeetvPS03aWrTMBcO0dqVx+Uwoms7inFlQYeOiP8yIROf72u54JyeP3hSJtACxbZ4q0AWAy\nqyNtADz6/Y6Y2kgrFWnXnO0D+JzKRR3LvGUTti1D4/GebQdoe2o7/h7HmMdp00XEE/OSIoKe5I+a\nFo66AdD2p7fpfHYXMJQC5lzsO4/R8ewuSr4l9LexRKQZUqnFc1nwmRuo+dKvRjz2XLpf3Q9A4Wdv\njNqfcc1amh97ecLnoM0Q96R1VVnUfn+PA8fBmgnXJ5mbDLTVcuSP31FajFmLz+/GH/Cg1Yh+Vaql\nABUqQqOkCbGYsigr2DLp9ubPu4wuezXBoH/Yb9npYn7KllIMQFvXcQCC56WGmW6Z/QE3TR0HKMq9\nAACrOZfFpddz+uxLAKO2E0at1o54ftNJfetuivMvBERElCVlN+FwiX6Gyz1eZEwVJoMNl2f0vsKS\n0uvIywpHbwpx4szztHUfj4PkiUVSGn7UV7tJzxGdoNx5unFKSyyp+eOWmbfgUgAKSjdPtziSGSYz\ndynlKz5C5ZFnlRZFMkOMlerFaE5n+Yb7UGvku3OuoFJrWLr+Xg7s/CkAHvfMLkqmZmgpWmzGZB09\nN/K+1yc2QTSXiVWnS30+O5E6fWQ8tcrn4DQtWQyAY/cehSVRFveZWkXaTb1cTH5Iw4+R0WZkiM/M\nDEXaD9j7J3XcvT8op6fVQ+Ue0XepOdyPz63sApxao2L9dVnc+TUx8Z+WHZ/c5S6HWBh+84nmuNQ3\n1wm4xQRl94l2DDYj+jSxwK6zTP3/pTFquei/rwMgZ33hiGUM6abIZ+aKPMrvEpOLL9/2R1ztA1Fl\n1Vo1y+7fMGW5JMpQV+2JpCsxGFUULdCzYKkx8vvx/dFOGScPDW0XL9BjMKoiaTQgOnVMmI6W6An3\nnMKJjeVzC6Onfs+vb6axdwfIG5zL1epUpKZr6OsJjHOUwJYx+phyLNKzYj8uOy/6evV2xSbbdNHV\n7ic1XchvMqs5sMtJIBDfdDLjcXy/K/L52A86uP2TIjXL3Z/NQKtV8a/fzAZgz/YBGs5M3tjz+H5X\npA2A2z+ZHmkD4F+/mR1TGxf/h+gXGtON/O3WZ4YZ3M0UYaOP1t+/DkDn1vdjOi5sGNL3/uxKA9Hz\n+kE6nnln3HK+djt1/+spAMof/kxU2hdjaS7W9Yvo3zN22H/7zqMA5N93FWrzkHGd7dIVtP7+9Qkb\n1NguXSG+nOc81/v2kXGNUCQSyUwRor37JAXZqwGwmLJZuuBWapuFsZnH24deZ40YZcwvvFQYbPtF\nGjGd1hxTKz6/0ImplgI2LvsUtS2ifoezDbVKQ5ZtMWWFl0TKBwJeahreUkzm6vo3yUxbCIDZmElR\n7kbSU0sBaOk4zICrnWBI9HUMOismQzoZacJhJBj0s//k4zFdl3jh9Tk4ceZ5AFYsvB2jPpULV34G\ngKb2A/T21eELiP+BRq3DaLCRahHpvDLTFmB3NHK48ukR6y4r3EJR3qbIdnffWYJBP3mZK2KWr6P3\nNIFA4ju2JKXhx5vP9rBqszD82HBlKr/9YSvBGe70JhPjLRKlZy9i/pLrZ0gaiRLkFW/E7RQWcfXV\noykaZbn8vhIAVl2Xx8kdHWz/vVjE8TiVHWQnIwHf8MkilVpMFFSsvQedwTrTIkkURqc3U77yNgCO\n7fndjLS56XqxuPTgf89Ho1WNmY953+v7Z0Sm2YDU6ZJk0Okzjae2HkKDYwGFIlmZKqThB4C/s4tA\nfz+alJQZbde8YhkAupxsfO0dM9p2MmBZMzwf+UwSsE/O6HThulTyykyRbb8vRN2xfupPiIXz5ion\nzdXiD8DRMz1esTklJi64USxqbfloHrac+Bh7nMtzP64FYMCu7ILsbMHVKe6Rtz71XNT+oqsWcsF/\nfGhKdS+9b12UwUftS6doeFN4+7o6BlCpVVjyxTswY1kuBReXMNAsjJ/ON/oACHgD/P3yX0fty1ot\nJi8v+ekNAHzwH28A0Lxj5Ah+cj5MOYIBOHNKOF5UrDZSNF/PgoqhRcYTB1xR5Xu7ArQ2indV3jwd\nCyoMzCs71/BjuBNH2PCgqdZLYame8mWifr1Bhdcz/v8+HK1hNJlmmpqTnki0E4DyZQb273LGdGzp\n4sm9f8P/k1jaKV9ujNquOTm6Y81McHSvi7LFQn6tTkXFGiPH9in3PxzoD/KHn3UBIhrNp7+RjWow\nqMymKy1TMvw4tw2AP/ysK9IGiCg6sbSRViqiPp15tUYxo48w/XsrYzb4mLUM/g/a/7w95kN8XX0A\ndP3jA7LvvCTqt/QrV41r+BF0i/ds7/YjZFw/ZFypNhtIu2QZPW8cilkWANvlI/ele946PKF6JBLJ\n9FJVv42MQaMGo8FGQfYaCrLXjFjW73dz8NSTFOWJaBixLv7bHSLKqb2/kQVFV7Bi4R2jlg0EfRyu\n/DNu7+jj4emW2R9ws++EWAtYVf5R0lKKsJpENKXy4qvGPLanr3bc+qeD1s4j4ksoxNL5t6DRiP5f\ncd4mis8x3BiJUGh0xZ9lWxS1nZE6n4zU+ROS7d1DP8MZGC/yiPKoxy8ikUgkEolEIpFIJBKJRCKR\nSCQSiUQikUgkEolEIklEkjLix45/9HLl7SKs3KrNVu78TDZP/7JdYakSF0tK3pD343kWT0ZzOkvW\n3K2Yd6Rk5ihdfA0Ajr4WutsTL1zgki0iL3Lx8lQyCoy8/tjI3kyS8fH7h0f8WLD0JgBSbEUzLY4k\nQcjIEWHhcovW09awb9rbu/PL4l772y+aeOm3LcNyVUsmR0Snj6DPAanT5wiJrtNnmqDLha9NjAV0\nebmKyGBaUi6+jPB8zjU8Z2oxr4o9VGZcGHzv2a6/mo7H/zSzbScBlnWrFW3f3xufNHNanYoFa1JZ\nsCZ1xN/7u330tHqwdwgvT3uHF3uHl75O4RnscQXxeYL4vaJT4vME8XlC6I3CH8acqsWcqsGaoaNk\nmYiQN39VCtb06U2RWHOwjx1Pt0xrG5JB4vB6zr+4NPK95b069v1w+7Ay9mrhDd/8Ti3HHvsAjXHs\nqbeANzrKZdAXvR3yB0csJ0kMwulZKlYbyZunZeHScyN+DB+bnxpM95I3T0fZYgOFJUPvmMpjw8uH\nefP5fj7xhUyMZvHOuvLWVF75y9jvV5Uarr8rLbLt94fYtc0Rw1lNH/vfcUbJdOUtqeNG4igsFZ6e\nZYsMY5YbjS3Xiyg8z/x67BSjaekaNmyxRLZ7OgPUnFA24sfb/+jn5ntske07P5WuaMSPczn0fvT/\nzWiKv3/pZNoIzz24e0Z/nmaK9r+Mn9pktuOqbgLA1zXx1H+9O44Oi/hhXT1/1HWO8+l6ZV9UxA+A\njGvWTSjih7EsF2NJTtQ+V5VIzedpmJ5Igyq1uM+zl15ExsJ1GGxijKsC3PYOemoOAtB+bCeh4MT7\nBsbB+rKXXUJqYTk6y9A72evooa/xdKR+b79ynu2Lbv4fWHPLqH/nGQA6T+3GmldGzorLALDklKI1\nmPC5xL3V13iatsNv4unrir2RATGQfwAAIABJREFU4NBkpSW7mJwVW7DmiQgAWlMKQb8PV4/op/dU\nH6Tz1HuEgmNPcBpSxRrHsru+MWa5yn/8EkerXP+IJ16fg91HHwNEWo/s9CUYDeL+DgUDuL19dNmr\nADjb9A5en4NUq4jkF2vED51WRFI707Sd3v46ivMvBCDNWoRWa8TrddBlrx5sYycuz9h9j5mQ2eMV\nz8ie478hJ2MJuZnLIzLrdRZUiHeq1z+Ax9tPt12k0G3rPh5T/dNFa9dRuuw1FOWK93imrRyLKQut\nRkRnC4b8uD199DtbAejqrVJc5kQhKQ0/QkH4wYMiDcRXflbE3V/MJbtADAL++kg7rQ2Jn2NnJlFr\ndJgsQuG4HKJDEk77sHTdJ9DpY8tfJUlyBjvFFWs/xoFdv4zcC4lCTunQ4Prkzk4ZrnYKBM4z/MjK\nX0FB6WaFpJEkGguW3kRPRyVed9+0tpORJ/Ty9r92SKOPOBLW6ee+w1VqDUvXfQJA6vS5QoLrdCXw\n1NYDyhl+qM3i2TOUFOOprVNEhkTBXVUz84Yfg1jWrsb+xtt4G5sVaT8R0eXloi+ap6gMgTgZfoxH\nSoaOlIzpNdKIN/ZOL4994ZTsKyUpoWBsY9aAW6bwmc1UHRsyDCgs1VNSLsZBTkeQ2urhRgMnBw0/\nLrsxhYo1RlJsYn7O7QyOmcJi6x96ufUTNlLTRfkHv5ZF5RH3mKlIPvmVLBYuGzKWeO3ZPjpblb0f\n333dQU+nWKhMz9Jw1YdTefN5MTYdyQBEq1Pxxe/nDNs/ESpWiwWCWz5u4/kne4f9Hk5V8vnv5WAw\nDRnRv/IXOwGF56aO7HFFrsu6i81cdLWVB74m5nh/+1DXuPJl52sjqWL2bB+ebupcLrsxhQ/eGsDl\njE0pXXFztCFmXdX4RjLhNoCY2plMG53HxdgofUH6uGWnE3+PA1dVk6IyJAJhI4nJ4GnsxN87gNY2\nNGesNuoxFGZGfh/z+PoOnCfqMS8tjuwzLSrEWCrGjO7atnFlsF02PM1Lz1sTSxUzETQ6AwuvexAA\nS27psN/NWfMwZ4m+ffqCNXQcn5hxUe7KyynYIFLJhQ1MzsVoyx0yDKnYTN3Ov9BdrWx6ZmN6HgA5\nyy9h3qZbhzk76a3iWc9asomMhes48/rvAehrPDVu3QGfh6wlInVE0cW3o1JFXxONXoM1twwAa24Z\ntrIVVL/yK4BRjW6CfqHLBzrq0RotaA2WwbqMI5aXxBefX+jMyrpXqax7ddzy9S3vR32Oh1o1NN7s\n7jtLd9/UjXemW+YhQrR3n6S9e2oOZPtPPj6p4+pb3p+wzD6/kzNNOwAin5Nl7/HfTOn4ZCIpDT/W\nbkkhI0eIXnnYRflKM1fdKV7wV92ZTnuTj+524eXj906sg/6Nu8/EV9gEwZoqcsSGFwbKFl8r9qcV\njnpMouD1OPC4hHWp29mD29WDxyUs5TwuOwG/h0BA/L+DQR/BgDeyTQg0WgMarR6NVgx0NBoDeoMF\nk1UMHM3WHMzWbIxm0Wk8X8HPNjRaI0vXfZyD7/wCgGAwMSahrOdM0nbUxZbfVTIyfv/QQFint1C+\n4iMKSjM5QiHReXY7e3E7u3A5uwa3u3E7uwkMnmP4eQ8MdqqDAS+hUAC1RtxParUOtUaHZnBbb0zD\nYLJhMArLWaMpHUtqPkZzxoyen5JodSYWrbiNY3t/P63tnD0mJnPK11g5+PbwyTXJ5LGmFkQt9Jct\nvjYp9DkM6XS3U+jxsE73uMSiYFinB4ODej2s0we7c1KnR5OoOl0JwsYW1k0bxik5vZgqFs95ww/X\n6SrlGlepyLj9w7T+/BGxPcejrwCkXXmZ0iLgt8+M4Ucy4XOLxa5HP3eS3rbEclxZv0HP/Q8KY7Y1\n6/Sk29Q4BoS8x4/6efIPTra9qrwXtVJ0HmohtVTMPxVcXMqyBzZy+o/C89bv9CkpWty56BI9TzwV\n+zjp/z0ywP/5r4l7dCc74YgfABsvt6DTiwWpI3tcIxp1hQ0/ADZfZY18rz7hGdMIzN4T4L+/0sr3\nfiXm91JsGh7eWsxrfxNGE0c+cOIaCJFbqOWKW8Ri+dI1YpGpvka8Zx79T+WNhX3eEA9/T0Rq+9Yv\n8lFr4L8eF2OZV/7Sx8H3nPh8Qn/PK9Nz7R2pFC8QxjSVR90sWjGxhbNQCFrqxbP5+e/lsPEyC+8O\nRj3p6fSTmavl2jvE/EDYQKStSZR/6pHEyOH+wy8Kb/NHni8mt1DHXQ+K5/KKm1N573UHzYPnF/CH\nsKSoyS8W12vJKiOli/S8+7o43/EMP77wvRwMD6k5skfMyZ086KbxrJeBfnFjarQq8ubp2PwhsYi5\n+kKhK5pqxf21+62x6z+3DYAje5yRNgAG+oORNgA2f8gSaSPcTixtHHhYRDi95rHryVuXT+t+ZaJq\nuWpkNC8AT8vUniNPY0eU4QeAYV7W4G9jG34AdL+yL8rwAyD96rUAtPzqlTGPValV2C6NNmgP+QLY\nd06fR3nxlo9GGXz0NZyk/Zgw7vAO9KAzpZK+QETzy1q8icILbo657uylF1F4wU2R7f6WajpPvIen\nT1xHtUaHKbOQvDUfAkBnTqX0srvxu8U7JBwJZKZJny/OV2dKwdFeS/vRnQB4ettRaXWkFVUAkLv6\nStRaHfOv+mcATvz1v/E6xo62YEjJpOii2wAIuJ20Hn4TR+vg+mAohCkjn/x1Yh1Nb00npaCcnBWX\nAtB2+K0R6/Q5hV4+vfVn0eexYC1lV9wzkVOXJCIyyLIkCZjds+ESiUQikUgkEolEIpFIJBKJRCKR\nSCQSiUQikUgks5ikjPjxH78vHfP3nEIdOYXJFeJ1urGk5gPQ0XyYtIwy5s3forBE0YQ9VB29jfT1\n1tPXIzwl+3rqpp6OIMZ0nCqVCJeZkl5ERvZi0rMXie20ecNCiCU7lpQ85i+9EYDqY1sVlkYw0Cs8\nFFKzDXhdMnfxVAj4hjyIFi6/BZ3eMkZp5fF5B7B3i7Bo9q6z2LvPMtAnQkGGZijutVYn8vOl2OaR\nYismI2cJAKm24ln3/ANk5FaQkb0YgO6O6bHYf/VxkV/v0w/N553nOmmoFHmIve7h/9P3X5xA/k0J\nltR8OpoPAySsTnf0NgJEdHpYr0udHn8SUacrgbsmMXLjmpZV0PvKNqXFUBRfa1sktYfGljZO6fhj\nXFBGyuYLAOh/d/eMt59IaGxpWDasVVoMAt1je9rNNTzOAL948AQANQcTKzrCrR8x8dBP0zg/8nda\nmtix+WI9my/W8/OfCM/PX/zUMdMiKs6J3+0jb7PwHDbnWKm4dy3ldwhv4PptVdS9WknX0VYlRZTM\nMLWVIlqB1xPClqGJ7D9xwDVi+apjYrzu94dISx8qf27kkNHY/dYA33lAjJW/9pM8UtI03PgxoWvD\nn+dzbJ+L7z4ojok1hcd08/Y/xLsvt1DH/V/NQqMR/fMb707jxruHn8df/p+IGLB3p5Mf/Wli6ctU\nKvj2AyLdxn/+qpBNV1jYdMXocyQdLX7+/eOifKJcr94uMUf2uQ838M2f50WiYGTna7nlE7Zxj/f7\nYouAFgIMRhUbtojrE/4ci/oaL9+6X9xfXs/47YTbCNcfaxsA37q/OaY2+pvEmHP3f7/HlT+9mrq3\nagHoOtWJxz72c3bmlZpx648Vv11GMwYI9I/8LowVf+/w66ixxB75x/7eSfLuF3Vo08SzY7tM6O22\nx18n6B09aqZl1Xy06daoff17TxNwTO2cRm0vp4T0+asi2/a649Rs+x2REKiAu6eN/mYRYdHd2868\nTbeMW6/OIt6r4eggnSffA6B+17PDyjraztJzRkQyW3rH19AaLczb/GFARNBQIqKiziyiWDlaaqh6\n+VFCweh3s7NDpH11djaw4Jr7UWtF1KOC9ddRu/2pMevWmqz4XaI/e2rrT4dFCHF2NkZSxiy765uo\ntXoyF28ERo/4MToyGqVEIpkZktLwQzJxbJkLAcjKX8H8ihsVX/QIDqZi6Wo/SUfzYbrbT0XtV4Jw\naom+7lr6umupPf0aIFJlZOZWkF+6GRhcNJoFFAyeT3fHabrbppbXKx601YhwjanZBjIKTQpLk9zY\nshaiN6QAkF2wWmFphuMa6KC9SQwiOlqO4uwfP6fmdOP3iUFbT0cVPR1V1Fe9CYDOYCUzp4KcwjWA\nuLazhZLFVwPTZ/hx73dKAAj4Qmy+KXPMstLwY2LYMheSlS8mKhJFp3e1Cz0S1ulK6nOQOn0u4msV\nusTf04s2ffwJ8OnCUFKENiMd/xxf6A6ne7FesF6R9jM+IiZA3Wdq8bXM3QXY9BuvRaXRjF9wGgl6\nPPg6pZ4P09vu5ZHPneTs4cQy+EhJEX2J7/0gdZjRx0j8jy+KRZAXX3BzpmZupRlzdzl585/FQsmy\nBzZSeuMStGbheDT/1qXMv3UpfbVCB1Q/c5Tal04R9CfG4rFkeggExELOmVMelqwaWow8fmDkBebw\nwvWZk56otCWVR2OzcA6nuvj4ZbXc+gkbFw6m3Sgs0WM0qejtCkTSybz1Qh87X0lcA62nH+vm8G4n\nt/2LSJ+0cqOJtAwNfb2iL3/ykJutf+jlwLti0TYzZ3LT2OFUJZ+6vo6P3Gfj0uvFfElhqQ4V0NIg\nxi7vvOrgr7/pwelIzGe2u8PPl+9uZN3FYvH6yltSWb7eGEmHrjeocDqCtDaI93LlMTd7tg/ElB4F\n4H98uJ4rb01lxQYxJ1dYqsOWoUVvEDrC6w3R2+Wn5qS4V3e95uDNrf34/bEvZobbAFixwRRpIyx/\nuA2AmpOeSBtAzO189I3oVArzr1sQ9TkW8TT8CHkSK5WbUgQ9U+snBN3Dr6PaYoj5+JA/QO+bhwDI\n+ogYN4cNR1IvXkbvW4dHPdZ2+cph+3reOhJz2xMlozw6bWnTnn8wlrFAx7F3yF1xWcSwYzSyKy4C\nQK3VEfR5aNz9wpjl/W7xzug4sYv8tddgTBOpdS3ZxQy0K5fWtOXAtmFGH+dirz+Bo6UGa7541m1l\nK1Hvepagf+xnseWgcNwYLS2MzyneQT1nDpO5aEPkeqh1BoK+GL2TJBKJZAZJSsOPmxccVVqEpCM1\nXXikLF33cUXlsHfX0lz7Ll1twsNJ6YWhWPB5B2ht2Edrg8gRmWIrprBsM1n5ovOnViflYxRh0crb\n2Lf9x5GFb6XY85zwECjflMGyy7J54f9UAhAMSGvYibJ49V1KixCFz+uko1kMstoaD9DfW6+wRLHj\n8zhobdhLa8NeAMwpuRSWXkTuvHWAyH+ZrKTYigAR/WM6Foo/c+HBuNcpEaSmFyeEPgciOj0Z9DlI\nnT4XcJ08HYn2oBTm1Svpe2uHojIojeuY6OsrZfih0gv9nHPfPTQ/9HNCvuR4R8ULw/xSAKwblbn+\n5+JrblHEMzDROPWBiILz6387RV9n4t2Pl10hFlAs1tiMScPGIdfdYOThXyTuovJ0EfYYP/DQTk7+\nYT9lNy8FoOyGJZhyLKSWikXstf++hYV3rmD3t14HoO9stzICS2aEz946sXHuv948tXFxf2+AJ3/R\nxZO/iL9x3ZVllXGvczROHnLz/c+3xFS2q90/Kdl0evFuczuDPPVwN089HN9ncSavF8D+Xc6oz3jR\nVOfjiZ9Pr7HmTLSx50dzO+JbvFCp4+NgMmU/lZEqmGDXsvu1/QBkfXgznFNdxjVrRzT8UBtFxIjU\nC5ZE7ff3DuA4UD2xxieANa8MAE9fJyAieoxFKBSkv7mKjPKx+/ypRUPn4WivG9cQIoy7J9pRz5xd\npIjhRygojAEdrWfGLWuvPxEx/FBr9ZgzC3G0jR0dtPdsbMY84f9LGK3BjFcafkgkkgQkBl8OiUQi\nkUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEkkiktxujZKkoKejkvoqkfPM3j2+ZWai099bz6mD9RhO\nvgLA/GU3kZ0/PPRbsqA3pDK/4gYqjwzP6zeTHHhZhOHe+JECyjdl8KEHhZXztkeS/56Zi/i8wvOk\n8cwOms++SyAwO0JcOvvbqDr6HHWVwmuvdMm15M1br3iqjalQuujqaYn48Ylvi1QvT/znyN4ARrMI\nPX/1x3Ow2LQc3iE8YU/s7ou7LJL4Edbps0Gfg9TpsxHXiVOKR/ywrFklI36cFGnEQoGAoqlGdPl5\nZN19Bx1P/FnsmAORJ1R6PVkfu2NwQ/n+ibexWWkRFGXA7ufvP6ll5zNirBNKzOwBlC+e3NTQokke\nN5twtQ9w4jciOuDJ3+0jd2MRi+5eBUDOukJSS9O56KHrANh2z18IuOdWahyJJBFQXhtKZpJTfz2h\ntAjJzeADozbo41OdYWqRctXG4ccHnSOn0hoNb6tI4eE4WI117VAKZ/OSIgzF2XjqO6LKp164ZMS2\ne7cfIRSYvs6cMS0bAHdv7Cmx3faOsQuoVJgyCiKbqYWLWPupn0xKPq3BMqnjpopvQMwXhiN/jMX5\nUVIMadljRvzwuwfwOWObhzw/rYtKrWxKTYlEIhkNOUqXTAvhVA7VR7fSb29UWJrpweMWnY6T+/9I\nS9ZCFi4TucTNKblKijUp8oo30j6YiqO3c/pC1o1FKCgm4n/3uUPc89AKrv2cCMuWnm/kjV+dpath\n7oatTyb8PheNZ96h6ew7AAT8szPkndcj8jtWHv4rzbXvsXD5h4GhtFrJhDWtkMy8ZXS1Ho9rvVs+\nkgWARquiaJGJupPCGOivP2vE2Rfg0w/NByAzX09rrZuv/GoRAI9+pYa920bOqylRhv7eeqqPbhXf\npU5PeBJBpyuJ+3QVoWAQlVq5wIaGkiK06TYA/D29ismhJEGP0P/uympMFYsVlcWyfi1Bl5gc7nrm\nOUVlmQmy7r4TXV7ivLu8jU1KizDjuAcC7HhaGHq8+utGHD2Jl9rlfCzmyb0zzWa5nHouoWCI1t31\ntO4W8yFL71vH0vs3YMlPAaBwSxn126qUFFEikUgkkjHRWEziS5xUvDbNPKXjdRkpw/YFHBMz/AjT\n/er+KMMPgIyr1tLy29ei9tkuG9kZZKS0MPFArRUGJiqNWKoLeGKfAw/6xr4WWr0pbmNjpQwdAuOc\nY1RZb/S10+iN45Sf3L0kkUgkiYw0/JDElWDAR+3p12gcXPSdC151IBZW9u/8KQDF5R+iZNGVJJtP\nwcLltwKwf8dPCCngirbk4kwAUrMN1B2xU7wiDYALbivkgtsK6WkWHTF7u4eAb2LyPXzvvvgKKxlG\nR4vIh1h99O/4vAMKSzOzOOxNHH7vEUBEAClacCnJ9vzPm39J3A0/TFYxIMwtNnDgzV4WbxCD9U9+\nr4xffrGaFRenAvDv1x6lq8XL5pvEO+D6T+ZLw48EIBgQi1QRnT5H9DlInZ7sBN1uPGfrMC4oU04I\nlQrzajFZ2Pf2TuXkSACch48qbvgBkHLJZgBCPh/df/+HwtJMH2nXfAjLutVKixGFp2Hyhh/V+/tI\ny9JhSkn8aYumKmHguntrOzufacXZl1xRHQYGJqcr7Pa5pWMmSuXTR1h6/4bItnVeWkzHBX3RHq1a\nS3y8riUSiUQy+wg70p2PSjO5xX5DUdZUxBleX0Hm1I4vHH68u2GcKBej0L+3El9nH7qs1Mi+tEtX\n0Pq4iOobCgTRpluxrIweS7pqWkS7ddHRJKaL0ATmX8aNgnFeBMCemgM073t1MmIR8DgnddxUUakm\nYHAywYiHodD4UUQkEoC9x3+jtAgSScwo5wonkUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpkS\nie86I0kK7N0iV9rpQ3/B7exWWBplCHvU1lVuo9/ewJI1HwNAqx07pFiiYLbmAFBQupmms7tmvP0H\nfrV2zN/TC4xRn5LEwO9zUnV0Kx2DaQXmKuHn/+zJl7F3nRl6/nUmJcWKmbSMMkxm4UXhcnbFpU6P\nS1yTHz1Qid8X4uXfi5DnP35deMEbzcJiv69beMQefFukQ7j3uyVxaV8yeezdZzl96C8AUqdLnZ6U\nuE6cUjbiB2BZswqQET8GDh0h444Po9IkRv7j1CsuRW2x0PWXZwEI+ZIrKsNopF11OQDpN16rsCTR\nBF1ufE3Nkz7+D9+s4olvVzFvscgnXr4+jYVrUihYJLZzS4xo9TPvy+L3Bqk5JNL+nXq/l0NvdtN4\nOrkj3p0+Nbln4djRxE9jE080Bi06qx53V2werxnLo9MuuTpiu0+cbY6o7ezVBQDUvngqpuMlEolk\nrrDpf26m+7SYw6jcehqAzAoRscLR4sDTO/vTOAQHRj5HTaplUvWZlxRNRZxhmBbPm/SxxpIcNKnR\nqWKCLi/elsnNU4SCIXq2HSDn7ssi+7Rp5kj6l/69laRtrkCljo4aMV0pXsIE/b5B+QKo1Bq0htjn\nEtW6cVKZeFxDEVxVKlQaHZ6+zknLqgQavSHmslpD9P1yfuoXiUQimQtIww/JlGlrPEDlkb8CMYQX\nmyN0t53k4Du/AGDZ+nsxpyROnu3xKFl0Fe1NB/B5lQnfJkkOejoqAWHs5fX0KyxNYtHdfooj7/8/\nAFZs+hQ6/eQG2zOLityi9YBI7REPHL1iAUGjVeH3hQiv+ZlTNVhtQ92PcKpR76ChiN4gg5EpSVin\nS30+hNTpyYfrxCnSb7pOURkMpcUAaLMy8XfGx6AuGQkOOHGfqsS0rEJpUSJYL1iPviAPgPZfP46/\np1dhiaaAWk36zdeTduVlSksyIu7KKkLBqaUCCQWh4aRYLG84OcBbTw79ptaoyJpnIG++mODNLjKS\nmqUnNVPkSU/N1JGSqcOSJrZ1BhVavTpiLKLTq9Do1AQDYjLc5wni8wRx9fvpbfcCYO/w0d3sprla\nvEebKp00VzvxeWZXipPtb3sAcDhCWK3jh8h2Dohr9sLWaVhQOz9E9wRDdo9Z9XkLOZy/PQ7GDBPX\nPnM3HftFCqO2vY30VnXh6hw06AiBId1EzlphqLHwjhUAePvE9W3aeTamdsIGIr2VndgWZVFy7SIA\n3F0DNO04i29A3J9asx5TphlHox2AvlqZLlEikcwtii8vJeCNHrve8PjNALz/w11UPV+pgFQzi7/X\nMeJ+c3kBEx6FqMB26Yopy3QuxhLhlGAozMTTNDGJ0kaQxXGwBqaQibZ72wGy79oCDKXDSbtoKSAM\nP1IvjB63hAJBencem3yDE8Bj78CYnofBlhPzMYbUsVPphEJBXD3CEcuUkY8lu5ihVLbJkdJXZxGp\n8tRaPUG/d8yyxvOunbt3ZtLzSCQSSSIxaww/0rPFqWy+No2KdWYyc8XkjsmqweUI0NkqLCdPHXSy\n+7U+utrmlmfKdFFX+QZ1lduUFiMhcQ0I69lD7z7MygsfxJpWqLBEsaHVmShaeAVnTrw4o+3+29LX\nZ7Q9yeRprd9D1dHngCGveEk0jj7h3Xr4/cdYuekB9IYUhSUan9x56wCordw25A0wBQ7vEAtp336q\ngpMf9LNwjRWAtjoP//ul5Tj7xOTMmits7H6pm1WXpg3+Pvs9chKRuso3Bj+lTh8JqdOTC29jE/4u\n4QWmzcxQRojBhcqUTRvoeXFyOZRnC459BxLK8ANAXyQ8D/P//Uv0PP8ijg/2iR/ioP9mAk2ayEue\n/c//hHHhAoWlGR3XiemNThAMhGivc9Mu+w5TZsAh7v1vf93Oj35mY6wgPR5PiC99XvTzursmNxbI\nWSf06IrPbkJn1qOz6gHQWfWoddGNX/fXu/G7xPyNb8CLf8AXMXx496uv4OmN9uRMKbax8TtXAKC1\n6NFZhurXGKKnwC57+BYCbn+kPlG/l73ffxsY2ZBCpVaRs0G8Q8KfY+Ht9/D+N4Rhtdc+sXv14I/f\nYcvPb0JjFHIvvmcNi+9ZM6zc4V+8N6q8EolEMpsxpOrxDBrXzVWclSNHV0u5YDFamwV/b+xRydKv\nXI2xbHocHXI/cSX1//VMTGV1WaKvm3nDhmG/9Uwx+oa/x0H/ByI6TOpmMUZJWV8OgMZixLy0OKp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bwFlyotyqiYrTmR0PXhxcN4s/iiTEAYbtz69cUcfk3kU5yo4Uf7WSeFFSnoTeIdsfKqHN78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y3/+2lQfu7+bKq8Se7BVX6ll2vnZEQ8DU1vrZulk887ZudrN1i4e21pHb13d3uXnnG28B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\n9kHGilIMOVloLIPJQ6Ew4QE3QZdqJeg7VotnXzXe6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GlXCerd/ScnNHaiNBotqyo/Fktc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K/e\nrHcYeUsGBhg8vl/vMApO+IVDFN3zagCcN+/EXF1BsqsHAM1ixmC30/1PXxx336L77sRSW0myXZ34\nTvUPkIklMJWqi/HWVXWQzTL4/V+MP3k2S/9Xv0fFn74fgOK334tjzzZSvSpRxFxZhmVFFZHmYwA4\nbtg67jAjvzc47BjsNix11bnbzFXlFN17OwCZaIxMNEai5QoAya6+PB6h6YulVSLMMf8vWFN0E9tK\nX6fmz6YIJwc41v84AP748m4TMlv2yhW6zh/v6yKbXp4XXgFs5fq0eQFpsSME5LcwdikyGOQSt5jc\nSLLG1k0PUlS0cszt4YhqcXfy9HcIhTqvu0Vjw/r7qarYOe64GholxWvYtUONefT4Vxkcmryd3UIp\nyL+K3/izytz3j32lj8G+ma+Q2LjLyeotdi6eVCcgTh4Izzo+IcTCMmvWKbdZrh9+xPQ4i2twNr2J\nuh1vAGCg7QS9Lc0Mdg334ZUVrEIsOvb1dWjW2WX429erPuGzHUcUBtvqGiy1aiVpJhzF6HHiunkT\nAM7tqwGIHL0IQGjfKX2CFOMyWK04dmzDtUclethWNYL0udVNNpkkcvQEoX0HAIieuyCflcSkMuHx\nEz8sNeocT3RwdokfL0+wmCsGp0pMKfutd4BhbMfk/q8/CkB6YGz8/V9/FGuj+pxh9BaNus19+yuI\nnj5P9GhhvNc4VqzGaCucSkp9+55Y1hdhZyo9FKT7H74AgPfNd2NZWYN5hfobzYSjJC5PvLI+8H9P\n4by5CcuKKgBsVWVomkY6qM61Rl46RfCXzxM72zLhGPHzrXT9/efV/G96DdY19ViqK9Rtl9vp/qcv\nYa5V8UyU+FH2wQfUN+P8vZorfXjvf+2o3w1+T63UHPq/pyeMay50Rc7TFTk/r3MsZ7YK/RIPAGI9\nyzv5wF5Zq9vcUvFDiOVL08a+1wsxQtMMbNv8LoBrlT5exulQ5wd3bH0fh498MZcIUl9326ikj1hs\ngN7+06SS6pjW622g2NuI0WgBYNOGt7O/+bOk0vF5uz/5kr8KIYQQQgghhBBCCCGEEEIIIYQQQogC\nVZAVP978wbLc90//cHBWFT/W73Twnj+v5MATAUAqfghRiNym0im3SZFcgEjEUjFSJq60bjulddtJ\nRNTqvd5LzfS2NBML9U+2uxBiATmGKzToPYYoHI7tq/E9cPuEtyfa++j87KMLGJGYkMGAfd0aXHt2\nAeDYuhnNLJV59BRvvUJoXzMA4cMvkYnGdI5IFJJEeyfOcX5v37IegOjJszMa12BTFSBdt9ww09Am\n5XvP2wAwFheNuS30QjORw8cn3DcTjtD3le8AUPGR94+pUuR7z9vo+NS/AuNXDFlM3GsKo81LOqrO\n6w2eaNY5ksKVuKrKXPd89qFp7Rfef5Tw/qOzn79VVU7o+devjHu7pX7yyg6Xf+svZh2DKDy2Cv0q\nToBUndCz1UtUWr0IsWxlMvlfG85mM6QziXmMZn5lpKL9tFWUbxtV6SMQuErrFdXSOZEIUlS0koaV\nqsWh2exg/br7OXP2BwA01Kvzhj29JwA4ffZR0unrnj+XobbmJtauvhcAq9VDVWUTV9t/Ne/3ayoF\nmfgxlwZ61cXgurU2nSMRQsxUiblqym0SmfHLCovFLREdwmIfe4J1oVkcKoaaTXdSs+kOAj2qNG1v\nSzP9V4+RSRXuh0Yh9JBNq4MVc2UJvgfvxLG5AQCD00qqP0Bo32kA+r/9FJn46L8v5841eF9/IwDW\n+gpMxe7cbdV//sC4851/21+Pmte5cw0A3tffmPcYI+OMjAHg3LWWmr94kK5/+z4AsQvt+N51F45N\n9QBoJiPx1i78P3wegNCBMxOOLRZOqj9AokMl8JmKXWgmI8nuAQBC+0/j/8HzZKL6l2ZcrixVlTiH\nEz1cN+zE6PHoHNHylg4ECTUfAq6DiFwAACAASURBVCC0r5lkV7fOEYlCFr84fs9j582qfdPQz58m\nPYN2L9433wOA0eOeYsvpc99xC/ZtG8e9LdXTh//bj005RuzMBQACv3wWz2tuG3WbwenA91vqs0f3\nv3wRMov3hG6hJH4MHN0HQDYliz+EWE70T/xY3skHej3+mUScxECvLnMLIfQ3ncSPULSbfSe/MI/R\niMWmovxaW8BYfIjDR/9n1HMmEGyjv18tPti180MUeerYvvU9AGiakXC4h1NnHgHGf661te+jqlK1\ng3G7qvH5Nkjix2IQCaqD6qKSZf9QCFGQ3MYSvKaKKbeLpoMLEI2Ya4cf+1uKKlZT1qAuABXXbsZo\nsuoclYanfBUAnvJV1De9if4ralVTb8sBgn2tOsYmRGEwel0A1P3jByCVJnKyFQCD1Yx940qK77sZ\nAGtDFW2f/OqofbOpNPHWLgDirV24dq3FsqIcgNC+0yQ6x1bkyWayY8YY2X9kDADLivIJxxhvnBGu\nPWqlctlvvo5MKErk+CV1f5xWHJvqc8kk3Z//EUN7D03wqIiFEnjqJQJPvaR3GGKY0e3CuWsHAK7d\nu7DU6tuffbnLptNET5wCVKJH5NSZRX0hWhSWeMsVUv5BTCXeUb832NRCnLIPvYue//gKmXAkr/E0\ns5nit9yD+7Yb5zxWAEtdDcVvfv3YG4b/Jnr/51tk4/kngA/+8OfYNqzBsqJ61O9taxoB8N5zB4M/\n/uXMA54nI6u4zW7vFFsuAtksAy+9oHcUQogFZLTaAbB4S/QJIKuOEWM9HfrMrzOTUyVdmlz6JGvH\nejty/wZCiOUnmc6/AqXRqPc1BbHQ3K5rx11dXYfHTd6IRPsAOHfhJ2xc/1as1muLkM9d+NEUyUVZ\n+vrP5OZyOSvnJvBZMugdgBBCCCGEEEIIIYQQQgghhBBCCCGEmJllX+bCaFL9Va02bYothRCL0Vrn\n7ry2C6UH5jkSMS+yWYa6zjPUdR4Ag8lCSe0WyhpUOeiiijVj+mQvNKPZSvkq9TwsX7WbaKCX3ksH\nAOi9dIhkNKBneEIsSiOtUEIHztD5mUfIJq9lT5vLvaz8l99W221pwLa2lti5a2VzI8daiBxryf1s\nKvXkKn4EnjmSVzuVkf1HvppK1eoky4ryvMe4nutGVQJ+4LEX6P36L0etOHJsaaD2/70bgLL33U3o\nwGnSgfxWMguxFGkmE44tmwBw7m7CvnE9mkHWI+gp0a5WqIb2NRM+eJh0KDx/k2katnWqcprBYcNg\nt2Gwq5W6mn3kZ1X9YeR7zWgcdyijt4iKP3w/maha5ZWJRsnE4rmfs9EYmeH/ATKxGPHzl0a950zF\nUr8Ck9eD9rKYxsQ4fB8sdeNXrCn73d9QMYQiZKIxsrGRmGNkIjEysXju52w0RuKKet9LB+fx30IP\nmQzBJ1+g+C33jHuztbGO6k/+EYFfPgdA9MQZUr1+smlVqcvgsGMuL8W2QVXqct+2B6N3dFvIRFsn\nltqpW4FORrNaAPC9/x1oprGnzQZ/9Liaq/XqtMbNptL0/c/DVH3sw2oes3nU7UX33EHszEVi51vG\n2103zvq1eoeQt1DrWZLBQb3DEEIsIFvlSIsRfc4Nxf2qzUgmuTxbAOvfYqdd1/mFEPqKJ/M/724x\nOecxErEYmc323PfR2OTXB7u7j9LY8BpswxU//AMXGRi8NOUcsdi1Yw+TyTbDSOfWsk/8WLtN/cOH\ng1K+VohCss6xBwCfeUVe2w+lpN/jUpBJJehrPURfq2qVYLF78NXvxFevEkEc3tmd5J0Ldk8ZddvU\nyewVW1/HUOdZelpUIshA+ymymbSe4QmxKGTT6nNX93//eMwFuGTPIMEXTgBQdNcubKtqRiV+LEaZ\nsLqA1/fNJ8aUmY0cv0Rw32kA3Ddvwn3TJgZ/0bzgMQqhJ2tDPQCuPU04d27PXegX+hhpoxE6eJjQ\nvmYSbQt3wlyzmKn4ow/MzVhmE7aNa6a1T/vH/pFUz/jtvMZT+u63zDqJAK618shX35ceBiDcfGTW\ncy82wSeex9Gkei1bG8YeyxmLPLnEkIkSRCYSbj5K8InnqPzo780qxtIH7wfAXFE25rbY+RaGfvbU\njMdOdvYw8MhPACh5x5tG32gw4PutB+j41L8C5N3yZr45V07v70xPg8cP6B2CEGKB6Z94sLiPVefb\nSDswvSz3x1+I5S4WH8p7W5PRismoLsynptEiRhSudCaFaXghiXmKpAy7oxSr9Vrbskx6+gmdk7eF\nWTjLMvHDaNK4+W6VtXPvu0sBaDkZ1TMkIUSenMYi1jluosySX8IHQJYs/uTy7LW51CWiATpOP03H\n6acBcBZX46tvwle/EwCzza1jdKBpBrzVG/BWbwAgFQ/T23qY3uFEkMhgp57hCaGb+CX13E8Phsa9\nPdl77cDN4Fj8PThjw/dnZEXyy0VPqAxx982bsK2uAUn8GKWuaCcby+4Y9bssKoHmFxf+WY+QxCyZ\nSlSPddfuJpy7mzCX+XSOaJnLqGS76OmzhPY1Ezl+Epj4NUuI+ZRNp+n7768DUP4Hv4m5umLWY0aP\nngKg/6HvqDlS6rmtmcavFjMZ101NOG/cOe5tmUiU/i9/e0yS53QFn34RAPuW9di3bBh1m7G4CN97\n3gZAz+e+Oqt55oJmMOKoadA7jLxkEnGCF07oHYZYAIGfPzvqq1jebBU6Jx70LO+KE9cqruhDKn4I\nsbyFY33T2t5pV4ndQ6HpVe4ThSka7cftqgag2LuKK20vTLjtmlX3oF1XPczn20Cxt5GBwcmrMbqc\n145n44nFUfldauoKIYQQQgghhBBCCCGEEEIIIYQQQhSoRVvx47VvV6vEHvzI5Ks//v5bjaRT01tt\n4Sk2YjCO7vv3xKOT9/cRYj45jJ6pN5ojBgwLOt9MaGiYNNVX2azZ8Jh8lJhVieVS8/Qz+f3JdpLZ\n+JzGKBan8EAH4YEOrhz5KQBFlWvxNTRRUrsZAIPRPNnu885kdVK17laq1t0KQNjfRk/LAfovvwRA\nKiHVp8TykPIHJ98gc91nO02fXs3TkQ5MXoo9dV1lE2OR9BR9ua7QGcIJ1XrBbLSxrvQ2bObF/VlF\njGawqZKZju1bce3ZhW3V8OrwAvj7XcqSPb2E9jUTOnAQgPTQ4lh9IkTKr/ogd/3Df1L8lntx3rwL\nmH6FjkwsxtCP9xLY+5z6xXAljmS7qsRlWTm9VcjmirKx7Veu4//m93Oxz4X+rz5C1Sf+CKPHNer3\n9m0bAXDffgvBJ5+fs/lmwl5Vh8Gy+KuvAQQvniSbWhzllYUQC8curV50pWerl2w6TbyvS7f5hRD6\nC4SnV+Xd41DVH6Tix/LQ3382V/GjtHQdNdV7aO/Yn7vdaLSyetXd6vaStQAMDrUC4C2qZ/PGt3Po\npS8CEImOrS5jMtmoKN+W+zkQWBzPq0Wb+BEcVKU5TWYNV9HEB/9u7/RLd77ck98f4OnH5u7gXYjp\n0DBwq/fXF2w+u9G9oPMtBldjZ/QOQSywbFaVNB/sPMNg5xmMZnWysmTFVsrqm/CUr1Ib6nxByllS\nS0NJLSt33AfAQNsJeloOMNR9Xm0wyzLSQixaw20HlgrNMEURvetfauTPeoxEOkJ/9HLu58biPdiQ\nxI9Fbfg5b1+/FtfuJhxbVYKlZtY3wXK5y8RihA8fJbRPtZOKX2rVN6BxZOMJLn/gz/QOI2+dn/pX\nvUOYUten/3Pe5+j/2vdGfZ0rmVic/m88yuBP9gLg3LUV69pGLNWVABhcDgw2K9mkupifHgqSaOsk\nevIsAJHmI2RiYxP8O//u32cUT7K7lyu//7EZ7TsT6WCItj/51ILNNxOOFY16h5C34NljeocghFhg\nBosVS3GZrjEs58QPg9WGxVui2/zxvk6yGWldKMRyFo71ApDOJDAaLFNuX+JRn22v9uyfYkuxFLR1\n7Ke25iZAJWmsW3MfDfWq1XQiHsBuL8VovPa86ew6xLkLPwHgxhv+EKu1iF07fxuAy1eeoc9/lmQy\nDIDLWcWqxtdgsVxL4u/qfmlB7tdUFm3ix69+rnq7v/j4EGu2ONh+i3rwdtzqYtMN11ZK9nUmSSXz\nP4OezUIskqG9RZ0cePbHg+z7pax8EmKpCqUH6Em06h2G0Fk6qV7ze1ua6W1pxuLwAuCr30lZfRP2\notn3Fp8Ng1G9HZeu3E7pyu0kIioZsbelmZ5LB4mH+vUMTwgxhamqeJiKryUxpAKhSbYUYnGzVFXi\n3LML1w07ATB6JEFHV9kssQsXc4ke4SPHyCaSOgclxPSlB9X5n8De52CkeodYFOxVdXqHMKVsWiUG\nhS7Jgg8hlhtbeY2uC3qSQ37SseVbuVVVW9Hv8V/OSTdCCGVk8Wf/0EXKizdMuX1pkUr8MBrMpDNy\n7LzUJRJBTp1Rixe2bHoATTNiMatzuCNfR3R1v8SZc4+RzaqEwpOnv8v2be/DZFIVblc1vpZVja+d\ncK7evlP4By7Ox92YtimWJwohhBBCCCGEEEIIIYQQQgghhBBCiMVq0Vb8GJHNwLmjEc4dVb3Tv/eF\nXr57bCMWm8pZ+ev3tXL5XEzPEMUc2LhbVXR5/9/U8Ud3nxrT3aBmlcqq+n9fX4O72ETAr1Z0fOgV\nxxc0TlF4ToWfJyt19cXLjFTU6Dj1JB2nnsRZonrCljXsorRuO2aba7Ld591IRZKazXdRs/lOAj0t\n9Lao1bz9V46SSUtGshAjrl/drllm1mZitmNYG1VJes1qJhsf+/fp2NyQ+z52YXr9R6ej2r2RFUXb\ncZpVuV2TwUw8HSEY7wGgI3iKrtDZCfc3GSzUe3dR4Vyj4jYXg6aRSKkyhoPxTs73P0ckObpFYpGt\nCoBa92aK7Suwm4arQGgakeQA7YGTXB48CDAv78lFtipq3arVSG7+4ZV/CzH/UmZ0u3Du2oFr9y4A\nLLX69fAWSsrvJ7RfPZ9D+w6S8vt1jkgIsZTZq1bqHcKUIm0tAGSSCZ0jEUIsNFtFra7zx3radZ1f\nb7YKfY8Not3L+/EXQlzTO3g2r4ofI+1gyks20dl3ZL7DWlLyPZ+m5SpxaSyGftd9/acBaD78eVau\neCUezwp1QzZLONJDe8cBAPr9o8+XDg61cvT4/7Jp/VsBsFjc447f23sSgFNnHpmP8Gdk0Sd+vFwm\nnaX1bIy12xx6hyLmQSqZHZP0AdB+USX3fPDm49z6ayU8+Gdy0llM7kLkEAADyS6dIxGFIOxvy329\nfPhHFFWtA1QiSHHNplwrFn1oeMpX4SlfBUB90xvpv3KU3hb1oSTYd1nH2ITQX7Lr2kVP5841BJ+f\nflLobMcwWIcPHN97Nz1f+inZdCZ3m2NLI67d6jUlE08QevHktOObSkPxbgDWld5GIN5NW+AooA6v\nHGYvPru6aBNK9I+b+GExqs/Ve2oewGkpYTCmklMuD6n3UpdF9c0uta/kRPoXY+f33gCAz9FAf6SV\nnvB5ADTNSIVzNet9r8I0fHB9wf/CnNznl8/vc6jkmpH5Nc0IsCDzLyWayYRjyyacu5sAsG9cj2aQ\nIpF6yiaShI8eAyC0r5nY+YuMe8AkhFgWfO+4g8iJSwBEjqmEh5L7bwWg9C23kegeoPMz6qRj4mrP\nrOYyuYowuRZ/S6/QpYmTWoUQS5u9Ut/Ej2jX8m41YivXOfFGWr2Ilyly1lBTrhYtFLtWYrW4c+cG\nUqkogUgnPQPqInBn3xEywy0dROHrGTjN+pX3YDTkt5irvvIVdPYdZTEkJhSKTCaV55Yq8cNktJJK\nL56iDaFQJydPf2da+wwMXOTFA58BoNy3GbenBoNBXSeKxwP0958lEFx870UFl/gBcOm0JH4sNacO\nqH73f3rvaZ0j0UOWLBk06bw0Z67ETnExeljvMESBymYzDHao16LBjtMYzTZK67YB4KtvwlPegJ49\nTI1mG+Wr9lC+ag8A0UAPvS3N9F5Sq3+TsaBusQmhh8DTR3MXPDy3bcNSW0aivQ8Ag9WMwWGj7ZNf\nnXIMUBdORsYASLT35cYAJhwnerIVAPetW3FsX028pVPNb7Pg2NIAwxfOe7/yc9KByIzv60Sq3RtV\nvOkoL7Z9I9fjdISmqfkNwyc8Xm6D73YAnJYSzvU/S8vA/nG3M2jGcU+MnOrdC0A6kySdHV3x5KL/\nV7xy5fup9WwB5ifx4lTv3lxvVj3mL3TWhnpce1Sih3Pndgx2u74BCeKXVFJnaN8BwoePkoktnpMl\nQgh9eW7bRujgtUQH2+oaSt/+agC6/v0H2NfWUv7euwFo+9TXZjWXvapuVvsvlHCrJH4IsVxJxQ99\n2fRKvBk+3o33duozv1hUtOFztGvrXktdxU0Tbmcxu/AVrcFXpCqc1lXcyJHz3yQaH5xwH1E4UukY\nXf3HqSnbmdf2Lns5NWU7ae89NM+RLR3pdHxa21stblLRwj+XkU6rqoKd3Yfp7C6Ma45ypVkIIYQQ\nQgghhBBCCCGEEEIIIYQQokAVZMWPsy9F2PVq1U8nlZJSPLNhthp478dr2XGbKt/p8pqw2g1EQ2o1\n59Pf9/PQp65Sv0Gt/PvjzzXy9795gd/5B1UyvHGLk6G+JH/1FrXCYqAnyZptTt72EdXrfdVmB0az\nxuXTUQC+8tdXaR3+HsBXbeHvvrcOl1c9FZPxDO/ZcXRW90kzwDv+WLWCeeWbSnB5TQz1JXn2h6qM\n+7c/0zGr8edalixP+L9GiVk9ZqXmGnzmWpxGr86RFZYsKtv7XKSZ1ugxnaMRS0k6GaPnolr93nNx\nP1ZnMb56tTLaV9+E3VOmZ3jYPeXUbb+HFdteB8Bgxxl6Ww4wMFy1JJuRsoViaUsNBLn68YcAVf7c\nuqoaa30FAJlglFjL1KuAUgOqUs7Vjz+UGwPAWl+R1xjJXrVCpPtLP8X34J04Nqu2I5rZSOxiB/7v\nPwdA6MCZGdzDqSVSYQBc5lK81moGYqPLDI5UAEm/rBIIgMlgodKlWtGEkwMTVvsAJiyDmkhPXMUk\nlUkQTPRSOtxuRkPLuy9ovvSev9CYSktwDbdycd7QhLnMp3NEIh0IABDaf4jQvgMke3p1jkgIsVgZ\ni5y5ymYAxW+4ieAz6vg3+PxxIsdbaPjPP5iTuWzl1XMyznzKxGPEZMW3EMuSZjJjLS3XNYbl3GpE\nM5mxluhzPizuV5+VM8mELvOLxWVl1SsAJq32MR6XvZztax5k/6n/BqbTxkIsVle6X6S6bAdwrRLM\nZNaueA3+gGqdGI0PzGtsS0E8Ob0q417XCsJRObehh4JM/Nj7vQH2fk/+EOfCve8tp3Gzgz98zSkA\n0qksf/6lVXRfVmV7HvrU1VHbl1SYefdf1PK1T6tSdp2tcRo22RnouVZWOzSU4oUfq3+fL/zFFVKJ\nDO/8c5WI8aFPr+Sjb7x20aOvI8EHbz5O0+1FAPz+v9TP+j7del8JN75eJU188sFzBPpTVDfasDkX\nb4GbdDZJb+IKQO6r1aDaGZWaa/GZaygxV4/6vbimL9nGubC6UBVM+3WORix18fAA7SdVW4P2k3tx\nldZR1qAuoJXWbcdkdeoS10grh+KajRTXbCQZVy20+loP03vxAJGhLl3iEuJ64ZcucO7Nn5hyO/8P\nnhv1dSrxVvX8bv/7b848uOFxZjKGZlYfqRNXe+j4h4dnFcNMnB9uX7Krupo9tQ/gj6rPEm2BE3SH\nzpLOTnwCw20pz71+jOw3XUZN3f8az1bKnI04zcUAmA02jAbz6BYzmgbZuU28MGomajxbAXLzmw2q\nPc9CzL/YGWw2HDvU4+PavQvbqgb1OAjdZNMqiSpy/CShfc1ETw+3KciMTc5aCHev+PCkt4eS/QA8\n3/WNhQhHTGJLyV1UOdYxmFAXupt7fzCmvddisLH4VQDUuVS7xJHnzshzScxcOhjFVKSONzJWM649\nG7j6V1++tkE2i2Yav7XbdFl9lXMyznyKdLQuu/d1MXNv+KvN7HlgZe7n/3jTs3Sfl1aphcpWXq1W\n/+kkFQ6SCgV0m19vej7+yznhRoxmMtporL5txvu77OVU+1SiQFtP81yFJXQSivbQ2XcEIPfvOhmT\n0caONQ8CcPDMQ7lFVWJ8kbifbDaTO4c4lXLvBtp7C6M1ylJTkIkfYu6s2urg5P4g8ei1k0XHXwjm\nEjFezmw18NOHejh/JDxq++t1tsbpbB3d72nvt9WKlE8+vHbez7dbHddeeGLhDOFAelS8hSKeUatX\nO+Ln6Iify/3ebSyh1FxDqUUl0xSbqnIXXJaLSHqInqS6ONUeO0soLYlgQj+h/iuE+tXzsfXwj/BW\nr6dsuCKIt2YjBoM+f59mqwuAqnWvpGrdKwn7VSJfT0sz/ZdfIpWITra7EKKADMZUNbPnrnyZRu8e\najybANhaUUfSdzstgyo5snWgeUy1C5PRkvs+Nc1+nQBmg5U9te8AwGXx0RO+SOug6pEaTwVJZeKs\n9d1GkXV+Lh6NzO+yqKoVI/PHU+rz6XzPvygZDNjXrwXAtbsJx9bNaGazzkEJgERbB6F9BwgdVCc/\nMuGJq9UspHhaHSuZDbbRiVJi0al2bkBDo8RaC4Dd6CGSkr7ky0ngmaPUfOxduZ8jRy8Su3itqqll\nRTkp/9xcyC6ExI9oe6veIQghdGKrqNV1/uWefGCrqNFt7uX+2ItryovXYzRYpt5wEtWlKlFZEj+W\nhgvtTwJQXrwBk9E25fZOu6pctGvD+zhy/mEiMX0T1U1GG5UlmwFVdbej7yVd47leJpMiHO3F5ajI\na3ufdw1uRyXBiCxGXWiLtwSCEEIIIYQQQgghhBBCCCGEEEIIIYSY1PIqEyDG6GiJsWG3G7NFlXtO\np2HDDS4un5l4Jfjl05OvEi8qNXH/76iVIZtv9uBwGdAManyjScNg0Ein56/kx7M/8LPzVapiyX8+\nvZkDjw/yk690c/HY4lhRN1vBtJ9g2k9r7DgABgx4zRVUWdcAUGtdN+n+qWyCjviFeY9zdrJksqoE\ndpoU8UyESFqtWgqm+0lkpFKBWJyymTQDbScZaDsJgMlip7RuO77hVjBuX71usTlLVgDQULKClTvu\nw9+mXkN6Ww4w1H1BSiQLsQTEUyFO9z3Buf5nAKh0raeheDfrSlXpU6vRyZm+p0btk85c64tsNU2/\nVdVKb1Ou2kbr4MEx4wPz2oZgZP7WwYMACz7/YmGprgLAubsJ1w07MXo8OkckQFX0CB08TGjfAUBV\n/FiMnur4n9z3JoMFi8EOwCsq37nsKgsudh3h01Q51tEXuwxANDU0p+NX2FdjMdi4Gj4xp+OKudP3\nzb0krvYAoNksBJ8+Oup2o8OK/9H8WuVNRDOoyj+WYt+sxlkI0c6ZtakTQhQ+e6XOFT962nWdX292\nHSuuxLqX92Mvrilyzv556HaqY2lNMyyLcwdLXTyhWnCdufx/bG68P+/9nDYfN276EBfangDgak8z\n2eHrU/PJYDBT4q4HoMq3jXLvhlz18Mtdv5r3+aerP3Ax74ofoLFl1Vs5cOqLwMyqDC8Ei0VVTbfb\nSzEZbRhzlZGzpNNJ0sNxR2N+4vHCaPEmZ3GWuR98vpvNN7n5719tASAcTHPxWITv/OvEJyWTicnf\nAP/kvxqJBNWL4t+95zz+7iTrdqoLCX/z3cmTEuZCPJrhHz9wEYDGzQ5e+84y/va76/juZ1Uf5B98\nYWmVFsqQwZ/sJJJWLzpTJX7EM1FOh19YiNCEWPZSiSjdF16k+8KLAFhdpZTVN+Gr3wmAza3PyVSD\n0YRvpep16Fu5g3hkkN4WVdKw91Iz8ZBfl7iEEHMjnU0B0B48QVfoDLfUvQ+AWs+WMYkRwXhfrv2L\n11aNhjamHcxknJbS3PddobNjbtc0A05zybTvw3TnH2/uhZhfL0a3OjB17tqBa/cNWGqrdY5IAJDJ\nED19luA+9Z4aPX6SbHr+TxbNpVQmQSqXECZJoYvNcf8vOe7/5ZyPq6EWamwuuZNsNi2JH4tZNkvg\nmaMT3hxqHv/9cDosJark9UgCyGIW612cCXVCiPmne6uXruXdbsRWLokfQn9Wy+wXPBiGE93NJgeJ\nZGjW44nFobP/KCWeBqp9O/Lex2iwsK7udQDUVdxEW28z3X51XBSNz769psloxWWvoMTTAECJp5Ei\nV23uOVgIuv0nWFl5c97bO20+dm94PwDHLn6XULRnvkLLi6YZqShTrXTKfJsoLl6FyTR1S6AR6XSC\nwaFLAPT1n6Gr+wjpdGKKvRZe4Tyj5tGGJgcWm+p6c/SF5fXiXlZrobTKwofvPAVAcDA1q/HMVgNr\nd7r42/ecB8DfnQSgqiH/P5651HIiwuc/epljzwf40KdXAksv8WNELKN6cyezccyaVedohBDjiYf6\naTvxOG0nHgdUBRBf/U5K61Q/SZN1+qvt54LV4aV2810A1G6+k0D3RXqGE0H8V4+RSSd1iUsIkR+7\nWVU6iybHrvzODv838v3LJTMxesKqEliFcw0NxXtoGdg37jyaZkBDy1XlAlVlZITN5B6zT6N3N+Y8\n+qrO1Mj84829EPMvFM2kDtscWzbh2rML2waV6KsZpHOn3pJd3YT2q4ozoQOHSAcKYwWIENfzWNSq\nLbPBSiK9NCplLmVGjzpmsK4sx2Cf+Ng/dODMjMYvhEofAKlIiFRIXnOFWG40o0pKs/oqdY1juVb8\nGEkKtJbp8/gnh/yk41IJWihGg3nOxjIZLCy+y7diNk61/hibRZ0vK/E0Tmtfu9XLmtq7WFOrzpeH\nY30Ewx2EY/2AqiySSsdy58eMBjNGoxWjwYJpuGKE1ezGYfPhtPtyPxe6oXA7gbB6//M4a/Lax2lX\nSeU3bv4degfO0D2grkUPha4SSwTGVFbRNHWey2S0YTbZsQxXJ7aYXVjNbmzDCV82axGZTIqTl36Y\nVxy+0vWsXX0vNltxXtuPx2i0UFqizseVlqyjsf5Ozl/8GQBd3S/NeNy5JmcKhRBCCCGEEEIIIYQQ\nQgghhBBCCCEKlFT8AP7yEwrMqwAAIABJREFUCyspKlEPxX2rjusczcKKRzNYbAa+fHBr7nexSIaj\nz6lVE5/701Zikfx7myXjGYb6kmy+UWWvnT4QYuV6O2/60MJlAe+6oyjXaubq+RgGA6zd4aTn6uLs\nITXXgql+SsxS7luIQhDsayXY10rrIZWZWlS1jtK6bRTXbALAZLHrEJWGp2I1norVAKR3vYn+y0fo\nuaQqgIT6LusQkxCLU/jgOc69+RO6xqBpBm5b+QEAhuJdBGLdxNOqCobJYMHnaMRh9gJwwT9+q7dT\nvXsB8FjKWVt6K+XOVQAMxjrIZFPYTWqFhM/RwP72bxFK9OX27QieZKW3CYCNZXfitJSQyagKciWO\nOopttQxE2yi2j18K2KCZKLJVYjJYhmO2YjZee+2rdm8cbj2hPseFEv2jVqOPzL+x7E6A3PwljjqA\nKedf7KyN9bh278K5U1WGMtj1eF8Q18tEY4QPHwEgtK+ZeKu8L4rC57PV6R2CyJP75s1U/v4b1Q9G\nA9n4xJX5Lrzr0zOaw+wpjBZp8R5p8yLEcmQtqwL0a0c1Um0iMdivy/x6s/pUlTDNqM9lpWj38m6x\nI0ZLZ+auQvG1dpdiqchm0xy98G0Atq1+INdiZSacNh9OW2FUxZtv59vUOcSmdb8xrf00NMqLN1Be\nvGHU76/9HWcxaEY0Lf/392Bk6u4ONVW7AVi79r5ci1OAbDZDONxNKKzGiMcDpNLxa5XPNQ2DwYTJ\nqCosWq1FuJwVOJ3D74OaAbPZycb1bxm+3cPlK8/kHft8ksQPwOle/H1L55rdqe7zX39rLV/6+BUO\nPalKg2cy4Ckx8Sf/pUof3f3ucn44zdYon/uzy7zvEysAeMNvVXD1XJTP/4U6Ifrxr60Zte17P17L\nzfeW4PSoeExmja8d255L3Pjix65w+KkhfvsfVJuWptuLcHqMGE3qD/R/j24jEszwn3/cCsDJ/UHc\nJSbe/Zfq5H5JhZlUMsuFo2E+++FL07ofhSqY9kvihxAFJptVCXaDHacZ7DidO4FRVLmWktotFNdu\nxGx16RKb0WyjfPWNlK++EYBooJueiwfoHU4EScWlHLgQespmM1waOACAz1FPtXsDhuFyp8l0hFDC\nz5EulfDRFTo77hgj7VJ+1fY1Gry7qXCqz2t1RdvJkiWeUu3kusPniafDo/YNxHs43PEoAKtLb6Gx\neA+Zkde0aDv72x7GY6uYMPHCbvawp+aBCe/f1op7Rv18qvcJrgwdHjP/6tJbAHLzD0ZV6cmp5l9s\nTKUluHY34bxBJdOYy+TEgu6yWWLnVDuk4L5mIkePk01KCzSxdGhokvhRQHwP3kH/d54GwP/YC5Ad\n28ZttizeAkn88OvbI1wIoQ97xQpd5491L88WLyNsFfoeV8Uk8UNcJ56Yfcu3TFYtXEmm5PzmUpRK\nq0VEL537Bpsb76eiZJPOERU+f6AFgPbeQ9SUNc16vLls2fRyLmcFa9bcC6jj3nQ6zqXWJwDo6DpM\nKjX91mEmk1qQVVW5k4b6O3KJIY31dzIwcJFAUP/3qWWf+GG2aJjM2tQbLjGNWxyASrT41U8HRt3W\n35mg81IMAFeRuvjYelr9Abxt9WGmcvS5AB++8+S4tz24cXSfo4f+po2H/mbqP4TPfzT/lXRPPdLP\nU48sz6xrUBU/hBCFLZtRyW8jiSA0a7h9KgGuuHoj3ur1OLxVw1sv7HuY3VPByh1vYMW21wHgv3KM\nrnPPE+q/sqBxCCGuOdv/zKivM5VMxzjX/yzn+p+d1n69kUujvr5cMNFLe+DEuLeFE35+fuGfphfo\nOPNPNPdU8+vNYLPh2LEV1+5dANhWNYC2/I5NFptUvx+A0P5mQvsPkvIPTLHH/LEZXVQ4VBWuUusK\n3OYyLMNVcTQ0Upk4oZSKry/WytXQCZKZmG7xTqXIUkmdawsAxdYarEYnRi3/0xK/bPsv0tnJE2+s\nRnWsW+fahs+2Esdw1SKjZiaRiTIY7wSgI3KGnmjLtO/D3Ss+nPv+YO8P6Ytdzq0cqnKso8a5EZdZ\nXTg3G2wkMlHCyWv/RpeCUx9T31b1PgDspvH7QI9UQdrb/oVpx1/j3IjXqj5Hesw+XGbfqH8Di9Ex\n6j6OpyVwEIBzQ+NXkhpLJScYNCMrXFuosq8FwGH2YtasJIafs4FEN23hk3RHL0462uYSVeWp1rmJ\nE/69tIdPA7DOews1zg25pOrOyDnODj2f673tsZSxwfsqPJZyABLpCJdDR2gNLp5+zFMxlXoYemL4\nOTQPSR9QOBU/EgO9eoeQF0+5DYBNd1Wy6iYflWtVX3JnqQWDQSMaUK9pvZdCnH++l+ZH1HFVdGjq\nJMM3fWorTferi+Cnn+zmm39wELPVyE3vrAdgy93VlKxwMNwqnYH2KKf2dvHC/6rXvlgoNe37MnI/\nACrXenL3AyAaSObuB0DzI1fyuh8Tsf5/9t47MLKrPvv/TK8a9VFfSbur7U3b1/a6wlJcsDEGgsGU\nhBL4kTeUkACBF5JAeEMSeolNCNg4YMDGYGMb3Ns2be+70hatepemtzvz++NornZW0mikGe2MVufz\njzRz7zn3e87cuXPvOc95vnY9G+6qYXH8eEsdWAoMjH7F8Q4GcfcFubBf/G637Ozj/N6BlL8aUSWG\nzqBl8zuF+G31WyopqbVhyhMTEP7hEB0nRtj/aBsAJ56f3sI4AJ1eQ+Od4jNataOc8iUOLAXC6S7o\nDtPT4ub4s6Lefb+9SCSUuuvyfCX7woP5Lvyoyurx53v/SxIZ8bZTzaa06nB7xbNB/P5RcnUSjUU4\ncvbXLPCIhY0NNW9EO43nUMl4Tl18Cqu5mMK8umyHMinVVdvQjjqIKEqI/QfvVx0+ZkpcLNLW/jqD\nQy1sbPwYADqdkaqqLbhOZV/4oc12ABKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCSSmTHvJU1W\n+/xL8wLQ3SpWCFnzdGy4OZ9DLwtbLKNFy4ab89n0RpEL/hsfTr7SRpJ7uBXp+JFLvONjJTz/6DBD\nfdNbSSORJBCL4e67AIC77wIXDz+FwSxWgOaXLSbPuRBHqUjRZXE4r8hqca1W3EKU1K2npG497j6x\n2r7z5IsMdZyY9eNLJBLJnESrxbJsCfbNwg7TumYVGsPs2VpKUiMWEquRvYcO49ndRKBl1AVillbT\np8oix2YW529Bk2S9hlFnpWjU4aLIVEWdvZF9fY8D4Arn1mr4RY7NNORvm3a58Ki7hS8yTIzkK/Eq\nrctYWXQzIBw+Lsess1NuFemsyq0N9AcucmjgKWDMRWM6CMcSA+tLbgeg2Dzeft6ss2PWiZR9eq0x\nJcePYFSk1TLFrOoKpUzRkL9NjedKoUW0YWvZu3AYSsdtj7u0lFrqKbXU0+EV95JHB5+dsm67oYil\nBSLdV11eY8K22rx1AGqfbyp9OwatWd1u0TtYVnC96pITdw7JZQLnujAvFavdvfvOzMoxDPlzxPFj\nMLeucRNx08cauOmvxTVHq5v4Gc1ebFL/1m8s5tr7xHPdzz6yh65TqVvYOxfbsZeY+NB/b8W5aOLv\neFlDHmUNeWy8W1yr/ufDe+g960mpHQA3/XXDpO2ItyHeDoBr71s47XYArL1NOArc/o+rMNsnH7o2\nVlkprLKyYF0hAEuvd/K9u1J3rtMbtXzsl9dSscwx4XZ7iYml1ztZer1wCTrweDuP/ePhlOsvqbPx\n3u9voqTONuF2a6GR+k3F1G8S/XXN++p58K/30n/BO+H+EkHWHSd6s7+SN5tk33Flfve/JJHeoVMs\nqw0BoNMaZ1RH50Dq13XJ3Odiz25ApCtZuuAtFDkWZjmi8cTTDrl9XVmOJDnRaISDZx5m9aK7ASgt\nWJbliMZTWLBI/b+re3/abh+X4/X20NW9HxDuIoX59Rmtf6bknPBDp9dQVm2ku01csKPK7A62Wezz\n0/RkoEv07/c+fYG/+Ewln/quOCFDgSgd54J8/7MXADi+252tECUzxKOIAdFkA8SS2cc6em257zNO\nml5wS+GHJOOEA+L63H/xEN7hTly9QqiXX9aAc9GWKx5PXqn4HVlaWo93qIP2o38CkCIQiUQy7zFW\nVmAbFXrYN61H55h4ckFy5Qmev4BndxPe/YcAiAanP/E/m7jD/WjQqqlN+gMXGQ524ouICTSNRoNN\nX8gC+xpAiBCMOivrSt4KwKtdDxIju+IVgDKLSFUTF314wkKofnL4ZUZCPWqalAJTJcsLblBTswAc\n7H9yyrQfcSpty1hT9Cb1dTSm0OE9wWBQWIIrsTAWvYMK61JxPGM5JeYFbC59OwC7e3+tpgRJFbPO\nztriN6uCD19khD7/efyK+Ix0GgNWfQGlljoA+vyTp6W6lN09j6j/6zQGjDohVthYchc2Q+G0Yryc\npt7H0GgSnxU3lr5ttD15hKMB9vT+Nmkdoej0ciGvKn4jAA5DKe5wP52jAgtfZASD1kKRWUziVVrF\nYGGVbQUAQ8FO2r0Tp5GNU2KuU4Ujp4ZfIRwNsiT/GkB8J2rsq7HoxXU3Eg1xavhVNbXN0oLt6DR6\nau1CMDIXhB/DT+6i/JPinHW9eIjQxV6ioYlTabhfOzqjYxjy8qfeKQcIjeT+opeuUy5VKBHyK5zd\n2UfrQZH6aajDh0arobReiDQ2v6uWvFITtiIxcfXu/1jPd25/mWg0tet4UbWV+364idJ6G0f/JCYK\nml/rwzcUwlEmriEb7qqmalUBeaPpZz7wwBa+c8fLBKdI+RIXbmh1GrUdAK0Hh9R2AJTW29V2ANiK\njGo7gJTasvXeOm77/MqE9wYuCiFE86t9DHf51XUO+eUWqtcUULVKLF7b99vppR99+7+spWKZg55m\n8Wx98PftDLT5sBUI4WD9pmJVhAKw/s5qWvcPsv93bUnrLagUKdk+/NA12AqNxEbbffzZbs6MfiYA\ntkIjy28pY9mNZQAU1Vj5q59t4/vveBUAT39u3ZfkAhqtFnNpZVZjCHTPZ+GBJqv9H/G4iHjlXIVk\njIgS4Fyn+I1pqH7jtMt7/L109s+dlH+SzOHx97L/9M8pLRApKGvLr6MwrzYrscSf2Qdd5+jsO0Dv\n0ClApKfJdZRoiEPNvwKgxrmJxdW3oNeZpyh15TCZxsbf3J7ZEdJcWq/ROHGq1itNzgk//unn9aze\namPv8+Kh4l8+0jpun18dWpGx48UfTuYru54eYtfT2ctZLck80ZiCVxnBrktvQFCSHuuuFYM3uiSr\nYSSSmWDJL6egYhn55WISJa+kHp3BlOWoErEVVrH0epGb3tV7jtaDf8A7OJ8HRyQSyXxCl5eHbaOY\nRLRv3oixOruD05IxlBEXnr378OxuAiDcm9urxXv95zgy8CdV+BAXgFxOq1sIV64pfw9WfT5WvZgA\nKzJXMxBIPjl1JVjo2Kj+r8Qi7Ov7PQABJXHgvs9/Hn9khGvL3wuABg3VtpVTCj/MOjG4srJQOH3E\nRQl7ex9VRSaXEu+vJfnXstCxEYdRrORemn8dJ4dfnlbbFtjXYtJZaRkRK8fOuvZOKLaJi1tmkkda\niYXxR8RnH2V6wpSJ8EbGP/9HL8lpHotFJ+y3dIi7fHR4T3Bs8LlxfdTuPQbAYKCDVUW3qO8vyFs7\npfDDbijixNBLAFz0iBWb8e/KuuK3otXocFrESr7Xux/GHe5Xy5p1dhY6NuEwivh0GsOk37NcwfmX\nb4WIOA8c21cn3XdGwg+NFp3ZOpPQrjgR90i2Q5iS0y/38Nt/ENecE893E/JP/h3e+dB5PvHodoqq\nRf8X19qo21TEuT2pfR81Wg2VK/J55DMHVOHH5TT95iL3fGMda94q7k0cTjM3frSBP/1HctHT6Zd7\nAPjtPxxKuR0gxCjxdgBTtqV8qYO3fm5szDeqxPjjvx5n76+FoCM2iXAkLjQJeqd3jaxY5uDA4+08\n/uUj4niX1b/v0TbONw1w51fXqO9tvbduSuHH3V9bCwhhRzQa43//j1gJeurFnnH77v9dG9fcJxZR\nvPVzK7CXmLj1H0QfPPJZORl5OaaScjT67E1nRMMhgnPAbWi2MBaVojVmb/wp0NuRtWNLcpfWrtcB\nMBnsLChL3V3Q4+/lUPP/Eo3m/uS6ZPboGz6j/s2zVlBRLH7znYXLsZhmZ34tEBJz34Oucwy6zjHg\nEs+7ofDULmy5ibh/auvdS9fAEWqcmwCoLGnEai7O/NFGn129gT76hk8l3VdRQqpruUFvyXgsot4x\noYuihGblGNNFWgJIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRzFFyzvFjaaNQtq+7bnJLFGte\nZnPcSiRXG+7IwFXl+PGFH9aoKda/9XcdfOjzZVz3FmHTZLHp6GoN8S8fEytAOi8kqur0Bg3v+ngp\nN98l7GpLKg0M90d49Y9CWfnQf/YS9I/PFe4oFNeZe//Wyaab8ihyisul163QcT7Ec78dBuDPv05c\nMffBz5Wx7U0OKmvH8gr+8JnF4+q/o0Gkv1BmOZ2VZK6jIa+0lqKaNRRVrwLAZJsbObfjOJwLWb3j\n/9DTsguAi4efQgkHshyVRCKRZA6NQY91tbAkt2/eiHn5UjRaqa/PBWKRCL6jx1WHD/+pMxAdf9+X\ny3T6kq9gAYjExP3vedc+Vl7ilpB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DXheAGv+EsY9ir1xE2fpbANT+C46I+7x4/+WKi0W6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eg29J7PFHHLx8Uqv//+s4dffLmRNReK4/m+LxZTWKbj5FER859+0Mr+bfHr49qLrNz22WJK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Mw9A3gT6qPdjAKd/+FPZGJFugpQmAjk3v4FpzaTyFvKXk7NqdzS/8kUggcTkl68zZQH8A\niK/udPw1TdPipYCEEOMrFnBgNqsT85WrTaxcbRr2fe+8qT7zDz/YcU7xlH//xU7+9/c55OapfYzN\nrnHnvbYhl9+3Jzjka+nwp1dymTffOOxyKy5U2/TXvz/7GAXgC5/vAOC1lyZGwMRPfugG4PzlJkpm\n6BMuazBAfl9pmMuv1HP5lRae3aACR77ycKfE2yaBsbAA103XAaiyLkIIIYQYNyG3KsV66nc/JXfN\nVeSuWg+AJmXWhJgQ0h740Zy6wIOJzpxbiKZP7+0ebwoDbwxmO6XLbsRkU/cVopEw7tZaqt8bWGa6\n9YQKxtDpTZRdcANmh7qxFwr00tNcQ+uJ7WPug9/dHg88yZ29goMv/fCsZSpW3wmAq2QBepMVTafO\nz5fd+W+Egz5OvP8kAN1Nx2k9sQOdXl2fiPU1FOgFGLSvoUAvJ7c+DcCMpR9j5srb4qVt9r/wn/Ht\nBMS3VSxQZLBtJSa+1h4VuBQlioY2zNJgNWUBsHjGDew5/czA8kBCiLi8XBVr0Nqmymx9NOhjpLy+\njvj/rRbXuXcsCSZd4Id17tyEr3dv2TJOPUmu3iOqbpBt4cJBX8+95RYAdGYzXW+/TTQcHre+nYve\nI0eGHBOoccXGBEyacU1Edn3miJc97j23aGSRPi2vPkc0HMK16hIANKO6wRn2qJuCLa88R8+BPcO2\n41y0dMBzz9H+2mNdnjqKc5YAcKZlO73+DkbKZnZRnL2ETvf0PdEW4lx88f4OPnaDlVVr1Il/5VwD\nRcV6bHZ1cqfTaXi9UerPqO/LvbsDvPAnH5veTxzsNVInjoe48aoWPvkZdaHg0ivMlJUbsFrU+j2e\nCK2tEY4eVhEqEy3wQ68f/iR4JHTJaeacmUwaX/tWBh+/WwXfaBocORTk2BG1/dvbB57A6zSwO3RU\nzFIXlpZcYELT4ObbrQBs3Rzgj7/vHccRTB2aUZ02ZV69nsz169D0iQNwhBBCCJFa0UiElvdepqda\nTWopue5ezDmJMwQLIVLPnCcZP9LFUjgjresP97oJubtS1n7L8a20HN864uWbj22i+VjijMi9HXVs\nfeLhUfXj4Cv/X8LXazY9pR5H2F6sj8P1NaalesuAx7Ne79tGo9lWYuIKhDwAtLtPkuMYeabRgswq\nVhg+wYG65wHw+CXbixAf5rCr84aW1oPDLJlYKNQ/aVCvN59TW8ki4fBCCCGEEEIIIYQQQgghhBBC\nCCGEEJPUpMn4EZtVZyoqSricr7p6PLqTdO7tKm1X9rXXorNaz16grxZb9nXX4Vy1is6NG9X7duwg\nGppAheg/wr19+9BjAtC0+JgAOjdunPBjmqgsOseIlvNGemgPNqS4NyJlIhFaX3uB9ndeB8CUW0A0\nHCbQrH6n0cjI0rcFO1QawPZ33yAaCtJb07/vrK5/kxVz7wNg9YIHaO48TKdblYLxBbsJh/vrA+r1\nJqymTDLtalZBflYVGhrH6jae2ziFmKZ6PVE2/K6XDb9LX1aG9rYIP/zPHoD447n65B1tSWlnONev\nb0n5Op583MOTj3vG/P777xv5LItvfieTWz5ujZdn+fsvdfLcM94Rv/+qj1n40c/70wzefJtVMn6M\ngWlGCXmf+QQAxvy8NPdGiOkjI6eC4oo1OLNnAmAyOwiHgwR8qsxDd3sNZ469ic+T/NlrRrM6tyoo\nW052wXxsGYUA6A1mwkEf7m5Va7vhxAe0NSQuoWmx51AyW2Xry8qrxGTNjB+zB/3ddLfX0nJmFwCd\nLYOXrbXYVWmyktmXxNsAdewfawOg5cyuIdsQYqryNapskyce/x55a68hZ8WlAPHU+kKI8ZXOUi/R\nUAh/W3Pa1p9u1oL0ZvzwNk/fbCtCpNqp1q2jyvgB4LKXctHc+wFo7TlOW88JurzqHkIg3Eso7I+X\nj9FpevR6Ewadylhg1FsxG+yYjCojsNngwGLMwGpS5yHBsI9tJ55IytjE+NDrDBh0Fgx9WSkMOjMG\nvfkjzy0YP/K6ST90CfDBFGTOB1TZoVDYRyii7iXF/h8KqwwZsf/HXg+GfYTCfkIRldU61WWKDAY1\nzmBw7Nd4ATStP79GJDIx7mtPmsAPY566yDpUWuVYoECgsXHc+pRMEb/6Y2579lny7r474bLGnBzy\n7rgDUIEgPVu30rN5MwDB1tbUdnSUIn7/iMcEkHfHHfExAfRs3jzhxjRRmXSWES0nQR+Dq1qjbowt\nuDibjY+doat5YNmE3DIVvHTPN+cye1kmXc3qC+m5H9Sw7fmm8e0s/fsMX13tmN7f/vbrQ77W6T7N\ntqOPA1BVei1F2Yspyl48onY73LUcPv0SPb2Tc18shBATQVGJOt6NlWh5/WV1UjSaoA+AV1/04XZH\ncTjUiXzZTLkBMloZ6y7GddP1UtpFiHFUVnWlepynHt1d6iZCd3sNBoMFe1YJAAWlyzl58KWU9KGo\nYk1fH9YTCnrp7VbHtgFfNxZHLlm5lQBk5VZydOdTNJ8evJSmzZnPkku+gL6vdntPRy3uzjoMJlv8\n9YKy5fFa7oMFbcTaABV0HWsDwGCyxdsAVfddAj/EdBUNh2h++3m69m8DoHD9rdjL56S5V0JML5pO\nl9aSS76WekjxjaKJzJLmwI/pXGZHiFRr6amm3XOKbHv5qN4XC+zIc1aS56xMWn96fON/P0QMz2Zy\nsaTsVgAV1KEzY9Sr+4YfDlBIJbs5e8DjWIUjIcIRP8Gwv++5n83HHyMamx13joLBXkwmJ8a+4Kax\nslr7x+kPJGcC5bmaNIEfhqyshK/HAj6i4fB4dCdlerZvx1hQQNbll49oeb3dTta6dWStWweAr6aG\nnu3b8ezeDUDE50v09nERGxMwonHFxgSQtW5dfEwAnt27J8SYJiL9CD/OXaHUz4aejJZfr/5GV99a\nyNY/Nw0I/DAYdfz1z1XgQ8EsGwFvmJwZ6gvz09+dT2eTn2PbOse/0ykUy/Cx+dAvsFty4hk9rKas\n+IVrgFDYj9ffEV/eG5ha20EIIdJh7jz1nd6X8I39+4Jjbivgj0Jf4Iffl5yTo6lOMxjIuft2ABwr\nl6e5N0JML9mFC+IBH6Ggl0NbfkVX24mBC/XtHO3OgnjARLI11LwPgKernvamQ0QjA68zlFSqjAIV\nC69jRuWlQwZ+FJZfiN5g5vjeZ/raPbt2uz2zmKDfPWRfYm0AHN/7zJBtAAnbSZbdLzay+0UJ8hYT\nl79N3Yg49dTPyKhaSsFlNwBgzHAlepsQIglM2QVo+vTdbpjWgQeaDnNe4kzpqeZrOpPW9Qsx1e0/\n8zxrKj8HEM/SIMSH6XUmMqyF6e5GUuh1BvQ6AybDhwMzNCA51zbdnkayTU5ysucC0NA4+Dl9Ipqm\nIzdnfvx5V9eppPTtXI1PiI8QQgghhBBCCCGEEEIIIYQQQgghhEi6SZPxQ+90Jnw90jt16pW3v/AC\ngQZVjiP31lvRWa0jfq+logJLRQW5N90EgHvPHro/+AB/7djKQSRL+wsvABBoaBjzmAByb7opPiYg\n7eOaSEaaqikQGV2a+Oli1tIMANobfJw5NHCm3MqbCyiYpdIx73qlhUcfOkj5IrVPevi357Pu0zOm\nXMaPD/P42vD42tKy7paa7WlZrxBCpJNerw14brVqQyyZ2AXLTWTn9B8fHK+eGLUmJzKdzUrB/Z/F\nXDEz3V0RYlqa0ZdJA6DmwAtnZ/sA6Evt6ulOXdaJoF/V+W1r2D/o6/XH3wVg5oJrsTrzh26oLztJ\nJDJ0ZlJPV33izmj93wFDtTNsG0JMU92Hd9NzTH2OXeevIXfVegw2R5p7JSacJKUMF2DJL07r+n3N\n0zfjhDmnAJ3RNPyCKeRrnsYZV4QYB95AJ7tqNwCwbOad6LRJc3tXiAmnueUA2a455OUtBCA3Zz6t\nbYdG1casiiuxmDPjz5ua9yS1j2M1afYMminxgctUK//h3rkTAO+RI7iuvhrnqlUAI64vHttezhUr\ncK5Yga+mBoDOjRvpPTS6P95kcu/cGR8TgHPVqlHVTNdMpviYQJW2SfeYJopIVN3M0WvGhMsFo/6E\nr09XGXnqM3NyT/dZr11ydwmRsLoQsOE71UTCUWr6lqvZ001FX9CISL7jm3+X7i4IIcS4O9BX2iUa\nVff7brtTBR8+9ZteGuqGL2u4dJn6TvvejweWSvzT0xL8ORS9Q90EKnjwrzCVpPeCtRDTlU5nwOkq\ni9+Aa63bneYeDS0ajQCqtIrJkhEPwo/9PKblzG6KKy6i8rxbAHBkltB4cjOe7oYRryvWBkDlebfE\n2wBG1Y4Q01U0rK6VtG9/h869W8hedjEAOcsvRW89t5reYnLz1p+kfef7dB+ZuN83k40l7aVGpm/g\ngaWgJK3rjwT8BDpa09oHIaaDdvdJALbX/IalZbdjMtjS2yEhJqmGxp2UzViLzZYLwOKF91DfsI36\nvpIvbvfZE000TUdmRikApTPWkpe7IP5aR+cJ2juqx6Hnw5s8gR/DBAdE/FPzZnbY46H1j3+k8803\nAci67DKcK1cOGwjzUbGMGYWf+xy+kydpe0bVGPafGf9I6NiYADrffDM+Jhg+wOejLBUV8TEBtD3z\nTFrGNBGEouom0XCBH9Ek1cCaavQGNZPO0zVwNnTJPAelCxzsfUOdvHQ2DtzXdNT7mXmeBH58WIat\nGJejDIBTzZvT3BshhJh8mhpVcMezG7zc8nErefnqhuJzr+Xx3DNe9u1R3/ldnRF0esjIUK+XV+i5\ncLWZJecPPBZ46XkVIP3Ki1MrUDpZdDYbhV+8HwBj0dSohSrEZGQ0O9B0egL+HgDCoUCaewSu/Hnk\nlpyHPUPdyDJZnOj0ZvR6tZ/VdImvU/R01HJgy2PMWnwjAEUVqymqWI27S92YaqzZRNPpHUQTZASJ\ntQEwa/GN8TYA3F118TaAhO0IIdSNydZNrwMqECRr8Uqyl6tMQ6asnHR2TYyDSDBA96GdtO98H5Ds\nBKlgTmfGj2gEX8v0DYi0Fs5I6/p9LfWSPUeIcdThOc0Hxx6hqvgqAAoz56e5R0JMLtFomL37f82y\n8/8KAKPRTknxhZQUX9j3+sBJHQvn34FOb0KnDbwG4POpSgAHDv1+HHo9MiOrDSGEEEIIIYQQQggh\nhBBCCCGEEEIIISacSZPxY7iIUc2YOMvBZBfq6ACg9ZlnaH/55XiGjIxVqzDmJ6gpPAjLzJmUfOlL\nAHS8/jodr76atojcUEdHfEwAzpUrxzwmgJIvfal/TDCtIo0DUTWL10zi9F4Gqf02qM4mlckjp8Qy\n4OcX36VmK2zaMHgNcbNdP6n/zDS9HsuMsvhz7ylVFirTrlJEziq6BLslj2CoF4C27uOcbtmGP+ge\nss2cjFnMKbkCkIwfQghxLv75H7swmeG6G60AOBwad3/Sxt2fHNn7fb4oj/3Cw4+/35PCXk5umslE\nwQOfk0wfQkwAmqYy8KX94FrTqFp+LwC5xecRCnrpaDoMQFvjQUKBXsIhde4wa/FNGIyWIZsC6Gg6\nzI7mIwBkF8ynsHwlroIqACqX3k5J5SUc2Kwyevg8bUO2AbCj+Ui8DQBXQVW8DYADmx8bsg0hxECR\nYID2ne/RvusDADLmLsZ1/kXYy2b3LaGlr3MiOaJRes+coOvwLgC6D+4i7JfSh6lkyUtfxg9/WzPR\nUDBt6083S0GaM35M4zI7QqSLP+RmT63KrH/CUkB57goKMtV5hkFnTvr6ItEwPd4mAJp7jia9fSHG\nW6+3lW07fwrA/Hm348qqiL8WK+caYzBYz3p/e0c1B/syfQSCnhT2dHQmzR3gSDDxgZvOkvhiy1QS\n8XrpevttALrefhtLRQXOFSsAsC9dis48gp26Tv3Ruq66CmNeHs1PPql+nqaLbBGvOvHqevvt+JgA\nnCtWjHxMADpdfEyAGle6LxyOE09YpRRy6rMTLmfROcajO5PO4Q9UcNXFdxVzyT0lhAIqldPq2wtp\nPuVl31uDX0DNLrbgbk9/GuqxMmRkUfrZL8SfH/36Q2TaS1gx7zMA6DQ9kUgIm9kFqICQsvwLOXDq\nTwA0dRwa/04LIcQ04fdHefjBTp58XAXf3XCLlSUXGJlRqtIK2m06wpEo3l51rNPUGOF4dYjN76sb\nkq+/7KOtLTJ449Nd3w3mvPvuxTyzbJiFhRDjIeB3E41GMJrV+YpObyQSHv8bOLlFi8ktPg9QgRh7\n3v0xQf/gF3Fmn3fTyBrtOydtbzxIe+NBzNZMACoW3Uhu8WIql9wGwP4PfjFsO7E2AMzWzHgbAJVL\nbhu+DSHEQH1pnLuP7KH7yJ54yZes81aRtXglBrsznb0ToxLFW1/bH+hxeDchd3ea+zR96G0ODI70\nlUKevoEH6rzGkl+S1l5M3+0/vVQ3vUN10zvp7sao1LZtH/A4VfX4mth/5nkO1r0EQJZtBi57KU6L\nmmBtNbkwGx3xgBBd3+TgSFSVvQ9HggTDPvwhNdnTH3TjDXTg8bcC4Pa34fY1E4mmprTkrlN/SEm7\nQ3nj4H+Ny3q6vQ28su/b47IuUH8H47m+qSBWqmXXnl+SmVlOQZ66FpCZUYrZkoVBrz4z4XAAf6CH\nrq6TADQ176Wz7/8TzeQJ/HAPPbscQG89O9pmuvDV1OCrUbP02/70JxzLlpGxdi0ApoKCYd/vOP98\nAo0qm0Hn66+nrqOjEBuPr6YmPiaAjLVrRzwmgEBj44QZU6rFAj+GY9Nnprgnk9PGx88AsPKmQu78\n+pz4z6ORKBu+fYxoZGAAUWaeCYCiShuH3u8Yv44m2WBBc7OLL4tfnN55/Elau6rjtcvysuYxp+QK\nlsy6A4Dq+jc40fDuuPVXCCGmo53bAwMexbnLuuZKAGyLF6a5J0KImEg4iLuzDqerFIDc4sU0n945\n7v2wZxbF/9/WsH/QoA+LXd0Y1hvGNgHF7+0C4Mj2J3Fd9y0ycmaOuZ1YG8CY2xFC9At0qkkfze+8\nQMt7L2EvU9cHMqqW4py7GL0lcZZVMX4iwQC9Z07grlFZkXqO7iPYPXmvz0x26cz2AeBrPpPW9aeL\nKVtNftSZkj+7fzSm6/YfTCyTedHfPIDObo/f16r9529Ni/WL9IoFZrR7TtHuOZXm3ggxuXR1naKr\na/J/bnTDLyKEEEIIIYQQQgghhBBCCCGEEEIIISaiSZPxI9TVlfB1YywLhKZNm9Ieg4n4/XR/8AHd\nmzYBYF+4ENe112IqTFy33HXVVQC4t28n1DmyzBHjJTYmgO5Nm+JjAkY0rok4plToCKqsLQyT/MZl\nkBr2g2mtVeWGvnvHDtbeUUyshNf255up2XN2atCKpSpzSv1RD5ufbRy3fiabznz2LMUseyn17XsA\naO2qBvqjhZs6DtLWXc3imbcCUFl8OSaDncOnXx6nHgshhBDnxjJvDlnXXpnubgghBlF3/B2qlt8L\nQMXCG+jtacHdeXrQZa2OPIJ+N6GgN6l98Hv7zx2tzvyzXjcYLfHSLMPJKVpET0ctAd/gpQYcWTPQ\n6034etuHbQMYtJ1YG0DCdoQQoxeNRHCfPAKA++QRtFc3YC9XGUCccxbhqKjCmJm43K5IBnWd19fc\ngKfmcPx30numhmg4lM6OiQ+x5BcNv1AKTddSI9aCGWldfzSirhf6WyfvtdFkCzY3A1D7jW/iWL6M\n7Ouvm1brHzfD3Aec8cOv0fGb5/Bs2TOOnRJCiPSbNIEfsS8sotF4Te4Pi5UrMObmEmxpGc+uTUx9\nX3qe/fvxHDyIa/16AFxXXz3o4ppelXHIWLuW9uefH58+jkU0Gh8TgGv9+iHHBGpcE35MSdIRUgfY\n4WgIvTb0RzvDkItFZ8cXGbxO9XTXeLyXDd+pHna53a+1DHicrPSDlHrRaXr8waHLa4XCAXYffwqA\nqrJrKcu/EJ3OCMDBU8+lpqPnyIVKfVmlW8amSPKCVDQ0okzfYMOpYu7ffJNIUJXQqP6F1EEU4+f7\n/5XFTTdZ2LEjCMDd97QRkmvXKaWzWsn9xF2Dnk8IIdKvtW4PdX2lXkpmX8LSSx+kp0MFfvg87RiM\nlniZFasjj11v/YBQ18DAj9xiVZPXYLJiMFiwZ/annrc58imdewUAoaCXcMgXb9/rVsf1bQ37Ka9S\n55jZBfNZvPZ+PF31ABjNDrLy5uBzq1rX3W01ZORUDDmesnlXYs8oxNPTBIC/t4Nw2I/Z6gIgw1UG\nRDl16JVh2wDw9DTF2wAwW13xNoCE7YjpSdM0oiOcHJWVVcG8eTezZcsP+n4i5zkfFY2E42VFYo+x\nMguOiirs5XOxlcwEQG+1p6WPk13Y58XbqILdfA21eOtP0VuvUm6HvXIdayKz5Jekdf2+5ukZ+GEp\nSO9297eqY5xoOJzWfojpp/jbD1H/te9DJJLurgghxIQyaQI/In51YSPY1oYxN3fI5SwVFRL48VGR\nCB2vvqr+r9PhunLoGY62qqrJESTR94Xe8eqrU2dM5yiekSFQQ7F5zpDLaWiUmOdx3Dv+9bLFxDNY\nxo9efztZ9tKE74sFOxyqfZFgyMesootVe5qeXn9b8juaJFGSezKwSncNmyMvS/CHSJqMqqU4KxdR\n9/wT6e6KSKFYzMFtt1nR62HNGjVTu7TUQE2NRH6kUvbtN2HIykx3N4SY0oLN53Y+XrNfnbt1Nh+l\nsGI1Ga5yAByZJYTDAQJelQ207vi7+Hs7znr/vOX3AKBpZ1e2tTryKJ8/cOLAyYMvAXDm2Juq/34P\n+97/OQDl868lI7scp6sMgIC3m6ZT2zh95HUASuZcmjDw48yxN8kvW4Y9Q82CtjnyQNMIBtTNy7bG\ng9SfeJeu1hPDtgFgzyiKtwEQDHjibQAJ2xHT08oL/5atfYEc0ejw50KRaAQJ+BidQLva57W3t9C+\n4934z805+VhLKuKBIJaCUsw5BfGJV9NXlGCX2nf725vxtzXFMzV4G04RaG9F/gYnp7oXfkPdC79J\ndzcGWOpcT4Fp8O/pnT2v0BKoHeceJV/TW88NeBSjZ6mcDYDra5aPPAAAIABJREFU2msxl84g2nff\nIdjcTNMvfknYrSbHmcvLcF17DebSvmumej2Bunra/vgMAIG6sQUfjXT9Zd/6Z9qe+ROeXbsGvL/8\nO2oCU8tvfkvvvv2jWnesTWDQdge0qWlkX/cxHMuXA6Cz2wj39ODevgOAjhdfGtW6x0rvygDAWJQ3\nLusTE9cs61JKLfMBMGoWesLtHPaoygFdofG9R7wq8yYyDWdnixyJk969ABzp3ZLMLolhWK05eL0T\n9z7WuTj7SogQQgghhBBCCCGEEEIIIYQQQgghhJgUJk3GjxhvdXXCjB/OlSvp2bp1HHs0uXS+9hoZ\na9agtw+ectKYN/kiJWNjAgYd12Qc07k47TuYMOMHwEzrYmp9BwAIRv3j0S0xBEtpeVrXby4oPutn\njR37qSy+HACXo5wO96mEbVTXv0Eo7ANg7owr4/+fSDpQUb6bI68mpT0zVgDsOJPSnhAxzspFGJ2S\njWCqi2Vcf/ppLzfdZOGdd9R3cW2tZPtIldgsKsfK5WnuiRBTS7CpGd+x4wD4jh3HV32ccHdPUtru\naD5KR/PRUb/v/T9/+ZzX3dujSs0e2vqrhMvVHn6N2sOvDfl6S91uWup2n1NfktGGmJ7M5gxstpFf\nD+nsrGHb1h+msEfTi7+tGX9bM517+2dvajodpuwCACz5xZhzCzG51DVOU1YuJlcOOtPZWTkni2go\nRNDdRainE4BgTyeBzjYCbWqf6m9vwt/WTDQUTGc3hRAizpibS+H9nweg6/WNtDzxZLxsjaViZjzb\nBkCktxf3zl20/u73gNrnZd94Pbl33gFA/X99P6XrTzfHsguwL11Cw09+CkDY7caYn4/ObB643FqV\nqc40uwxjUR7GIpUFoeUnT+K641oMuarkYfMPf0Wg5gzmWSqDSuYtV2GuKIG+zFjB0w20P/FnArWq\n5KJmNFD41Qfi7QGUP/KvA9Z96i+/NqD0iyE3m8KvPqD6M7OEcHsXHU+r0oi9W/cmYauIdMkzlTHH\ntmLAz7IM+Sxxrgfg3Y7fSYZukdDqlQ/R1V1LQ6OqjNDcso9QaOLd1xqLSRf40XvgABmrVg35uqWi\nAlNREYGGhnHs1eQRDYfxnz6Nrapq0Nc1gwHNaCQanDwnYbExAYOOazKO6Vx0hpppC54hxzhjyGUM\nmon59osA2Ot+Y7y6NmnkllpZfauqo126wIEt08iBd1Tap5d+qoIgrBlq92lzGuhqDhAKjq2ESNlf\nfikJPU6u2uYtlORcAEBu5pxhAz8ATjapNGqhsI/55dePep2X6m4G4FB0OzO1KhxkAeChm0ORbXTT\nn8ZbQ0elthiAIq0cA0Y6oiqw43B0J176T4os2FihuwIj6iQkQpi3Is+ctf5LdDcCcCS6izJtDk6y\nAfDTS3V0H01RtY/RoWeF7nLsZMTfe7nu9gFtvRHZMODAslybR6mmgrGMmOihg6MRdfE+Ni5n33jP\n061hV+RdFujUgWsG2QTwsS3yOn58fePXmK0tpkgr72vTTAAfDVH1ezoe3ZdoU4uJqi8dvb18DoH2\n5jR3RoyXv32ok799KN29mPo0nY6cO25JdzeEmPSCjU3x4A5QgR7hnolzMVqIsSoruwSAGTPWYDBa\n6elRNxiqjz1PT08dOp0691u+4kHa245SXf1i/L12ewHLVzwIwN69v6KjvTr+WpZrFrMqrsSZUQJA\nNBqlt7eZvXtUQFEg4B7R+mPWXvxPHDn8DGXlanmHo4je3hYOH3oaIN7XC5Y90Nc3FfRx6WX/MmC8\nb7/1T339iWCxZMWXNxptRCIh3n3nm4NuJ03TM2v21RQWng+AwWChs/MER4/8GSCeKvmitV8F4NjR\nPzOj9CKcTjV+v7+LE8dfpbl5+t5oiUYi+FvV9crY40fprWpCkzHThdGegd6uzj0Ndqf61/e6zmxB\nZ7ag7wsU0Zkt6AxG0OvRdOqmmabTqf9r/euPhsPxm2LRSJhoOEw0Eu5/PRQg7FfnnhGfl7DfS9jn\nVc/9vr7nvQCEPD2EeroI9gV6hL2eJG0pIYQYH5nrLsV/8iQAHS///+ydeZwcZZ3/31V9T0/PfU8y\nyUwmJzlNIECQMwICcqqIiAoIKrur7q6u7qqrKOruirg/gV3EA2XBCKigKIjcCYGEhNx3ZpKZydx3\nT0/f3VW/P56ununM2TM9Z5736wWT7qp6jqrqp556ns/z+b6UsM27N/F5FW5rJ9zWnvCd551tFP3d\nPeKDovSt8piA/KcaxRobWw2JhSua30+wdugxY+f61bT84BEyrhThwQu+9ClaH/glzvWrAcjYeD7t\nP3uaqFc8Y3zb99D52O/QI+KZlPXRD5J7+0003fsgAHo4QtO3H8S2QIRgLPrGPQOEHqeT8cH30/6z\nZwAIVteSfuHZ5N35EQAaDlcT9cjn1kwlx1w86PcONR0AuykdfzQ1CxIks5fMjDIyM0SbsqjyGto6\nDtPcLMJedXYdH1WozOnIjBN++I8cIerxYHINvco6/yMfoeGhh8SHYRr+MxXFPPRl18PhGSmQmI11\nGg+Hve+wIesmQEySD0axTax89UTbOemfXh3JqeSCm0v46DcXYjIrCd+31PgSPi88W0zUf/bh5Tz5\nzaO8/czsEZtFoiG2H/05AKFwch3g+vZd+ENuXI7CMeW9SFnNfu0d/Ih8FyjLWaGez9uaGFzV0Vmg\nLCdPEZ273doWQgSYpywGYI16Idu0v6Ih2v4APrZoz5OnCGeT5cr6YfNfoqzloP4ubl28yJUoFZyl\nnEOXLibiQwTZrr1MJrkAnK1eNkDo0Z8SpZwSpZy92tZYebyUKgtYo14EwNvai4Tpc92x4WChsorj\n2l4AfHhwKdlx0QdAkVJGoTKX97Q3YmUK4CQDkzK1j/TMs4SiP3v1+dhyClAt4oUw4vMQaG3EfXAn\nAD1H9w5+/DJxfM77NmDL73OiCbTU0/Hu63iqDg6bvxp7Ac1ddxGuRSvjK+cURSHi9eBvFC+jrVte\nINSdXPy+ostuIGeteFHt3LWF5lcGiofS5oo2Ne/cy0grmR9/LoW62ujev4OOnZvFjqd1GLNXn0/W\ninOw54t7WjFbMDtdLPuXBwYty+H7vxyP99qf8Z5/iWQ2k37u2ViKi6a6GBLJzEDXCTW3ABA8Xh0T\nepwAmFYrDiWSVFFcso7iYtGP2rf/cYKBbkpKzgFg1eo72L7tAcKxd6JDB59i7bp7aGsTse57eupZ\nuuzDNDYK11lD9OFwiHeFVavuoK72DQ4degoATYuSmTkvLvhINn+AyoVXc+jgJgD8/i7KKzayfMWt\nAGx75340LcLOHWKCJCOjjLXrPp8g9DidQKCbt7f+AIDcvCUsW3bzkOeqvGIjubmL2bv3MQDCoV7m\nll3IqtW3A/Du9h+jxQQEAIsWX8/hw8/Q466L13Xpsg/T1SXEY+Ek3zVHw1nnpHP3d8UimC9deSTZ\n+bdR8audywH46Tfqeeev3SlP3xBPRP1eZseaQ4lEIpm+WAqLCMSEFyNhSk8n6wMbsS8Si7tUux1F\nUVBiDhWKoqAn+eBJJv+ppnfnTtKWLWHuN4TA07d/P+433iRYd2rQ/SOt7YRONRE4JJ771oq5BKvq\nMOcKx4/0S8QYbaRFjMH2tiSKanrfeJfCr90tBDWQtKgGoHfrLvx7D8c/97y4mawbLwfAMqeI6OHq\npNOUTA9GuhuS/S2OlyrfezhNWVhUG1ZFOIVbVTsWxY5VFSJdu+rErFgntVySoenoPEZOdiVKbDGm\nqpopzF9BYb5YcBwKeWhu3UtzzBGk19syZWVNlsFnhCUSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQi\nkUgk054Z5/ihaxruLVvIueqqIfexzZtHzhVXAND54ouTVbQZgWI2YystHXJ7pKdnEkuTGmZjncaL\nN9rNUa+II7vEed6w+y5KW4+KmWr/rsko2rRl8blCbfyxby8iEtTY+rSw12085uVj3140YP9DmzsB\n8HsirN6YN27HDy0oXB/0cGhc6SSLYrEOiMUIyTt99Kejp5qOnrEpphv1E7jpc2M4ru/lQuU6chTh\nINKlt1KmLGS//g4AnliolOO6cDEw3DCMsCfJ0qTX0K43xj/X6UepVJaTrmQC0KknF4JjvrKEE/rB\neDkBavTDcYeSPKWYJr0mvk3FxCmOJZyDTj1RTWqKPbqjRACIEBb7T2HYwtz1l1J4kQjxE2iup2vv\nO/HyWLNycc5fRLBD1GMwx4nCiz9E7jmXABDqbKV73zYMP+L0iiXMvfFOWl4XFtIdO94YcLwpLZ35\ntwiLbVtuAf7GGjrf2xLbqmPLK8Y5X/yOIy89nVTdCi+5lpy176dzl0hvMLePzGXvo/RqsdIy1NVG\n1/7t8bjVaXMXUHjJtaTNKQfg1LO/ov/FCnW20rV3G4oqtLjFl3+YUGcb7e++Pmh5dG3ghR7v+ZdI\nZjOKxUzmBy+f6mJIJNMTXSfU1EzgeCx0S9UJglXVRHul5bHkzKGs7CJqTr4CQG8sxEpt7RsAzC17\nP7l5i2luiq3y6m2ipuY1liwVoR5bW/djUq1UVyWOO80tE05xPT11nIylbWC4hYwlf4Cmpp24Yw4a\nANVVL1B0gXD0yM5eQGfn8TGcheFRY2FD5s7dwMEDm+LlNPIvLFwJQEHByrg1MkBz8y462o/EP5+q\n20JFxeWkpwsXLsP5I9VEw6IjPMkLPWcEigof/6diLrlRhDZ1pJs4vsfLz+4VIYUaqqXHiEQiOcNI\nIjxLwR2fRgsEaH7kUQCibjf28vkUf+EfJiX/IZOwWMZ1/GjT1UMhWn7+S2xzhbOW64INFH/hH+iO\nhajpfuXVhP01n3im6BExfqn1ipAuelRLSN+UIUJzZF5zCfZllSgO4Y4Qd1MZh+NHuL458QtdRw+J\n8To1lo9kZtIVaaKclQO+742KMfiANrlule3hetrD9cPuM9+xksVpw7uRSyaPvft/jdXipKBA3EdF\nhavJcM2Jb7daXZTNuYCyORcA4OltpLl5Ny2tYmx9PPNnE82ME34AuLdsIfMCcbJNGRmD7pO1cSMA\nitVKx5/+NGFvfIrJJOJjjgNbWRnE0gg2NIyw9/jIvOgiVIdjyO2B46kZJLCVibhIRKOzpk4zjdqA\nGFBymXMotS0edt/KtLVkWQoAONi7hYA2fRutieIDn5kLgBbVeeDW3dQd7IsBN5jwIxIWndSmKi/F\nC53jzr/tr38EwP3etnGnlQyZ686j8NqPTGqew+EjsVMWIUwQPw7EOfbjRMVEr+5O2M8ItdKru0kn\nc8z59zIw3ShRTCT3EqXGDLUcpLNcOZflyrmD7ucgbcB3Hn14u+AmvZZcitmgXg1Am15PrX6MHjqT\nKmMqyVz2vrgl8ckn/ntAKBJFVVFMg3c50krnk3vOJfhOiYHf2mcejYsmANTNVso++lkKLxbCht6a\nowTbEoVWRZdejy1XtGGtb/6Z9u2vDcjHsN4c8pl9Wj+h4EJxfnPPvpjO97bQ/OpAwQeIGNzFl38E\nb6z8dU8/MqD+pdfcGg9l41p4Fp7jfQP+3roqqKuKh4YpvvzDRLw9MfHL6BjP+ZdIZjuuDedhzhr7\nc0EimRXEnnGhxiYCVScIHBfhKAJVJ9C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Klzlwd4hnmNWm0FqffDjD158VE0Y7XxPn7Y8/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\nT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NeVW8AYjk9S411+ciscFxN4y6XV9TTRxDQBTxsAk8HCXo6WO/87017o+y6AlXZdCj9c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7iYOR/4sfiTXy5Jv0f/5Usl6bdYuNqExPdjN/+aBTcsYcOfb81yxPQhFonJoA+JRDKn6D31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+QZiTLm09e2nq3E55wUkAqKo+LtIZjn31TycVfgRCHt7eextAvO3NRgfACd32+UXJF/xJ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            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 0:00:05.470788\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_n7RaKj2U96-",
        "colab_type": "text"
      },
      "source": [
        "Observations:
\n",
        "A look at the word cloud shows that \"c#\", \"java\", \"php\", \"asp.net\", \"javascript\", \"c++\" are some of the most frequent tags."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hpc8IWjqU96_",
        "colab_type": "text"
      },
      "source": [
        "
 3.2.6 The top 20 tags 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov3WmIEHU97A",
        "colab_type": "code",
        "outputId": "8e4867c3-81da-445d-a7b3-fc8d4c283be0",
        "colab": {}
      },
      "source": [
        "i=np.arange(30)\n",
        "tag_df_sorted.head(30).plot(kind='bar')\n",
        "plt.title('Frequency of top 20 tags')\n",
        "plt.xticks(i, tag_df_sorted['Tags'])\n",
        "plt.xlabel('Tags')\n",
        "plt.ylabel('Counts')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y2rGxMcYYUzs2NsYYY2rHxsYYY0zt2NgYY4ypHRsbY4wxtWNjY4wxpnZsbIwx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\nN6qNmQUAFQtAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlmizz7LU97D",
        "colab_type": "text"
      },
      "source": [
        "Observations:
\n",
        "1. Majority of the most frequent tags are programming language.\n",
        "2. C# is the top most frequent programming language.\n",
        "3. Android, IOS, Linux and windows are among the top most frequent operating systems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-Z7F0_mU97F",
        "colab_type": "text"
      },
      "source": [
        "
 3.3 Cleaning and preprocessing of Questions 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWzF-nN6U97G",
        "colab_type": "text"
      },
      "source": [
        "
 3.3.1 Preprocessing 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeR1aoQaU97H",
        "colab_type": "text"
      },
      "source": [
        "
     \n",
        "    
  1.  Sample 1M data points 
    2. \n",
        "    
  2.  Separate out code-snippets from Body 
    3. \n",
        "    
  3.  Remove Spcial characters from Question title and description (not in code)
    4. \n",
        "    
  4.  Remove stop words (Except 'C') 
    5. \n",
        "    
  5.  Remove HTML Tags 
    6. \n",
        "    
  6.  Convert all the characters into small letters 
    7. \n",
        "    
  7.  Use SnowballStemmer to stem the words 
    8. \n",
        "
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qr2xhpsAU97I",
        "colab_type": "code",
        "outputId": "3d6bf96b-0fea-4315-b0b8-9fd3257f52c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "def striphtml(data):\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, ' ', str(data))\n",
        "    return cleantext\n",
        "stop_words = set(stopwords.words('english'))\n",
        "stemmer = SnowballStemmer(\"english\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCDUa4KxU97L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#http://www.sqlitetutorial.net/sqlite-python/create-tables/\n",
        "def create_connection(db_file):\n",
        "    \"\"\" create a database connection to the SQLite database\n",
        "        specified by db_file\n",
        "    :param db_file: database file\n",
        "    :return: Connection object or None\n",
        "    \"\"\"\n",
        "    try:\n",
        "        conn = sqlite3.connect(db_file)\n",
        "        print(type(conn))\n",
        "        return conn\n",
        "    except Error as e:\n",
        "        print(e)\n",
        " \n",
        "    return None\n",
        "\n",
        "def create_table(conn, create_table_sql):\n",
        "    \"\"\" create a table from the create_table_sql statement\n",
        "    :param conn: Connection object\n",
        "    :param create_table_sql: a CREATE TABLE statement\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    try:\n",
        "        c = conn.cursor()\n",
        "        c.execute(create_table_sql)\n",
        "    except Error as e:\n",
        "        print(e)\n",
        "        \n",
        "def checkTableExists(dbcon):\n",
        "    cursr = dbcon.cursor()\n",
        "    str = \"select name from sqlite_master where type='table'\"\n",
        "    table_names = cursr.execute(str)\n",
        "    print(\"Tables in the databse:\")\n",
        "    tables =table_names.fetchall() \n",
        "    print(tables[0][0])\n",
        "    return(len(tables))\n",
        "\n",
        "def create_database_table(database, query):\n",
        "    conn = create_connection(database)\n",
        "    if conn is not None:\n",
        "        create_table(conn, query)\n",
        "        checkTableExists(conn)\n",
        "    else:\n",
        "        print(\"Error! cannot create the database connection.\")\n",
        "    conn.close()\n",
        "\n",
        "# sql_create_table = \"\"\"CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);\"\"\"\n",
        "# create_database_table(\"Processed.db\", sql_create_table)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qz092AdFU97P",
        "colab_type": "code",
        "outputId": "b00a3eec-60d8-4c08-83c8-54debd0a4123",
        "colab": {}
      },
      "source": [
        "# http://www.sqlitetutorial.net/sqlite-delete/\n",
        "# https://stackoverflow.com/questions/2279706/select-random-row-from-a-sqlite-table\n",
        "start = datetime.now()\n",
        "read_db = 'train_no_dup.db'\n",
        "write_db = 'Processed.db'\n",
        "if os.path.isfile(read_db):\n",
        "    conn_r = create_connection(read_db)\n",
        "    if conn_r is not None:\n",
        "        reader =conn_r.cursor()\n",
        "        reader.execute(\"SELECT Title, Body, Tags From no_dup_train ORDER BY RANDOM() LIMIT 1000000;\")\n",
        "\n",
        "if os.path.isfile(write_db):\n",
        "    conn_w = create_connection(write_db)\n",
        "    if conn_w is not None:\n",
        "        tables = checkTableExists(conn_w)\n",
        "        writer =conn_w.cursor()\n",
        "        if tables != 0:\n",
        "            writer.execute(\"DELETE FROM QuestionsProcessed WHERE 1\")\n",
        "            print(\"Cleared All the rows\")\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tables in the databse:\n",
            "QuestionsProcessed\n",
            "Cleared All the rows\n",
            "Time taken to run this cell : 0:06:32.806567\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FELLR5FBU97U",
        "colab_type": "text"
      },
      "source": [
        "__ we create a new data base to store the sampled and preprocessed questions __"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "clKVIuAcU97W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#http://www.bernzilla.com/2008/05/13/selecting-a-random-row-from-an-sqlite-table/\n",
        "\n",
        "start = datetime.now()\n",
        "preprocessed_data_list=[]\n",
        "reader.fetchone()\n",
        "questions_with_code=0\n",
        "len_pre=0\n",
        "len_post=0\n",
        "questions_proccesed = 0\n",
        "for row in reader:\n",
        "\n",
        "    is_code = 0\n",
        "\n",
        "    title, question, tags = row[0], row[1], row[2]\n",
        "\n",
        "    if '' in question:\n",
        "        questions_with_code+=1\n",
        "        is_code = 1\n",
        "    x = len(question)+len(title)\n",
        "    len_pre+=x\n",
        "\n",
        "    code = str(re.findall(r'(.*?)', question, flags=re.DOTALL))\n",
        "\n",
        "    question=re.sub('(.*?)', '', question, flags=re.MULTILINE|re.DOTALL)\n",
        "    question=striphtml(question.encode('utf-8'))\n",
        "\n",
        "    title=title.encode('utf-8')\n",
        "\n",
        "    question=str(title)+\" \"+str(question)\n",
        "    question=re.sub(r'[^A-Za-z]+',' ',question)\n",
        "    words=word_tokenize(str(question.lower()))\n",
        "\n",
        "    #Removing all single letter and and stopwords from question exceptt for the letter 'c'\n",
        "    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))\n",
        "\n",
        "    len_post+=len(question)\n",
        "    tup = (question,code,tags,x,len(question),is_code)\n",
        "    questions_proccesed += 1\n",
        "    writer.execute(\"insert into QuestionsProcessed(question,code,tags,words_pre,words_post,is_code) values (?,?,?,?,?,?)\",tup)\n",
        "    if (questions_proccesed%100000==0):\n",
        "        print(\"number of questions completed=\",questions_proccesed)\n",
        "\n",
        "no_dup_avg_len_pre=(len_pre*1.0)/questions_proccesed\n",
        "no_dup_avg_len_post=(len_post*1.0)/questions_proccesed\n",
        "\n",
        "print( \"Avg. length of questions(Title+Body) before processing: %d\"%no_dup_avg_len_pre)\n",
        "print( \"Avg. length of questions(Title+Body) after processing: %d\"%no_dup_avg_len_post)\n",
        "print (\"Percent of questions containing code: %d\"%((questions_with_code*100.0)/questions_proccesed))\n",
        "\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMihWt4uU97b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dont forget to close the connections, or else you will end up with locks\n",
        "conn_r.commit()\n",
        "conn_w.commit()\n",
        "conn_r.close()\n",
        "conn_w.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8VzP3hsU97e",
        "colab_type": "code",
        "outputId": "5efb2876-7d07-438f-aeac-edeecc694f5b",
        "colab": {}
      },
      "source": [
        "if os.path.isfile(write_db):\n",
        "    conn_r = create_connection(write_db)\n",
        "    if conn_r is not None:\n",
        "        reader =conn_r.cursor()\n",
        "        reader.execute(\"SELECT question From QuestionsProcessed LIMIT 10\")\n",
        "        print(\"Questions after preprocessed\")\n",
        "        print('='*100)\n",
        "        reader.fetchone()\n",
        "        for row in reader:\n",
        "            print(row)\n",
        "            print('-'*100)\n",
        "conn_r.commit()\n",
        "conn_r.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Questions after preprocessed\n",
            "====================================================================================================\n",
            "('ef code first defin one mani relationship differ key troubl defin one zero mani relationship entiti ef object model look like use fluent api object composit pk defin batch id batch detail id use fluent api object composit pk defin batch detail id compani id map exist databas tpt basic idea submittedtransact zero mani submittedsplittransact associ navig realli need one way submittedtransact submittedsplittransact need dbcontext class onmodelcr overrid map class lazi load occur submittedtransact submittedsplittransact help would much appreci edit taken advic made follow chang dbcontext class ad follow onmodelcr overrid must miss someth get follow except thrown submittedtransact key batch id batch detail id zero one mani submittedsplittransact key batch detail id compani id rather assum convent creat relationship two object configur requir sinc obvious wrong',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('explan new statement review section c code came accross statement block come accross new oper use way someon explain new call way',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('error function notat function solv logic riddl iloczyni list structur list possibl candid solut list possibl coordin matrix wan na choos one candid compar possibl candid element equal wan na delet coordin call function skasuj look like ni knowledg haskel cant see what wrong',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('step plan move one isp anoth one work busi plan switch isp realli soon need chang lot inform dns wan wan wifi question guy help mayb peopl plan correct chang current isp new one first dns know receiv new ip isp major chang need take consider exchang server owa vpn two site link wireless connect km away citrix server vmware exchang domain control link place import server crucial step inform need know avoid downtim busi regard ndavid',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('use ef migrat creat databas googl migrat tutori af first run applic creat databas ef enabl migrat way creat databas migrat rune applic tri',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('magento unit test problem magento site recent look way check integr magento site given point unit test jump one method would assum would big job write whole lot test check everyth site work anyon involv unit test magento advis follow possibl test whole site custom modul nis exampl test would amaz given site heavili link databas would nbe possibl fulli test site without disturb databas better way automaticlli check integr magento site say integr realli mean fault site ship payment etc work correct',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('find network devic without bonjour write mac applic need discov mac pcs iphon ipad connect wifi network bonjour seem reason choic turn problem mani type router mine exampl work block bonjour servic need find ip devic tri connect applic specif port determin process run best approach accomplish task without violat app store sandbox',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('send multipl row mysql databas want send user mysql databas column user skill time nnow want abl add one row user differ time etc would code send databas nthen use help schema',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('insert data mysql php powerpoint event powerpoint present run continu way updat slide present automat data mysql databas websit',)\n",
            "----------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSJXxeS0U97i",
        "colab_type": "code",
        "outputId": "15a89762-1ef2-4791-e451-d78ef9401d34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Taking 1 Million entries to a dataframe.\n",
        "#check here\n",
        "write_db = 'drive/My Drive/Stackoverflow/data/Processed.db'\n",
        "if os.path.isfile(write_db):\n",
        "    conn_r = create_connection(write_db)\n",
        "    if conn_r is not None:\n",
        "        preprocessed_data = pd.read_sql_query(\"\"\"SELECT question, Tags FROM QuestionsProcessed LIMIT 500000 \"\"\", conn_r)\n",
        "conn_r.commit()\n",
        "conn_r.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANsenX1EU97l",
        "colab_type": "code",
        "outputId": "6b2e7334-9060-4000-e301-5f2d22f92836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "preprocessed_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "
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              "    \n",
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              "      \n",
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              "      \n",
              "    \n",
              "  \n",
              "
questiontags
0chang cpu soni vaio pcg grx tri everywher find...cpu motherboard sony-vaio replacement disassembly
1display size grayscal qimag qt abl display ima...c++ qt qt4
2datagrid selecteditem set back null eventtocom...mvvm silverlight-4.0
3filter string collect base listview item resol...c# winforms string listview collections
4disabl home button without use type keyguard c...android android-layout android-manifest androi...
\n",
              "
"
            ],
            "text/plain": [
              "                                            question                                               tags\n",
              "0  chang cpu soni vaio pcg grx tri everywher find...  cpu motherboard sony-vaio replacement disassembly\n",
              "1  display size grayscal qimag qt abl display ima...                                         c++ qt qt4\n",
              "2  datagrid selecteditem set back null eventtocom...                               mvvm silverlight-4.0\n",
              "3  filter string collect base listview item resol...            c# winforms string listview collections\n",
              "4  disabl home button without use type keyguard c...  android android-layout android-manifest androi..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6vsCoLOU97r",
        "colab_type": "code",
        "outputId": "d9b66538-e777-4c7d-f28d-177eb75c76c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"number of data points in sample :\", preprocessed_data.shape[0])\n",
        "print(\"number of dimensions :\", preprocessed_data.shape[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of data points in sample : 500000\n",
            "number of dimensions : 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qB0bL2drU97w",
        "colab_type": "text"
      },
      "source": [
        "
4. Machine Learning Models 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZ3-VPbqU97w",
        "colab_type": "text"
      },
      "source": [
        "
 4.1 Converting tags for multilabel problems 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K88oaTD9U97y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "
Xy1y2y3y4
x10110
x11000
x10100
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttr2m-qiU97z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# binary='true' will give a binary vectorizer\n",
        "vectorizer = CountVectorizer(tokenizer = lambda x: x.split(), binary='true')\n",
        "multilabel_y = vectorizer.fit_transform(preprocessed_data['tags'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VCwyfHxU972",
        "colab_type": "text"
      },
      "source": [
        "__ We will sample the number of tags instead considering all of them (due to limitation of computing power) __"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FtgktWvU973",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tags_to_choose(n):\n",
        "    t = multilabel_y.sum(axis=0).tolist()[0]\n",
        "    sorted_tags_i = sorted(range(len(t)), key=lambda i: t[i], reverse=True)\n",
        "    multilabel_yn=multilabel_y[:,sorted_tags_i[:n]]\n",
        "    return multilabel_yn\n",
        "\n",
        "def questions_explained_fn(n):\n",
        "    multilabel_yn = tags_to_choose(n)\n",
        "    x= multilabel_yn.sum(axis=1)\n",
        "    return (np.count_nonzero(x==0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxbL2OyqU978",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "questions_explained = []\n",
        "total_tags=multilabel_y.shape[1]\n",
        "total_qs=preprocessed_data.shape[0]\n",
        "for i in range(500, total_tags, 100):\n",
        "    questions_explained.append(np.round(((total_qs-questions_explained_fn(i))/total_qs)*100,3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbX6_QWtU98B",
        "colab_type": "code",
        "outputId": "7add8021-d595-4e9e-c506-611520b21a9c",
        "colab": {}
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(questions_explained)\n",
        "xlabel = list(500+np.array(range(-50,450,50))*50)\n",
        "ax.set_xticklabels(xlabel)\n",
        "plt.xlabel(\"Number of tags\")\n",
        "plt.ylabel(\"Number Questions coverd partially\")\n",
        "plt.grid()\n",
        "plt.show()\n",
        "# you can choose any number of tags based on your computing power, minimun is 50(it covers 90% of the tags)\n",
        "print(\"with \",5500,\"tags we are covering \",questions_explained[50],\"% of questions\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vma02s2fM7Oqw7Gtm9ryZPWVmvzez8UNZRxTtXb3UqoFaRCStKBXEMjP7N2CM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\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "with  5500 tags we are covering  99.04 % of questions\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgJKNRG0U98F",
        "colab_type": "code",
        "outputId": "cb3483f7-bcab-423b-f8d7-f5d7647b9b0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "multilabel_yx = tags_to_choose(500)\n",
        "print(\"number of questions that are not covered :\", questions_explained_fn(500),\"out of 500000\" )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of questions that are not covered : 45221 out of 500000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTuhd5XMU98L",
        "colab_type": "code",
        "outputId": "5ebbcac7-3608-42f4-d9da-96d167a77305",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Number of tags in sample :\", multilabel_y.shape[1])\n",
        "print(\"number of tags taken :\", multilabel_yx.shape[1],\"(\",(multilabel_yx.shape[1]/multilabel_y.shape[1])*100,\"%)\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of tags in sample : 29587\n",
            "number of tags taken : 500 ( 1.6899313887856153 %)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxH1ggjxU98R",
        "colab_type": "text"
      },
      "source": [
        "__ We consider top 15% tags which covers  99% of the questions __"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stMfn7tMU98U",
        "colab_type": "text"
      },
      "source": [
        "
4.2 Split the data into test and train (80:20) 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqeDZrwWU98U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_size=preprocessed_data.shape[0]\n",
        "train_size=int(0.80*total_size)\n",
        "\n",
        "x_train=preprocessed_data.head(train_size)\n",
        "x_test=preprocessed_data.tail(total_size - train_size)\n",
        "\n",
        "y_train = multilabel_yx[0:train_size,:]\n",
        "y_test = multilabel_yx[train_size:total_size,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNptwtj_a3IQ",
        "colab_type": "code",
        "outputId": "4cdc5c6d-9704-4c61-cd7f-4f5240ca211d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Number of data points in train data :\", x_train.shape)\n",
        "print(\"Number of data points in test data :\", x_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of data points in train data : (399993, 2)\n",
            "Number of data points in test data : (99999, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgNXo4eJU98X",
        "colab_type": "code",
        "outputId": "3f26bf9d-76db-4a9b-d9de-40f277aed8bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Number of data points in train data :\", y_train.shape)\n",
        "print(\"Number of data points in test data :\", y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of data points in train data : (399993, 500)\n",
            "Number of data points in test data : (99999, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "664323eyU98a",
        "colab_type": "text"
      },
      "source": [
        "
4.3 Featurizing data 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnV3O0WFU98b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = datetime.now()\n",
        "vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm=\"l2\", \\\n",
        "                             tokenizer = lambda x: x.split(), sublinear_tf=False, ngram_range=(1,3))\n",
        "x_train_multilabel = vectorizer.fit_transform(x_train['question'])\n",
        "x_test_multilabel = vectorizer.transform(x_test['question'])\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CB01RkDzU98f",
        "colab_type": "code",
        "outputId": "cbeda5ce-bbc3-4d6a-8324-53962dd50d56",
        "colab": {}
      },
      "source": [
        "print(\"Dimensions of train data X:\",x_train_multilabel.shape, \"Y :\",y_train.shape)\n",
        "print(\"Dimensions of test data X:\",x_test_multilabel.shape,\"Y:\",y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Diamensions of train data X: (799999, 88244) Y : (799999, 5500)\n",
            "Diamensions of test data X: (200000, 88244) Y: (200000, 5500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-JQh1bHU98j",
        "colab_type": "code",
        "outputId": "63863f36-79ad-4726-de7c-c5d728f8902e",
        "colab": {}
      },
      "source": [
        "# https://www.analyticsvidhya.com/blog/2017/08/introduction-to-multi-label-classification/\n",
        "#https://stats.stackexchange.com/questions/117796/scikit-multi-label-classification\n",
        "# classifier = LabelPowerset(GaussianNB())\n",
        "\"\"\"\n",
        "from skmultilearn.adapt import MLkNN\n",
        "classifier = MLkNN(k=21)\n",
        "\n",
        "# train\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "\n",
        "# predict\n",
        "predictions = classifier.predict(x_test_multilabel)\n",
        "print(accuracy_score(y_test,predictions))\n",
        "print(metrics.f1_score(y_test, predictions, average = 'macro'))\n",
        "print(metrics.f1_score(y_test, predictions, average = 'micro'))\n",
        "print(metrics.hamming_loss(y_test,predictions))\n",
        "\n",
        "\"\"\"\n",
        "# we are getting memory error because the multilearn package \n",
        "# is trying to convert the data into dense matrix\n",
        "# ---------------------------------------------------------------------------\n",
        "#MemoryError                               Traceback (most recent call last)\n",
        "# in ()\n",
        "#----> classifier.fit(x_train_multilabel, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nfrom skmultilearn.adapt import MLkNN\\nclassifier = MLkNN(k=21)\\n\\n# train\\nclassifier.fit(x_train_multilabel, y_train)\\n\\n# predict\\npredictions = classifier.predict(x_test_multilabel)\\nprint(accuracy_score(y_test,predictions))\\nprint(metrics.f1_score(y_test, predictions, average = 'macro'))\\nprint(metrics.f1_score(y_test, predictions, average = 'micro'))\\nprint(metrics.hamming_loss(y_test,predictions))\\n\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qh-EUIsAU98l",
        "colab_type": "text"
      },
      "source": [
        "
 4.4 Applying Logistic Regression with OneVsRest Classifier 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "2Cp-1EWpU98m",
        "colab_type": "code",
        "outputId": "6ecf8c51-1828-438f-dd78-4bf262a5efaa",
        "colab": {}
      },
      "source": [
        "# this will be taking so much time try not to run it, download the lr_with_equal_weight.pkl file and use to predict\n",
        "# This takes about 6-7 hours to run.\n",
        "classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'), n_jobs=-1)\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "predictions = classifier.predict(x_test_multilabel)\n",
        "\n",
        "print(\"accuracy :\",metrics.accuracy_score(y_test,predictions))\n",
        "print(\"macro f1 score :\",metrics.f1_score(y_test, predictions, average = 'macro'))\n",
        "print(\"micro f1 scoore :\",metrics.f1_score(y_test, predictions, average = 'micro'))\n",
        "print(\"hamming loss :\",metrics.hamming_loss(y_test,predictions))\n",
        "print(\"Precision recall report :\\n\",metrics.classification_report(y_test, predictions))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy : 0.081965\n",
            "macro f1 score : 0.0963020140154\n",
            "micro f1 scoore : 0.374270748817\n",
            "hamming loss : 0.00041225090909090907\n",
            "Precision recall report :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          0       0.62      0.23      0.33     15760\n",
            "          1       0.79      0.43      0.56     14039\n",
            "          2       0.82      0.55      0.66     13446\n",
            "          3       0.76      0.42      0.54     12730\n",
            "          4       0.94      0.76      0.84     11229\n",
            "          5       0.85      0.64      0.73     10561\n",
            "          6       0.70      0.30      0.42      6958\n",
            "          7       0.87      0.61      0.72      6309\n",
            "          8       0.70      0.40      0.50      6032\n",
            "          9       0.78      0.43      0.55      6020\n",
            "         10       0.86      0.62      0.72      5707\n",
            "         11       0.52      0.17      0.25      5723\n",
            "         12       0.55      0.10      0.16      5521\n",
            "         13       0.59      0.25      0.35      4722\n",
            "         14       0.61      0.22      0.32      4468\n",
            "         15       0.79      0.52      0.63      4536\n",
            "         16       0.58      0.27      0.37      4545\n",
            "         17       0.80      0.53      0.64      4069\n",
            "         18       0.61      0.24      0.35      3638\n",
            "         19       0.57      0.18      0.27      3218\n",
            "         20       0.33      0.06      0.10      3000\n",
            "         21       0.73      0.34      0.46      2585\n",
            "         22       0.59      0.29      0.38      2439\n",
            "         23       0.88      0.61      0.72      2199\n",
            "         24       0.64      0.39      0.48      2157\n",
            "         25       0.67      0.39      0.49      2123\n",
            "         26       0.86      0.65      0.74      1948\n",
            "         27       0.35      0.07      0.12      2027\n",
            "         28       0.59      0.29      0.39      2013\n",
            "         29       0.61      0.20      0.30      1801\n",
            "         30       0.48      0.24      0.32      1728\n",
            "         31       0.94      0.75      0.84      1725\n",
            "         32       0.60      0.26      0.36      1581\n",
            "         33       0.49      0.14      0.22      1533\n",
            "         34       0.81      0.33      0.47      1565\n",
            "         35       0.75      0.62      0.68      1568\n",
            "         36       0.76      0.50      0.60      1542\n",
            "         37       0.74      0.50      0.59      1536\n",
            "         38       0.37      0.12      0.19      1524\n",
            "         39       0.40      0.12      0.19      1345\n",
            "         40       0.65      0.38      0.48      1292\n",
            "         41       0.41      0.11      0.17      1264\n",
            "         42       0.69      0.25      0.37      1265\n",
            "         43       0.59      0.29      0.38      1171\n",
            "         44       0.41      0.15      0.22      1173\n",
            "         45       0.38      0.10      0.16      1137\n",
            "         46       0.62      0.12      0.20      1125\n",
            "         47       0.26      0.07      0.11      1116\n",
            "         48       0.44      0.15      0.22      1042\n",
            "         49       0.40      0.02      0.03      1096\n",
            "         50       0.63      0.38      0.48      1031\n",
            "         51       0.47      0.14      0.22      1033\n",
            "         52       0.87      0.68      0.76      1042\n",
            "         53       0.32      0.09      0.14      1027\n",
            "         54       0.53      0.14      0.22      1063\n",
            "         55       0.63      0.34      0.44      1048\n",
            "         56       0.78      0.42      0.54      1054\n",
            "         57       0.91      0.77      0.83      1058\n",
            "         58       0.37      0.10      0.16      1000\n",
            "         59       0.26      0.03      0.05       973\n",
            "         60       0.76      0.42      0.54       978\n",
            "         61       0.74      0.43      0.54       977\n",
            "         62       0.27      0.06      0.10       957\n",
            "         63       0.81      0.22      0.34       958\n",
            "         64       0.88      0.63      0.73       944\n",
            "         65       0.76      0.49      0.60       923\n",
            "         66       0.67      0.36      0.47       959\n",
            "         67       0.55      0.15      0.24       951\n",
            "         68       0.38      0.13      0.20       924\n",
            "         69       0.71      0.25      0.37       897\n",
            "         70       0.78      0.47      0.59       900\n",
            "         71       0.82      0.40      0.54       893\n",
            "         72       0.21      0.01      0.01       836\n",
            "         73       0.74      0.16      0.26       850\n",
            "         74       0.58      0.37      0.45       838\n",
            "         75       0.88      0.64      0.74       855\n",
            "         76       0.47      0.28      0.35       837\n",
            "         77       0.68      0.41      0.52       824\n",
            "         78       0.14      0.01      0.01       793\n",
            "         79       0.34      0.09      0.14       751\n",
            "         80       0.31      0.08      0.13       793\n",
            "         81       0.71      0.33      0.45       758\n",
            "         82       0.60      0.28      0.38       764\n",
            "         83       0.82      0.59      0.69       710\n",
            "         84       0.82      0.48      0.61       734\n",
            "         85       0.79      0.42      0.55       723\n",
            "         86       0.44      0.23      0.30       708\n",
            "         87       0.93      0.58      0.72       714\n",
            "         88       0.91      0.53      0.67       683\n",
            "         89       0.58      0.20      0.30       711\n",
            "         90       0.71      0.42      0.53       699\n",
            "         91       0.44      0.03      0.06       725\n",
            "         92       0.71      0.47      0.57       676\n",
            "         93       0.47      0.10      0.16       672\n",
            "         94       0.66      0.40      0.50       645\n",
            "         95       0.86      0.66      0.75       691\n",
            "         96       0.57      0.09      0.15       664\n",
            "         97       0.91      0.59      0.72       633\n",
            "         98       0.64      0.38      0.48       615\n",
            "         99       0.53      0.19      0.29       667\n",
            "        100       0.89      0.71      0.79       656\n",
            "        101       0.22      0.03      0.05       648\n",
            "        102       0.64      0.13      0.22       654\n",
            "        103       0.92      0.63      0.75       653\n",
            "        104       0.87      0.52      0.65       656\n",
            "        105       0.20      0.02      0.04       607\n",
            "        106       0.68      0.34      0.45       635\n",
            "        107       0.23      0.03      0.05       594\n",
            "        108       0.40      0.18      0.25       592\n",
            "        109       0.32      0.07      0.12       604\n",
            "        110       0.46      0.21      0.29       606\n",
            "        111       0.70      0.39      0.50       567\n",
            "        112       0.68      0.27      0.38       571\n",
            "        113       0.61      0.36      0.45       578\n",
            "        114       0.47      0.18      0.26       564\n",
            "        115       0.35      0.13      0.19       537\n",
            "        116       0.93      0.66      0.77       583\n",
            "        117       0.59      0.09      0.15       534\n",
            "        118       0.66      0.35      0.46       566\n",
            "        119       0.20      0.04      0.07       567\n",
            "        120       0.48      0.16      0.24       497\n",
            "        121       0.55      0.19      0.29       536\n",
            "        122       0.24      0.05      0.08       528\n",
            "        123       0.81      0.53      0.64       550\n",
            "        124       0.50      0.21      0.29       563\n",
            "        125       0.35      0.06      0.10       545\n",
            "        126       0.49      0.18      0.27       544\n",
            "        127       0.95      0.76      0.84       549\n",
            "        128       0.63      0.34      0.44       495\n",
            "        129       0.94      0.59      0.73       509\n",
            "        130       0.34      0.11      0.16       501\n",
            "        131       0.28      0.04      0.07       524\n",
            "        132       0.48      0.26      0.34       485\n",
            "        133       0.55      0.37      0.45       515\n",
            "        134       0.32      0.04      0.08       536\n",
            "        135       0.77      0.38      0.51       526\n",
            "        136       0.67      0.34      0.45       493\n",
            "        137       0.40      0.08      0.14       501\n",
            "        138       0.31      0.05      0.09       501\n",
            "        139       0.29      0.02      0.04       523\n",
            "        140       0.88      0.64      0.74       508\n",
            "        141       0.33      0.11      0.16       490\n",
            "        142       0.77      0.50      0.60       482\n",
            "        143       0.49      0.25      0.33       461\n",
            "        144       0.74      0.48      0.58       496\n",
            "        145       0.62      0.17      0.26       521\n",
            "        146       0.39      0.13      0.19       481\n",
            "        147       0.00      0.00      0.00       486\n",
            "        148       0.37      0.09      0.14       497\n",
            "        149       0.54      0.09      0.16       470\n",
            "        150       0.37      0.11      0.17       459\n",
            "        151       0.74      0.45      0.56       464\n",
            "        152       0.50      0.24      0.32       482\n",
            "        153       0.46      0.09      0.15       507\n",
            "        154       0.29      0.04      0.07       503\n",
            "        155       0.90      0.59      0.71       456\n",
            "        156       0.50      0.27      0.35       480\n",
            "        157       0.54      0.26      0.35       443\n",
            "        158       0.92      0.70      0.80       457\n",
            "        159       0.57      0.08      0.13       478\n",
            "        160       0.16      0.03      0.05       470\n",
            "        161       0.37      0.18      0.24       468\n",
            "        162       0.24      0.05      0.09       428\n",
            "        163       0.40      0.08      0.13       462\n",
            "        164       0.73      0.32      0.45       493\n",
            "        165       0.93      0.68      0.79       437\n",
            "        166       0.40      0.20      0.26       435\n",
            "        167       0.30      0.02      0.03       448\n",
            "        168       0.53      0.16      0.25       436\n",
            "        169       0.36      0.10      0.15       437\n",
            "        170       0.38      0.09      0.15       410\n",
            "        171       0.59      0.32      0.41       450\n",
            "        172       0.69      0.39      0.50       435\n",
            "        173       0.91      0.67      0.77       427\n",
            "        174       0.45      0.16      0.24       427\n",
            "        175       0.43      0.17      0.24       424\n",
            "        176       0.64      0.43      0.52       410\n",
            "        177       0.67      0.29      0.40       426\n",
            "        178       0.74      0.49      0.59       459\n",
            "        179       0.52      0.13      0.20       433\n",
            "        180       0.71      0.36      0.48       452\n",
            "        181       0.91      0.62      0.74       427\n",
            "        182       0.46      0.13      0.20       410\n",
            "        183       0.28      0.02      0.04       404\n",
            "        184       0.69      0.42      0.52       406\n",
            "        185       0.68      0.41      0.52       411\n",
            "        186       0.22      0.02      0.03       394\n",
            "        187       0.90      0.65      0.75       414\n",
            "        188       0.64      0.10      0.18       430\n",
            "        189       0.16      0.04      0.06       389\n",
            "        190       0.28      0.03      0.05       418\n",
            "        191       0.36      0.16      0.22       371\n",
            "        192       0.83      0.57      0.68       363\n",
            "        193       0.91      0.55      0.69       389\n",
            "        194       0.44      0.04      0.07       411\n",
            "        195       0.49      0.22      0.31       383\n",
            "        196       0.95      0.74      0.83       423\n",
            "        197       0.91      0.54      0.68       378\n",
            "        198       0.69      0.38      0.49       382\n",
            "        199       0.12      0.01      0.02       344\n",
            "        200       0.71      0.31      0.44       383\n",
            "        201       0.77      0.34      0.47       390\n",
            "        202       0.18      0.02      0.04       405\n",
            "        203       0.43      0.07      0.11       365\n",
            "        204       0.42      0.14      0.21       346\n",
            "        205       0.21      0.05      0.08       378\n",
            "        206       0.67      0.27      0.39       390\n",
            "        207       0.33      0.07      0.11       379\n",
            "        208       0.39      0.11      0.17       386\n",
            "        209       0.42      0.15      0.22       339\n",
            "        210       0.27      0.07      0.12       382\n",
            "        211       0.37      0.05      0.08       374\n",
            "        212       0.62      0.38      0.47       364\n",
            "        213       0.94      0.76      0.84       372\n",
            "        214       0.96      0.63      0.76       350\n",
            "        215       0.76      0.38      0.50       352\n",
            "        216       0.00      0.00      0.00       351\n",
            "        217       0.64      0.29      0.40       329\n",
            "        218       0.72      0.31      0.44       341\n",
            "        219       0.94      0.71      0.81       331\n",
            "        220       0.49      0.27      0.35       342\n",
            "        221       0.76      0.39      0.52       339\n",
            "        222       0.29      0.04      0.06       332\n",
            "        223       0.43      0.12      0.18       327\n",
            "        224       0.31      0.06      0.11       324\n",
            "        225       0.51      0.21      0.30       352\n",
            "        226       0.65      0.30      0.41       317\n",
            "        227       0.54      0.12      0.20       355\n",
            "        228       0.57      0.19      0.29       341\n",
            "        229       0.58      0.37      0.46       334\n",
            "        230       0.64      0.49      0.56       304\n",
            "        231       0.43      0.04      0.07       321\n",
            "        232       0.77      0.50      0.61       311\n",
            "        233       0.32      0.10      0.15       312\n",
            "        234       0.09      0.01      0.02       306\n",
            "        235       0.03      0.00      0.01       305\n",
            "        236       0.16      0.02      0.04       340\n",
            "        237       0.58      0.30      0.40       316\n",
            "        238       0.65      0.23      0.34       297\n",
            "        239       0.35      0.13      0.19       305\n",
            "        240       0.73      0.44      0.55       310\n",
            "        241       0.67      0.36      0.47       307\n",
            "        242       0.58      0.16      0.25       316\n",
            "        243       0.26      0.07      0.11       314\n",
            "        244       0.51      0.12      0.19       316\n",
            "        245       0.67      0.46      0.55       313\n",
            "        246       0.79      0.46      0.58       325\n",
            "        247       0.60      0.36      0.45       291\n",
            "        248       0.33      0.01      0.02       311\n",
            "        249       0.57      0.24      0.33       314\n",
            "        250       0.38      0.05      0.09       309\n",
            "        251       0.30      0.08      0.13       300\n",
            "        252       0.55      0.27      0.36       325\n",
            "        253       0.76      0.51      0.61       316\n",
            "        254       0.43      0.09      0.15       306\n",
            "        255       0.54      0.19      0.28       289\n",
            "        256       0.49      0.11      0.18       304\n",
            "        257       0.16      0.02      0.04       268\n",
            "        258       0.85      0.58      0.69       266\n",
            "        259       0.06      0.00      0.01       298\n",
            "        260       0.55      0.36      0.43       292\n",
            "        261       0.25      0.05      0.08       289\n",
            "        262       0.50      0.01      0.01       305\n",
            "        263       0.00      0.00      0.00       281\n",
            "        264       0.59      0.25      0.35       295\n",
            "        265       0.16      0.02      0.04       281\n",
            "        266       0.83      0.52      0.64       269\n",
            "        267       0.45      0.12      0.19       312\n",
            "        268       0.75      0.40      0.52       294\n",
            "        269       0.34      0.05      0.09       285\n",
            "        270       0.56      0.33      0.42       279\n",
            "        271       0.50      0.28      0.36       269\n",
            "        272       0.59      0.38      0.46       277\n",
            "        273       0.69      0.31      0.43       272\n",
            "        274       0.36      0.01      0.03       285\n",
            "        275       0.94      0.69      0.80       295\n",
            "        276       0.46      0.19      0.27       283\n",
            "        277       0.65      0.29      0.40       250\n",
            "        278       0.57      0.20      0.30       281\n",
            "        279       0.86      0.58      0.69       270\n",
            "        280       0.62      0.35      0.44       272\n",
            "        281       0.32      0.07      0.11       278\n",
            "        282       0.00      0.00      0.00       264\n",
            "        283       0.85      0.59      0.70       281\n",
            "        284       0.78      0.53      0.63       261\n",
            "        285       0.33      0.09      0.14       283\n",
            "        286       0.00      0.00      0.00       275\n",
            "        287       0.29      0.03      0.05       274\n",
            "        288       0.37      0.04      0.06       284\n",
            "        289       0.00      0.00      0.00       260\n",
            "        290       0.54      0.24      0.34       245\n",
            "        291       0.07      0.00      0.01       267\n",
            "        292       0.33      0.07      0.11       263\n",
            "        293       0.30      0.09      0.14       268\n",
            "        294       0.33      0.11      0.16       270\n",
            "        295       0.48      0.06      0.10       261\n",
            "        296       0.84      0.59      0.69       240\n",
            "        297       0.43      0.22      0.29       250\n",
            "        298       0.81      0.51      0.63       245\n",
            "        299       0.11      0.01      0.01       283\n",
            "        300       0.51      0.21      0.30       236\n",
            "        301       0.78      0.51      0.62       267\n",
            "        302       0.19      0.02      0.04       243\n",
            "        303       0.26      0.04      0.06       276\n",
            "        304       0.89      0.71      0.79       280\n",
            "        305       0.37      0.14      0.20       249\n",
            "        306       0.24      0.02      0.04       258\n",
            "        307       0.00      0.00      0.00       262\n",
            "        308       0.53      0.20      0.29       248\n",
            "        309       0.58      0.25      0.35       244\n",
            "        310       0.33      0.06      0.09       254\n",
            "        311       0.41      0.10      0.16       263\n",
            "        312       0.52      0.25      0.33       232\n",
            "        313       0.75      0.55      0.63       235\n",
            "        314       0.61      0.11      0.19       248\n",
            "        315       0.49      0.16      0.25       263\n",
            "        316       0.33      0.08      0.12       264\n",
            "        317       0.61      0.06      0.12       216\n",
            "        318       0.05      0.00      0.01       230\n",
            "        319       0.53      0.27      0.36       230\n",
            "        320       0.00      0.00      0.00       239\n",
            "        321       0.45      0.08      0.13       265\n",
            "        322       0.69      0.32      0.44       253\n",
            "        323       0.23      0.04      0.06       238\n",
            "        324       0.72      0.37      0.49       232\n",
            "        325       0.22      0.05      0.08       239\n",
            "        326       0.49      0.18      0.26       261\n",
            "        327       0.64      0.14      0.23       261\n",
            "        328       0.67      0.47      0.55       231\n",
            "        329       0.46      0.13      0.20       264\n",
            "        330       0.18      0.02      0.03       242\n",
            "        331       0.80      0.37      0.50       231\n",
            "        332       0.63      0.28      0.39       234\n",
            "        333       0.50      0.32      0.39       212\n",
            "        334       0.26      0.05      0.09       221\n",
            "        335       0.15      0.03      0.05       242\n",
            "        336       0.57      0.30      0.40       211\n",
            "        337       0.20      0.01      0.03       212\n",
            "        338       0.00      0.00      0.00       222\n",
            "        339       0.22      0.02      0.04       227\n",
            "        340       0.66      0.30      0.41       216\n",
            "        341       0.57      0.26      0.36       231\n",
            "        342       0.45      0.22      0.29       233\n",
            "        343       0.17      0.03      0.04       232\n",
            "        344       0.28      0.02      0.04       209\n",
            "        345       0.37      0.11      0.17       216\n",
            "        346       0.27      0.09      0.13       222\n",
            "        347       0.48      0.19      0.28       243\n",
            "        348       0.51      0.26      0.35       222\n",
            "        349       0.57      0.12      0.20       228\n",
            "        350       0.44      0.12      0.18       205\n",
            "        351       0.58      0.30      0.39       177\n",
            "        352       0.77      0.39      0.52       234\n",
            "        353       0.96      0.57      0.71       230\n",
            "        354       0.47      0.21      0.29       195\n",
            "        355       0.90      0.42      0.57       209\n",
            "        356       0.06      0.00      0.01       205\n",
            "        357       0.50      0.11      0.18       211\n",
            "        358       0.43      0.16      0.23       230\n",
            "        359       0.27      0.08      0.12       211\n",
            "        360       0.39      0.09      0.14       221\n",
            "        361       0.24      0.04      0.08       200\n",
            "        362       0.82      0.15      0.25       219\n",
            "        363       0.36      0.07      0.12       222\n",
            "        364       0.62      0.27      0.38       213\n",
            "        365       0.94      0.36      0.52       199\n",
            "        366       0.80      0.37      0.51       200\n",
            "        367       0.76      0.29      0.42       199\n",
            "        368       0.57      0.26      0.36       212\n",
            "        369       0.93      0.71      0.80       214\n",
            "        370       0.10      0.02      0.03       197\n",
            "        371       0.20      0.03      0.05       212\n",
            "        372       0.41      0.14      0.21       210\n",
            "        373       0.43      0.03      0.05       211\n",
            "        374       0.41      0.15      0.22       213\n",
            "        375       0.00      0.00      0.00       216\n",
            "        376       0.87      0.53      0.66       195\n",
            "        377       0.95      0.67      0.79       187\n",
            "        378       0.15      0.03      0.04       191\n",
            "        379       0.17      0.02      0.04       178\n",
            "        380       0.79      0.48      0.60       193\n",
            "        381       0.13      0.02      0.04       187\n",
            "        382       0.67      0.03      0.06       193\n",
            "        383       0.17      0.04      0.06       204\n",
            "        384       0.28      0.15      0.19       193\n",
            "        385       0.12      0.02      0.04       207\n",
            "        386       0.84      0.45      0.59       211\n",
            "        387       0.06      0.00      0.01       210\n",
            "        388       0.31      0.04      0.06       223\n",
            "        389       0.24      0.09      0.13       203\n",
            "        390       0.72      0.24      0.36       199\n",
            "        391       0.40      0.08      0.13       200\n",
            "        392       0.22      0.05      0.09       183\n",
            "        393       0.62      0.31      0.41       189\n",
            "        394       0.96      0.66      0.78       194\n",
            "        395       0.53      0.18      0.27       183\n",
            "        396       0.43      0.21      0.28       189\n",
            "        397       0.71      0.34      0.46       191\n",
            "        398       0.34      0.06      0.11       206\n",
            "        399       0.33      0.01      0.03       221\n",
            "        400       0.28      0.04      0.07       196\n",
            "        401       0.28      0.09      0.14       179\n",
            "        402       0.28      0.08      0.12       187\n",
            "        403       0.51      0.22      0.31       203\n",
            "        404       0.46      0.12      0.19       205\n",
            "        405       0.35      0.08      0.13       218\n",
            "        406       0.19      0.04      0.06       196\n",
            "        407       0.72      0.35      0.47       206\n",
            "        408       0.31      0.06      0.10       203\n",
            "        409       0.70      0.43      0.53       187\n",
            "        410       0.85      0.54      0.66       208\n",
            "        411       0.83      0.45      0.58       193\n",
            "        412       0.33      0.02      0.03       192\n",
            "        413       0.66      0.36      0.46       182\n",
            "        414       0.45      0.19      0.27       175\n",
            "        415       0.64      0.49      0.55       181\n",
            "        416       0.00      0.00      0.00       202\n",
            "        417       0.92      0.44      0.60       202\n",
            "        418       0.17      0.01      0.02       195\n",
            "        419       0.78      0.25      0.38       177\n",
            "        420       0.26      0.07      0.11       168\n",
            "        421       0.80      0.45      0.58       187\n",
            "        422       0.92      0.46      0.62       209\n",
            "        423       0.66      0.16      0.26       177\n",
            "        424       0.35      0.06      0.10       182\n",
            "        425       0.52      0.14      0.23       187\n",
            "        426       0.22      0.04      0.07       185\n",
            "        427       0.43      0.13      0.20       185\n",
            "        428       0.42      0.18      0.25       185\n",
            "        429       0.92      0.46      0.61       175\n",
            "        430       0.90      0.49      0.64       190\n",
            "        431       0.31      0.03      0.05       185\n",
            "        432       0.71      0.03      0.05       189\n",
            "        433       0.60      0.20      0.30       184\n",
            "        434       0.79      0.36      0.49       200\n",
            "        435       0.20      0.01      0.01       167\n",
            "        436       0.21      0.01      0.03       209\n",
            "        437       0.50      0.07      0.12       200\n",
            "        438       0.29      0.09      0.14       169\n",
            "        439       0.44      0.15      0.23       170\n",
            "        440       0.25      0.04      0.07       182\n",
            "        441       0.62      0.34      0.44       156\n",
            "        442       0.20      0.02      0.03       170\n",
            "        443       0.00      0.00      0.00       189\n",
            "        444       0.00      0.00      0.00       172\n",
            "        445       0.33      0.11      0.16       180\n",
            "        446       0.21      0.06      0.10       175\n",
            "        447       0.48      0.12      0.19       187\n",
            "        448       0.00      0.00      0.00       170\n",
            "        449       0.41      0.24      0.30       170\n",
            "        450       0.35      0.10      0.16       176\n",
            "        451       0.62      0.15      0.24       194\n",
            "        452       0.61      0.31      0.41       175\n",
            "        453       0.19      0.04      0.07       187\n",
            "        454       0.11      0.01      0.01       181\n",
            "        455       0.62      0.14      0.23       177\n",
            "        456       0.50      0.18      0.26       170\n",
            "        457       0.24      0.03      0.05       182\n",
            "        458       0.68      0.37      0.48       172\n",
            "        459       0.00      0.00      0.00       190\n",
            "        460       0.43      0.16      0.23       183\n",
            "        461       0.94      0.63      0.75       182\n",
            "        462       0.35      0.16      0.22       173\n",
            "        463       0.91      0.69      0.79       171\n",
            "        464       0.58      0.27      0.37       173\n",
            "        465       0.77      0.41      0.53       184\n",
            "        466       0.72      0.22      0.34       175\n",
            "        467       0.43      0.19      0.26       162\n",
            "        468       0.12      0.01      0.02       176\n",
            "        469       0.91      0.46      0.61       177\n",
            "        470       0.52      0.07      0.13       167\n",
            "        471       0.27      0.06      0.10       192\n",
            "        472       0.50      0.32      0.39       168\n",
            "        473       0.32      0.05      0.09       188\n",
            "        474       0.31      0.05      0.08       163\n",
            "        475       0.44      0.17      0.24       160\n",
            "        476       0.89      0.56      0.69       180\n",
            "        477       0.92      0.46      0.61       182\n",
            "        478       0.49      0.27      0.35       171\n",
            "        479       0.57      0.18      0.27       174\n",
            "        480       0.96      0.52      0.68       162\n",
            "        481       0.21      0.04      0.06       169\n",
            "        482       0.33      0.03      0.06       157\n",
            "        483       0.77      0.48      0.59       200\n",
            "        484       0.58      0.21      0.31       177\n",
            "        485       0.51      0.26      0.34       175\n",
            "        486       0.64      0.51      0.57       185\n",
            "        487       0.96      0.52      0.67       167\n",
            "        488       0.00      0.00      0.00       192\n",
            "        489       0.30      0.09      0.14       176\n",
            "        490       0.00      0.00      0.00       167\n",
            "        491       0.33      0.01      0.01       177\n",
            "        492       0.47      0.26      0.33       160\n",
            "        493       0.46      0.22      0.30       159\n",
            "        494       0.15      0.03      0.04       159\n",
            "        495       0.31      0.10      0.15       162\n",
            "        496       0.82      0.46      0.59       167\n",
            "        497       0.17      0.02      0.03       168\n",
            "        498       0.40      0.12      0.19       154\n",
            "        499       0.00      0.00      0.00       184\n",
            "        500       0.14      0.03      0.05       167\n",
            "        501       0.41      0.20      0.27       153\n",
            "        502       0.78      0.55      0.65       143\n",
            "        503       0.22      0.07      0.10       177\n",
            "        504       0.69      0.32      0.44       177\n",
            "        505       0.90      0.50      0.64       152\n",
            "        506       0.80      0.40      0.54       179\n",
            "        507       0.60      0.12      0.20       171\n",
            "        508       0.61      0.28      0.39       151\n",
            "        509       0.51      0.23      0.32       162\n",
            "        510       0.63      0.24      0.35       158\n",
            "        511       0.18      0.03      0.05       164\n",
            "        512       0.00      0.00      0.00       149\n",
            "        513       0.78      0.60      0.68       174\n",
            "        514       0.51      0.15      0.23       172\n",
            "        515       0.34      0.14      0.20       144\n",
            "        516       0.57      0.15      0.23       164\n",
            "        517       0.88      0.67      0.76       152\n",
            "        518       0.60      0.02      0.03       175\n",
            "        519       0.29      0.04      0.06       168\n",
            "        520       0.52      0.11      0.18       145\n",
            "        521       0.89      0.38      0.53       165\n",
            "        522       0.91      0.55      0.69       151\n",
            "        523       0.93      0.57      0.71       171\n",
            "        524       0.89      0.53      0.66       160\n",
            "        525       0.59      0.41      0.49       139\n",
            "        526       0.57      0.19      0.29       165\n",
            "        527       0.57      0.22      0.31       148\n",
            "        528       0.64      0.21      0.32       178\n",
            "        529       0.31      0.06      0.10       152\n",
            "        530       0.11      0.01      0.01       143\n",
            "        531       0.57      0.20      0.30       174\n",
            "        532       0.63      0.20      0.30       135\n",
            "        533       0.35      0.05      0.09       179\n",
            "        534       0.26      0.04      0.08       135\n",
            "        535       0.29      0.09      0.14       157\n",
            "        536       0.88      0.53      0.66       163\n",
            "        537       0.79      0.39      0.53       127\n",
            "        538       0.34      0.13      0.19       130\n",
            "        539       0.55      0.20      0.29       155\n",
            "        540       0.43      0.18      0.25       165\n",
            "        541       0.35      0.11      0.16       139\n",
            "        542       0.38      0.05      0.09       159\n",
            "        543       0.44      0.18      0.25       140\n",
            "        544       0.76      0.17      0.28       143\n",
            "        545       0.44      0.12      0.19       147\n",
            "        546       0.47      0.18      0.26       153\n",
            "        547       0.76      0.28      0.41       165\n",
            "        548       0.35      0.10      0.16       149\n",
            "        549       0.62      0.26      0.37       123\n",
            "        550       0.82      0.06      0.11       148\n",
            "        551       0.68      0.41      0.51       145\n",
            "        552       0.50      0.04      0.07       157\n",
            "        553       0.46      0.23      0.31       151\n",
            "        554       0.50      0.01      0.01       152\n",
            "        555       0.43      0.17      0.24       147\n",
            "        556       0.72      0.35      0.47       143\n",
            "        557       0.47      0.20      0.28       139\n",
            "        558       0.92      0.54      0.68       165\n",
            "        559       0.37      0.10      0.16       147\n",
            "        560       0.27      0.13      0.17       139\n",
            "        561       0.29      0.08      0.12       152\n",
            "        562       0.45      0.26      0.33       132\n",
            "        563       0.41      0.17      0.24       150\n",
            "        564       0.30      0.08      0.13       165\n",
            "        565       0.73      0.38      0.50       147\n",
            "        566       0.27      0.05      0.08       151\n",
            "        567       0.52      0.24      0.33       153\n",
            "        568       0.48      0.19      0.27       148\n",
            "        569       0.17      0.04      0.06       142\n",
            "        570       0.11      0.02      0.04       140\n",
            "        571       0.07      0.01      0.01       149\n",
            "        572       1.00      0.02      0.04       146\n",
            "        573       0.51      0.29      0.37       135\n",
            "        574       0.73      0.24      0.36       137\n",
            "        575       0.50      0.11      0.18       142\n",
            "        576       0.24      0.10      0.14       145\n",
            "        577       0.82      0.25      0.38       145\n",
            "        578       0.72      0.33      0.45       131\n",
            "        579       0.40      0.15      0.22       142\n",
            "        580       0.00      0.00      0.00       143\n",
            "        581       0.38      0.09      0.15       139\n",
            "        582       0.57      0.15      0.24       150\n",
            "        583       0.00      0.00      0.00       121\n",
            "        584       0.57      0.28      0.38       148\n",
            "        585       0.61      0.41      0.49       134\n",
            "        586       0.64      0.37      0.47       151\n",
            "        587       0.74      0.11      0.20       150\n",
            "        588       0.48      0.11      0.18       141\n",
            "        589       0.20      0.03      0.05       137\n",
            "        590       0.79      0.36      0.50       154\n",
            "        591       0.52      0.22      0.31       126\n",
            "        592       0.85      0.49      0.62       144\n",
            "        593       0.29      0.06      0.10       130\n",
            "        594       0.46      0.15      0.22       148\n",
            "        595       0.13      0.02      0.03       115\n",
            "        596       0.64      0.46      0.53       142\n",
            "        597       0.95      0.46      0.62       123\n",
            "        598       0.63      0.21      0.32       150\n",
            "        599       0.00      0.00      0.00       134\n",
            "        600       0.24      0.04      0.07       154\n",
            "        601       0.36      0.08      0.14       165\n",
            "        602       0.50      0.02      0.04       150\n",
            "        603       0.49      0.15      0.23       137\n",
            "        604       0.89      0.53      0.67       133\n",
            "        605       0.38      0.14      0.21       146\n",
            "        606       0.88      0.12      0.21       129\n",
            "        607       0.17      0.03      0.05       151\n",
            "        608       0.86      0.55      0.67       138\n",
            "        609       0.36      0.13      0.19       124\n",
            "        610       0.40      0.01      0.03       144\n",
            "        611       0.00      0.00      0.00       150\n",
            "        612       0.00      0.00      0.00       130\n",
            "        613       0.21      0.05      0.08       127\n",
            "        614       0.41      0.17      0.24       141\n",
            "        615       0.10      0.02      0.03       133\n",
            "        616       0.54      0.29      0.38       132\n",
            "        617       0.67      0.02      0.03       131\n",
            "        618       0.21      0.03      0.06       125\n",
            "        619       0.63      0.37      0.46       123\n",
            "        620       0.00      0.00      0.00       148\n",
            "        621       0.12      0.01      0.02       117\n",
            "        622       0.72      0.47      0.57       129\n",
            "        623       0.36      0.04      0.06       113\n",
            "        624       0.88      0.51      0.64       110\n",
            "        625       0.92      0.63      0.75       121\n",
            "        626       0.22      0.08      0.12       125\n",
            "        627       0.95      0.59      0.73       132\n",
            "        628       0.67      0.30      0.42       116\n",
            "        629       0.81      0.38      0.52       126\n",
            "        630       0.29      0.04      0.07       126\n",
            "        631       0.28      0.06      0.10       148\n",
            "        632       0.91      0.61      0.74       140\n",
            "        633       0.50      0.02      0.03       128\n",
            "        634       0.40      0.16      0.22       128\n",
            "        635       0.00      0.00      0.00       140\n",
            "        636       0.95      0.41      0.57       130\n",
            "        637       0.62      0.23      0.34       126\n",
            "        638       0.75      0.08      0.15       143\n",
            "        639       0.67      0.31      0.42       121\n",
            "        640       0.16      0.04      0.07       117\n",
            "        641       0.36      0.12      0.19       112\n",
            "        642       0.46      0.14      0.21       137\n",
            "        643       0.96      0.61      0.74       141\n",
            "        644       0.71      0.37      0.49       127\n",
            "        645       0.28      0.06      0.10       128\n",
            "        646       0.10      0.01      0.01       124\n",
            "        647       0.11      0.03      0.05       138\n",
            "        648       0.13      0.03      0.04       119\n",
            "        649       0.00      0.00      0.00       137\n",
            "        650       0.33      0.01      0.02       121\n",
            "        651       0.07      0.02      0.03       108\n",
            "        652       0.72      0.41      0.52       122\n",
            "        653       0.61      0.26      0.36       139\n",
            "        654       0.40      0.02      0.03       112\n",
            "        655       0.53      0.14      0.22       125\n",
            "        656       0.64      0.19      0.29       124\n",
            "        657       0.30      0.08      0.12       117\n",
            "        658       0.50      0.20      0.28       116\n",
            "        659       0.37      0.08      0.14       130\n",
            "        660       0.15      0.02      0.03       121\n",
            "        661       0.75      0.35      0.48       124\n",
            "        662       0.48      0.12      0.19       121\n",
            "        663       0.84      0.63      0.72       126\n",
            "        664       0.00      0.00      0.00       118\n",
            "        665       0.18      0.06      0.09       113\n",
            "        666       0.00      0.00      0.00       128\n",
            "        667       0.53      0.12      0.20       139\n",
            "        668       0.29      0.04      0.07       131\n",
            "        669       0.26      0.05      0.08       127\n",
            "        670       0.47      0.07      0.12       125\n",
            "        671       0.33      0.02      0.03       111\n",
            "        672       0.55      0.37      0.44       127\n",
            "        673       0.72      0.48      0.57       130\n",
            "        674       0.19      0.02      0.04       130\n",
            "        675       0.60      0.20      0.30       126\n",
            "        676       0.15      0.02      0.03       104\n",
            "        677       0.53      0.14      0.22       127\n",
            "        678       0.57      0.15      0.24       130\n",
            "        679       0.26      0.10      0.14       112\n",
            "        680       0.43      0.09      0.15       131\n",
            "        681       0.00      0.00      0.00       140\n",
            "        682       0.53      0.35      0.42       114\n",
            "        683       0.78      0.12      0.22       112\n",
            "        684       0.35      0.06      0.10       115\n",
            "        685       0.66      0.15      0.24       128\n",
            "        686       0.57      0.10      0.17       122\n",
            "        687       0.25      0.03      0.05       109\n",
            "        688       0.29      0.02      0.03       108\n",
            "        689       0.00      0.00      0.00       125\n",
            "        690       0.50      0.01      0.02       117\n",
            "        691       0.36      0.09      0.15       127\n",
            "        692       0.80      0.35      0.49       129\n",
            "        693       0.42      0.16      0.23       118\n",
            "        694       0.72      0.37      0.49       151\n",
            "        695       0.67      0.29      0.41       112\n",
            "        696       0.81      0.22      0.34       119\n",
            "        697       0.19      0.05      0.07       109\n",
            "        698       0.58      0.33      0.42       122\n",
            "        699       0.96      0.49      0.65       102\n",
            "        700       0.29      0.07      0.11       102\n",
            "        701       0.46      0.26      0.33       107\n",
            "        702       0.25      0.03      0.05       105\n",
            "        703       0.25      0.01      0.02       113\n",
            "        704       0.62      0.27      0.37        98\n",
            "        705       0.21      0.05      0.08       100\n",
            "        706       0.72      0.33      0.45       131\n",
            "        707       0.45      0.21      0.29       112\n",
            "        708       0.44      0.03      0.06       119\n",
            "        709       0.28      0.07      0.11       105\n",
            "        710       0.18      0.03      0.04       117\n",
            "        711       0.39      0.14      0.21       115\n",
            "        712       0.41      0.10      0.16       129\n",
            "        713       0.68      0.27      0.38       101\n",
            "        714       0.57      0.10      0.17       122\n",
            "        715       0.00      0.00      0.00        97\n",
            "        716       0.38      0.16      0.23       116\n",
            "        717       0.43      0.08      0.14       110\n",
            "        718       0.38      0.04      0.08       113\n",
            "        719       0.75      0.49      0.59       110\n",
            "        720       0.78      0.05      0.10       130\n",
            "        721       0.00      0.00      0.00       104\n",
            "        722       0.89      0.66      0.75       119\n",
            "        723       0.00      0.00      0.00       108\n",
            "        724       0.43      0.22      0.29       112\n",
            "        725       0.32      0.05      0.08       126\n",
            "        726       0.93      0.67      0.78       120\n",
            "        727       0.30      0.05      0.09       130\n",
            "        728       0.67      0.02      0.04       103\n",
            "        729       0.70      0.17      0.28       111\n",
            "        730       0.33      0.03      0.05       110\n",
            "        731       0.00      0.00      0.00        96\n",
            "        732       0.55      0.05      0.10       112\n",
            "        733       0.39      0.08      0.13        90\n",
            "        734       0.28      0.11      0.15        95\n",
            "        735       0.80      0.39      0.52       116\n",
            "        736       0.40      0.02      0.03       128\n",
            "        737       0.25      0.09      0.13        93\n",
            "        738       0.89      0.15      0.26       107\n",
            "        739       0.58      0.29      0.39        99\n",
            "        740       0.40      0.04      0.07       105\n",
            "        741       0.46      0.05      0.09       116\n",
            "        742       0.68      0.43      0.53       105\n",
            "        743       0.40      0.19      0.26        84\n",
            "        744       0.44      0.14      0.21       102\n",
            "        745       0.69      0.23      0.34       111\n",
            "        746       0.36      0.10      0.15       104\n",
            "        747       0.44      0.14      0.21       110\n",
            "        748       0.58      0.21      0.30        92\n",
            "        749       0.87      0.57      0.69       106\n",
            "        750       0.00      0.00      0.00       116\n",
            "        751       0.28      0.09      0.14       109\n",
            "        752       0.85      0.54      0.66       104\n",
            "        753       1.00      0.01      0.02       119\n",
            "        754       0.27      0.06      0.10        96\n",
            "        755       0.17      0.04      0.06       104\n",
            "        756       0.00      0.00      0.00       101\n",
            "        757       0.50      0.19      0.28       114\n",
            "        758       0.00      0.00      0.00       112\n",
            "        759       0.67      0.04      0.08        95\n",
            "        760       0.00      0.00      0.00       102\n",
            "        761       0.31      0.11      0.17       105\n",
            "        762       0.57      0.25      0.35       109\n",
            "        763       0.09      0.01      0.02       112\n",
            "        764       0.94      0.40      0.56       116\n",
            "        765       0.60      0.31      0.41       109\n",
            "        766       0.00      0.00      0.00        96\n",
            "        767       0.50      0.09      0.15       114\n",
            "        768       0.00      0.00      0.00        99\n",
            "        769       0.65      0.15      0.25        98\n",
            "        770       0.48      0.21      0.30       107\n",
            "        771       0.00      0.00      0.00       103\n",
            "        772       0.00      0.00      0.00        96\n",
            "        773       0.00      0.00      0.00       106\n",
            "        774       0.76      0.33      0.46        97\n",
            "        775       0.27      0.03      0.06        91\n",
            "        776       0.00      0.00      0.00       101\n",
            "        777       0.76      0.38      0.50       109\n",
            "        778       0.00      0.00      0.00       104\n",
            "        779       0.33      0.08      0.13       116\n",
            "        780       0.00      0.00      0.00       102\n",
            "        781       0.85      0.26      0.40       106\n",
            "        782       0.64      0.15      0.24       108\n",
            "        783       0.80      0.08      0.15        95\n",
            "        784       0.91      0.36      0.52       108\n",
            "        785       0.94      0.43      0.59       113\n",
            "        786       0.40      0.06      0.10       109\n",
            "        787       0.78      0.41      0.54       112\n",
            "        788       0.00      0.00      0.00       104\n",
            "        789       0.43      0.17      0.25        92\n",
            "        790       0.44      0.06      0.11       116\n",
            "        791       0.29      0.04      0.07        96\n",
            "        792       0.58      0.15      0.24       118\n",
            "        793       0.64      0.27      0.38       106\n",
            "        794       0.26      0.06      0.10        93\n",
            "        795       0.80      0.31      0.45       103\n",
            "        796       0.39      0.12      0.18       104\n",
            "        797       0.57      0.09      0.16        89\n",
            "        798       0.55      0.06      0.11        97\n",
            "        799       0.00      0.00      0.00        92\n",
            "        800       0.55      0.14      0.22        85\n",
            "        801       1.00      0.04      0.08        93\n",
            "        802       0.79      0.28      0.41        93\n",
            "        803       0.36      0.13      0.19       102\n",
            "        804       0.65      0.12      0.20       108\n",
            "        805       0.87      0.37      0.52       111\n",
            "        806       0.61      0.14      0.23        98\n",
            "        807       0.20      0.03      0.06        94\n",
            "        808       0.15      0.02      0.04        84\n",
            "        809       0.84      0.32      0.46       100\n",
            "        810       0.22      0.02      0.04        92\n",
            "        811       0.37      0.11      0.17        88\n",
            "        812       0.39      0.13      0.20       104\n",
            "        813       0.50      0.04      0.08        90\n",
            "        814       0.38      0.07      0.12       109\n",
            "        815       0.23      0.04      0.06        81\n",
            "        816       0.70      0.22      0.33        96\n",
            "        817       0.98      0.53      0.69        88\n",
            "        818       0.56      0.24      0.33       101\n",
            "        819       0.94      0.45      0.61       103\n",
            "        820       0.00      0.00      0.00        94\n",
            "        821       0.72      0.17      0.27       108\n",
            "        822       0.29      0.06      0.09        90\n",
            "        823       0.81      0.44      0.57        97\n",
            "        824       0.50      0.02      0.04        90\n",
            "        825       0.52      0.23      0.32       102\n",
            "        826       0.12      0.01      0.02        85\n",
            "        827       0.20      0.02      0.03       109\n",
            "        828       0.30      0.03      0.05       103\n",
            "        829       0.98      0.40      0.56       106\n",
            "        830       0.88      0.26      0.40       108\n",
            "        831       0.50      0.04      0.07        84\n",
            "        832       0.00      0.00      0.00        98\n",
            "        833       0.77      0.26      0.39        92\n",
            "        834       0.50      0.10      0.17        91\n",
            "        835       0.87      0.28      0.43        92\n",
            "        836       0.28      0.07      0.11       104\n",
            "        837       0.63      0.24      0.34       102\n",
            "        838       0.22      0.07      0.11       111\n",
            "        839       0.00      0.00      0.00        96\n",
            "        840       0.41      0.15      0.22        86\n",
            "        841       0.34      0.10      0.16       105\n",
            "        842       0.20      0.01      0.02        92\n",
            "        843       0.39      0.16      0.23        86\n",
            "        844       0.00      0.00      0.00       108\n",
            "        845       0.45      0.06      0.11        82\n",
            "        846       0.22      0.04      0.07       101\n",
            "        847       0.97      0.60      0.74        94\n",
            "        848       1.00      0.41      0.58       101\n",
            "        849       0.39      0.14      0.20        88\n",
            "        850       0.88      0.36      0.51        81\n",
            "        851       0.79      0.10      0.18       109\n",
            "        852       0.45      0.13      0.20       101\n",
            "        853       0.25      0.03      0.06        91\n",
            "        854       0.29      0.06      0.10        95\n",
            "        855       0.20      0.01      0.02        99\n",
            "        856       0.14      0.01      0.02        79\n",
            "        857       0.67      0.32      0.43        91\n",
            "        858       0.00      0.00      0.00        89\n",
            "        859       0.42      0.09      0.15        91\n",
            "        860       0.49      0.19      0.28        88\n",
            "        861       0.32      0.07      0.11       101\n",
            "        862       0.51      0.30      0.37        81\n",
            "        863       0.69      0.20      0.31       101\n",
            "        864       0.28      0.11      0.16        80\n",
            "        865       0.00      0.00      0.00        97\n",
            "        866       0.88      0.46      0.60        94\n",
            "        867       0.00      0.00      0.00        97\n",
            "        868       0.29      0.07      0.11        91\n",
            "        869       0.35      0.09      0.14        88\n",
            "        870       0.53      0.25      0.34       112\n",
            "        871       0.93      0.57      0.71        94\n",
            "        872       0.00      0.00      0.00        84\n",
            "        873       0.89      0.53      0.66        74\n",
            "        874       0.91      0.53      0.67        80\n",
            "        875       0.46      0.23      0.31        79\n",
            "        876       0.56      0.07      0.12        71\n",
            "        877       0.77      0.26      0.39        92\n",
            "        878       1.00      0.08      0.15        99\n",
            "        879       0.56      0.14      0.23        98\n",
            "        880       0.37      0.18      0.24        82\n",
            "        881       0.70      0.35      0.47        80\n",
            "        882       0.91      0.55      0.69        94\n",
            "        883       0.07      0.01      0.02       102\n",
            "        884       0.88      0.22      0.35        95\n",
            "        885       0.91      0.57      0.70        87\n",
            "        886       0.20      0.01      0.02        88\n",
            "        887       0.41      0.08      0.13        90\n",
            "        888       0.84      0.46      0.60       104\n",
            "        889       0.20      0.01      0.02        93\n",
            "        890       0.14      0.02      0.04        83\n",
            "        891       0.00      0.00      0.00        92\n",
            "        892       0.58      0.17      0.26        88\n",
            "        893       0.00      0.00      0.00        74\n",
            "        894       1.00      0.40      0.57        98\n",
            "        895       0.47      0.22      0.30        73\n",
            "        896       0.00      0.00      0.00        87\n",
            "        897       0.29      0.03      0.05        73\n",
            "        898       0.58      0.22      0.32        86\n",
            "        899       0.24      0.08      0.12       100\n",
            "        900       0.43      0.14      0.21        93\n",
            "        901       0.82      0.36      0.50        86\n",
            "        902       0.38      0.07      0.12       107\n",
            "        903       0.43      0.03      0.06        97\n",
            "        904       0.52      0.17      0.26        88\n",
            "        905       0.00      0.00      0.00        94\n",
            "        906       0.14      0.02      0.04        83\n",
            "        907       0.00      0.00      0.00        85\n",
            "        908       0.00      0.00      0.00        90\n",
            "        909       0.14      0.01      0.02        83\n",
            "        910       0.60      0.07      0.13        83\n",
            "        911       0.19      0.03      0.06        87\n",
            "        912       0.94      0.38      0.54        87\n",
            "        913       0.56      0.10      0.18        86\n",
            "        914       0.52      0.16      0.25        91\n",
            "        915       0.25      0.02      0.04        87\n",
            "        916       0.00      0.00      0.00        92\n",
            "        917       0.00      0.00      0.00        92\n",
            "        918       0.81      0.37      0.51        78\n",
            "        919       0.44      0.10      0.16        81\n",
            "        920       0.00      0.00      0.00        87\n",
            "        921       0.00      0.00      0.00        95\n",
            "        922       0.85      0.27      0.41        82\n",
            "        923       0.33      0.02      0.04        89\n",
            "        924       0.00      0.00      0.00        73\n",
            "        925       0.41      0.09      0.14        82\n",
            "        926       0.43      0.03      0.06        91\n",
            "        927       0.38      0.10      0.15        83\n",
            "        928       0.33      0.03      0.05        79\n",
            "        929       0.55      0.07      0.12        89\n",
            "        930       0.29      0.07      0.11        85\n",
            "        931       0.00      0.00      0.00        95\n",
            "        932       0.25      0.01      0.02        80\n",
            "        933       0.50      0.07      0.12        72\n",
            "        934       0.64      0.29      0.40        79\n",
            "        935       0.52      0.15      0.23        75\n",
            "        936       0.70      0.22      0.34        85\n",
            "        937       0.47      0.09      0.16        75\n",
            "        938       0.23      0.09      0.13        69\n",
            "        939       0.00      0.00      0.00        85\n",
            "        940       0.11      0.01      0.02        72\n",
            "        941       0.00      0.00      0.00        69\n",
            "        942       0.44      0.09      0.14        94\n",
            "        943       0.00      0.00      0.00        85\n",
            "        944       0.94      0.36      0.52        89\n",
            "        945       0.19      0.04      0.06        77\n",
            "        946       0.78      0.15      0.25        93\n",
            "        947       0.00      0.00      0.00        81\n",
            "        948       0.95      0.50      0.66        78\n",
            "        949       0.00      0.00      0.00        75\n",
            "        950       0.00      0.00      0.00        80\n",
            "        951       0.12      0.01      0.02        88\n",
            "        952       0.29      0.03      0.05        80\n",
            "        953       1.00      0.71      0.83        85\n",
            "        954       0.83      0.55      0.66        71\n",
            "        955       0.00      0.00      0.00        80\n",
            "        956       0.81      0.37      0.51        68\n",
            "        957       0.87      0.52      0.65        75\n",
            "        958       0.43      0.13      0.20        90\n",
            "        959       0.81      0.15      0.25        87\n",
            "        960       0.89      0.38      0.53        87\n",
            "        961       0.74      0.29      0.42        68\n",
            "        962       0.65      0.26      0.37        86\n",
            "        963       0.57      0.19      0.28        85\n",
            "        964       0.43      0.15      0.23        78\n",
            "        965       0.76      0.44      0.56        88\n",
            "        966       0.93      0.46      0.61        85\n",
            "        967       0.52      0.23      0.32        70\n",
            "        968       0.33      0.04      0.07        82\n",
            "        969       0.88      0.47      0.61        92\n",
            "        970       0.31      0.05      0.09        73\n",
            "        971       0.00      0.00      0.00        77\n",
            "        972       0.46      0.16      0.24        82\n",
            "        973       0.80      0.10      0.18        80\n",
            "        974       0.12      0.01      0.02        83\n",
            "        975       0.98      0.58      0.73        76\n",
            "        976       0.00      0.00      0.00        85\n",
            "        977       0.00      0.00      0.00        65\n",
            "        978       0.57      0.11      0.19        72\n",
            "        979       0.33      0.02      0.04        85\n",
            "        980       0.23      0.05      0.08        64\n",
            "        981       0.25      0.03      0.05        76\n",
            "        982       0.58      0.07      0.13        96\n",
            "        983       0.94      0.31      0.46        94\n",
            "        984       0.29      0.02      0.04        87\n",
            "        985       0.33      0.01      0.03        75\n",
            "        986       0.00      0.00      0.00        79\n",
            "        987       0.00      0.00      0.00        86\n",
            "        988       0.50      0.01      0.02        88\n",
            "        989       0.00      0.00      0.00        84\n",
            "        990       0.52      0.14      0.22        95\n",
            "        991       0.37      0.15      0.22        71\n",
            "        992       0.57      0.38      0.46        68\n",
            "        993       0.00      0.00      0.00        75\n",
            "        994       0.00      0.00      0.00        90\n",
            "        995       0.95      0.43      0.60        83\n",
            "        996       0.89      0.43      0.58        79\n",
            "        997       0.71      0.08      0.14        64\n",
            "        998       0.27      0.04      0.07        74\n",
            "        999       0.81      0.36      0.50        81\n",
            "       1000       0.00      0.00      0.00        74\n",
            "       1001       0.14      0.02      0.03        62\n",
            "       1002       0.67      0.25      0.37        71\n",
            "       1003       0.00      0.00      0.00        72\n",
            "       1004       0.50      0.08      0.14        75\n",
            "       1005       0.93      0.53      0.67        72\n",
            "       1006       0.52      0.15      0.23        81\n",
            "       1007       0.00      0.00      0.00        74\n",
            "       1008       0.17      0.01      0.03        72\n",
            "       1009       0.00      0.00      0.00        75\n",
            "       1010       0.47      0.16      0.24        91\n",
            "       1011       0.59      0.18      0.27        90\n",
            "       1012       0.62      0.25      0.36        80\n",
            "       1013       0.00      0.00      0.00        88\n",
            "       1014       0.80      0.06      0.11        71\n",
            "       1015       0.57      0.11      0.18        74\n",
            "       1016       0.88      0.22      0.35        68\n",
            "       1017       0.70      0.39      0.50        71\n",
            "       1018       0.65      0.21      0.32        80\n",
            "       1019       0.00      0.00      0.00        83\n",
            "       1020       0.46      0.08      0.14        74\n",
            "       1021       0.93      0.49      0.64        78\n",
            "       1022       0.86      0.32      0.47        77\n",
            "       1023       0.12      0.01      0.02        78\n",
            "       1024       0.68      0.31      0.43        67\n",
            "       1025       0.50      0.01      0.02        80\n",
            "       1026       0.69      0.23      0.35        77\n",
            "       1027       0.80      0.32      0.46        88\n",
            "       1028       0.24      0.06      0.09        70\n",
            "       1029       0.00      0.00      0.00        79\n",
            "       1030       0.33      0.07      0.12        67\n",
            "       1031       0.88      0.47      0.61        75\n",
            "       1032       0.56      0.28      0.38        64\n",
            "       1033       0.88      0.21      0.34        70\n",
            "       1034       0.17      0.06      0.09        69\n",
            "       1035       0.44      0.10      0.16        72\n",
            "       1036       0.30      0.04      0.07        79\n",
            "       1037       0.24      0.05      0.08        84\n",
            "       1038       0.00      0.00      0.00        87\n",
            "       1039       0.68      0.35      0.46        65\n",
            "       1040       0.72      0.36      0.48        73\n",
            "       1041       0.00      0.00      0.00        77\n",
            "       1042       0.27      0.05      0.09        77\n",
            "       1043       0.16      0.07      0.09        60\n",
            "       1044       0.00      0.00      0.00        73\n",
            "       1045       0.00      0.00      0.00        67\n",
            "       1046       0.43      0.04      0.07        83\n",
            "       1047       1.00      0.40      0.57        70\n",
            "       1048       1.00      0.02      0.03        65\n",
            "       1049       0.62      0.14      0.22        74\n",
            "       1050       0.50      0.02      0.03        62\n",
            "       1051       0.58      0.16      0.25        70\n",
            "       1052       0.00      0.00      0.00        69\n",
            "       1053       0.25      0.08      0.12        72\n",
            "       1054       0.44      0.15      0.23        72\n",
            "       1055       0.90      0.52      0.66        73\n",
            "       1056       0.74      0.34      0.46        92\n",
            "       1057       0.67      0.05      0.10        73\n",
            "       1058       0.31      0.12      0.17        68\n",
            "       1059       0.00      0.00      0.00        71\n",
            "       1060       0.33      0.10      0.16        69\n",
            "       1061       0.85      0.24      0.37        72\n",
            "       1062       0.44      0.29      0.35        66\n",
            "       1063       0.14      0.01      0.02        84\n",
            "       1064       0.00      0.00      0.00        78\n",
            "       1065       0.81      0.45      0.58        66\n",
            "       1066       0.21      0.04      0.07        69\n",
            "       1067       0.11      0.01      0.02        80\n",
            "       1068       1.00      0.01      0.03        71\n",
            "       1069       0.52      0.18      0.27        60\n",
            "       1070       0.20      0.01      0.02        77\n",
            "       1071       0.88      0.29      0.43        80\n",
            "       1072       0.25      0.06      0.10        80\n",
            "       1073       0.00      0.00      0.00        74\n",
            "       1074       0.21      0.04      0.07        69\n",
            "       1075       0.44      0.07      0.12        56\n",
            "       1076       0.32      0.13      0.18        63\n",
            "       1077       0.58      0.19      0.29        58\n",
            "       1078       0.00      0.00      0.00        63\n",
            "       1079       0.83      0.24      0.37        85\n",
            "       1080       0.52      0.15      0.24        78\n",
            "       1081       0.00      0.00      0.00        84\n",
            "       1082       0.74      0.42      0.54        73\n",
            "       1083       0.09      0.02      0.03        55\n",
            "       1084       0.51      0.26      0.34        70\n",
            "       1085       0.69      0.26      0.38        85\n",
            "       1086       0.00      0.00      0.00        68\n",
            "       1087       0.40      0.02      0.05        82\n",
            "       1088       0.00      0.00      0.00        67\n",
            "       1089       0.81      0.44      0.57        78\n",
            "       1090       0.70      0.11      0.19        64\n",
            "       1091       0.35      0.09      0.15        75\n",
            "       1092       0.38      0.16      0.23        61\n",
            "       1093       0.65      0.17      0.28        63\n",
            "       1094       0.00      0.00      0.00        77\n",
            "       1095       0.36      0.13      0.19        70\n",
            "       1096       0.86      0.34      0.48        71\n",
            "       1097       0.44      0.12      0.18        69\n",
            "       1098       0.58      0.22      0.32        63\n",
            "       1099       0.80      0.49      0.61        67\n",
            "       1100       0.57      0.06      0.11        68\n",
            "       1101       0.00      0.00      0.00        57\n",
            "       1102       0.90      0.54      0.67        69\n",
            "       1103       0.14      0.01      0.03        70\n",
            "       1104       0.40      0.05      0.09        75\n",
            "       1105       0.21      0.05      0.08        62\n",
            "       1106       0.25      0.01      0.03        72\n",
            "       1107       0.00      0.00      0.00        76\n",
            "       1108       0.00      0.00      0.00        72\n",
            "       1109       0.00      0.00      0.00        86\n",
            "       1110       0.85      0.43      0.57        82\n",
            "       1111       0.00      0.00      0.00        70\n",
            "       1112       0.50      0.01      0.03        72\n",
            "       1113       0.65      0.24      0.35        70\n",
            "       1114       0.20      0.02      0.03        57\n",
            "       1115       0.25      0.04      0.07        68\n",
            "       1116       0.00      0.00      0.00        64\n",
            "       1117       0.29      0.03      0.05        66\n",
            "       1118       0.50      0.11      0.18        81\n",
            "       1119       0.68      0.24      0.35        63\n",
            "       1120       0.15      0.06      0.09        62\n",
            "       1121       0.00      0.00      0.00        79\n",
            "       1122       0.80      0.21      0.34        56\n",
            "       1123       0.24      0.06      0.09        71\n",
            "       1124       0.00      0.00      0.00        78\n",
            "       1125       0.80      0.06      0.11        66\n",
            "       1126       0.00      0.00      0.00        62\n",
            "       1127       0.75      0.18      0.29        66\n",
            "       1128       0.00      0.00      0.00        70\n",
            "       1129       0.94      0.46      0.62        65\n",
            "       1130       0.85      0.37      0.51        63\n",
            "       1131       0.89      0.52      0.66        79\n",
            "       1132       0.38      0.07      0.12        67\n",
            "       1133       0.00      0.00      0.00        64\n",
            "       1134       0.20      0.03      0.05        67\n",
            "       1135       0.73      0.21      0.32        78\n",
            "       1136       0.44      0.07      0.13        54\n",
            "       1137       0.00      0.00      0.00        64\n",
            "       1138       0.39      0.09      0.15        76\n",
            "       1139       0.00      0.00      0.00        64\n",
            "       1140       0.00      0.00      0.00        67\n",
            "       1141       0.06      0.01      0.02        70\n",
            "       1142       0.44      0.06      0.11        66\n",
            "       1143       0.74      0.40      0.52        62\n",
            "       1144       0.00      0.00      0.00        67\n",
            "       1145       0.43      0.06      0.11        47\n",
            "       1146       0.35      0.09      0.14        69\n",
            "       1147       0.71      0.40      0.51        63\n",
            "       1148       0.37      0.10      0.16        70\n",
            "       1149       0.41      0.13      0.19        55\n",
            "       1150       0.57      0.33      0.42        49\n",
            "       1151       0.57      0.07      0.12        58\n",
            "       1152       0.00      0.00      0.00        65\n",
            "       1153       0.00      0.00      0.00        67\n",
            "       1154       0.00      0.00      0.00        66\n",
            "       1155       0.94      0.52      0.67        62\n",
            "       1156       0.62      0.07      0.12        72\n",
            "       1157       0.90      0.42      0.57        62\n",
            "       1158       0.00      0.00      0.00        60\n",
            "       1159       0.43      0.16      0.23        64\n",
            "       1160       0.30      0.05      0.09        59\n",
            "       1161       0.10      0.02      0.03        55\n",
            "       1162       0.51      0.29      0.37        63\n",
            "       1163       0.77      0.36      0.49        64\n",
            "       1164       0.00      0.00      0.00        54\n",
            "       1165       0.32      0.10      0.15        62\n",
            "       1166       0.00      0.00      0.00        73\n",
            "       1167       0.46      0.21      0.29        56\n",
            "       1168       0.33      0.03      0.06        60\n",
            "       1169       0.35      0.11      0.17        63\n",
            "       1170       0.80      0.05      0.10        73\n",
            "       1171       0.60      0.31      0.41        58\n",
            "       1172       0.29      0.03      0.06        59\n",
            "       1173       0.23      0.04      0.07        68\n",
            "       1174       0.45      0.14      0.22        63\n",
            "       1175       0.98      0.60      0.74        70\n",
            "       1176       0.87      0.42      0.57        62\n",
            "       1177       0.00      0.00      0.00        62\n",
            "       1178       0.00      0.00      0.00        45\n",
            "       1179       0.97      0.37      0.53        79\n",
            "       1180       0.70      0.12      0.21        58\n",
            "       1181       0.88      0.30      0.44        71\n",
            "       1182       0.12      0.02      0.03        56\n",
            "       1183       0.00      0.00      0.00        63\n",
            "       1184       0.00      0.00      0.00        72\n",
            "       1185       0.33      0.04      0.06        56\n",
            "       1186       0.82      0.19      0.30        75\n",
            "       1187       0.17      0.02      0.03        57\n",
            "       1188       0.45      0.08      0.14        60\n",
            "       1189       0.25      0.02      0.03        65\n",
            "       1190       0.50      0.01      0.03        68\n",
            "       1191       0.59      0.16      0.25        62\n",
            "       1192       0.00      0.00      0.00        68\n",
            "       1193       0.00      0.00      0.00        66\n",
            "       1194       0.40      0.04      0.06        57\n",
            "       1195       0.11      0.01      0.03        67\n",
            "       1196       0.88      0.10      0.18        69\n",
            "       1197       0.36      0.06      0.10        66\n",
            "       1198       0.40      0.03      0.06        62\n",
            "       1199       0.33      0.08      0.14        59\n",
            "       1200       0.92      0.21      0.34        57\n",
            "       1201       1.00      0.31      0.47        62\n",
            "       1202       0.87      0.47      0.61        58\n",
            "       1203       0.00      0.00      0.00        67\n",
            "       1204       0.63      0.35      0.45        74\n",
            "       1205       0.50      0.02      0.04        55\n",
            "       1206       0.55      0.09      0.16        65\n",
            "       1207       0.47      0.11      0.17        75\n",
            "       1208       0.63      0.20      0.30        61\n",
            "       1209       0.69      0.39      0.49        62\n",
            "       1210       0.14      0.02      0.03        59\n",
            "       1211       0.50      0.19      0.28        47\n",
            "       1212       0.00      0.00      0.00        59\n",
            "       1213       0.95      0.36      0.52        59\n",
            "       1214       1.00      0.03      0.05        74\n",
            "       1215       0.25      0.02      0.03        65\n",
            "       1216       0.00      0.00      0.00        60\n",
            "       1217       0.53      0.19      0.27        54\n",
            "       1218       0.00      0.00      0.00        62\n",
            "       1219       0.93      0.68      0.79        78\n",
            "       1220       0.85      0.57      0.68        72\n",
            "       1221       0.75      0.35      0.48        60\n",
            "       1222       0.43      0.14      0.21        63\n",
            "       1223       0.00      0.00      0.00        66\n",
            "       1224       0.56      0.14      0.23        69\n",
            "       1225       0.00      0.00      0.00        69\n",
            "       1226       0.80      0.18      0.29        68\n",
            "       1227       0.53      0.17      0.26        58\n",
            "       1228       0.00      0.00      0.00        51\n",
            "       1229       0.00      0.00      0.00        59\n",
            "       1230       0.00      0.00      0.00        75\n",
            "       1231       0.50      0.11      0.18        64\n",
            "       1232       0.00      0.00      0.00        66\n",
            "       1233       0.29      0.03      0.06        58\n",
            "       1234       0.00      0.00      0.00        63\n",
            "       1235       0.06      0.02      0.03        62\n",
            "       1236       0.00      0.00      0.00        57\n",
            "       1237       1.00      0.01      0.03        77\n",
            "       1238       0.81      0.40      0.54        52\n",
            "       1239       0.86      0.30      0.45        63\n",
            "       1240       0.90      0.40      0.55        48\n",
            "       1241       0.00      0.00      0.00        71\n",
            "       1242       0.79      0.18      0.29        62\n",
            "       1243       0.43      0.10      0.16        61\n",
            "       1244       0.00      0.00      0.00        53\n",
            "       1245       0.09      0.01      0.02        75\n",
            "       1246       0.38      0.05      0.10        55\n",
            "       1247       0.50      0.02      0.04        55\n",
            "       1248       0.00      0.00      0.00        49\n",
            "       1249       0.33      0.05      0.09        74\n",
            "       1250       0.97      0.47      0.64        59\n",
            "       1251       0.38      0.14      0.21        56\n",
            "       1252       0.33      0.10      0.15        63\n",
            "       1253       0.59      0.21      0.31        48\n",
            "       1254       0.95      0.60      0.73        62\n",
            "       1255       0.00      0.00      0.00        69\n",
            "       1256       0.30      0.05      0.08        65\n",
            "       1257       0.00      0.00      0.00        62\n",
            "       1258       0.39      0.14      0.20        51\n",
            "       1259       0.62      0.12      0.21        64\n",
            "       1260       0.00      0.00      0.00        64\n",
            "       1261       0.00      0.00      0.00        63\n",
            "       1262       0.93      0.22      0.36        58\n",
            "       1263       0.36      0.07      0.12        54\n",
            "       1264       0.00      0.00      0.00        62\n",
            "       1265       0.00      0.00      0.00        59\n",
            "       1266       0.90      0.46      0.60        57\n",
            "       1267       0.14      0.02      0.03        51\n",
            "       1268       0.25      0.04      0.07        46\n",
            "       1269       0.97      0.53      0.68        55\n",
            "       1270       0.88      0.10      0.18        69\n",
            "       1271       0.60      0.14      0.22        65\n",
            "       1272       0.38      0.08      0.14        60\n",
            "       1273       0.35      0.10      0.16        59\n",
            "       1274       0.25      0.05      0.08        62\n",
            "       1275       0.00      0.00      0.00        52\n",
            "       1276       0.40      0.07      0.12        57\n",
            "       1277       0.29      0.03      0.06        61\n",
            "       1278       0.70      0.11      0.19        62\n",
            "       1279       0.93      0.57      0.71        47\n",
            "       1280       0.25      0.03      0.06        63\n",
            "       1281       0.58      0.11      0.19        61\n",
            "       1282       0.60      0.18      0.28        50\n",
            "       1283       0.27      0.08      0.12        52\n",
            "       1284       0.68      0.23      0.35        56\n",
            "       1285       0.67      0.04      0.07        57\n",
            "       1286       0.71      0.10      0.18        49\n",
            "       1287       0.57      0.14      0.23        56\n",
            "       1288       0.57      0.27      0.36        49\n",
            "       1289       0.00      0.00      0.00        55\n",
            "       1290       0.00      0.00      0.00        68\n",
            "       1291       0.90      0.50      0.64        52\n",
            "       1292       0.29      0.03      0.05        73\n",
            "       1293       0.88      0.43      0.58        67\n",
            "       1294       0.00      0.00      0.00        54\n",
            "       1295       0.25      0.06      0.10        34\n",
            "       1296       1.00      0.34      0.51        56\n",
            "       1297       0.00      0.00      0.00        66\n",
            "       1298       1.00      0.03      0.06        68\n",
            "       1299       0.57      0.06      0.11        64\n",
            "       1300       0.91      0.50      0.65        64\n",
            "       1301       0.00      0.00      0.00        48\n",
            "       1302       0.00      0.00      0.00        63\n",
            "       1303       0.00      0.00      0.00        62\n",
            "       1304       0.50      0.02      0.04        54\n",
            "       1305       0.23      0.10      0.14        51\n",
            "       1306       0.22      0.07      0.11        55\n",
            "       1307       0.00      0.00      0.00        53\n",
            "       1308       0.61      0.31      0.41        54\n",
            "       1309       0.67      0.16      0.26        61\n",
            "       1310       0.00      0.00      0.00        42\n",
            "       1311       0.25      0.02      0.03        55\n",
            "       1312       0.00      0.00      0.00        64\n",
            "       1313       0.00      0.00      0.00        58\n",
            "       1314       0.90      0.36      0.51        50\n",
            "       1315       0.00      0.00      0.00        57\n",
            "       1316       0.59      0.22      0.32        46\n",
            "       1317       1.00      0.05      0.09        42\n",
            "       1318       0.50      0.22      0.30        74\n",
            "       1319       0.00      0.00      0.00        55\n",
            "       1320       0.00      0.00      0.00        59\n",
            "       1321       1.00      0.02      0.04        56\n",
            "       1322       0.00      0.00      0.00        61\n",
            "       1323       0.00      0.00      0.00        43\n",
            "       1324       0.47      0.18      0.26        45\n",
            "       1325       0.62      0.09      0.16        56\n",
            "       1326       0.72      0.35      0.47        52\n",
            "       1327       0.52      0.20      0.29        56\n",
            "       1328       0.00      0.00      0.00        56\n",
            "       1329       0.56      0.10      0.17        51\n",
            "       1330       0.00      0.00      0.00        54\n",
            "       1331       0.50      0.12      0.19        51\n",
            "       1332       0.00      0.00      0.00        48\n",
            "       1333       0.00      0.00      0.00        51\n",
            "       1334       0.00      0.00      0.00        38\n",
            "       1335       0.91      0.42      0.58        50\n",
            "       1336       0.00      0.00      0.00        48\n",
            "       1337       0.38      0.10      0.15        52\n",
            "       1338       0.58      0.21      0.31        52\n",
            "       1339       0.25      0.04      0.06        56\n",
            "       1340       0.50      0.04      0.07        52\n",
            "       1341       1.00      0.02      0.03        58\n",
            "       1342       0.00      0.00      0.00        56\n",
            "       1343       0.33      0.03      0.06        62\n",
            "       1344       0.93      0.32      0.47        44\n",
            "       1345       0.38      0.06      0.10        53\n",
            "       1346       0.20      0.02      0.03        53\n",
            "       1347       0.00      0.00      0.00        52\n",
            "       1348       0.50      0.10      0.17        58\n",
            "       1349       0.64      0.36      0.46        50\n",
            "       1350       0.00      0.00      0.00        62\n",
            "       1351       0.96      0.39      0.55        59\n",
            "       1352       0.00      0.00      0.00        57\n",
            "       1353       0.63      0.24      0.35        50\n",
            "       1354       0.67      0.11      0.19        55\n",
            "       1355       0.00      0.00      0.00        55\n",
            "       1356       0.17      0.02      0.03        56\n",
            "       1357       0.16      0.08      0.11        38\n",
            "       1358       0.20      0.04      0.06        53\n",
            "       1359       1.00      0.23      0.37        44\n",
            "       1360       1.00      0.23      0.38        56\n",
            "       1361       0.25      0.04      0.06        56\n",
            "       1362       1.00      0.33      0.49        46\n",
            "       1363       0.73      0.22      0.34        49\n",
            "       1364       0.00      0.00      0.00        66\n",
            "       1365       0.33      0.05      0.09        60\n",
            "       1366       0.86      0.11      0.19        56\n",
            "       1367       0.00      0.00      0.00        63\n",
            "       1368       0.53      0.15      0.23        67\n",
            "       1369       1.00      0.44      0.61        59\n",
            "       1370       0.94      0.33      0.48        49\n",
            "       1371       0.76      0.25      0.38        51\n",
            "       1372       0.20      0.02      0.04        50\n",
            "       1373       0.93      0.40      0.56        63\n",
            "       1374       0.20      0.02      0.03        55\n",
            "       1375       0.00      0.00      0.00        60\n",
            "       1376       0.52      0.18      0.27        60\n",
            "       1377       0.00      0.00      0.00        42\n",
            "       1378       0.94      0.30      0.45        54\n",
            "       1379       0.00      0.00      0.00        50\n",
            "       1380       0.00      0.00      0.00        45\n",
            "       1381       0.60      0.06      0.12        47\n",
            "       1382       0.11      0.02      0.03        54\n",
            "       1383       0.33      0.04      0.08        45\n",
            "       1384       0.00      0.00      0.00        52\n",
            "       1385       0.73      0.23      0.35        48\n",
            "       1386       0.60      0.06      0.11        50\n",
            "       1387       0.17      0.02      0.04        47\n",
            "       1388       0.75      0.16      0.26        57\n",
            "       1389       0.00      0.00      0.00        49\n",
            "       1390       0.55      0.27      0.36        44\n",
            "       1391       0.00      0.00      0.00        58\n",
            "       1392       0.77      0.19      0.30        54\n",
            "       1393       0.38      0.12      0.18        51\n",
            "       1394       0.50      0.02      0.04        51\n",
            "       1395       0.83      0.21      0.33        48\n",
            "       1396       0.67      0.13      0.22        61\n",
            "       1397       1.00      0.02      0.03        61\n",
            "       1398       0.62      0.15      0.24        55\n",
            "       1399       0.74      0.25      0.37        57\n",
            "       1400       0.50      0.06      0.11        49\n",
            "       1401       0.50      0.04      0.07        56\n",
            "       1402       0.54      0.13      0.22        52\n",
            "       1403       0.75      0.12      0.21        49\n",
            "       1404       0.92      0.80      0.86        41\n",
            "       1405       0.75      0.32      0.44        57\n",
            "       1406       0.33      0.02      0.04        54\n",
            "       1407       0.70      0.55      0.62        47\n",
            "       1408       0.38      0.07      0.12        41\n",
            "       1409       1.00      0.39      0.56        49\n",
            "       1410       1.00      0.44      0.61        48\n",
            "       1411       0.17      0.02      0.03        55\n",
            "       1412       0.73      0.13      0.23        60\n",
            "       1413       1.00      0.01      0.03        67\n",
            "       1414       0.00      0.00      0.00        50\n",
            "       1415       0.00      0.00      0.00        53\n",
            "       1416       0.40      0.10      0.16        59\n",
            "       1417       0.53      0.14      0.22        66\n",
            "       1418       0.67      0.04      0.08        50\n",
            "       1419       0.80      0.11      0.20        36\n",
            "       1420       0.30      0.06      0.11        47\n",
            "       1421       0.00      0.00      0.00        46\n",
            "       1422       0.38      0.10      0.16        51\n",
            "       1423       0.82      0.18      0.30        49\n",
            "       1424       0.50      0.07      0.12        56\n",
            "       1425       0.00      0.00      0.00        51\n",
            "       1426       0.67      0.04      0.07        53\n",
            "       1427       0.30      0.06      0.11        47\n",
            "       1428       0.00      0.00      0.00        39\n",
            "       1429       0.97      0.56      0.71        50\n",
            "       1430       0.86      0.20      0.33        59\n",
            "       1431       0.00      0.00      0.00        67\n",
            "       1432       0.00      0.00      0.00        53\n",
            "       1433       0.38      0.08      0.14        72\n",
            "       1434       0.62      0.10      0.17        51\n",
            "       1435       0.54      0.12      0.20        56\n",
            "       1436       0.67      0.11      0.18        56\n",
            "       1437       0.57      0.16      0.25        51\n",
            "       1438       0.00      0.00      0.00        46\n",
            "       1439       0.67      0.04      0.07        52\n",
            "       1440       0.00      0.00      0.00        41\n",
            "       1441       1.00      0.04      0.08        47\n",
            "       1442       1.00      0.02      0.04        45\n",
            "       1443       0.10      0.02      0.03        54\n",
            "       1444       0.15      0.04      0.06        52\n",
            "       1445       0.00      0.00      0.00        52\n",
            "       1446       0.61      0.25      0.35        44\n",
            "       1447       1.00      0.17      0.29        47\n",
            "       1448       0.00      0.00      0.00        48\n",
            "       1449       0.33      0.02      0.03        56\n",
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            "       1451       0.12      0.02      0.03        65\n",
            "       1452       0.50      0.07      0.13        55\n",
            "       1453       0.29      0.07      0.11        61\n",
            "       1454       0.00      0.00      0.00        62\n",
            "       1455       0.65      0.22      0.33        49\n",
            "       1456       0.20      0.02      0.03        53\n",
            "       1457       0.62      0.31      0.41        42\n",
            "       1458       0.75      0.05      0.10        59\n",
            "       1459       0.00      0.00      0.00        49\n",
            "       1460       0.71      0.10      0.18        50\n",
            "       1461       0.00      0.00      0.00        45\n",
            "       1462       0.42      0.11      0.17        47\n",
            "       1463       0.71      0.33      0.45        45\n",
            "       1464       1.00      0.04      0.08        50\n",
            "       1465       0.33      0.05      0.08        62\n",
            "       1466       0.00      0.00      0.00        51\n",
            "       1467       0.33      0.02      0.03        62\n",
            "       1468       0.93      0.48      0.63        54\n",
            "       1469       0.50      0.11      0.17        38\n",
            "       1470       0.81      0.26      0.40        65\n",
            "       1471       1.00      0.29      0.45        52\n",
            "       1472       0.50      0.09      0.15        44\n",
            "       1473       0.17      0.04      0.06        50\n",
            "       1474       0.00      0.00      0.00        56\n",
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            "       1476       0.12      0.02      0.03        58\n",
            "       1477       0.00      0.00      0.00        39\n",
            "       1478       0.96      0.48      0.64        50\n",
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            "       1481       0.83      0.33      0.47        57\n",
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            "       1484       1.00      0.10      0.18        59\n",
            "       1485       0.93      0.28      0.43        47\n",
            "       1486       0.50      0.02      0.04        53\n",
            "       1487       0.00      0.00      0.00        42\n",
            "       1488       0.00      0.00      0.00        47\n",
            "       1489       0.33      0.02      0.04        52\n",
            "       1490       0.72      0.30      0.42        44\n",
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            "       1492       0.81      0.25      0.39        51\n",
            "       1493       0.00      0.00      0.00        39\n",
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            "       1495       0.40      0.12      0.19        49\n",
            "       1496       0.62      0.16      0.26        49\n",
            "       1497       0.00      0.00      0.00        51\n",
            "       1498       1.00      0.04      0.07        52\n",
            "       1499       0.50      0.06      0.11        48\n",
            "       1500       0.00      0.00      0.00        51\n",
            "       1501       0.25      0.02      0.03        56\n",
            "       1502       0.00      0.00      0.00        48\n",
            "       1503       0.82      0.48      0.61        58\n",
            "       1504       0.50      0.02      0.04        44\n",
            "       1505       0.00      0.00      0.00        45\n",
            "       1506       0.20      0.02      0.04        44\n",
            "       1507       0.00      0.00      0.00        55\n",
            "       1508       0.33      0.04      0.08        45\n",
            "       1509       0.62      0.17      0.27        46\n",
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            "       1511       0.00      0.00      0.00        43\n",
            "       1512       0.89      0.19      0.31        42\n",
            "       1513       0.00      0.00      0.00        44\n",
            "       1514       0.58      0.33      0.42        45\n",
            "       1515       1.00      0.48      0.65        42\n",
            "       1516       1.00      0.36      0.53        42\n",
            "       1517       0.22      0.10      0.14        49\n",
            "       1518       1.00      0.18      0.30        51\n",
            "       1519       0.50      0.02      0.04        47\n",
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            "       1522       0.22      0.05      0.09        38\n",
            "       1523       0.00      0.00      0.00        44\n",
            "       1524       0.67      0.04      0.07        55\n",
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            "       1529       0.67      0.06      0.12        63\n",
            "       1530       0.77      0.25      0.38        40\n",
            "       1531       0.00      0.00      0.00        40\n",
            "       1532       0.22      0.04      0.07        48\n",
            "       1533       0.00      0.00      0.00        49\n",
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            "       1535       1.00      0.19      0.32        42\n",
            "       1536       1.00      0.06      0.11        54\n",
            "       1537       0.64      0.12      0.21        56\n",
            "       1538       0.50      0.03      0.05        38\n",
            "       1539       0.00      0.00      0.00        47\n",
            "       1540       0.44      0.10      0.16        40\n",
            "       1541       0.82      0.20      0.32        46\n",
            "       1542       1.00      0.15      0.26        46\n",
            "       1543       0.25      0.02      0.04        42\n",
            "       1544       0.70      0.33      0.45        48\n",
            "       1545       1.00      0.02      0.05        41\n",
            "       1546       0.00      0.00      0.00        35\n",
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            "       1548       0.20      0.04      0.06        55\n",
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            "       1550       1.00      0.12      0.22        48\n",
            "       1551       0.84      0.68      0.75        40\n",
            "       1552       0.67      0.04      0.07        51\n",
            "       1553       0.75      0.07      0.12        44\n",
            "       1554       0.91      0.20      0.32        51\n",
            "       1555       0.00      0.00      0.00        59\n",
            "       1556       0.50      0.18      0.27        60\n",
            "       1557       1.00      0.07      0.12        46\n",
            "       1558       0.67      0.05      0.09        43\n",
            "       1559       0.00      0.00      0.00        52\n",
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            "       1561       0.95      0.50      0.66        38\n",
            "       1562       0.40      0.10      0.15        42\n",
            "       1563       0.30      0.06      0.10        49\n",
            "       1564       1.00      0.15      0.25        48\n",
            "       1565       1.00      0.38      0.56        52\n",
            "       1566       0.97      0.63      0.76        46\n",
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            "       1568       0.81      0.44      0.57        39\n",
            "       1569       0.57      0.09      0.15        47\n",
            "       1570       0.60      0.12      0.21        48\n",
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            "       1573       0.00      0.00      0.00        31\n",
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            "       1575       0.14      0.02      0.04        49\n",
            "       1576       1.00      0.43      0.61        46\n",
            "       1577       0.25      0.02      0.03        55\n",
            "       1578       0.00      0.00      0.00        42\n",
            "       1579       0.89      0.20      0.32        41\n",
            "       1580       0.00      0.00      0.00        47\n",
            "       1581       0.40      0.08      0.13        50\n",
            "       1582       0.00      0.00      0.00        47\n",
            "       1583       0.50      0.11      0.18        54\n",
            "       1584       0.50      0.04      0.08        49\n",
            "       1585       0.25      0.06      0.09        35\n",
            "       1586       0.00      0.00      0.00        43\n",
            "       1587       0.64      0.13      0.22        53\n",
            "       1588       0.00      0.00      0.00        49\n",
            "       1589       0.00      0.00      0.00        44\n",
            "       1590       0.50      0.05      0.09        39\n",
            "       1591       0.00      0.00      0.00        36\n",
            "       1592       0.00      0.00      0.00        46\n",
            "       1593       0.75      0.22      0.34        55\n",
            "       1594       0.91      0.21      0.34        47\n",
            "       1595       1.00      0.22      0.35        51\n",
            "       1596       0.00      0.00      0.00        42\n",
            "       1597       0.00      0.00      0.00        50\n",
            "       1598       0.53      0.20      0.29        40\n",
            "       1599       0.00      0.00      0.00        38\n",
            "       1600       0.00      0.00      0.00        47\n",
            "       1601       0.88      0.38      0.53        37\n",
            "       1602       0.25      0.02      0.03        62\n",
            "       1603       0.00      0.00      0.00        43\n",
            "       1604       0.00      0.00      0.00        66\n",
            "       1605       0.33      0.03      0.06        33\n",
            "       1606       0.00      0.00      0.00        35\n",
            "       1607       1.00      0.29      0.44        42\n",
            "       1608       0.96      0.57      0.71        44\n",
            "       1609       0.67      0.05      0.09        40\n",
            "       1610       0.91      0.46      0.61        46\n",
            "       1611       0.33      0.04      0.07        55\n",
            "       1612       0.88      0.35      0.50        43\n",
            "       1613       0.00      0.00      0.00        51\n",
            "       1614       0.69      0.24      0.35        38\n",
            "       1615       0.00      0.00      0.00        47\n",
            "       1616       0.45      0.10      0.16        51\n",
            "       1617       0.00      0.00      0.00        52\n",
            "       1618       0.25      0.02      0.04        43\n",
            "       1619       1.00      0.03      0.05        37\n",
            "       1620       0.00      0.00      0.00        50\n",
            "       1621       0.00      0.00      0.00        44\n",
            "       1622       0.56      0.12      0.20        41\n",
            "       1623       0.50      0.13      0.21        46\n",
            "       1624       1.00      0.05      0.09        42\n",
            "       1625       0.94      0.33      0.49        48\n",
            "       1626       0.20      0.02      0.04        51\n",
            "       1627       0.00      0.00      0.00        37\n",
            "       1628       0.20      0.04      0.07        48\n",
            "       1629       0.00      0.00      0.00        43\n",
            "       1630       0.00      0.00      0.00        50\n",
            "       1631       0.00      0.00      0.00        41\n",
            "       1632       0.29      0.04      0.08        45\n",
            "       1633       0.90      0.40      0.55        45\n",
            "       1634       0.43      0.11      0.17        56\n",
            "       1635       0.71      0.27      0.39        44\n",
            "       1636       1.00      0.33      0.50        39\n",
            "       1637       0.74      0.27      0.40        51\n",
            "       1638       0.00      0.00      0.00        31\n",
            "       1639       0.00      0.00      0.00        53\n",
            "       1640       1.00      0.19      0.31        59\n",
            "       1641       0.20      0.03      0.05        35\n",
            "       1642       0.38      0.10      0.15        52\n",
            "       1643       0.00      0.00      0.00        32\n",
            "       1644       0.00      0.00      0.00        45\n",
            "       1645       0.00      0.00      0.00        50\n",
            "       1646       0.36      0.08      0.13        52\n",
            "       1647       0.53      0.26      0.34        39\n",
            "       1648       0.25      0.02      0.03        56\n",
            "       1649       0.75      0.32      0.45        37\n",
            "       1650       0.30      0.07      0.12        42\n",
            "       1651       0.62      0.09      0.16        55\n",
            "       1652       0.89      0.47      0.62        34\n",
            "       1653       0.83      0.12      0.22        40\n",
            "       1654       0.00      0.00      0.00        45\n",
            "       1655       0.00      0.00      0.00        56\n",
            "       1656       0.00      0.00      0.00        50\n",
            "       1657       0.00      0.00      0.00        46\n",
            "       1658       0.84      0.37      0.52        43\n",
            "       1659       0.88      0.45      0.59        49\n",
            "       1660       0.80      0.23      0.36        52\n",
            "       1661       1.00      0.02      0.04        54\n",
            "       1662       0.00      0.00      0.00        43\n",
            "       1663       0.00      0.00      0.00        59\n",
            "       1664       0.00      0.00      0.00        45\n",
            "       1665       0.00      0.00      0.00        51\n",
            "       1666       0.00      0.00      0.00        47\n",
            "       1667       0.17      0.02      0.04        50\n",
            "       1668       0.86      0.30      0.44        40\n",
            "       1669       0.25      0.03      0.05        38\n",
            "       1670       1.00      0.14      0.24        37\n",
            "       1671       0.50      0.02      0.04        51\n",
            "       1672       0.86      0.51      0.64        47\n",
            "       1673       0.86      0.12      0.21        49\n",
            "       1674       0.25      0.02      0.04        45\n",
            "       1675       0.00      0.00      0.00        46\n",
            "       1676       0.00      0.00      0.00        45\n",
            "       1677       0.38      0.07      0.11        45\n",
            "       1678       0.00      0.00      0.00        43\n",
            "       1679       1.00      0.02      0.04        52\n",
            "       1680       0.60      0.07      0.13        41\n",
            "       1681       0.00      0.00      0.00        41\n",
            "       1682       0.00      0.00      0.00        35\n",
            "       1683       0.67      0.05      0.09        41\n",
            "       1684       0.50      0.11      0.19        35\n",
            "       1685       1.00      0.02      0.04        53\n",
            "       1686       0.00      0.00      0.00        43\n",
            "       1687       0.00      0.00      0.00        39\n",
            "       1688       0.00      0.00      0.00        38\n",
            "       1689       0.50      0.18      0.26        51\n",
            "       1690       0.50      0.06      0.11        47\n",
            "       1691       0.00      0.00      0.00        30\n",
            "       1692       0.64      0.23      0.34        30\n",
            "       1693       0.00      0.00      0.00        47\n",
            "       1694       0.00      0.00      0.00        51\n",
            "       1695       0.00      0.00      0.00        43\n",
            "       1696       0.86      0.30      0.44        40\n",
            "       1697       0.00      0.00      0.00        33\n",
            "       1698       0.00      0.00      0.00        45\n",
            "       1699       0.00      0.00      0.00        42\n",
            "       1700       1.00      0.42      0.59        45\n",
            "       1701       0.83      0.38      0.53        39\n",
            "       1702       0.00      0.00      0.00        56\n",
            "       1703       1.00      0.36      0.53        44\n",
            "       1704       0.83      0.34      0.48        44\n",
            "       1705       1.00      0.40      0.57        40\n",
            "       1706       1.00      0.23      0.37        35\n",
            "       1707       0.00      0.00      0.00        32\n",
            "       1708       1.00      0.27      0.42        45\n",
            "       1709       0.00      0.00      0.00        37\n",
            "       1710       0.00      0.00      0.00        47\n",
            "       1711       0.25      0.07      0.11        30\n",
            "       1712       0.00      0.00      0.00        38\n",
            "       1713       0.00      0.00      0.00        39\n",
            "       1714       0.73      0.31      0.43        36\n",
            "       1715       0.00      0.00      0.00        38\n",
            "       1716       0.20      0.02      0.03        55\n",
            "       1717       0.60      0.07      0.13        42\n",
            "       1718       0.55      0.24      0.33        46\n",
            "       1719       0.54      0.14      0.22        51\n",
            "       1720       0.27      0.11      0.16        35\n",
            "       1721       0.85      0.47      0.61        36\n",
            "       1722       0.89      0.42      0.57        38\n",
            "       1723       0.92      0.30      0.45        40\n",
            "       1724       0.67      0.04      0.07        53\n",
            "       1725       0.00      0.00      0.00        27\n",
            "       1726       0.20      0.02      0.04        48\n",
            "       1727       0.83      0.50      0.62        38\n",
            "       1728       0.18      0.05      0.08        38\n",
            "       1729       0.86      0.11      0.19        57\n",
            "       1730       0.85      0.47      0.60        47\n",
            "       1731       0.00      0.00      0.00        48\n",
            "       1732       0.00      0.00      0.00        41\n",
            "       1733       0.15      0.06      0.09        33\n",
            "       1734       0.33      0.05      0.09        37\n",
            "       1735       0.50      0.04      0.08        45\n",
            "       1736       0.95      0.41      0.57        44\n",
            "       1737       0.80      0.26      0.39        47\n",
            "       1738       1.00      0.38      0.55        48\n",
            "       1739       0.25      0.02      0.04        48\n",
            "       1740       0.00      0.00      0.00        51\n",
            "       1741       0.91      0.24      0.38        42\n",
            "       1742       0.93      0.29      0.44        45\n",
            "       1743       1.00      0.14      0.24        43\n",
            "       1744       0.00      0.00      0.00        50\n",
            "       1745       1.00      0.25      0.40        40\n",
            "       1746       0.67      0.16      0.26        49\n",
            "       1747       0.00      0.00      0.00        37\n",
            "       1748       0.83      0.42      0.56        36\n",
            "       1749       0.40      0.05      0.09        41\n",
            "       1750       0.00      0.00      0.00        41\n",
            "       1751       0.91      0.29      0.44        34\n",
            "       1752       0.00      0.00      0.00        37\n",
            "       1753       0.80      0.20      0.31        41\n",
            "       1754       0.00      0.00      0.00        46\n",
            "       1755       0.00      0.00      0.00        35\n",
            "       1756       0.59      0.22      0.32        46\n",
            "       1757       0.00      0.00      0.00        44\n",
            "       1758       0.50      0.05      0.09        43\n",
            "       1759       0.17      0.03      0.06        30\n",
            "       1760       0.00      0.00      0.00        46\n",
            "       1761       0.00      0.00      0.00        39\n",
            "       1762       0.00      0.00      0.00        41\n",
            "       1763       0.00      0.00      0.00        47\n",
            "       1764       0.86      0.18      0.29        34\n",
            "       1765       0.00      0.00      0.00        32\n",
            "       1766       0.71      0.29      0.41        42\n",
            "       1767       0.90      0.24      0.38        38\n",
            "       1768       0.00      0.00      0.00        35\n",
            "       1769       0.57      0.12      0.20        33\n",
            "       1770       0.67      0.05      0.10        39\n",
            "       1771       0.00      0.00      0.00        37\n",
            "       1772       0.54      0.15      0.23        48\n",
            "       1773       1.00      0.33      0.49        46\n",
            "       1774       0.67      0.14      0.23        44\n",
            "       1775       0.50      0.02      0.03        63\n",
            "       1776       0.80      0.10      0.18        40\n",
            "       1777       1.00      0.03      0.05        39\n",
            "       1778       0.50      0.08      0.14        38\n",
            "       1779       0.00      0.00      0.00        44\n",
            "       1780       0.92      0.55      0.69        44\n",
            "       1781       0.67      0.05      0.09        40\n",
            "       1782       0.33      0.05      0.08        43\n",
            "       1783       0.00      0.00      0.00        39\n",
            "       1784       0.44      0.09      0.15        44\n",
            "       1785       0.71      0.13      0.22        38\n",
            "       1786       0.00      0.00      0.00        39\n",
            "       1787       1.00      0.05      0.09        44\n",
            "       1788       0.00      0.00      0.00        46\n",
            "       1789       0.70      0.17      0.28        40\n",
            "       1790       0.75      0.27      0.39        45\n",
            "       1791       0.00      0.00      0.00        39\n",
            "       1792       0.20      0.05      0.08        41\n",
            "       1793       0.71      0.21      0.33        47\n",
            "       1794       0.38      0.07      0.12        43\n",
            "       1795       0.76      0.38      0.51        34\n",
            "       1796       0.72      0.40      0.51        45\n",
            "       1797       1.00      0.19      0.32        31\n",
            "       1798       0.25      0.06      0.09        36\n",
            "       1799       0.68      0.27      0.39        55\n",
            "       1800       0.00      0.00      0.00        30\n",
            "       1801       0.00      0.00      0.00        35\n",
            "       1802       1.00      0.23      0.37        48\n",
            "       1803       0.12      0.03      0.04        38\n",
            "       1804       0.00      0.00      0.00        35\n",
            "       1805       0.00      0.00      0.00        32\n",
            "       1806       0.71      0.27      0.39        37\n",
            "       1807       1.00      0.19      0.32        37\n",
            "       1808       0.00      0.00      0.00        36\n",
            "       1809       0.00      0.00      0.00        42\n",
            "       1810       0.00      0.00      0.00        42\n",
            "       1811       0.00      0.00      0.00        35\n",
            "       1812       0.57      0.10      0.17        39\n",
            "       1813       0.71      0.28      0.40        36\n",
            "       1814       0.43      0.06      0.11        48\n",
            "       1815       1.00      0.44      0.62        45\n",
            "       1816       0.75      0.26      0.39        34\n",
            "       1817       0.67      0.19      0.29        32\n",
            "       1818       1.00      0.27      0.43        44\n",
            "       1819       0.00      0.00      0.00        46\n",
            "       1820       0.00      0.00      0.00        40\n",
            "       1821       0.00      0.00      0.00        37\n",
            "       1822       0.00      0.00      0.00        35\n",
            "       1823       0.00      0.00      0.00        33\n",
            "       1824       0.00      0.00      0.00        38\n",
            "       1825       1.00      0.05      0.10        38\n",
            "       1826       0.73      0.18      0.29        45\n",
            "       1827       0.00      0.00      0.00        36\n",
            "       1828       0.00      0.00      0.00        45\n",
            "       1829       0.96      0.68      0.80        38\n",
            "       1830       0.17      0.03      0.05        35\n",
            "       1831       0.75      0.26      0.39        34\n",
            "       1832       0.50      0.03      0.06        33\n",
            "       1833       0.60      0.13      0.21        23\n",
            "       1834       0.50      0.02      0.04        44\n",
            "       1835       0.00      0.00      0.00        50\n",
            "       1836       1.00      0.05      0.09        44\n",
            "       1837       0.86      0.26      0.40        46\n",
            "       1838       0.00      0.00      0.00        33\n",
            "       1839       0.60      0.20      0.30        45\n",
            "       1840       0.00      0.00      0.00        37\n",
            "       1841       1.00      0.03      0.05        39\n",
            "       1842       0.00      0.00      0.00        40\n",
            "       1843       0.00      0.00      0.00        41\n",
            "       1844       0.33      0.05      0.08        43\n",
            "       1845       0.00      0.00      0.00        36\n",
            "       1846       0.00      0.00      0.00        38\n",
            "       1847       0.00      0.00      0.00        33\n",
            "       1848       0.00      0.00      0.00        37\n",
            "       1849       1.00      0.12      0.21        34\n",
            "       1850       0.00      0.00      0.00        42\n",
            "       1851       0.60      0.41      0.48        37\n",
            "       1852       0.80      0.11      0.19        37\n",
            "       1853       0.91      0.24      0.38        41\n",
            "       1854       1.00      0.45      0.62        40\n",
            "       1855       0.00      0.00      0.00        40\n",
            "       1856       0.00      0.00      0.00        39\n",
            "       1857       0.00      0.00      0.00        30\n",
            "       1858       0.33      0.02      0.04        49\n",
            "       1859       0.67      0.28      0.39        29\n",
            "       1860       0.00      0.00      0.00        45\n",
            "       1861       0.25      0.05      0.08        40\n",
            "       1862       0.90      0.23      0.37        39\n",
            "       1863       0.00      0.00      0.00        37\n",
            "       1864       0.81      0.35      0.49        37\n",
            "       1865       0.91      0.28      0.43        36\n",
            "       1866       0.00      0.00      0.00        39\n",
            "       1867       0.38      0.07      0.12        42\n",
            "       1868       0.73      0.25      0.37        44\n",
            "       1869       0.00      0.00      0.00        39\n",
            "       1870       0.00      0.00      0.00        46\n",
            "       1871       0.00      0.00      0.00        43\n",
            "       1872       0.14      0.03      0.05        34\n",
            "       1873       0.40      0.04      0.08        47\n",
            "       1874       0.57      0.10      0.17        39\n",
            "       1875       0.33      0.03      0.05        36\n",
            "       1876       0.56      0.14      0.22        37\n",
            "       1877       0.00      0.00      0.00        47\n",
            "       1878       0.50      0.06      0.11        48\n",
            "       1879       0.67      0.19      0.29        32\n",
            "       1880       0.87      0.28      0.43        46\n",
            "       1881       0.17      0.03      0.05        38\n",
            "       1882       0.00      0.00      0.00        36\n",
            "       1883       0.00      0.00      0.00        40\n",
            "       1884       0.38      0.09      0.14        34\n",
            "       1885       0.00      0.00      0.00        41\n",
            "       1886       0.00      0.00      0.00        42\n",
            "       1887       0.00      0.00      0.00        38\n",
            "       1888       1.00      0.02      0.04        49\n",
            "       1889       1.00      0.42      0.59        36\n",
            "       1890       0.70      0.19      0.30        36\n",
            "       1891       0.67      0.23      0.34        44\n",
            "       1892       0.33      0.04      0.07        24\n",
            "       1893       0.00      0.00      0.00        36\n",
            "       1894       1.00      0.39      0.56        46\n",
            "       1895       0.00      0.00      0.00        33\n",
            "       1896       1.00      0.12      0.21        42\n",
            "       1897       0.00      0.00      0.00        35\n",
            "       1898       0.00      0.00      0.00        31\n",
            "       1899       0.71      0.33      0.45        36\n",
            "       1900       0.00      0.00      0.00        30\n",
            "       1901       0.62      0.10      0.18        49\n",
            "       1902       0.67      0.12      0.20        34\n",
            "       1903       1.00      0.07      0.14        40\n",
            "       1904       0.00      0.00      0.00        42\n",
            "       1905       0.00      0.00      0.00        44\n",
            "       1906       0.84      0.34      0.48        47\n",
            "       1907       0.00      0.00      0.00        46\n",
            "       1908       0.57      0.33      0.42        36\n",
            "       1909       1.00      0.06      0.11        35\n",
            "       1910       0.00      0.00      0.00        46\n",
            "       1911       0.00      0.00      0.00        39\n",
            "       1912       0.85      0.29      0.43        38\n",
            "       1913       0.00      0.00      0.00        38\n",
            "       1914       0.73      0.19      0.30        43\n",
            "       1915       0.84      0.52      0.64        31\n",
            "       1916       0.33      0.08      0.12        39\n",
            "       1917       0.00      0.00      0.00        38\n",
            "       1918       0.75      0.20      0.32        45\n",
            "       1919       0.58      0.19      0.29        37\n",
            "       1920       0.00      0.00      0.00        29\n",
            "       1921       0.00      0.00      0.00        31\n",
            "       1922       0.61      0.34      0.44        41\n",
            "       1923       0.17      0.02      0.03        54\n",
            "       1924       0.80      0.12      0.22        32\n",
            "       1925       0.00      0.00      0.00        32\n",
            "       1926       0.00      0.00      0.00        38\n",
            "       1927       0.94      0.38      0.54        42\n",
            "       1928       0.00      0.00      0.00        41\n",
            "       1929       0.00      0.00      0.00        47\n",
            "       1930       1.00      0.40      0.57        30\n",
            "       1931       1.00      0.05      0.09        41\n",
            "       1932       0.00      0.00      0.00        40\n",
            "       1933       0.62      0.19      0.29        43\n",
            "       1934       0.00      0.00      0.00        42\n",
            "       1935       0.33      0.06      0.10        36\n",
            "       1936       0.57      0.29      0.38        42\n",
            "       1937       1.00      0.03      0.05        36\n",
            "       1938       0.94      0.50      0.65        32\n",
            "       1939       1.00      0.12      0.21        50\n",
            "       1940       0.33      0.03      0.05        35\n",
            "       1941       0.00      0.00      0.00        41\n",
            "       1942       0.80      0.20      0.32        40\n",
            "       1943       0.00      0.00      0.00        38\n",
            "       1944       0.84      0.47      0.60        34\n",
            "       1945       0.00      0.00      0.00        42\n",
            "       1946       0.90      0.32      0.47        28\n",
            "       1947       0.00      0.00      0.00        37\n",
            "       1948       0.00      0.00      0.00        32\n",
            "       1949       0.00      0.00      0.00        32\n",
            "       1950       0.69      0.35      0.46        26\n",
            "       1951       0.00      0.00      0.00        49\n",
            "       1952       0.00      0.00      0.00        32\n",
            "       1953       0.50      0.03      0.06        31\n",
            "       1954       0.71      0.12      0.21        40\n",
            "       1955       0.00      0.00      0.00        47\n",
            "       1956       1.00      0.07      0.13        43\n",
            "       1957       0.00      0.00      0.00        38\n",
            "       1958       0.77      0.26      0.39        38\n",
            "       1959       0.00      0.00      0.00        34\n",
            "       1960       0.32      0.21      0.25        39\n",
            "       1961       1.00      0.03      0.06        34\n",
            "       1962       0.20      0.02      0.04        42\n",
            "       1963       0.60      0.09      0.16        32\n",
            "       1964       0.00      0.00      0.00        41\n",
            "       1965       0.33      0.02      0.04        42\n",
            "       1966       0.00      0.00      0.00        37\n",
            "       1967       0.00      0.00      0.00        41\n",
            "       1968       0.86      0.60      0.71        30\n",
            "       1969       0.50      0.24      0.32        25\n",
            "       1970       0.50      0.15      0.23        40\n",
            "       1971       0.00      0.00      0.00        43\n",
            "       1972       0.00      0.00      0.00        42\n",
            "       1973       0.00      0.00      0.00        32\n",
            "       1974       0.00      0.00      0.00        33\n",
            "       1975       1.00      0.21      0.35        28\n",
            "       1976       0.00      0.00      0.00        35\n",
            "       1977       0.92      0.22      0.36        49\n",
            "       1978       1.00      0.33      0.49        49\n",
            "       1979       0.00      0.00      0.00        34\n",
            "       1980       0.00      0.00      0.00        28\n",
            "       1981       1.00      0.24      0.38        34\n",
            "       1982       0.00      0.00      0.00        30\n",
            "       1983       0.50      0.03      0.05        40\n",
            "       1984       0.00      0.00      0.00        38\n",
            "       1985       0.00      0.00      0.00        42\n",
            "       1986       0.00      0.00      0.00        32\n",
            "       1987       0.00      0.00      0.00        37\n",
            "       1988       0.25      0.03      0.05        34\n",
            "       1989       0.75      0.15      0.24        41\n",
            "       1990       0.00      0.00      0.00        34\n",
            "       1991       0.00      0.00      0.00        34\n",
            "       1992       0.00      0.00      0.00        30\n",
            "       1993       0.67      0.17      0.27        36\n",
            "       1994       0.83      0.16      0.26        32\n",
            "       1995       0.00      0.00      0.00        38\n",
            "       1996       0.00      0.00      0.00        32\n",
            "       1997       0.00      0.00      0.00        39\n",
            "       1998       0.00      0.00      0.00        32\n",
            "       1999       0.73      0.18      0.29        44\n",
            "       2000       0.50      0.02      0.05        41\n",
            "       2001       1.00      0.24      0.39        37\n",
            "       2002       0.30      0.08      0.12        38\n",
            "       2003       0.00      0.00      0.00        31\n",
            "       2004       0.00      0.00      0.00        35\n",
            "       2005       0.80      0.24      0.36        34\n",
            "       2006       0.80      0.24      0.36        34\n",
            "       2007       1.00      0.06      0.12        31\n",
            "       2008       0.00      0.00      0.00        40\n",
            "       2009       1.00      0.25      0.40        40\n",
            "       2010       0.40      0.05      0.09        39\n",
            "       2011       0.62      0.14      0.22        37\n",
            "       2012       0.00      0.00      0.00        35\n",
            "       2013       0.00      0.00      0.00        27\n",
            "       2014       0.00      0.00      0.00        38\n",
            "       2015       0.00      0.00      0.00        34\n",
            "       2016       0.00      0.00      0.00        33\n",
            "       2017       0.00      0.00      0.00        31\n",
            "       2018       1.00      0.06      0.11        34\n",
            "       2019       0.00      0.00      0.00        40\n",
            "       2020       0.00      0.00      0.00        29\n",
            "       2021       0.00      0.00      0.00        34\n",
            "       2022       0.00      0.00      0.00        37\n",
            "       2023       0.54      0.23      0.33        30\n",
            "       2024       0.00      0.00      0.00        34\n",
            "       2025       0.00      0.00      0.00        36\n",
            "       2026       0.92      0.22      0.36        49\n",
            "       2027       0.00      0.00      0.00        22\n",
            "       2028       0.94      0.38      0.55        39\n",
            "       2029       0.00      0.00      0.00        36\n",
            "       2030       1.00      0.49      0.65        37\n",
            "       2031       0.90      0.28      0.43        32\n",
            "       2032       1.00      0.17      0.29        41\n",
            "       2033       0.00      0.00      0.00        28\n",
            "       2034       0.30      0.08      0.12        38\n",
            "       2035       0.00      0.00      0.00        26\n",
            "       2036       0.00      0.00      0.00        33\n",
            "       2037       0.00      0.00      0.00        32\n",
            "       2038       0.80      0.22      0.34        37\n",
            "       2039       0.00      0.00      0.00        32\n",
            "       2040       0.55      0.15      0.24        40\n",
            "       2041       0.40      0.07      0.12        29\n",
            "       2042       0.00      0.00      0.00        30\n",
            "       2043       0.00      0.00      0.00        33\n",
            "       2044       0.00      0.00      0.00        35\n",
            "       2045       0.50      0.18      0.26        34\n",
            "       2046       0.50      0.03      0.06        31\n",
            "       2047       0.50      0.06      0.11        32\n",
            "       2048       0.00      0.00      0.00        36\n",
            "       2049       1.00      0.02      0.05        43\n",
            "       2050       0.00      0.00      0.00        27\n",
            "       2051       0.50      0.10      0.16        31\n",
            "       2052       0.00      0.00      0.00        34\n",
            "       2053       0.00      0.00      0.00        32\n",
            "       2054       0.71      0.11      0.19        45\n",
            "       2055       0.00      0.00      0.00        39\n",
            "       2056       0.95      0.58      0.72        33\n",
            "       2057       0.40      0.05      0.09        38\n",
            "       2058       0.25      0.03      0.05        33\n",
            "       2059       0.00      0.00      0.00        44\n",
            "       2060       1.00      0.46      0.63        35\n",
            "       2061       0.40      0.10      0.16        40\n",
            "       2062       0.00      0.00      0.00        31\n",
            "       2063       1.00      0.44      0.61        32\n",
            "       2064       0.00      0.00      0.00        45\n",
            "       2065       0.93      0.40      0.56        35\n",
            "       2066       0.00      0.00      0.00        37\n",
            "       2067       0.40      0.06      0.10        35\n",
            "       2068       0.00      0.00      0.00        43\n",
            "       2069       0.00      0.00      0.00        26\n",
            "       2070       0.00      0.00      0.00        40\n",
            "       2071       1.00      0.46      0.63        37\n",
            "       2072       0.00      0.00      0.00        31\n",
            "       2073       0.40      0.11      0.18        35\n",
            "       2074       0.00      0.00      0.00        35\n",
            "       2075       0.00      0.00      0.00        31\n",
            "       2076       0.00      0.00      0.00        30\n",
            "       2077       0.83      0.18      0.29        28\n",
            "       2078       0.00      0.00      0.00        37\n",
            "       2079       0.00      0.00      0.00        38\n",
            "       2080       0.00      0.00      0.00        28\n",
            "       2081       0.00      0.00      0.00        28\n",
            "       2082       0.00      0.00      0.00        33\n",
            "       2083       1.00      0.11      0.19        28\n",
            "       2084       1.00      0.26      0.41        23\n",
            "       2085       0.84      0.46      0.59        35\n",
            "       2086       0.60      0.08      0.14        39\n",
            "       2087       0.00      0.00      0.00        31\n",
            "       2088       0.00      0.00      0.00        25\n",
            "       2089       0.77      0.46      0.58        37\n",
            "       2090       0.00      0.00      0.00        34\n",
            "       2091       0.00      0.00      0.00        34\n",
            "       2092       0.00      0.00      0.00        38\n",
            "       2093       0.00      0.00      0.00        36\n",
            "       2094       0.29      0.06      0.10        33\n",
            "       2095       0.40      0.05      0.09        40\n",
            "       2096       0.67      0.11      0.18        38\n",
            "       2097       0.33      0.04      0.07        25\n",
            "       2098       0.00      0.00      0.00        33\n",
            "       2099       1.00      0.19      0.32        42\n",
            "       2100       0.00      0.00      0.00        29\n",
            "       2101       0.00      0.00      0.00        29\n",
            "       2102       0.50      0.06      0.10        35\n",
            "       2103       0.67      0.10      0.17        40\n",
            "       2104       0.00      0.00      0.00        42\n",
            "       2105       0.00      0.00      0.00        36\n",
            "       2106       0.00      0.00      0.00        33\n",
            "       2107       0.00      0.00      0.00        33\n",
            "       2108       0.00      0.00      0.00        34\n",
            "       2109       0.00      0.00      0.00        42\n",
            "       2110       0.00      0.00      0.00        28\n",
            "       2111       0.40      0.05      0.09        40\n",
            "       2112       1.00      0.04      0.08        24\n",
            "       2113       0.00      0.00      0.00        36\n",
            "       2114       0.43      0.09      0.15        33\n",
            "       2115       0.00      0.00      0.00        32\n",
            "       2116       0.67      0.15      0.24        27\n",
            "       2117       0.00      0.00      0.00        30\n",
            "       2118       0.79      0.38      0.51        29\n",
            "       2119       0.50      0.07      0.12        28\n",
            "       2120       0.94      0.46      0.62        35\n",
            "       2121       0.00      0.00      0.00        35\n",
            "       2122       0.00      0.00      0.00        37\n",
            "       2123       0.00      0.00      0.00        35\n",
            "       2124       0.40      0.06      0.10        35\n",
            "       2125       0.00      0.00      0.00        37\n",
            "       2126       0.00      0.00      0.00        35\n",
            "       2127       0.40      0.06      0.11        32\n",
            "       2128       0.36      0.13      0.20        30\n",
            "       2129       0.00      0.00      0.00        32\n",
            "       2130       0.00      0.00      0.00        41\n",
            "       2131       1.00      0.04      0.07        26\n",
            "       2132       0.00      0.00      0.00        34\n",
            "       2133       0.00      0.00      0.00        29\n",
            "       2134       0.00      0.00      0.00        36\n",
            "       2135       0.00      0.00      0.00        29\n",
            "       2136       0.00      0.00      0.00        35\n",
            "       2137       0.83      0.37      0.51        27\n",
            "       2138       0.00      0.00      0.00        35\n",
            "       2139       0.85      0.37      0.51        30\n",
            "       2140       0.00      0.00      0.00        33\n",
            "       2141       0.67      0.05      0.10        38\n",
            "       2142       0.00      0.00      0.00        37\n",
            "       2143       1.00      0.10      0.18        31\n",
            "       2144       0.71      0.14      0.24        35\n",
            "       2145       1.00      0.37      0.54        38\n",
            "       2146       1.00      0.17      0.29        35\n",
            "       2147       0.38      0.15      0.22        33\n",
            "       2148       0.00      0.00      0.00        32\n",
            "       2149       0.67      0.05      0.10        37\n",
            "       2150       0.00      0.00      0.00        41\n",
            "       2151       0.00      0.00      0.00        39\n",
            "       2152       0.00      0.00      0.00        36\n",
            "       2153       0.00      0.00      0.00        31\n",
            "       2154       0.00      0.00      0.00        30\n",
            "       2155       1.00      0.42      0.59        26\n",
            "       2156       0.00      0.00      0.00        32\n",
            "       2157       0.00      0.00      0.00        38\n",
            "       2158       0.00      0.00      0.00        33\n",
            "       2159       0.00      0.00      0.00        32\n",
            "       2160       0.33      0.03      0.06        32\n",
            "       2161       0.00      0.00      0.00        34\n",
            "       2162       0.50      0.22      0.31        27\n",
            "       2163       0.00      0.00      0.00        37\n",
            "       2164       1.00      0.03      0.06        30\n",
            "       2165       0.00      0.00      0.00        35\n",
            "       2166       0.56      0.21      0.30        24\n",
            "       2167       0.00      0.00      0.00        37\n",
            "       2168       0.87      0.50      0.63        26\n",
            "       2169       0.00      0.00      0.00        27\n",
            "       2170       0.00      0.00      0.00        39\n",
            "       2171       0.00      0.00      0.00        25\n",
            "       2172       0.00      0.00      0.00        33\n",
            "       2173       0.00      0.00      0.00        39\n",
            "       2174       0.94      0.43      0.59        35\n",
            "       2175       1.00      0.33      0.50        30\n",
            "       2176       0.00      0.00      0.00        36\n",
            "       2177       0.33      0.04      0.06        28\n",
            "       2178       0.00      0.00      0.00        34\n",
            "       2179       0.00      0.00      0.00        35\n",
            "       2180       0.00      0.00      0.00        23\n",
            "       2181       0.00      0.00      0.00        34\n",
            "       2182       0.00      0.00      0.00        27\n",
            "       2183       1.00      0.08      0.15        25\n",
            "       2184       0.00      0.00      0.00        33\n",
            "       2185       1.00      0.15      0.26        33\n",
            "       2186       0.33      0.16      0.21        19\n",
            "       2187       0.00      0.00      0.00        38\n",
            "       2188       0.00      0.00      0.00        20\n",
            "       2189       0.00      0.00      0.00        32\n",
            "       2190       0.33      0.06      0.11        31\n",
            "       2191       0.67      0.12      0.21        33\n",
            "       2192       0.00      0.00      0.00        28\n",
            "       2193       1.00      0.06      0.11        36\n",
            "       2194       0.00      0.00      0.00        35\n",
            "       2195       0.00      0.00      0.00        26\n",
            "       2196       0.00      0.00      0.00        32\n",
            "       2197       0.00      0.00      0.00        34\n",
            "       2198       1.00      0.03      0.06        33\n",
            "       2199       0.00      0.00      0.00        27\n",
            "       2200       0.60      0.10      0.17        31\n",
            "       2201       0.00      0.00      0.00        22\n",
            "       2202       0.00      0.00      0.00        28\n",
            "       2203       0.75      0.19      0.30        32\n",
            "       2204       0.00      0.00      0.00        34\n",
            "       2205       0.00      0.00      0.00        27\n",
            "       2206       1.00      0.11      0.21        35\n",
            "       2207       0.00      0.00      0.00        32\n",
            "       2208       1.00      0.03      0.06        31\n",
            "       2209       0.00      0.00      0.00        34\n",
            "       2210       0.00      0.00      0.00        31\n",
            "       2211       0.00      0.00      0.00        38\n",
            "       2212       1.00      0.03      0.07        29\n",
            "       2213       1.00      0.08      0.15        24\n",
            "       2214       0.00      0.00      0.00        26\n",
            "       2215       0.60      0.08      0.14        39\n",
            "       2216       0.50      0.11      0.18        28\n",
            "       2217       0.00      0.00      0.00        29\n",
            "       2218       0.00      0.00      0.00        39\n",
            "       2219       0.00      0.00      0.00        26\n",
            "       2220       0.00      0.00      0.00        29\n",
            "       2221       1.00      0.41      0.58        22\n",
            "       2222       0.00      0.00      0.00        28\n",
            "       2223       1.00      0.08      0.15        37\n",
            "       2224       0.00      0.00      0.00        31\n",
            "       2225       0.20      0.03      0.04        40\n",
            "       2226       1.00      0.18      0.31        33\n",
            "       2227       0.00      0.00      0.00        41\n",
            "       2228       0.00      0.00      0.00        33\n",
            "       2229       0.00      0.00      0.00        29\n",
            "       2230       0.00      0.00      0.00        34\n",
            "       2231       0.00      0.00      0.00        28\n",
            "       2232       0.86      0.23      0.36        26\n",
            "       2233       0.00      0.00      0.00        27\n",
            "       2234       1.00      0.23      0.38        26\n",
            "       2235       1.00      0.39      0.57        33\n",
            "       2236       0.00      0.00      0.00        33\n",
            "       2237       0.64      0.19      0.30        36\n",
            "       2238       1.00      0.16      0.27        38\n",
            "       2239       0.00      0.00      0.00        27\n",
            "       2240       0.93      0.37      0.53        35\n",
            "       2241       0.00      0.00      0.00        41\n",
            "       2242       0.50      0.03      0.06        30\n",
            "       2243       0.00      0.00      0.00        29\n",
            "       2244       0.00      0.00      0.00        37\n",
            "       2245       0.50      0.15      0.24        39\n",
            "       2246       0.00      0.00      0.00        29\n",
            "       2247       0.00      0.00      0.00        30\n",
            "       2248       0.00      0.00      0.00        37\n",
            "       2249       0.00      0.00      0.00        33\n",
            "       2250       0.50      0.04      0.07        27\n",
            "       2251       0.00      0.00      0.00        31\n",
            "       2252       0.00      0.00      0.00        27\n",
            "       2253       0.00      0.00      0.00        32\n",
            "       2254       0.73      0.23      0.35        35\n",
            "       2255       0.00      0.00      0.00        37\n",
            "       2256       0.00      0.00      0.00        33\n",
            "       2257       0.82      0.45      0.58        20\n",
            "       2258       0.00      0.00      0.00        28\n",
            "       2259       0.43      0.13      0.20        23\n",
            "       2260       0.00      0.00      0.00        31\n",
            "       2261       1.00      0.10      0.19        29\n",
            "       2262       0.60      0.12      0.19        26\n",
            "       2263       0.00      0.00      0.00        32\n",
            "       2264       0.00      0.00      0.00        35\n",
            "       2265       0.00      0.00      0.00        33\n",
            "       2266       0.67      0.23      0.34        35\n",
            "       2267       0.00      0.00      0.00        30\n",
            "       2268       0.50      0.05      0.08        22\n",
            "       2269       0.00      0.00      0.00        31\n",
            "       2270       0.00      0.00      0.00        32\n",
            "       2271       0.00      0.00      0.00        28\n",
            "       2272       0.83      0.19      0.31        26\n",
            "       2273       0.00      0.00      0.00        27\n",
            "       2274       0.00      0.00      0.00        33\n",
            "       2275       0.00      0.00      0.00        33\n",
            "       2276       0.50      0.09      0.15        22\n",
            "       2277       0.00      0.00      0.00        33\n",
            "       2278       0.00      0.00      0.00        36\n",
            "       2279       1.00      0.32      0.49        34\n",
            "       2280       0.00      0.00      0.00        24\n",
            "       2281       0.00      0.00      0.00        26\n",
            "       2282       0.40      0.09      0.15        22\n",
            "       2283       0.20      0.04      0.06        28\n",
            "       2284       0.00      0.00      0.00        43\n",
            "       2285       0.00      0.00      0.00        31\n",
            "       2286       0.00      0.00      0.00        30\n",
            "       2287       0.00      0.00      0.00        32\n",
            "       2288       0.00      0.00      0.00        28\n",
            "       2289       0.88      0.19      0.31        37\n",
            "       2290       0.00      0.00      0.00        23\n",
            "       2291       0.00      0.00      0.00        33\n",
            "       2292       0.50      0.03      0.06        33\n",
            "       2293       0.00      0.00      0.00        29\n",
            "       2294       0.00      0.00      0.00        28\n",
            "       2295       0.00      0.00      0.00        29\n",
            "       2296       0.00      0.00      0.00        24\n",
            "       2297       0.00      0.00      0.00        28\n",
            "       2298       1.00      0.15      0.27        26\n",
            "       2299       0.00      0.00      0.00        28\n",
            "       2300       1.00      0.10      0.18        31\n",
            "       2301       0.00      0.00      0.00        28\n",
            "       2302       0.00      0.00      0.00        34\n",
            "       2303       0.50      0.04      0.07        27\n",
            "       2304       0.00      0.00      0.00        31\n",
            "       2305       0.00      0.00      0.00        38\n",
            "       2306       0.00      0.00      0.00        37\n",
            "       2307       0.83      0.36      0.50        28\n",
            "       2308       1.00      0.04      0.07        28\n",
            "       2309       0.00      0.00      0.00        26\n",
            "       2310       1.00      0.21      0.35        28\n",
            "       2311       0.00      0.00      0.00        29\n",
            "       2312       1.00      0.11      0.19        38\n",
            "       2313       0.50      0.04      0.07        25\n",
            "       2314       1.00      0.05      0.09        22\n",
            "       2315       0.00      0.00      0.00        33\n",
            "       2316       0.00      0.00      0.00        30\n",
            "       2317       0.00      0.00      0.00        37\n",
            "       2318       0.00      0.00      0.00        26\n",
            "       2319       0.20      0.05      0.08        21\n",
            "       2320       0.00      0.00      0.00        29\n",
            "       2321       0.00      0.00      0.00        23\n",
            "       2322       0.00      0.00      0.00        33\n",
            "       2323       0.00      0.00      0.00        29\n",
            "       2324       0.00      0.00      0.00        29\n",
            "       2325       0.40      0.10      0.15        21\n",
            "       2326       0.00      0.00      0.00        36\n",
            "       2327       0.00      0.00      0.00        34\n",
            "       2328       0.00      0.00      0.00        25\n",
            "       2329       1.00      0.07      0.13        28\n",
            "       2330       0.00      0.00      0.00        30\n",
            "       2331       0.79      0.38      0.51        29\n",
            "       2332       0.00      0.00      0.00        32\n",
            "       2333       0.00      0.00      0.00        34\n",
            "       2334       0.50      0.03      0.06        30\n",
            "       2335       0.00      0.00      0.00        29\n",
            "       2336       1.00      0.03      0.06        30\n",
            "       2337       0.00      0.00      0.00        26\n",
            "       2338       0.92      0.40      0.56        30\n",
            "       2339       0.00      0.00      0.00        35\n",
            "       2340       0.00      0.00      0.00        26\n",
            "       2341       0.00      0.00      0.00        33\n",
            "       2342       1.00      0.15      0.27        39\n",
            "       2343       0.80      0.15      0.26        26\n",
            "       2344       0.00      0.00      0.00        39\n",
            "       2345       0.00      0.00      0.00        36\n",
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            "       2348       0.60      0.10      0.17        31\n",
            "       2349       0.50      0.05      0.09        20\n",
            "       2350       0.00      0.00      0.00        32\n",
            "       2351       0.00      0.00      0.00        32\n",
            "       2352       0.00      0.00      0.00        28\n",
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            "       2354       0.92      0.33      0.49        36\n",
            "       2355       0.67      0.06      0.11        33\n",
            "       2356       0.00      0.00      0.00        31\n",
            "       2357       0.60      0.09      0.16        32\n",
            "       2358       0.12      0.05      0.07        19\n",
            "       2359       0.00      0.00      0.00        29\n",
            "       2360       0.00      0.00      0.00        27\n",
            "       2361       0.00      0.00      0.00        25\n",
            "       2362       1.00      0.04      0.08        24\n",
            "       2363       0.00      0.00      0.00        35\n",
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            "       2369       0.00      0.00      0.00        29\n",
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            "       2372       0.00      0.00      0.00        32\n",
            "       2373       0.67      0.06      0.12        31\n",
            "       2374       0.00      0.00      0.00        30\n",
            "       2375       0.00      0.00      0.00        20\n",
            "       2376       0.83      0.18      0.29        28\n",
            "       2377       0.00      0.00      0.00        35\n",
            "       2378       0.00      0.00      0.00        24\n",
            "       2379       1.00      0.04      0.08        23\n",
            "       2380       0.00      0.00      0.00        31\n",
            "       2381       0.67      0.05      0.10        38\n",
            "       2382       0.00      0.00      0.00        26\n",
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            "       2385       0.00      0.00      0.00        24\n",
            "       2386       0.54      0.33      0.41        21\n",
            "       2387       0.00      0.00      0.00        28\n",
            "       2388       0.00      0.00      0.00        22\n",
            "       2389       1.00      0.18      0.30        28\n",
            "       2390       0.88      0.20      0.33        35\n",
            "       2391       0.00      0.00      0.00        23\n",
            "       2392       0.00      0.00      0.00        27\n",
            "       2393       0.00      0.00      0.00        24\n",
            "       2394       1.00      0.43      0.61        23\n",
            "       2395       0.00      0.00      0.00        24\n",
            "       2396       1.00      0.03      0.06        31\n",
            "       2397       0.00      0.00      0.00        28\n",
            "       2398       0.00      0.00      0.00        35\n",
            "       2399       0.40      0.08      0.13        25\n",
            "       2400       0.00      0.00      0.00        33\n",
            "       2401       0.00      0.00      0.00        22\n",
            "       2402       0.25      0.03      0.05        36\n",
            "       2403       0.00      0.00      0.00        29\n",
            "       2404       0.50      0.08      0.13        26\n",
            "       2405       0.00      0.00      0.00        26\n",
            "       2406       0.58      0.42      0.49        26\n",
            "       2407       1.00      0.04      0.07        26\n",
            "       2408       1.00      0.03      0.06        32\n",
            "       2409       0.00      0.00      0.00        29\n",
            "       2410       0.00      0.00      0.00        26\n",
            "       2411       0.00      0.00      0.00        30\n",
            "       2412       0.00      0.00      0.00        30\n",
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            "       2414       0.00      0.00      0.00        33\n",
            "       2415       0.00      0.00      0.00        22\n",
            "       2416       0.00      0.00      0.00        27\n",
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            "       2418       0.00      0.00      0.00        33\n",
            "       2419       1.00      0.03      0.07        29\n",
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            "       2421       0.00      0.00      0.00        28\n",
            "       2422       0.00      0.00      0.00        25\n",
            "       2423       0.78      0.32      0.45        22\n",
            "       2424       0.50      0.03      0.05        35\n",
            "       2425       1.00      0.11      0.19        28\n",
            "       2426       0.50      0.03      0.06        34\n",
            "       2427       0.00      0.00      0.00        23\n",
            "       2428       0.00      0.00      0.00        30\n",
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            "       2431       0.50      0.04      0.08        23\n",
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            "       2434       0.78      0.48      0.60        29\n",
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            "       2441       1.00      0.33      0.50        30\n",
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            "       2444       0.00      0.00      0.00        30\n",
            "       2445       1.00      0.42      0.59        24\n",
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            "       2447       0.80      0.13      0.22        31\n",
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            "       2452       1.00      0.07      0.13        29\n",
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            "       2455       0.17      0.04      0.06        27\n",
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            "       2458       0.71      0.16      0.26        31\n",
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            "       2460       0.00      0.00      0.00        30\n",
            "       2461       1.00      0.18      0.30        28\n",
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            "       2470       0.92      0.41      0.56        27\n",
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            "       2476       0.92      0.37      0.52        30\n",
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            "       2478       0.67      0.07      0.13        28\n",
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            "       2481       0.00      0.00      0.00        30\n",
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            "       2484       0.62      0.22      0.32        23\n",
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            "       2489       1.00      0.03      0.06        32\n",
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            "       2491       0.00      0.00      0.00        24\n",
            "       2492       0.50      0.19      0.27        27\n",
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            "       2497       0.50      0.03      0.06        29\n",
            "       2498       1.00      0.18      0.30        34\n",
            "       2499       0.92      0.44      0.59        25\n",
            "       2500       0.00      0.00      0.00        30\n",
            "       2501       0.00      0.00      0.00        27\n",
            "       2502       0.50      0.14      0.22        28\n",
            "       2503       0.00      0.00      0.00        22\n",
            "       2504       0.00      0.00      0.00        26\n",
            "       2505       0.00      0.00      0.00        28\n",
            "       2506       0.33      0.04      0.08        23\n",
            "       2507       0.00      0.00      0.00        17\n",
            "       2508       0.00      0.00      0.00        25\n",
            "       2509       0.00      0.00      0.00        34\n",
            "       2510       0.00      0.00      0.00        24\n",
            "       2511       0.40      0.11      0.17        19\n",
            "       2512       0.00      0.00      0.00        27\n",
            "       2513       0.00      0.00      0.00        30\n",
            "       2514       0.75      0.12      0.21        24\n",
            "       2515       0.00      0.00      0.00        26\n",
            "       2516       0.00      0.00      0.00        18\n",
            "       2517       0.00      0.00      0.00        36\n",
            "       2518       1.00      0.03      0.06        30\n",
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            "       2521       1.00      0.33      0.50        21\n",
            "       2522       0.00      0.00      0.00        12\n",
            "       2523       0.00      0.00      0.00        27\n",
            "       2524       0.89      0.35      0.50        23\n",
            "       2525       0.00      0.00      0.00        31\n",
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            "       2527       0.00      0.00      0.00        30\n",
            "       2528       0.00      0.00      0.00        24\n",
            "       2529       0.87      0.33      0.47        40\n",
            "       2530       0.25      0.03      0.05        33\n",
            "       2531       0.00      0.00      0.00        17\n",
            "       2532       0.00      0.00      0.00        29\n",
            "       2533       0.00      0.00      0.00        24\n",
            "       2534       1.00      0.07      0.13        28\n",
            "       2535       0.00      0.00      0.00        26\n",
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            "       2540       0.67      0.20      0.31        30\n",
            "       2541       1.00      0.07      0.13        29\n",
            "       2542       0.00      0.00      0.00        23\n",
            "       2543       0.75      0.09      0.17        32\n",
            "       2544       1.00      0.19      0.31        27\n",
            "       2545       1.00      0.08      0.15        38\n",
            "       2546       1.00      0.04      0.07        26\n",
            "       2547       0.00      0.00      0.00        31\n",
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            "       2550       0.67      0.08      0.14        26\n",
            "       2551       0.45      0.24      0.31        21\n",
            "       2552       0.00      0.00      0.00        28\n",
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            "       2554       0.67      0.11      0.18        19\n",
            "       2555       1.00      0.17      0.30        23\n",
            "       2556       0.60      0.39      0.47        23\n",
            "       2557       0.00      0.00      0.00        19\n",
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            "       2561       0.14      0.06      0.08        17\n",
            "       2562       1.00      0.10      0.18        20\n",
            "       2563       0.80      0.16      0.27        25\n",
            "       2564       0.00      0.00      0.00        21\n",
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            "       2569       0.75      0.27      0.40        22\n",
            "       2570       1.00      0.12      0.22        24\n",
            "       2571       0.00      0.00      0.00        20\n",
            "       2572       0.00      0.00      0.00        26\n",
            "       2573       1.00      0.07      0.12        30\n",
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            "       2582       0.00      0.00      0.00        21\n",
            "       2583       1.00      0.13      0.23        23\n",
            "       2584       0.00      0.00      0.00        27\n",
            "       2585       0.86      0.70      0.78        27\n",
            "       2586       0.00      0.00      0.00        25\n",
            "       2587       1.00      0.21      0.34        29\n",
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            "       2589       0.00      0.00      0.00        28\n",
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            "       2592       1.00      0.05      0.10        20\n",
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            "       2597       0.67      0.06      0.11        32\n",
            "       2598       0.60      0.10      0.18        29\n",
            "       2599       0.00      0.00      0.00        20\n",
            "       2600       0.00      0.00      0.00        18\n",
            "       2601       0.00      0.00      0.00        14\n",
            "       2602       0.00      0.00      0.00        29\n",
            "       2603       0.25      0.04      0.07        26\n",
            "       2604       0.00      0.00      0.00        25\n",
            "       2605       0.00      0.00      0.00        23\n",
            "       2606       1.00      0.05      0.09        22\n",
            "       2607       0.00      0.00      0.00        25\n",
            "       2608       1.00      0.04      0.08        25\n",
            "       2609       0.00      0.00      0.00        30\n",
            "       2610       0.00      0.00      0.00        26\n",
            "       2611       0.00      0.00      0.00        26\n",
            "       2612       0.00      0.00      0.00        30\n",
            "       2613       0.00      0.00      0.00        28\n",
            "       2614       0.00      0.00      0.00        28\n",
            "       2615       0.00      0.00      0.00        32\n",
            "       2616       0.00      0.00      0.00        23\n",
            "       2617       0.00      0.00      0.00        21\n",
            "       2618       0.00      0.00      0.00        26\n",
            "       2619       0.00      0.00      0.00        29\n",
            "       2620       0.86      0.32      0.46        19\n",
            "       2621       0.00      0.00      0.00        28\n",
            "       2622       0.00      0.00      0.00        23\n",
            "       2623       0.00      0.00      0.00        26\n",
            "       2624       0.00      0.00      0.00        24\n",
            "       2625       0.00      0.00      0.00        24\n",
            "       2626       0.00      0.00      0.00        30\n",
            "       2627       0.00      0.00      0.00        28\n",
            "       2628       0.83      0.29      0.43        17\n",
            "       2629       0.00      0.00      0.00        31\n",
            "       2630       0.00      0.00      0.00        30\n",
            "       2631       0.00      0.00      0.00        33\n",
            "       2632       0.00      0.00      0.00        31\n",
            "       2633       0.86      0.16      0.27        37\n",
            "       2634       0.00      0.00      0.00        21\n",
            "       2635       0.00      0.00      0.00        30\n",
            "       2636       0.00      0.00      0.00        22\n",
            "       2637       0.00      0.00      0.00        24\n",
            "       2638       0.00      0.00      0.00        29\n",
            "       2639       0.00      0.00      0.00        29\n",
            "       2640       0.00      0.00      0.00        20\n",
            "       2641       0.00      0.00      0.00        27\n",
            "       2642       0.00      0.00      0.00        28\n",
            "       2643       0.00      0.00      0.00        29\n",
            "       2644       0.89      0.31      0.46        26\n",
            "       2645       0.00      0.00      0.00        22\n",
            "       2646       0.00      0.00      0.00        20\n",
            "       2647       0.67      0.07      0.13        27\n",
            "       2648       0.00      0.00      0.00        30\n",
            "       2649       0.00      0.00      0.00        19\n",
            "       2650       0.00      0.00      0.00        15\n",
            "       2651       0.00      0.00      0.00        32\n",
            "       2652       0.00      0.00      0.00        19\n",
            "       2653       0.00      0.00      0.00        28\n",
            "       2654       1.00      0.35      0.52        23\n",
            "       2655       0.00      0.00      0.00        27\n",
            "       2656       0.00      0.00      0.00        26\n",
            "       2657       0.00      0.00      0.00        31\n",
            "       2658       0.00      0.00      0.00        21\n",
            "       2659       0.50      0.04      0.07        28\n",
            "       2660       0.00      0.00      0.00        24\n",
            "       2661       0.00      0.00      0.00        18\n",
            "       2662       0.83      0.19      0.31        26\n",
            "       2663       0.00      0.00      0.00        26\n",
            "       2664       0.00      0.00      0.00        28\n",
            "       2665       0.00      0.00      0.00        22\n",
            "       2666       0.67      0.07      0.13        28\n",
            "       2667       0.00      0.00      0.00        31\n",
            "       2668       0.00      0.00      0.00        18\n",
            "       2669       0.00      0.00      0.00        32\n",
            "       2670       0.00      0.00      0.00        24\n",
            "       2671       0.00      0.00      0.00        22\n",
            "       2672       0.00      0.00      0.00        23\n",
            "       2673       0.93      0.56      0.70        25\n",
            "       2674       0.50      0.04      0.07        26\n",
            "       2675       1.00      0.13      0.23        23\n",
            "       2676       0.00      0.00      0.00        23\n",
            "       2677       0.00      0.00      0.00        24\n",
            "       2678       0.00      0.00      0.00        26\n",
            "       2679       0.00      0.00      0.00        19\n",
            "       2680       0.00      0.00      0.00        19\n",
            "       2681       0.00      0.00      0.00        21\n",
            "       2682       0.89      0.27      0.41        30\n",
            "       2683       0.00      0.00      0.00        28\n",
            "       2684       0.00      0.00      0.00        26\n",
            "       2685       0.00      0.00      0.00        23\n",
            "       2686       0.50      0.11      0.18        28\n",
            "       2687       0.00      0.00      0.00        21\n",
            "       2688       0.00      0.00      0.00        32\n",
            "       2689       0.00      0.00      0.00        27\n",
            "       2690       1.00      0.17      0.30        23\n",
            "       2691       0.00      0.00      0.00        23\n",
            "       2692       0.00      0.00      0.00        24\n",
            "       2693       0.00      0.00      0.00        24\n",
            "       2694       0.00      0.00      0.00        20\n",
            "       2695       0.00      0.00      0.00        29\n",
            "       2696       0.00      0.00      0.00        20\n",
            "       2697       0.80      0.15      0.26        26\n",
            "       2698       0.00      0.00      0.00        30\n",
            "       2699       0.00      0.00      0.00        20\n",
            "       2700       0.00      0.00      0.00        25\n",
            "       2701       1.00      0.04      0.08        23\n",
            "       2702       0.00      0.00      0.00        24\n",
            "       2703       0.40      0.08      0.14        24\n",
            "       2704       0.00      0.00      0.00        29\n",
            "       2705       0.00      0.00      0.00        36\n",
            "       2706       0.20      0.03      0.06        29\n",
            "       2707       0.00      0.00      0.00        25\n",
            "       2708       0.00      0.00      0.00        21\n",
            "       2709       0.67      0.07      0.13        28\n",
            "       2710       0.00      0.00      0.00        14\n",
            "       2711       0.00      0.00      0.00        28\n",
            "       2712       0.00      0.00      0.00        21\n",
            "       2713       0.00      0.00      0.00        33\n",
            "       2714       0.00      0.00      0.00        21\n",
            "       2715       0.50      0.04      0.08        23\n",
            "       2716       0.00      0.00      0.00        26\n",
            "       2717       0.00      0.00      0.00        22\n",
            "       2718       0.50      0.07      0.12        30\n",
            "       2719       0.00      0.00      0.00        25\n",
            "       2720       0.00      0.00      0.00        25\n",
            "       2721       0.00      0.00      0.00        23\n",
            "       2722       0.00      0.00      0.00        20\n",
            "       2723       0.00      0.00      0.00        29\n",
            "       2724       0.00      0.00      0.00        20\n",
            "       2725       0.78      0.33      0.47        21\n",
            "       2726       0.00      0.00      0.00        25\n",
            "       2727       0.00      0.00      0.00        27\n",
            "       2728       0.00      0.00      0.00        24\n",
            "       2729       1.00      0.33      0.50        15\n",
            "       2730       0.00      0.00      0.00        26\n",
            "       2731       0.00      0.00      0.00        28\n",
            "       2732       0.00      0.00      0.00        30\n",
            "       2733       0.00      0.00      0.00        35\n",
            "       2734       0.80      0.17      0.28        24\n",
            "       2735       0.00      0.00      0.00        17\n",
            "       2736       0.50      0.19      0.28        26\n",
            "       2737       0.00      0.00      0.00        22\n",
            "       2738       0.00      0.00      0.00        33\n",
            "       2739       0.00      0.00      0.00        29\n",
            "       2740       0.00      0.00      0.00        28\n",
            "       2741       1.00      0.33      0.50        27\n",
            "       2742       1.00      0.52      0.69        23\n",
            "       2743       0.00      0.00      0.00        23\n",
            "       2744       0.00      0.00      0.00        20\n",
            "       2745       0.00      0.00      0.00        28\n",
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            "       2748       0.00      0.00      0.00        24\n",
            "       2749       0.00      0.00      0.00        28\n",
            "       2750       1.00      0.10      0.19        29\n",
            "       2751       0.00      0.00      0.00        25\n",
            "       2752       0.00      0.00      0.00        23\n",
            "       2753       0.00      0.00      0.00        30\n",
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            "       2757       1.00      0.06      0.11        18\n",
            "       2758       0.80      0.22      0.35        18\n",
            "       2759       0.00      0.00      0.00        23\n",
            "       2760       0.00      0.00      0.00        30\n",
            "       2761       0.00      0.00      0.00        18\n",
            "       2762       0.00      0.00      0.00        21\n",
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            "       2765       0.00      0.00      0.00        28\n",
            "       2766       1.00      0.06      0.11        18\n",
            "       2767       0.00      0.00      0.00        24\n",
            "       2768       1.00      0.25      0.40        24\n",
            "       2769       0.00      0.00      0.00        23\n",
            "       2770       0.00      0.00      0.00        19\n",
            "       2771       0.00      0.00      0.00        23\n",
            "       2772       1.00      0.11      0.19        19\n",
            "       2773       0.00      0.00      0.00        19\n",
            "       2774       1.00      0.24      0.38        21\n",
            "       2775       0.00      0.00      0.00        19\n",
            "       2776       0.00      0.00      0.00        23\n",
            "       2777       0.00      0.00      0.00        29\n",
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            "       2788       0.71      0.20      0.31        25\n",
            "       2789       0.00      0.00      0.00        21\n",
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            "       2793       0.00      0.00      0.00        18\n",
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            "       2797       1.00      0.05      0.09        21\n",
            "       2798       0.00      0.00      0.00        17\n",
            "       2799       0.00      0.00      0.00        22\n",
            "       2800       1.00      0.04      0.08        24\n",
            "       2801       0.50      0.11      0.17        19\n",
            "       2802       0.00      0.00      0.00        23\n",
            "       2803       0.00      0.00      0.00        17\n",
            "       2804       0.00      0.00      0.00        23\n",
            "       2805       0.00      0.00      0.00        22\n",
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            "       2807       0.00      0.00      0.00        18\n",
            "       2808       1.00      0.04      0.08        24\n",
            "       2809       1.00      0.04      0.08        24\n",
            "       2810       0.00      0.00      0.00        20\n",
            "       2811       0.00      0.00      0.00        20\n",
            "       2812       0.00      0.00      0.00        23\n",
            "       2813       0.00      0.00      0.00        24\n",
            "       2814       0.00      0.00      0.00        17\n",
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            "       2819       0.25      0.07      0.11        14\n",
            "       2820       0.00      0.00      0.00        22\n",
            "       2821       1.00      0.10      0.17        21\n",
            "       2822       0.00      0.00      0.00        24\n",
            "       2823       0.00      0.00      0.00        18\n",
            "       2824       0.00      0.00      0.00        26\n",
            "       2825       0.00      0.00      0.00        18\n",
            "       2826       0.75      0.15      0.25        20\n",
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            "       2828       0.00      0.00      0.00        25\n",
            "       2829       1.00      0.04      0.07        28\n",
            "       2830       0.00      0.00      0.00        19\n",
            "       2831       0.00      0.00      0.00        25\n",
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            "       2835       1.00      0.17      0.29        18\n",
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            "       2837       0.00      0.00      0.00        31\n",
            "       2838       1.00      0.08      0.15        24\n",
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            "       2840       0.00      0.00      0.00        20\n",
            "       2841       0.00      0.00      0.00        28\n",
            "       2842       1.00      0.23      0.37        35\n",
            "       2843       1.00      0.16      0.27        19\n",
            "       2844       0.00      0.00      0.00        24\n",
            "       2845       0.00      0.00      0.00        21\n",
            "       2846       1.00      0.08      0.15        25\n",
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            "       2848       0.00      0.00      0.00        26\n",
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            "       2851       1.00      0.16      0.27        19\n",
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            "       2856       0.00      0.00      0.00        18\n",
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            "       2858       0.00      0.00      0.00        22\n",
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            "       2860       0.00      0.00      0.00        22\n",
            "       2861       0.00      0.00      0.00        21\n",
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            "       2866       0.67      0.27      0.39        22\n",
            "       2867       0.00      0.00      0.00        28\n",
            "       2868       0.00      0.00      0.00        27\n",
            "       2869       0.00      0.00      0.00        24\n",
            "       2870       0.00      0.00      0.00        21\n",
            "       2871       0.00      0.00      0.00        22\n",
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            "       2873       0.00      0.00      0.00        26\n",
            "       2874       0.00      0.00      0.00        25\n",
            "       2875       1.00      0.05      0.09        21\n",
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            "       2877       0.00      0.00      0.00        22\n",
            "       2878       0.80      0.19      0.31        21\n",
            "       2879       1.00      0.11      0.20        27\n",
            "       2880       1.00      0.04      0.08        24\n",
            "       2881       0.00      0.00      0.00        26\n",
            "       2882       0.00      0.00      0.00        29\n",
            "       2883       0.00      0.00      0.00        26\n",
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            "       2885       0.33      0.05      0.09        19\n",
            "       2886       0.83      0.26      0.40        19\n",
            "       2887       0.00      0.00      0.00        18\n",
            "       2888       0.00      0.00      0.00        22\n",
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            "       2891       0.00      0.00      0.00        34\n",
            "       2892       0.00      0.00      0.00        18\n",
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            "       2898       0.00      0.00      0.00        19\n",
            "       2899       0.00      0.00      0.00        19\n",
            "       2900       0.00      0.00      0.00        28\n",
            "       2901       0.00      0.00      0.00        27\n",
            "       2902       0.00      0.00      0.00        19\n",
            "       2903       0.00      0.00      0.00        26\n",
            "       2904       0.00      0.00      0.00        21\n",
            "       2905       1.00      0.16      0.27        19\n",
            "       2906       0.00      0.00      0.00        19\n",
            "       2907       1.00      0.20      0.33        20\n",
            "       2908       0.00      0.00      0.00        19\n",
            "       2909       0.00      0.00      0.00        23\n",
            "       2910       0.00      0.00      0.00        20\n",
            "       2911       0.00      0.00      0.00        24\n",
            "       2912       1.00      0.05      0.09        22\n",
            "       2913       0.00      0.00      0.00        21\n",
            "       2914       0.00      0.00      0.00        28\n",
            "       2915       0.00      0.00      0.00        20\n",
            "       2916       0.00      0.00      0.00        24\n",
            "       2917       0.00      0.00      0.00        23\n",
            "       2918       1.00      0.04      0.08        25\n",
            "       2919       0.00      0.00      0.00        18\n",
            "       2920       1.00      0.14      0.25        21\n",
            "       2921       0.00      0.00      0.00        28\n",
            "       2922       0.00      0.00      0.00        17\n",
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            "       2924       0.00      0.00      0.00        25\n",
            "       2925       0.00      0.00      0.00        18\n",
            "       2926       0.00      0.00      0.00        20\n",
            "       2927       0.00      0.00      0.00        22\n",
            "       2928       1.00      0.05      0.09        21\n",
            "       2929       0.00      0.00      0.00        15\n",
            "       2930       0.00      0.00      0.00        21\n",
            "       2931       0.00      0.00      0.00        25\n",
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            "       2933       0.00      0.00      0.00        12\n",
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            "       2936       0.00      0.00      0.00        20\n",
            "       2937       0.67      0.09      0.16        22\n",
            "       2938       0.00      0.00      0.00        24\n",
            "       2939       1.00      0.16      0.28        31\n",
            "       2940       0.00      0.00      0.00        23\n",
            "       2941       0.00      0.00      0.00        24\n",
            "       2942       0.00      0.00      0.00        23\n",
            "       2943       0.00      0.00      0.00        22\n",
            "       2944       0.00      0.00      0.00        17\n",
            "       2945       0.00      0.00      0.00        22\n",
            "       2946       0.00      0.00      0.00        17\n",
            "       2947       0.00      0.00      0.00        27\n",
            "       2948       0.00      0.00      0.00        18\n",
            "       2949       0.00      0.00      0.00        23\n",
            "       2950       0.00      0.00      0.00        22\n",
            "       2951       0.80      0.21      0.33        19\n",
            "       2952       0.00      0.00      0.00        15\n",
            "       2953       1.00      0.16      0.27        19\n",
            "       2954       0.00      0.00      0.00        19\n",
            "       2955       0.00      0.00      0.00        17\n",
            "       2956       0.00      0.00      0.00        20\n",
            "       2957       1.00      0.06      0.12        16\n",
            "       2958       0.00      0.00      0.00        17\n",
            "       2959       0.00      0.00      0.00        24\n",
            "       2960       0.00      0.00      0.00        23\n",
            "       2961       0.00      0.00      0.00        28\n",
            "       2962       0.50      0.05      0.10        19\n",
            "       2963       0.00      0.00      0.00        17\n",
            "       2964       0.00      0.00      0.00        25\n",
            "       2965       0.00      0.00      0.00        24\n",
            "       2966       0.00      0.00      0.00        18\n",
            "       2967       0.00      0.00      0.00        22\n",
            "       2968       0.00      0.00      0.00        17\n",
            "       2969       0.00      0.00      0.00        16\n",
            "       2970       0.00      0.00      0.00        24\n",
            "       2971       0.00      0.00      0.00        25\n",
            "       2972       0.00      0.00      0.00        18\n",
            "       2973       0.00      0.00      0.00        24\n",
            "       2974       0.00      0.00      0.00        19\n",
            "       2975       0.00      0.00      0.00        27\n",
            "       2976       0.00      0.00      0.00        21\n",
            "       2977       0.67      0.09      0.15        23\n",
            "       2978       0.00      0.00      0.00        26\n",
            "       2979       0.00      0.00      0.00        22\n",
            "       2980       0.00      0.00      0.00        24\n",
            "       2981       0.00      0.00      0.00        19\n",
            "       2982       1.00      0.05      0.09        21\n",
            "       2983       0.00      0.00      0.00        23\n",
            "       2984       0.00      0.00      0.00        24\n",
            "       2985       1.00      0.09      0.16        23\n",
            "       2986       1.00      0.09      0.16        23\n",
            "       2987       0.00      0.00      0.00        25\n",
            "       2988       1.00      0.17      0.29        24\n",
            "       2989       0.00      0.00      0.00        17\n",
            "       2990       0.00      0.00      0.00        23\n",
            "       2991       0.00      0.00      0.00        27\n",
            "       2992       0.00      0.00      0.00        18\n",
            "       2993       1.00      0.21      0.35        19\n",
            "       2994       0.00      0.00      0.00        27\n",
            "       2995       0.40      0.08      0.13        25\n",
            "       2996       0.00      0.00      0.00        21\n",
            "       2997       0.00      0.00      0.00        16\n",
            "       2998       0.00      0.00      0.00        28\n",
            "       2999       0.00      0.00      0.00        25\n",
            "       3000       0.00      0.00      0.00        16\n",
            "       3001       0.00      0.00      0.00        23\n",
            "       3002       0.00      0.00      0.00        20\n",
            "       3003       0.00      0.00      0.00        28\n",
            "       3004       0.00      0.00      0.00        14\n",
            "       3005       1.00      0.05      0.09        21\n",
            "       3006       0.00      0.00      0.00        19\n",
            "       3007       0.00      0.00      0.00        26\n",
            "       3008       0.00      0.00      0.00        27\n",
            "       3009       0.50      0.04      0.07        26\n",
            "       3010       0.00      0.00      0.00        20\n",
            "       3011       0.00      0.00      0.00        21\n",
            "       3012       0.00      0.00      0.00        21\n",
            "       3013       0.00      0.00      0.00        15\n",
            "       3014       0.00      0.00      0.00        27\n",
            "       3015       0.67      0.11      0.18        19\n",
            "       3016       1.00      0.05      0.10        19\n",
            "       3017       0.00      0.00      0.00        20\n",
            "       3018       0.00      0.00      0.00        19\n",
            "       3019       1.00      0.06      0.12        16\n",
            "       3020       0.00      0.00      0.00        15\n",
            "       3021       0.50      0.06      0.10        18\n",
            "       3022       0.00      0.00      0.00        18\n",
            "       3023       0.00      0.00      0.00        21\n",
            "       3024       1.00      0.27      0.42        26\n",
            "       3025       0.00      0.00      0.00        18\n",
            "       3026       0.50      0.04      0.08        23\n",
            "       3027       0.00      0.00      0.00        28\n",
            "       3028       0.83      0.24      0.37        21\n",
            "       3029       0.75      0.14      0.23        22\n",
            "       3030       0.00      0.00      0.00        21\n",
            "       3031       0.00      0.00      0.00        19\n",
            "       3032       0.00      0.00      0.00        23\n",
            "       3033       0.00      0.00      0.00        21\n",
            "       3034       0.00      0.00      0.00        17\n",
            "       3035       0.00      0.00      0.00        20\n",
            "       3036       0.67      0.10      0.17        21\n",
            "       3037       0.00      0.00      0.00        26\n",
            "       3038       0.00      0.00      0.00        27\n",
            "       3039       0.00      0.00      0.00        21\n",
            "       3040       0.00      0.00      0.00        19\n",
            "       3041       0.00      0.00      0.00        20\n",
            "       3042       0.00      0.00      0.00        24\n",
            "       3043       0.00      0.00      0.00        28\n",
            "       3044       0.00      0.00      0.00        18\n",
            "       3045       0.00      0.00      0.00        26\n",
            "       3046       0.00      0.00      0.00        26\n",
            "       3047       0.00      0.00      0.00        23\n",
            "       3048       0.00      0.00      0.00        18\n",
            "       3049       0.00      0.00      0.00        23\n",
            "       3050       1.00      0.18      0.30        17\n",
            "       3051       0.50      0.04      0.07        26\n",
            "       3052       0.00      0.00      0.00        32\n",
            "       3053       0.00      0.00      0.00        24\n",
            "       3054       0.00      0.00      0.00        16\n",
            "       3055       0.00      0.00      0.00        21\n",
            "       3056       0.00      0.00      0.00        23\n",
            "       3057       0.00      0.00      0.00        28\n",
            "       3058       0.00      0.00      0.00        13\n",
            "       3059       0.00      0.00      0.00        17\n",
            "       3060       0.00      0.00      0.00        15\n",
            "       3061       0.00      0.00      0.00        19\n",
            "       3062       0.00      0.00      0.00        18\n",
            "       3063       0.00      0.00      0.00        18\n",
            "       3064       0.00      0.00      0.00        22\n",
            "       3065       0.00      0.00      0.00        16\n",
            "       3066       0.00      0.00      0.00        18\n",
            "       3067       0.00      0.00      0.00        18\n",
            "       3068       0.00      0.00      0.00        22\n",
            "       3069       0.00      0.00      0.00        27\n",
            "       3070       0.00      0.00      0.00        23\n",
            "       3071       0.00      0.00      0.00        16\n",
            "       3072       0.00      0.00      0.00        24\n",
            "       3073       1.00      0.50      0.67        20\n",
            "       3074       0.00      0.00      0.00        22\n",
            "       3075       1.00      0.04      0.08        25\n",
            "       3076       0.00      0.00      0.00        18\n",
            "       3077       0.00      0.00      0.00        21\n",
            "       3078       0.00      0.00      0.00        18\n",
            "       3079       0.00      0.00      0.00        15\n",
            "       3080       1.00      0.07      0.12        15\n",
            "       3081       0.00      0.00      0.00        20\n",
            "       3082       0.00      0.00      0.00        23\n",
            "       3083       0.00      0.00      0.00        17\n",
            "       3084       0.00      0.00      0.00        16\n",
            "       3085       0.00      0.00      0.00        25\n",
            "       3086       0.00      0.00      0.00        13\n",
            "       3087       0.00      0.00      0.00        24\n",
            "       3088       0.00      0.00      0.00        22\n",
            "       3089       0.00      0.00      0.00        25\n",
            "       3090       0.00      0.00      0.00        21\n",
            "       3091       0.00      0.00      0.00        15\n",
            "       3092       0.00      0.00      0.00        19\n",
            "       3093       0.00      0.00      0.00        21\n",
            "       3094       0.00      0.00      0.00        22\n",
            "       3095       0.00      0.00      0.00        22\n",
            "       3096       0.00      0.00      0.00        26\n",
            "       3097       0.00      0.00      0.00        23\n",
            "       3098       0.00      0.00      0.00        22\n",
            "       3099       0.00      0.00      0.00        17\n",
            "       3100       1.00      0.22      0.36        18\n",
            "       3101       0.00      0.00      0.00        19\n",
            "       3102       0.00      0.00      0.00        15\n",
            "       3103       0.00      0.00      0.00        17\n",
            "       3104       0.00      0.00      0.00        20\n",
            "       3105       0.00      0.00      0.00        16\n",
            "       3106       0.00      0.00      0.00        14\n",
            "       3107       0.00      0.00      0.00        22\n",
            "       3108       0.00      0.00      0.00        24\n",
            "       3109       0.00      0.00      0.00        20\n",
            "       3110       0.00      0.00      0.00        19\n",
            "       3111       0.00      0.00      0.00        23\n",
            "       3112       0.00      0.00      0.00        21\n",
            "       3113       0.00      0.00      0.00        19\n",
            "       3114       0.00      0.00      0.00        18\n",
            "       3115       0.00      0.00      0.00        22\n",
            "       3116       0.00      0.00      0.00        19\n",
            "       3117       0.00      0.00      0.00        20\n",
            "       3118       0.00      0.00      0.00        18\n",
            "       3119       0.00      0.00      0.00        23\n",
            "       3120       0.00      0.00      0.00        18\n",
            "       3121       0.00      0.00      0.00        19\n",
            "       3122       1.00      0.19      0.32        16\n",
            "       3123       0.00      0.00      0.00        20\n",
            "       3124       0.50      0.05      0.08        22\n",
            "       3125       0.17      0.07      0.10        14\n",
            "       3126       0.00      0.00      0.00        16\n",
            "       3127       0.00      0.00      0.00        18\n",
            "       3128       0.00      0.00      0.00        33\n",
            "       3129       0.00      0.00      0.00        19\n",
            "       3130       0.00      0.00      0.00        28\n",
            "       3131       0.00      0.00      0.00        22\n",
            "       3132       0.00      0.00      0.00        20\n",
            "       3133       0.25      0.06      0.10        17\n",
            "       3134       0.00      0.00      0.00        19\n",
            "       3135       0.00      0.00      0.00        20\n",
            "       3136       0.00      0.00      0.00        20\n",
            "       3137       0.00      0.00      0.00        21\n",
            "       3138       0.00      0.00      0.00        21\n",
            "       3139       0.00      0.00      0.00        22\n",
            "       3140       0.00      0.00      0.00        18\n",
            "       3141       0.00      0.00      0.00        15\n",
            "       3142       0.00      0.00      0.00        20\n",
            "       3143       0.00      0.00      0.00        17\n",
            "       3144       0.00      0.00      0.00        23\n",
            "       3145       0.00      0.00      0.00        19\n",
            "       3146       0.00      0.00      0.00        17\n",
            "       3147       1.00      0.31      0.48        16\n",
            "       3148       0.80      0.50      0.62        16\n",
            "       3149       0.00      0.00      0.00        23\n",
            "       3150       0.00      0.00      0.00        25\n",
            "       3151       0.00      0.00      0.00        25\n",
            "       3152       0.00      0.00      0.00        26\n",
            "       3153       0.00      0.00      0.00        27\n",
            "       3154       0.00      0.00      0.00        20\n",
            "       3155       1.00      0.33      0.50        18\n",
            "       3156       0.00      0.00      0.00        17\n",
            "       3157       0.75      0.21      0.33        14\n",
            "       3158       0.00      0.00      0.00        23\n",
            "       3159       0.00      0.00      0.00        19\n",
            "       3160       0.50      0.05      0.09        20\n",
            "       3161       0.00      0.00      0.00        18\n",
            "       3162       0.00      0.00      0.00        19\n",
            "       3163       0.00      0.00      0.00        21\n",
            "       3164       0.00      0.00      0.00        16\n",
            "       3165       0.00      0.00      0.00        22\n",
            "       3166       0.00      0.00      0.00        19\n",
            "       3167       0.00      0.00      0.00        21\n",
            "       3168       0.00      0.00      0.00        27\n",
            "       3169       0.00      0.00      0.00        21\n",
            "       3170       0.00      0.00      0.00        23\n",
            "       3171       0.00      0.00      0.00        15\n",
            "       3172       0.00      0.00      0.00        24\n",
            "       3173       0.00      0.00      0.00        18\n",
            "       3174       0.00      0.00      0.00        21\n",
            "       3175       0.00      0.00      0.00        14\n",
            "       3176       0.00      0.00      0.00        19\n",
            "       3177       0.00      0.00      0.00        22\n",
            "       3178       0.00      0.00      0.00        20\n",
            "       3179       0.00      0.00      0.00        18\n",
            "       3180       0.00      0.00      0.00        20\n",
            "       3181       0.00      0.00      0.00        27\n",
            "       3182       0.00      0.00      0.00        23\n",
            "       3183       0.00      0.00      0.00        13\n",
            "       3184       0.00      0.00      0.00        22\n",
            "       3185       0.00      0.00      0.00        20\n",
            "       3186       0.00      0.00      0.00        28\n",
            "       3187       0.00      0.00      0.00        19\n",
            "       3188       0.00      0.00      0.00        23\n",
            "       3189       0.00      0.00      0.00        25\n",
            "       3190       0.00      0.00      0.00        21\n",
            "       3191       0.00      0.00      0.00        20\n",
            "       3192       0.00      0.00      0.00        22\n",
            "       3193       0.00      0.00      0.00        21\n",
            "       3194       0.00      0.00      0.00        16\n",
            "       3195       0.00      0.00      0.00        21\n",
            "       3196       0.00      0.00      0.00        21\n",
            "       3197       1.00      0.05      0.10        20\n",
            "       3198       0.00      0.00      0.00        18\n",
            "       3199       0.00      0.00      0.00        23\n",
            "       3200       0.33      0.05      0.09        19\n",
            "       3201       1.00      0.06      0.11        18\n",
            "       3202       0.00      0.00      0.00        25\n",
            "       3203       0.00      0.00      0.00        21\n",
            "       3204       1.00      0.07      0.12        15\n",
            "       3205       0.00      0.00      0.00        18\n",
            "       3206       0.00      0.00      0.00        23\n",
            "       3207       0.00      0.00      0.00        15\n",
            "       3208       0.00      0.00      0.00        20\n",
            "       3209       0.00      0.00      0.00        21\n",
            "       3210       0.00      0.00      0.00        20\n",
            "       3211       0.00      0.00      0.00        22\n",
            "       3212       0.00      0.00      0.00        21\n",
            "       3213       0.00      0.00      0.00        22\n",
            "       3214       0.00      0.00      0.00        25\n",
            "       3215       0.00      0.00      0.00        16\n",
            "       3216       0.00      0.00      0.00         7\n",
            "       3217       1.00      0.18      0.30        17\n",
            "       3218       0.00      0.00      0.00        26\n",
            "       3219       0.00      0.00      0.00        19\n",
            "       3220       0.00      0.00      0.00        29\n",
            "       3221       0.00      0.00      0.00        25\n",
            "       3222       0.00      0.00      0.00        14\n",
            "       3223       1.00      0.12      0.21        17\n",
            "       3224       0.00      0.00      0.00        23\n",
            "       3225       0.00      0.00      0.00        22\n",
            "       3226       0.00      0.00      0.00        20\n",
            "       3227       0.00      0.00      0.00        24\n",
            "       3228       0.00      0.00      0.00        17\n",
            "       3229       0.00      0.00      0.00        31\n",
            "       3230       0.00      0.00      0.00        21\n",
            "       3231       0.00      0.00      0.00        22\n",
            "       3232       0.00      0.00      0.00        15\n",
            "       3233       0.00      0.00      0.00        21\n",
            "       3234       0.00      0.00      0.00        23\n",
            "       3235       0.00      0.00      0.00        21\n",
            "       3236       0.00      0.00      0.00        14\n",
            "       3237       0.00      0.00      0.00        21\n",
            "       3238       0.00      0.00      0.00        17\n",
            "       3239       0.00      0.00      0.00        22\n",
            "       3240       0.00      0.00      0.00        22\n",
            "       3241       0.00      0.00      0.00        15\n",
            "       3242       0.00      0.00      0.00        21\n",
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            "       3244       0.00      0.00      0.00        29\n",
            "       3245       0.00      0.00      0.00        17\n",
            "       3246       0.00      0.00      0.00        22\n",
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            "       3248       0.00      0.00      0.00        20\n",
            "       3249       0.00      0.00      0.00        22\n",
            "       3250       0.00      0.00      0.00        24\n",
            "       3251       0.00      0.00      0.00        19\n",
            "       3252       0.00      0.00      0.00        17\n",
            "       3253       0.00      0.00      0.00        16\n",
            "       3254       0.00      0.00      0.00        25\n",
            "       3255       0.00      0.00      0.00        15\n",
            "       3256       0.00      0.00      0.00        17\n",
            "       3257       0.00      0.00      0.00        15\n",
            "       3258       0.00      0.00      0.00        21\n",
            "       3259       0.00      0.00      0.00        14\n",
            "       3260       0.00      0.00      0.00        18\n",
            "       3261       0.00      0.00      0.00        24\n",
            "       3262       0.00      0.00      0.00        20\n",
            "       3263       0.00      0.00      0.00        16\n",
            "       3264       1.00      0.05      0.10        19\n",
            "       3265       0.00      0.00      0.00        21\n",
            "       3266       0.00      0.00      0.00        20\n",
            "       3267       0.00      0.00      0.00        22\n",
            "       3268       0.00      0.00      0.00        13\n",
            "       3269       0.00      0.00      0.00        18\n",
            "       3270       0.00      0.00      0.00        15\n",
            "       3271       0.00      0.00      0.00        19\n",
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            "       3273       0.00      0.00      0.00        18\n",
            "       3274       0.00      0.00      0.00        22\n",
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            "       3276       0.00      0.00      0.00        17\n",
            "       3277       0.00      0.00      0.00        20\n",
            "       3278       0.00      0.00      0.00        22\n",
            "       3279       0.00      0.00      0.00        21\n",
            "       3280       0.00      0.00      0.00        19\n",
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            "       3286       0.00      0.00      0.00        11\n",
            "       3287       0.00      0.00      0.00        16\n",
            "       3288       0.00      0.00      0.00        14\n",
            "       3289       0.00      0.00      0.00        27\n",
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            "       3291       0.00      0.00      0.00        24\n",
            "       3292       0.00      0.00      0.00        19\n",
            "       3293       0.00      0.00      0.00        15\n",
            "       3294       1.00      0.05      0.09        22\n",
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            "       3296       0.00      0.00      0.00        26\n",
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            "       3299       0.00      0.00      0.00        19\n",
            "       3300       0.00      0.00      0.00        16\n",
            "       3301       1.00      0.05      0.10        19\n",
            "       3302       1.00      0.06      0.11        17\n",
            "       3303       0.00      0.00      0.00        17\n",
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            "       3321       0.00      0.00      0.00        21\n",
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            "       3324       1.00      0.12      0.21        17\n",
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            "       3330       1.00      0.25      0.40        12\n",
            "       3331       0.33      0.06      0.11        16\n",
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            "       3333       0.00      0.00      0.00        23\n",
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            "       3367       1.00      0.06      0.12        16\n",
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            "       3389       1.00      0.06      0.12        16\n",
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            "       3394       1.00      0.25      0.40        20\n",
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            "       3404       0.33      0.10      0.15        10\n",
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            "       3602       1.00      0.08      0.14        13\n",
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            "       3616       0.00      0.00      0.00        23\n",
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            "       3618       0.00      0.00      0.00        21\n",
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            "       3620       0.00      0.00      0.00        20\n",
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            "       3624       0.00      0.00      0.00        18\n",
            "       3625       0.00      0.00      0.00        21\n",
            "       3626       1.00      0.25      0.40        12\n",
            "       3627       0.00      0.00      0.00        18\n",
            "       3628       0.50      0.07      0.12        14\n",
            "       3629       0.00      0.00      0.00        13\n",
            "       3630       0.00      0.00      0.00        10\n",
            "       3631       0.00      0.00      0.00        17\n",
            "       3632       0.00      0.00      0.00         8\n",
            "       3633       0.00      0.00      0.00        16\n",
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            "       3641       0.00      0.00      0.00        20\n",
            "       3642       0.50      0.09      0.15        11\n",
            "       3643       0.00      0.00      0.00        13\n",
            "       3644       0.00      0.00      0.00        19\n",
            "       3645       0.00      0.00      0.00        11\n",
            "       3646       0.33      0.08      0.12        13\n",
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            "       3648       0.00      0.00      0.00        19\n",
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            "       3650       0.00      0.00      0.00        12\n",
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            "       3665       1.00      0.04      0.08        24\n",
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            "       3694       0.40      0.14      0.21        14\n",
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            "       3709       0.00      0.00      0.00        13\n",
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            "       3744       1.00      0.15      0.27        13\n",
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            "       3764       1.00      0.21      0.35        19\n",
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            "       3780       1.00      0.15      0.27        13\n",
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            "       3955       0.00      0.00      0.00        15\n",
            "       3956       0.00      0.00      0.00        17\n",
            "       3957       0.00      0.00      0.00         9\n",
            "       3958       0.00      0.00      0.00        20\n",
            "       3959       1.00      0.33      0.50         9\n",
            "       3960       0.00      0.00      0.00        13\n",
            "       3961       0.00      0.00      0.00        18\n",
            "       3962       0.00      0.00      0.00        14\n",
            "       3963       0.00      0.00      0.00        15\n",
            "       3964       0.00      0.00      0.00        13\n",
            "       3965       0.00      0.00      0.00        16\n",
            "       3966       0.00      0.00      0.00        15\n",
            "       3967       0.00      0.00      0.00        15\n",
            "       3968       0.00      0.00      0.00        17\n",
            "       3969       0.00      0.00      0.00        20\n",
            "       3970       0.00      0.00      0.00        16\n",
            "       3971       0.00      0.00      0.00        19\n",
            "       3972       1.00      0.12      0.22        16\n",
            "       3973       0.00      0.00      0.00        15\n",
            "       3974       0.00      0.00      0.00         8\n",
            "       3975       0.00      0.00      0.00        16\n",
            "       3976       0.00      0.00      0.00        15\n",
            "       3977       0.00      0.00      0.00        14\n",
            "       3978       0.00      0.00      0.00        16\n",
            "       3979       0.00      0.00      0.00        13\n",
            "       3980       0.00      0.00      0.00        28\n",
            "       3981       0.00      0.00      0.00        16\n",
            "       3982       0.00      0.00      0.00        12\n",
            "       3983       0.00      0.00      0.00        13\n",
            "       3984       0.00      0.00      0.00        12\n",
            "       3985       0.00      0.00      0.00        15\n",
            "       3986       0.00      0.00      0.00        10\n",
            "       3987       0.00      0.00      0.00        20\n",
            "       3988       0.00      0.00      0.00        17\n",
            "       3989       0.00      0.00      0.00        14\n",
            "       3990       0.00      0.00      0.00        11\n",
            "       3991       0.00      0.00      0.00        14\n",
            "       3992       0.00      0.00      0.00        13\n",
            "       3993       1.00      0.23      0.38        13\n",
            "       3994       0.00      0.00      0.00        18\n",
            "       3995       0.00      0.00      0.00        13\n",
            "       3996       0.00      0.00      0.00        13\n",
            "       3997       0.00      0.00      0.00        19\n",
            "       3998       0.00      0.00      0.00        10\n",
            "       3999       1.00      0.13      0.24        15\n",
            "       4000       0.00      0.00      0.00        20\n",
            "       4001       0.00      0.00      0.00        16\n",
            "       4002       0.00      0.00      0.00        11\n",
            "       4003       0.00      0.00      0.00        14\n",
            "       4004       0.00      0.00      0.00        15\n",
            "       4005       0.00      0.00      0.00        21\n",
            "       4006       0.00      0.00      0.00        12\n",
            "       4007       0.00      0.00      0.00        15\n",
            "       4008       0.00      0.00      0.00         9\n",
            "       4009       0.50      0.06      0.11        16\n",
            "       4010       0.00      0.00      0.00        12\n",
            "       4011       0.00      0.00      0.00        16\n",
            "       4012       0.00      0.00      0.00        19\n",
            "       4013       0.00      0.00      0.00        13\n",
            "       4014       0.00      0.00      0.00        13\n",
            "       4015       0.00      0.00      0.00        13\n",
            "       4016       0.00      0.00      0.00        16\n",
            "       4017       0.00      0.00      0.00        17\n",
            "       4018       0.00      0.00      0.00        10\n",
            "       4019       0.00      0.00      0.00        12\n",
            "       4020       0.00      0.00      0.00        13\n",
            "       4021       0.00      0.00      0.00        17\n",
            "       4022       0.00      0.00      0.00        16\n",
            "       4023       0.00      0.00      0.00        14\n",
            "       4024       0.00      0.00      0.00        11\n",
            "       4025       0.00      0.00      0.00         8\n",
            "       4026       0.00      0.00      0.00         8\n",
            "       4027       0.00      0.00      0.00        18\n",
            "       4028       0.00      0.00      0.00        13\n",
            "       4029       0.00      0.00      0.00        11\n",
            "       4030       0.00      0.00      0.00        19\n",
            "       4031       0.00      0.00      0.00         9\n",
            "       4032       0.00      0.00      0.00        12\n",
            "       4033       0.00      0.00      0.00        14\n",
            "       4034       0.00      0.00      0.00        17\n",
            "       4035       0.00      0.00      0.00        10\n",
            "       4036       0.00      0.00      0.00        12\n",
            "       4037       0.00      0.00      0.00        13\n",
            "       4038       0.00      0.00      0.00        13\n",
            "       4039       0.00      0.00      0.00        13\n",
            "       4040       0.00      0.00      0.00        12\n",
            "       4041       0.00      0.00      0.00        17\n",
            "       4042       0.00      0.00      0.00        10\n",
            "       4043       0.00      0.00      0.00        15\n",
            "       4044       0.00      0.00      0.00        13\n",
            "       4045       0.00      0.00      0.00        20\n",
            "       4046       0.00      0.00      0.00        16\n",
            "       4047       0.00      0.00      0.00        12\n",
            "       4048       0.00      0.00      0.00        16\n",
            "       4049       0.00      0.00      0.00        14\n",
            "       4050       0.00      0.00      0.00        15\n",
            "       4051       0.00      0.00      0.00        20\n",
            "       4052       0.00      0.00      0.00        10\n",
            "       4053       0.00      0.00      0.00        14\n",
            "       4054       0.00      0.00      0.00        14\n",
            "       4055       0.00      0.00      0.00         5\n",
            "       4056       0.00      0.00      0.00        15\n",
            "       4057       1.00      0.07      0.12        15\n",
            "       4058       0.00      0.00      0.00        17\n",
            "       4059       0.00      0.00      0.00        13\n",
            "       4060       0.00      0.00      0.00        14\n",
            "       4061       0.00      0.00      0.00        10\n",
            "       4062       0.00      0.00      0.00        15\n",
            "       4063       0.00      0.00      0.00        15\n",
            "       4064       0.00      0.00      0.00        17\n",
            "       4065       0.00      0.00      0.00        17\n",
            "       4066       0.00      0.00      0.00        14\n",
            "       4067       0.00      0.00      0.00        15\n",
            "       4068       0.00      0.00      0.00        21\n",
            "       4069       0.00      0.00      0.00         9\n",
            "       4070       0.00      0.00      0.00         9\n",
            "       4071       0.00      0.00      0.00        21\n",
            "       4072       0.00      0.00      0.00        18\n",
            "       4073       0.00      0.00      0.00         9\n",
            "       4074       0.00      0.00      0.00        12\n",
            "       4075       0.00      0.00      0.00        20\n",
            "       4076       0.00      0.00      0.00        15\n",
            "       4077       0.00      0.00      0.00        15\n",
            "       4078       0.00      0.00      0.00         9\n",
            "       4079       0.00      0.00      0.00        15\n",
            "       4080       0.00      0.00      0.00        19\n",
            "       4081       0.00      0.00      0.00        10\n",
            "       4082       0.00      0.00      0.00        11\n",
            "       4083       0.00      0.00      0.00        12\n",
            "       4084       0.00      0.00      0.00        14\n",
            "       4085       0.00      0.00      0.00         9\n",
            "       4086       0.00      0.00      0.00         9\n",
            "       4087       0.00      0.00      0.00         9\n",
            "       4088       0.00      0.00      0.00        18\n",
            "       4089       0.00      0.00      0.00        14\n",
            "       4090       0.00      0.00      0.00        18\n",
            "       4091       0.00      0.00      0.00        14\n",
            "       4092       0.00      0.00      0.00        13\n",
            "       4093       0.00      0.00      0.00        16\n",
            "       4094       0.00      0.00      0.00        14\n",
            "       4095       0.00      0.00      0.00        19\n",
            "       4096       0.00      0.00      0.00        15\n",
            "       4097       0.00      0.00      0.00        14\n",
            "       4098       0.00      0.00      0.00        16\n",
            "       4099       0.00      0.00      0.00        21\n",
            "       4100       0.00      0.00      0.00        18\n",
            "       4101       0.00      0.00      0.00        15\n",
            "       4102       0.00      0.00      0.00        15\n",
            "       4103       0.00      0.00      0.00        17\n",
            "       4104       0.00      0.00      0.00        13\n",
            "       4105       0.00      0.00      0.00        15\n",
            "       4106       0.00      0.00      0.00        14\n",
            "       4107       0.00      0.00      0.00        13\n",
            "       4108       0.00      0.00      0.00        15\n",
            "       4109       0.00      0.00      0.00        15\n",
            "       4110       0.00      0.00      0.00        13\n",
            "       4111       0.00      0.00      0.00        16\n",
            "       4112       0.00      0.00      0.00        13\n",
            "       4113       0.00      0.00      0.00        12\n",
            "       4114       0.00      0.00      0.00        13\n",
            "       4115       0.00      0.00      0.00        11\n",
            "       4116       0.00      0.00      0.00        15\n",
            "       4117       0.00      0.00      0.00        12\n",
            "       4118       0.00      0.00      0.00        12\n",
            "       4119       0.00      0.00      0.00        18\n",
            "       4120       1.00      0.09      0.17        11\n",
            "       4121       0.00      0.00      0.00         9\n",
            "       4122       0.00      0.00      0.00        12\n",
            "       4123       0.00      0.00      0.00        11\n",
            "       4124       0.00      0.00      0.00         9\n",
            "       4125       0.00      0.00      0.00         9\n",
            "       4126       0.00      0.00      0.00        15\n",
            "       4127       0.00      0.00      0.00        16\n",
            "       4128       0.00      0.00      0.00        13\n",
            "       4129       0.00      0.00      0.00        11\n",
            "       4130       0.00      0.00      0.00         7\n",
            "       4131       0.00      0.00      0.00        12\n",
            "       4132       0.00      0.00      0.00        15\n",
            "       4133       1.00      0.08      0.15        12\n",
            "       4134       0.00      0.00      0.00        16\n",
            "       4135       0.00      0.00      0.00        16\n",
            "       4136       0.00      0.00      0.00        11\n",
            "       4137       0.00      0.00      0.00        12\n",
            "       4138       0.00      0.00      0.00        12\n",
            "       4139       0.00      0.00      0.00        21\n",
            "       4140       0.00      0.00      0.00        13\n",
            "       4141       0.00      0.00      0.00         7\n",
            "       4142       0.00      0.00      0.00        12\n",
            "       4143       0.00      0.00      0.00        19\n",
            "       4144       0.00      0.00      0.00        10\n",
            "       4145       0.00      0.00      0.00        13\n",
            "       4146       0.00      0.00      0.00        18\n",
            "       4147       0.00      0.00      0.00        14\n",
            "       4148       0.00      0.00      0.00        11\n",
            "       4149       0.00      0.00      0.00         7\n",
            "       4150       0.00      0.00      0.00        10\n",
            "       4151       0.00      0.00      0.00        18\n",
            "       4152       0.00      0.00      0.00        14\n",
            "       4153       0.00      0.00      0.00        16\n",
            "       4154       0.00      0.00      0.00        12\n",
            "       4155       0.00      0.00      0.00        10\n",
            "       4156       0.00      0.00      0.00        15\n",
            "       4157       0.00      0.00      0.00        16\n",
            "       4158       0.00      0.00      0.00        19\n",
            "       4159       0.00      0.00      0.00        10\n",
            "       4160       0.00      0.00      0.00        17\n",
            "       4161       0.00      0.00      0.00        18\n",
            "       4162       0.00      0.00      0.00        12\n",
            "       4163       0.00      0.00      0.00        11\n",
            "       4164       0.00      0.00      0.00         8\n",
            "       4165       0.00      0.00      0.00        17\n",
            "       4166       0.00      0.00      0.00        17\n",
            "       4167       0.00      0.00      0.00         8\n",
            "       4168       0.00      0.00      0.00        12\n",
            "       4169       0.00      0.00      0.00        19\n",
            "       4170       0.00      0.00      0.00        15\n",
            "       4171       0.00      0.00      0.00        10\n",
            "       4172       0.00      0.00      0.00        17\n",
            "       4173       0.00      0.00      0.00        12\n",
            "       4174       0.00      0.00      0.00        14\n",
            "       4175       0.00      0.00      0.00        18\n",
            "       4176       0.00      0.00      0.00         8\n",
            "       4177       0.00      0.00      0.00        20\n",
            "       4178       0.00      0.00      0.00        15\n",
            "       4179       0.00      0.00      0.00        16\n",
            "       4180       0.00      0.00      0.00        12\n",
            "       4181       0.00      0.00      0.00        18\n",
            "       4182       0.00      0.00      0.00         8\n",
            "       4183       0.00      0.00      0.00        18\n",
            "       4184       0.00      0.00      0.00        16\n",
            "       4185       0.00      0.00      0.00        12\n",
            "       4186       0.00      0.00      0.00        16\n",
            "       4187       0.00      0.00      0.00        14\n",
            "       4188       0.00      0.00      0.00        17\n",
            "       4189       0.00      0.00      0.00        13\n",
            "       4190       0.00      0.00      0.00        11\n",
            "       4191       0.00      0.00      0.00        14\n",
            "       4192       0.00      0.00      0.00        11\n",
            "       4193       0.00      0.00      0.00        11\n",
            "       4194       0.00      0.00      0.00        17\n",
            "       4195       0.00      0.00      0.00         6\n",
            "       4196       0.00      0.00      0.00        17\n",
            "       4197       0.00      0.00      0.00        13\n",
            "       4198       0.00      0.00      0.00        12\n",
            "       4199       0.00      0.00      0.00         9\n",
            "       4200       0.00      0.00      0.00        12\n",
            "       4201       0.00      0.00      0.00        13\n",
            "       4202       0.00      0.00      0.00        13\n",
            "       4203       0.00      0.00      0.00        15\n",
            "       4204       0.00      0.00      0.00        15\n",
            "       4205       0.00      0.00      0.00        11\n",
            "       4206       0.00      0.00      0.00        14\n",
            "       4207       0.00      0.00      0.00         9\n",
            "       4208       0.00      0.00      0.00        15\n",
            "       4209       0.00      0.00      0.00        14\n",
            "       4210       0.00      0.00      0.00        11\n",
            "       4211       0.00      0.00      0.00        12\n",
            "       4212       0.00      0.00      0.00        12\n",
            "       4213       0.00      0.00      0.00        14\n",
            "       4214       0.00      0.00      0.00         9\n",
            "       4215       0.00      0.00      0.00         7\n",
            "       4216       0.00      0.00      0.00        12\n",
            "       4217       0.00      0.00      0.00        11\n",
            "       4218       0.00      0.00      0.00        13\n",
            "       4219       1.00      0.09      0.17        11\n",
            "       4220       1.00      0.07      0.13        14\n",
            "       4221       0.00      0.00      0.00        11\n",
            "       4222       1.00      0.08      0.14        13\n",
            "       4223       0.00      0.00      0.00         4\n",
            "       4224       0.00      0.00      0.00        12\n",
            "       4225       0.00      0.00      0.00        13\n",
            "       4226       0.00      0.00      0.00         7\n",
            "       4227       0.00      0.00      0.00        14\n",
            "       4228       0.00      0.00      0.00         9\n",
            "       4229       0.00      0.00      0.00        14\n",
            "       4230       0.00      0.00      0.00        11\n",
            "       4231       0.00      0.00      0.00        13\n",
            "       4232       0.00      0.00      0.00        16\n",
            "       4233       0.00      0.00      0.00        20\n",
            "       4234       0.00      0.00      0.00        12\n",
            "       4235       0.00      0.00      0.00        12\n",
            "       4236       0.00      0.00      0.00        13\n",
            "       4237       0.00      0.00      0.00        11\n",
            "       4238       0.00      0.00      0.00        15\n",
            "       4239       0.00      0.00      0.00        10\n",
            "       4240       0.00      0.00      0.00        11\n",
            "       4241       0.00      0.00      0.00        17\n",
            "       4242       0.00      0.00      0.00        16\n",
            "       4243       0.00      0.00      0.00        17\n",
            "       4244       0.00      0.00      0.00        12\n",
            "       4245       0.00      0.00      0.00        16\n",
            "       4246       0.00      0.00      0.00        10\n",
            "       4247       0.00      0.00      0.00        19\n",
            "       4248       0.00      0.00      0.00         9\n",
            "       4249       0.00      0.00      0.00        15\n",
            "       4250       0.00      0.00      0.00        18\n",
            "       4251       0.00      0.00      0.00        11\n",
            "       4252       0.00      0.00      0.00         9\n",
            "       4253       0.00      0.00      0.00        16\n",
            "       4254       0.00      0.00      0.00        13\n",
            "       4255       0.00      0.00      0.00         7\n",
            "       4256       0.00      0.00      0.00        11\n",
            "       4257       0.00      0.00      0.00        17\n",
            "       4258       0.00      0.00      0.00        12\n",
            "       4259       0.00      0.00      0.00        12\n",
            "       4260       0.00      0.00      0.00        17\n",
            "       4261       0.00      0.00      0.00        12\n",
            "       4262       0.00      0.00      0.00        10\n",
            "       4263       0.00      0.00      0.00        21\n",
            "       4264       0.00      0.00      0.00        16\n",
            "       4265       0.00      0.00      0.00        13\n",
            "       4266       0.00      0.00      0.00        13\n",
            "       4267       0.00      0.00      0.00        12\n",
            "       4268       0.00      0.00      0.00        14\n",
            "       4269       0.00      0.00      0.00        16\n",
            "       4270       0.00      0.00      0.00        12\n",
            "       4271       0.00      0.00      0.00        10\n",
            "       4272       0.00      0.00      0.00        15\n",
            "       4273       0.00      0.00      0.00         9\n",
            "       4274       0.00      0.00      0.00        17\n",
            "       4275       0.00      0.00      0.00        16\n",
            "       4276       0.00      0.00      0.00         8\n",
            "       4277       0.00      0.00      0.00        14\n",
            "       4278       0.00      0.00      0.00        18\n",
            "       4279       0.00      0.00      0.00        17\n",
            "       4280       0.00      0.00      0.00        12\n",
            "       4281       0.00      0.00      0.00         4\n",
            "       4282       0.00      0.00      0.00        17\n",
            "       4283       0.00      0.00      0.00        14\n",
            "       4284       0.00      0.00      0.00        15\n",
            "       4285       0.00      0.00      0.00        22\n",
            "       4286       0.00      0.00      0.00        18\n",
            "       4287       0.00      0.00      0.00         9\n",
            "       4288       0.00      0.00      0.00        14\n",
            "       4289       0.00      0.00      0.00         9\n",
            "       4290       0.00      0.00      0.00        12\n",
            "       4291       0.00      0.00      0.00        11\n",
            "       4292       1.00      0.06      0.11        17\n",
            "       4293       0.00      0.00      0.00         8\n",
            "       4294       0.00      0.00      0.00         8\n",
            "       4295       0.00      0.00      0.00         9\n",
            "       4296       0.00      0.00      0.00         9\n",
            "       4297       0.00      0.00      0.00        19\n",
            "       4298       0.00      0.00      0.00        11\n",
            "       4299       0.00      0.00      0.00         6\n",
            "       4300       0.00      0.00      0.00        13\n",
            "       4301       0.00      0.00      0.00        14\n",
            "       4302       0.00      0.00      0.00        14\n",
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            "       4304       0.00      0.00      0.00         4\n",
            "       4305       0.00      0.00      0.00        13\n",
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            "       4308       0.00      0.00      0.00        19\n",
            "       4309       0.00      0.00      0.00        12\n",
            "       4310       0.00      0.00      0.00        15\n",
            "       4311       0.00      0.00      0.00        13\n",
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            "       4315       0.00      0.00      0.00        12\n",
            "       4316       0.00      0.00      0.00        11\n",
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            "       4318       0.00      0.00      0.00        13\n",
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            "       4320       0.00      0.00      0.00        10\n",
            "       4321       0.00      0.00      0.00        13\n",
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            "       4323       0.00      0.00      0.00        14\n",
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            "       4326       0.00      0.00      0.00        13\n",
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            "       4333       0.00      0.00      0.00        13\n",
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            "       4368       0.75      0.17      0.27        18\n",
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            "       4402       0.33      0.08      0.13        12\n",
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            "       4633       0.00      0.00      0.00         7\n",
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            "       4974       0.00      0.00      0.00         9\n",
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            "       4977       0.00      0.00      0.00         8\n",
            "       4978       0.00      0.00      0.00        16\n",
            "       4979       0.00      0.00      0.00        12\n",
            "       4980       0.00      0.00      0.00         6\n",
            "       4981       0.00      0.00      0.00        15\n",
            "       4982       0.00      0.00      0.00         4\n",
            "       4983       0.00      0.00      0.00         8\n",
            "       4984       0.00      0.00      0.00         9\n",
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            "       4986       0.00      0.00      0.00        14\n",
            "       4987       0.00      0.00      0.00         7\n",
            "       4988       0.00      0.00      0.00        12\n",
            "       4989       0.00      0.00      0.00        15\n",
            "       4990       0.00      0.00      0.00         9\n",
            "       4991       0.00      0.00      0.00        13\n",
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            "       4993       0.00      0.00      0.00         8\n",
            "       4994       0.00      0.00      0.00        10\n",
            "       4995       0.00      0.00      0.00        11\n",
            "       4996       0.00      0.00      0.00        10\n",
            "       4997       0.00      0.00      0.00         4\n",
            "       4998       0.00      0.00      0.00        13\n",
            "       4999       0.00      0.00      0.00         8\n",
            "       5000       0.00      0.00      0.00        11\n",
            "       5001       0.00      0.00      0.00         5\n",
            "       5002       0.00      0.00      0.00         9\n",
            "       5003       0.00      0.00      0.00         6\n",
            "       5004       0.00      0.00      0.00        10\n",
            "       5005       0.00      0.00      0.00         8\n",
            "       5006       0.00      0.00      0.00        15\n",
            "       5007       0.00      0.00      0.00        14\n",
            "       5008       1.00      0.12      0.22         8\n",
            "       5009       0.00      0.00      0.00        10\n",
            "       5010       0.00      0.00      0.00        11\n",
            "       5011       0.00      0.00      0.00        10\n",
            "       5012       0.00      0.00      0.00        11\n",
            "       5013       0.00      0.00      0.00        14\n",
            "       5014       0.00      0.00      0.00         8\n",
            "       5015       0.00      0.00      0.00        14\n",
            "       5016       0.00      0.00      0.00        14\n",
            "       5017       0.00      0.00      0.00        11\n",
            "       5018       0.00      0.00      0.00         9\n",
            "       5019       0.00      0.00      0.00        14\n",
            "       5020       0.00      0.00      0.00        10\n",
            "       5021       0.00      0.00      0.00        15\n",
            "       5022       0.00      0.00      0.00        11\n",
            "       5023       0.00      0.00      0.00         6\n",
            "       5024       0.00      0.00      0.00        14\n",
            "       5025       0.00      0.00      0.00         8\n",
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            "       5031       0.00      0.00      0.00         8\n",
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            "       5038       0.00      0.00      0.00        12\n",
            "       5039       0.00      0.00      0.00        17\n",
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            "       5041       0.00      0.00      0.00         9\n",
            "       5042       0.00      0.00      0.00         9\n",
            "       5043       0.00      0.00      0.00        14\n",
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            "       5047       0.00      0.00      0.00        10\n",
            "       5048       0.00      0.00      0.00         7\n",
            "       5049       0.00      0.00      0.00         9\n",
            "       5050       0.00      0.00      0.00         5\n",
            "       5051       0.00      0.00      0.00        10\n",
            "       5052       0.00      0.00      0.00        10\n",
            "       5053       0.00      0.00      0.00        14\n",
            "       5054       0.00      0.00      0.00        13\n",
            "       5055       0.00      0.00      0.00         7\n",
            "       5056       0.00      0.00      0.00        15\n",
            "       5057       0.00      0.00      0.00         8\n",
            "       5058       0.00      0.00      0.00        11\n",
            "       5059       0.00      0.00      0.00         9\n",
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            "       5061       0.00      0.00      0.00        13\n",
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            "       5064       0.00      0.00      0.00         8\n",
            "       5065       0.00      0.00      0.00         6\n",
            "       5066       0.00      0.00      0.00         7\n",
            "       5067       0.00      0.00      0.00        10\n",
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            "       5070       0.00      0.00      0.00        11\n",
            "       5071       0.00      0.00      0.00         8\n",
            "       5072       0.00      0.00      0.00         4\n",
            "       5073       0.00      0.00      0.00        14\n",
            "       5074       0.00      0.00      0.00        11\n",
            "       5075       0.00      0.00      0.00        14\n",
            "       5076       0.00      0.00      0.00         7\n",
            "       5077       0.00      0.00      0.00        10\n",
            "       5078       0.00      0.00      0.00        11\n",
            "       5079       0.00      0.00      0.00        10\n",
            "       5080       0.00      0.00      0.00        13\n",
            "       5081       0.00      0.00      0.00        12\n",
            "       5082       0.00      0.00      0.00         8\n",
            "       5083       0.00      0.00      0.00        15\n",
            "       5084       0.00      0.00      0.00        15\n",
            "       5085       0.00      0.00      0.00        11\n",
            "       5086       0.00      0.00      0.00        12\n",
            "       5087       0.00      0.00      0.00         9\n",
            "       5088       0.00      0.00      0.00         4\n",
            "       5089       0.00      0.00      0.00         8\n",
            "       5090       0.00      0.00      0.00        11\n",
            "       5091       0.00      0.00      0.00         6\n",
            "       5092       0.00      0.00      0.00         9\n",
            "       5093       0.00      0.00      0.00        10\n",
            "       5094       0.00      0.00      0.00        18\n",
            "       5095       0.00      0.00      0.00         6\n",
            "       5096       0.00      0.00      0.00        12\n",
            "       5097       0.00      0.00      0.00         9\n",
            "       5098       0.00      0.00      0.00        11\n",
            "       5099       0.00      0.00      0.00         7\n",
            "       5100       0.00      0.00      0.00        12\n",
            "       5101       0.00      0.00      0.00         7\n",
            "       5102       0.00      0.00      0.00         5\n",
            "       5103       0.00      0.00      0.00        11\n",
            "       5104       0.00      0.00      0.00        13\n",
            "       5105       0.00      0.00      0.00        10\n",
            "       5106       0.00      0.00      0.00        12\n",
            "       5107       0.00      0.00      0.00         7\n",
            "       5108       0.00      0.00      0.00        14\n",
            "       5109       0.00      0.00      0.00        11\n",
            "       5110       0.00      0.00      0.00         8\n",
            "       5111       0.00      0.00      0.00        10\n",
            "       5112       0.00      0.00      0.00        10\n",
            "       5113       0.00      0.00      0.00         9\n",
            "       5114       0.00      0.00      0.00        13\n",
            "       5115       0.00      0.00      0.00         8\n",
            "       5116       0.00      0.00      0.00        10\n",
            "       5117       0.00      0.00      0.00         8\n",
            "       5118       0.00      0.00      0.00        12\n",
            "       5119       0.00      0.00      0.00         8\n",
            "       5120       0.00      0.00      0.00         7\n",
            "       5121       0.00      0.00      0.00        12\n",
            "       5122       0.00      0.00      0.00         9\n",
            "       5123       0.00      0.00      0.00         9\n",
            "       5124       0.00      0.00      0.00         8\n",
            "       5125       0.00      0.00      0.00         8\n",
            "       5126       0.00      0.00      0.00         8\n",
            "       5127       0.00      0.00      0.00        13\n",
            "       5128       0.00      0.00      0.00         8\n",
            "       5129       0.00      0.00      0.00         9\n",
            "       5130       0.00      0.00      0.00         8\n",
            "       5131       0.00      0.00      0.00        10\n",
            "       5132       0.00      0.00      0.00        11\n",
            "       5133       0.00      0.00      0.00        11\n",
            "       5134       0.00      0.00      0.00         6\n",
            "       5135       0.00      0.00      0.00        11\n",
            "       5136       0.00      0.00      0.00        11\n",
            "       5137       0.00      0.00      0.00        12\n",
            "       5138       0.00      0.00      0.00         8\n",
            "       5139       0.00      0.00      0.00        10\n",
            "       5140       0.00      0.00      0.00        10\n",
            "       5141       0.00      0.00      0.00        10\n",
            "       5142       0.00      0.00      0.00        10\n",
            "       5143       0.00      0.00      0.00         5\n",
            "       5144       0.00      0.00      0.00        13\n",
            "       5145       0.00      0.00      0.00        11\n",
            "       5146       0.00      0.00      0.00        12\n",
            "       5147       0.00      0.00      0.00         9\n",
            "       5148       0.00      0.00      0.00        12\n",
            "       5149       0.00      0.00      0.00         8\n",
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            "       5151       0.00      0.00      0.00        10\n",
            "       5152       0.00      0.00      0.00        12\n",
            "       5153       0.00      0.00      0.00        12\n",
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            "       5155       0.00      0.00      0.00        10\n",
            "       5156       0.00      0.00      0.00         9\n",
            "       5157       0.00      0.00      0.00        13\n",
            "       5158       0.00      0.00      0.00        10\n",
            "       5159       0.00      0.00      0.00         6\n",
            "       5160       0.00      0.00      0.00        10\n",
            "       5161       0.00      0.00      0.00        12\n",
            "       5162       0.00      0.00      0.00         8\n",
            "       5163       0.00      0.00      0.00        10\n",
            "       5164       0.00      0.00      0.00         9\n",
            "       5165       0.00      0.00      0.00        11\n",
            "       5166       0.00      0.00      0.00         8\n",
            "       5167       0.00      0.00      0.00         9\n",
            "       5168       0.00      0.00      0.00         9\n",
            "       5169       0.00      0.00      0.00         8\n",
            "       5170       0.00      0.00      0.00        12\n",
            "       5171       0.00      0.00      0.00         6\n",
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            "       5173       0.00      0.00      0.00        11\n",
            "       5174       0.00      0.00      0.00         7\n",
            "       5175       0.00      0.00      0.00         7\n",
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            "       5177       0.00      0.00      0.00        10\n",
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            "       5180       0.00      0.00      0.00         7\n",
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            "       5182       0.00      0.00      0.00         5\n",
            "       5183       0.00      0.00      0.00        17\n",
            "       5184       0.00      0.00      0.00         4\n",
            "       5185       0.00      0.00      0.00         7\n",
            "       5186       0.00      0.00      0.00         7\n",
            "       5187       0.00      0.00      0.00        10\n",
            "       5188       0.00      0.00      0.00        11\n",
            "       5189       0.00      0.00      0.00        13\n",
            "       5190       1.00      0.10      0.18        10\n",
            "       5191       0.00      0.00      0.00         8\n",
            "       5192       0.00      0.00      0.00        14\n",
            "       5193       0.00      0.00      0.00        12\n",
            "       5194       0.00      0.00      0.00        18\n",
            "       5195       0.00      0.00      0.00        10\n",
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            "       5198       0.00      0.00      0.00         8\n",
            "       5199       0.00      0.00      0.00        11\n",
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            "       5201       0.00      0.00      0.00        12\n",
            "       5202       0.00      0.00      0.00        14\n",
            "       5203       0.00      0.00      0.00        13\n",
            "       5204       0.00      0.00      0.00         8\n",
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            "       5206       0.00      0.00      0.00        16\n",
            "       5207       0.00      0.00      0.00         9\n",
            "       5208       0.00      0.00      0.00         6\n",
            "       5209       0.00      0.00      0.00         8\n",
            "       5210       0.00      0.00      0.00        11\n",
            "       5211       0.00      0.00      0.00        11\n",
            "       5212       0.00      0.00      0.00        14\n",
            "       5213       0.00      0.00      0.00         6\n",
            "       5214       0.00      0.00      0.00         8\n",
            "       5215       0.00      0.00      0.00        11\n",
            "       5216       0.00      0.00      0.00        11\n",
            "       5217       0.00      0.00      0.00         9\n",
            "       5218       0.00      0.00      0.00         9\n",
            "       5219       0.00      0.00      0.00        10\n",
            "       5220       0.00      0.00      0.00        10\n",
            "       5221       0.00      0.00      0.00        10\n",
            "       5222       0.00      0.00      0.00         8\n",
            "       5223       0.00      0.00      0.00         8\n",
            "       5224       0.00      0.00      0.00         7\n",
            "       5225       0.00      0.00      0.00         7\n",
            "       5226       0.00      0.00      0.00         8\n",
            "       5227       0.00      0.00      0.00        13\n",
            "       5228       0.00      0.00      0.00         7\n",
            "       5229       0.00      0.00      0.00         6\n",
            "       5230       0.00      0.00      0.00         7\n",
            "       5231       0.00      0.00      0.00        10\n",
            "       5232       0.00      0.00      0.00         7\n",
            "       5233       0.00      0.00      0.00         9\n",
            "       5234       0.00      0.00      0.00         5\n",
            "       5235       0.00      0.00      0.00         1\n",
            "       5236       0.00      0.00      0.00        16\n",
            "       5237       0.00      0.00      0.00         7\n",
            "       5238       0.00      0.00      0.00        10\n",
            "       5239       0.00      0.00      0.00        14\n",
            "       5240       0.00      0.00      0.00         8\n",
            "       5241       0.00      0.00      0.00         8\n",
            "       5242       0.00      0.00      0.00         8\n",
            "       5243       0.00      0.00      0.00         5\n",
            "       5244       0.00      0.00      0.00        11\n",
            "       5245       0.00      0.00      0.00         8\n",
            "       5246       0.00      0.00      0.00        11\n",
            "       5247       0.00      0.00      0.00        11\n",
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            "       5250       0.00      0.00      0.00        10\n",
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            "       5252       0.00      0.00      0.00        11\n",
            "       5253       0.00      0.00      0.00        12\n",
            "       5254       0.00      0.00      0.00        12\n",
            "       5255       0.00      0.00      0.00        10\n",
            "       5256       0.00      0.00      0.00        12\n",
            "       5257       0.00      0.00      0.00        11\n",
            "       5258       0.00      0.00      0.00        10\n",
            "       5259       0.00      0.00      0.00         8\n",
            "       5260       0.00      0.00      0.00        11\n",
            "       5261       0.00      0.00      0.00        10\n",
            "       5262       0.00      0.00      0.00         9\n",
            "       5263       0.00      0.00      0.00        10\n",
            "       5264       0.00      0.00      0.00        12\n",
            "       5265       1.00      0.09      0.17        11\n",
            "       5266       0.00      0.00      0.00         8\n",
            "       5267       0.00      0.00      0.00        12\n",
            "       5268       0.00      0.00      0.00         7\n",
            "       5269       0.00      0.00      0.00         9\n",
            "       5270       0.00      0.00      0.00        11\n",
            "       5271       0.00      0.00      0.00         9\n",
            "       5272       0.00      0.00      0.00        11\n",
            "       5273       0.00      0.00      0.00         7\n",
            "       5274       0.00      0.00      0.00        11\n",
            "       5275       0.00      0.00      0.00        11\n",
            "       5276       0.00      0.00      0.00         9\n",
            "       5277       0.00      0.00      0.00         7\n",
            "       5278       0.00      0.00      0.00         7\n",
            "       5279       0.00      0.00      0.00         8\n",
            "       5280       0.00      0.00      0.00         5\n",
            "       5281       0.00      0.00      0.00         8\n",
            "       5282       0.00      0.00      0.00         8\n",
            "       5283       0.00      0.00      0.00        13\n",
            "       5284       0.00      0.00      0.00        11\n",
            "       5285       0.00      0.00      0.00         6\n",
            "       5286       0.00      0.00      0.00        13\n",
            "       5287       0.00      0.00      0.00        15\n",
            "       5288       0.00      0.00      0.00         7\n",
            "       5289       0.00      0.00      0.00         8\n",
            "       5290       0.00      0.00      0.00         6\n",
            "       5291       0.00      0.00      0.00         9\n",
            "       5292       0.00      0.00      0.00         6\n",
            "       5293       0.00      0.00      0.00         9\n",
            "       5294       0.00      0.00      0.00        13\n",
            "       5295       0.00      0.00      0.00        11\n",
            "       5296       0.00      0.00      0.00        10\n",
            "       5297       0.00      0.00      0.00        13\n",
            "       5298       0.00      0.00      0.00        14\n",
            "       5299       0.00      0.00      0.00        10\n",
            "       5300       0.00      0.00      0.00        14\n",
            "       5301       0.00      0.00      0.00        11\n",
            "       5302       0.00      0.00      0.00         6\n",
            "       5303       0.00      0.00      0.00         6\n",
            "       5304       0.00      0.00      0.00         7\n",
            "       5305       0.00      0.00      0.00         9\n",
            "       5306       0.00      0.00      0.00         6\n",
            "       5307       0.00      0.00      0.00        10\n",
            "       5308       0.00      0.00      0.00        11\n",
            "       5309       0.00      0.00      0.00        11\n",
            "       5310       0.00      0.00      0.00        14\n",
            "       5311       0.00      0.00      0.00        10\n",
            "       5312       0.00      0.00      0.00        11\n",
            "       5313       0.00      0.00      0.00        11\n",
            "       5314       0.00      0.00      0.00        11\n",
            "       5315       0.00      0.00      0.00        11\n",
            "       5316       0.00      0.00      0.00         2\n",
            "       5317       0.00      0.00      0.00         5\n",
            "       5318       0.00      0.00      0.00        11\n",
            "       5319       0.00      0.00      0.00        12\n",
            "       5320       0.00      0.00      0.00         7\n",
            "       5321       0.00      0.00      0.00         7\n",
            "       5322       0.00      0.00      0.00         9\n",
            "       5323       0.00      0.00      0.00         9\n",
            "       5324       0.00      0.00      0.00         8\n",
            "       5325       0.00      0.00      0.00        10\n",
            "       5326       0.00      0.00      0.00         3\n",
            "       5327       0.00      0.00      0.00        13\n",
            "       5328       0.00      0.00      0.00        13\n",
            "       5329       0.00      0.00      0.00         7\n",
            "       5330       0.00      0.00      0.00         8\n",
            "       5331       0.00      0.00      0.00         9\n",
            "       5332       0.00      0.00      0.00         8\n",
            "       5333       0.00      0.00      0.00        11\n",
            "       5334       0.00      0.00      0.00        11\n",
            "       5335       0.00      0.00      0.00         6\n",
            "       5336       0.00      0.00      0.00         6\n",
            "       5337       0.00      0.00      0.00         6\n",
            "       5338       0.00      0.00      0.00        11\n",
            "       5339       0.00      0.00      0.00        12\n",
            "       5340       0.00      0.00      0.00         9\n",
            "       5341       0.00      0.00      0.00         8\n",
            "       5342       0.00      0.00      0.00         8\n",
            "       5343       0.00      0.00      0.00         7\n",
            "       5344       0.00      0.00      0.00         5\n",
            "       5345       0.00      0.00      0.00        11\n",
            "       5346       0.00      0.00      0.00        13\n",
            "       5347       0.00      0.00      0.00        10\n",
            "       5348       0.00      0.00      0.00        11\n",
            "       5349       0.00      0.00      0.00         7\n",
            "       5350       0.00      0.00      0.00        10\n",
            "       5351       0.00      0.00      0.00         7\n",
            "       5352       0.00      0.00      0.00         7\n",
            "       5353       0.00      0.00      0.00        11\n",
            "       5354       0.00      0.00      0.00        12\n",
            "       5355       0.00      0.00      0.00        12\n",
            "       5356       0.00      0.00      0.00        10\n",
            "       5357       0.00      0.00      0.00         9\n",
            "       5358       0.00      0.00      0.00         8\n",
            "       5359       0.00      0.00      0.00         7\n",
            "       5360       0.00      0.00      0.00        10\n",
            "       5361       0.00      0.00      0.00         6\n",
            "       5362       0.00      0.00      0.00         6\n",
            "       5363       0.00      0.00      0.00         9\n",
            "       5364       0.00      0.00      0.00         9\n",
            "       5365       0.00      0.00      0.00        17\n",
            "       5366       0.00      0.00      0.00         8\n",
            "       5367       0.00      0.00      0.00         9\n",
            "       5368       0.00      0.00      0.00         8\n",
            "       5369       0.00      0.00      0.00         8\n",
            "       5370       0.00      0.00      0.00        18\n",
            "       5371       0.00      0.00      0.00        14\n",
            "       5372       0.00      0.00      0.00        10\n",
            "       5373       0.00      0.00      0.00         7\n",
            "       5374       0.00      0.00      0.00         6\n",
            "       5375       0.00      0.00      0.00        12\n",
            "       5376       0.00      0.00      0.00        13\n",
            "       5377       0.00      0.00      0.00         9\n",
            "       5378       0.00      0.00      0.00        10\n",
            "       5379       0.00      0.00      0.00        10\n",
            "       5380       0.00      0.00      0.00         9\n",
            "       5381       0.00      0.00      0.00         7\n",
            "       5382       0.00      0.00      0.00        10\n",
            "       5383       0.00      0.00      0.00         9\n",
            "       5384       0.00      0.00      0.00        12\n",
            "       5385       0.00      0.00      0.00        15\n",
            "       5386       0.00      0.00      0.00         7\n",
            "       5387       0.00      0.00      0.00         8\n",
            "       5388       0.00      0.00      0.00         4\n",
            "       5389       0.00      0.00      0.00         7\n",
            "       5390       0.00      0.00      0.00         8\n",
            "       5391       0.00      0.00      0.00         4\n",
            "       5392       0.00      0.00      0.00        10\n",
            "       5393       0.00      0.00      0.00         7\n",
            "       5394       0.00      0.00      0.00         8\n",
            "       5395       0.00      0.00      0.00        16\n",
            "       5396       0.00      0.00      0.00        13\n",
            "       5397       0.00      0.00      0.00        11\n",
            "       5398       0.00      0.00      0.00         5\n",
            "       5399       0.00      0.00      0.00         5\n",
            "       5400       0.00      0.00      0.00        12\n",
            "       5401       0.00      0.00      0.00         7\n",
            "       5402       0.00      0.00      0.00         5\n",
            "       5403       0.00      0.00      0.00        12\n",
            "       5404       0.00      0.00      0.00         5\n",
            "       5405       0.00      0.00      0.00        10\n",
            "       5406       0.00      0.00      0.00         7\n",
            "       5407       0.00      0.00      0.00        12\n",
            "       5408       0.00      0.00      0.00         9\n",
            "       5409       0.00      0.00      0.00         9\n",
            "       5410       0.00      0.00      0.00         8\n",
            "       5411       0.00      0.00      0.00         6\n",
            "       5412       0.00      0.00      0.00         8\n",
            "       5413       0.00      0.00      0.00         6\n",
            "       5414       0.00      0.00      0.00         8\n",
            "       5415       0.00      0.00      0.00        16\n",
            "       5416       0.00      0.00      0.00         9\n",
            "       5417       0.00      0.00      0.00        11\n",
            "       5418       0.00      0.00      0.00         9\n",
            "       5419       0.00      0.00      0.00        14\n",
            "       5420       0.00      0.00      0.00         6\n",
            "       5421       0.00      0.00      0.00        11\n",
            "       5422       0.00      0.00      0.00        12\n",
            "       5423       0.00      0.00      0.00         8\n",
            "       5424       0.00      0.00      0.00        13\n",
            "       5425       0.00      0.00      0.00         4\n",
            "       5426       0.00      0.00      0.00        10\n",
            "       5427       0.00      0.00      0.00         9\n",
            "       5428       0.00      0.00      0.00        12\n",
            "       5429       0.00      0.00      0.00        11\n",
            "       5430       0.00      0.00      0.00         9\n",
            "       5431       0.00      0.00      0.00        15\n",
            "       5432       0.00      0.00      0.00        12\n",
            "       5433       0.00      0.00      0.00         8\n",
            "       5434       0.00      0.00      0.00         6\n",
            "       5435       0.00      0.00      0.00        12\n",
            "       5436       0.00      0.00      0.00        11\n",
            "       5437       0.00      0.00      0.00        10\n",
            "       5438       0.00      0.00      0.00         7\n",
            "       5439       0.00      0.00      0.00         9\n",
            "       5440       0.00      0.00      0.00        12\n",
            "       5441       0.00      0.00      0.00        10\n",
            "       5442       0.00      0.00      0.00         7\n",
            "       5443       0.00      0.00      0.00        12\n",
            "       5444       0.00      0.00      0.00         7\n",
            "       5445       0.00      0.00      0.00         9\n",
            "       5446       0.00      0.00      0.00         7\n",
            "       5447       0.00      0.00      0.00         6\n",
            "       5448       0.00      0.00      0.00        12\n",
            "       5449       0.00      0.00      0.00         9\n",
            "       5450       0.00      0.00      0.00        10\n",
            "       5451       0.00      0.00      0.00         6\n",
            "       5452       0.00      0.00      0.00        11\n",
            "       5453       0.00      0.00      0.00         7\n",
            "       5454       0.00      0.00      0.00         9\n",
            "       5455       0.00      0.00      0.00        11\n",
            "       5456       0.00      0.00      0.00         7\n",
            "       5457       0.00      0.00      0.00         9\n",
            "       5458       0.00      0.00      0.00         8\n",
            "       5459       0.00      0.00      0.00        11\n",
            "       5460       0.00      0.00      0.00         7\n",
            "       5461       0.00      0.00      0.00        11\n",
            "       5462       0.00      0.00      0.00        10\n",
            "       5463       0.00      0.00      0.00         9\n",
            "       5464       0.00      0.00      0.00         9\n",
            "       5465       0.00      0.00      0.00         7\n",
            "       5466       0.00      0.00      0.00         9\n",
            "       5467       0.00      0.00      0.00        14\n",
            "       5468       0.00      0.00      0.00         9\n",
            "       5469       0.00      0.00      0.00        12\n",
            "       5470       0.00      0.00      0.00        11\n",
            "       5471       0.00      0.00      0.00         8\n",
            "       5472       0.00      0.00      0.00        15\n",
            "       5473       0.00      0.00      0.00         4\n",
            "       5474       0.00      0.00      0.00         8\n",
            "       5475       0.00      0.00      0.00         9\n",
            "       5476       0.00      0.00      0.00        11\n",
            "       5477       0.00      0.00      0.00         8\n",
            "       5478       0.00      0.00      0.00         6\n",
            "       5479       0.00      0.00      0.00         7\n",
            "       5480       0.00      0.00      0.00         7\n",
            "       5481       0.00      0.00      0.00        10\n",
            "       5482       0.00      0.00      0.00        12\n",
            "       5483       0.00      0.00      0.00         6\n",
            "       5484       0.00      0.00      0.00         9\n",
            "       5485       0.00      0.00      0.00         8\n",
            "       5486       0.00      0.00      0.00         8\n",
            "       5487       0.00      0.00      0.00         9\n",
            "       5488       0.00      0.00      0.00         7\n",
            "       5489       0.00      0.00      0.00        10\n",
            "       5490       0.00      0.00      0.00        12\n",
            "       5491       0.00      0.00      0.00         6\n",
            "       5492       0.00      0.00      0.00         8\n",
            "       5493       0.00      0.00      0.00        13\n",
            "       5494       0.00      0.00      0.00         6\n",
            "       5495       0.00      0.00      0.00        10\n",
            "       5496       0.00      0.00      0.00         7\n",
            "       5497       0.00      0.00      0.00         9\n",
            "       5498       0.00      0.00      0.00         6\n",
            "       5499       0.00      0.00      0.00        13\n",
            "\n",
            "avg / total       0.53      0.26      0.33    530065\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCR2BtdxU98s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier, 'lr_with_equal_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvjtTBZ6U98y",
        "colab_type": "text"
      },
      "source": [
        "
 4.5 Modeling with less data points (0.5M data points) and more weight to title and 500 tags only. 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0QKMrEwU98y",
        "colab_type": "code",
        "outputId": "afe297b6-86bf-4f98-95d6-a794a4d4dd85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "sql_create_table = \"\"\"CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);\"\"\"\n",
        "create_database_table(\"drive/My Drive/Stackoverflow/MyTitlemoreweight.db\", sql_create_table)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Tables in the databse:\n",
            "QuestionsProcessed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neuz4eObF2KP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_more = create_engine(\"sqlite:///drive/My Drive/Stackoverflow/MyTitlemoreweight.db\")\n",
        "    # no_dup = pd.DataFrame(df_no_dup, columns=['Title', 'Body', 'Tags'])\n",
        "    # no_dup.to_sql('no_dup_train',title_more)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHLunpYsU982",
        "colab_type": "code",
        "outputId": "326bb663-a5a4-4744-e003-d4b4c431bd84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# http://www.sqlitetutorial.net/sqlite-delete/\n",
        "# https://stackoverflow.com/questions/2279706/select-random-row-from-a-sqlite-table\n",
        "#check here\n",
        "\n",
        "read_db = 'drive/My Drive/Stackoverflow/data/train_no_dup.db'\n",
        "write_db = 'drive/My Drive/Stackoverflow/MyTitlemoreweight.db'\n",
        "train_datasize = 400000\n",
        "if os.path.isfile(read_db):\n",
        "    conn_r = create_connection(read_db)\n",
        "    if conn_r is not None:\n",
        "        reader =conn_r.cursor()\n",
        "        # for selecting first 0.5M rows\n",
        "        reader.execute(\"SELECT Title, Body, Tags From no_dup_train LIMIT 500001;\")\n",
        "        # for selecting random points\n",
        "        #reader.execute(\"SELECT Title, Body, Tags From no_dup_train ORDER BY RANDOM() LIMIT 500001;\")\n",
        "\n",
        "if os.path.isfile(write_db):\n",
        "    conn_w = create_connection(write_db)\n",
        "    if conn_w is not None:\n",
        "        tables = checkTableExists(conn_w)\n",
        "        writer =conn_w.cursor()\n",
        "        if tables != 0:\n",
        "            writer.execute(\"DELETE FROM QuestionsProcessed WHERE 1\")\n",
        "            print(\"Cleared All the rows\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Tables in the databse:\n",
            "QuestionsProcessed\n",
            "Cleared All the rows\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jvi2298wU986",
        "colab_type": "text"
      },
      "source": [
        "
 4.5.1 Preprocessing of questions 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNhcD1LYU987",
        "colab_type": "text"
      },
      "source": [
        "
     \n",
        "    
  1.  Separate Code from Body 
    2. \n",
        "    
  2.  Remove Spcial characters from Question title and description (not in code)
    3. \n",
        "    
  3.   Give more weightage to title : Add title three times to the question  
    4. \n",
        "   \n",
        "    
  4.  Remove stop words (Except 'C') 
    5. \n",
        "    
  5.  Remove HTML Tags 
    6. \n",
        "    
  6.  Convert all the characters into small letters 
    7. \n",
        "    
  7.  Use SnowballStemmer to stem the words 
    8. \n",
        "
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifSmL0M-U98-",
        "colab_type": "code",
        "outputId": "7b2abff5-7465-47b0-8fc3-dfd2df143d87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#http://www.bernzilla.com/2008/05/13/selecting-a-random-row-from-an-sqlite-table/\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "start = datetime.now()\n",
        "preprocessed_data_list=[]\n",
        "reader.fetchone()\n",
        "questions_with_code=0\n",
        "len_pre=0\n",
        "len_post=0\n",
        "questions_proccesed = 0\n",
        "for row in reader:\n",
        "    \n",
        "    is_code = 0\n",
        "    \n",
        "    title, question, tags = row[0], row[1], str(row[2])\n",
        "    \n",
        "    if '' in question:\n",
        "        questions_with_code+=1\n",
        "        is_code = 1\n",
        "    x = len(question)+len(title)\n",
        "    len_pre+=x\n",
        "    \n",
        "    code = str(re.findall(r'(.*?)', question, flags=re.DOTALL))\n",
        "    \n",
        "    question=re.sub('(.*?)', '', question, flags=re.MULTILINE|re.DOTALL)\n",
        "    question=striphtml(question.encode('utf-8'))\n",
        "    \n",
        "    title=title.encode('utf-8')\n",
        "    \n",
        "    # adding title three time to the data to increase its weight\n",
        "    # add tags string to the training data\n",
        "    \n",
        "    question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
        "    \n",
        "#     if questions_proccesed<=train_datasize:\n",
        "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question+\" \"+str(tags)\n",
        "#     else:\n",
        "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
        "\n",
        "    question=re.sub(r'[^A-Za-z0-9#+.\\-]+',' ',question)\n",
        "    words=word_tokenize(str(question.lower()))\n",
        "    \n",
        "    #Removing all single letter and and stopwords from question exceptt for the letter 'c'\n",
        "    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))\n",
        "    \n",
        "    len_post+=len(question)\n",
        "    tup = (question,code,tags,x,len(question),is_code)\n",
        "    questions_proccesed += 1\n",
        "    writer.execute(\"insert into QuestionsProcessed(question,code,tags,words_pre,words_post,is_code) values (?,?,?,?,?,?)\",tup)\n",
        "    if (questions_proccesed%100000==0):\n",
        "        print(\"number of questions completed=\",questions_proccesed)\n",
        "\n",
        "no_dup_avg_len_pre=(len_pre*1.0)/questions_proccesed\n",
        "no_dup_avg_len_post=(len_post*1.0)/questions_proccesed\n",
        "\n",
        "print( \"Avg. length of questions(Title+Body) before processing: %d\"%no_dup_avg_len_pre)\n",
        "print( \"Avg. length of questions(Title+Body) after processing: %d\"%no_dup_avg_len_post)\n",
        "print (\"Percent of questions containing code: %d\"%((questions_with_code*100.0)/questions_proccesed))\n",
        "\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "number of questions completed= 100000\n",
            "number of questions completed= 200000\n",
            "number of questions completed= 300000\n",
            "number of questions completed= 400000\n",
            "Avg. length of questions(Title+Body) before processing: 1237\n",
            "Avg. length of questions(Title+Body) after processing: 423\n",
            "Percent of questions containing code: 56\n",
            "Time taken to run this cell : 0:18:04.853608\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x54WQvZAU99B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# never forget to close the conections or else we will end up with database locks\n",
        "conn_r.commit()\n",
        "conn_w.commit()\n",
        "conn_r.close()\n",
        "conn_w.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpN1ZM2bU99F",
        "colab_type": "text"
      },
      "source": [
        "__ Sample quesitons after preprocessing of data __"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytEecnCtU99H",
        "colab_type": "code",
        "outputId": "c35b6fad-a146-4f15-b3d0-56050cdea371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        }
      },
      "source": [
        "if os.path.isfile(write_db):\n",
        "    conn_r = create_connection(write_db)\n",
        "    if conn_r is not None:\n",
        "        reader =conn_r.cursor()\n",
        "        reader.execute(\"SELECT question From QuestionsProcessed LIMIT 50\")\n",
        "        print(\"Questions after preprocessed\")\n",
        "        print('='*100)\n",
        "        reader.fetchone()\n",
        "        for row in reader:\n",
        "            print(row)\n",
        "            print('-'*100)\n",
        "conn_r.commit()\n",
        "conn_r.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Questions after preprocessed\n",
            "====================================================================================================\n",
            "('countabl subaddit lebesgu measur let lbrace rbrace sequenc set sigma algebra mathcal want show left bigcup right leq sum left right countabl addit measur defin set sigma algebra mathcal think use monoton properti somewher proof start appreci littl help nthank ad han answer make follow addit construct given han answer clear bigcup bigcup cap emptyset neq left bigcup right left bigcup right sum left right also construct subset monoton left right leq left right final would sum leq sum result follow',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('hql equival sql queri hql queri replac name class properti name error occur hql error',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('undefin symbol architectur objc class skpsmtpmessag referenc error import framework send email applic background import framework skpsmtpmessag somebodi suggest get error collect ld return exit status import framework correct sorc taken framework follow mfmailcomposeviewcontrol question lock field updat answer drag drop folder project click copi nthat',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('java lang nosuchmethoderror javax servlet servletcontext geteffectivesessiontrackingmod ljava util set want servlet process input standalon java program deploy servlet jboss put servlet class file web inf class web xml gave servlet url map java client program open connect servlet use url object use localhost get folow error error org apach catalina connector coyoteadapt except error occur contain request process java lang nosuchmethoderror javax servlet servletcontext geteffectivesessiontrackingmod ljava util set org apach catalina connector coyoteadapt postparserequest coyoteadapt java org apach catalina connector coyoteadapt servic coyoteadapt java org apach coyot http http processor process http processor java org apach coyot http http protocol http connectionhandl process http protocol java org apach tomcat util net jioendpoint worker run jioendpoint java web xml file content',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('obtain updat locat use gps servic app two button start track stop track strart track button click gps start listen locat stop listen use besid toast everi new updat locat want thing use background servic alway updat locat even activ close toast appear everi new updat locat pleas hint link would appreci',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('specifi initi vector iv match block size algorithm use cryptostream troubl use cryptostream file encrypt code tri manag blocksiz seem work could fix error',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('uncaught typeerror properti addlistgroup object object domwindow function ask day get repli creat exact chain select exampl demo http www yxscript com cs cs zip look okay open googl chrome select honda first box program fail press ctrl shift tri find issu tell uncaught typeerror properti addlistgroup object object domwindow function clue solv error within content honda js',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('subqueri return row error new web program tri make twitter clone point tabl user id name id auto generat id name user tweet id content user id id auto generat id content text tweet user id id user made post follow id user id follow id id auto generat id user id user follow follow id user follow new sql well tri build sql statement would return tweet current log user everyon follow tri use statement work sometim time get error say subqueri return row statement put exampl would id current log user luck statement help would great appreci thank',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('feof file alway wrong start see lot post late found good link refer explain wrong thought take stab explain',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('sum limit bigl bigl lfloor frac bigr rfloor bigl lfloor frac bigr rfloor bigr frac work problem give troubl prime number form show sum limit biggl biggl lfloor frac biggr rfloor biggl lfloor frac biggr rfloor biggr frac two thing know prime form equiv text mod solv lfloor rfloor lfloor rfloor either think second one use realli see appli',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('frac work valu frac frac nwork valu frac',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('mathbb oplus mathbb isomorph mathbb mn mathbb mn modul would like know gcd case mathbb oplus mathbb isomorph mathbb mn mathbb mn modul know isomorph group show isomorph modul ring mathbb mn',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('mathcal modulo normal closur mathbb time mathbb consid braid group mathcal three strand known mathcal langl xyx yxi rangl nand center mathcal infinit cyclic generat xy group mathcal modulo normal closur langl xy rangl index langl xy rangl mathcal hard show langl xy rangl isomorph mathbb time mathbb',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('continu function show lim alpha int frac alpha alpha let continu function alpha gt love help find follow limit lim alpha int frac alpha first tri bound function sinc continu close interv lim alpha int frac alpha leq lim alpha int frac alpha get number depend alpha assum integr alway diverg sinc continu divid expon bigger one multipli infti mayb use hopit somehow thank',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('cvcreatecontourtre dll opencv imgproc deprec get error unabl find entri point name cvcreatecontourtre dll opencv imgproc program emgucv latest binari opencv check dll export opencv imgproc appear method export method deprec refactor document seem state method avail idea',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('fi fl result big like symbol use lyx xetex output font dejavu san write input text fi fl output text display strang big like symbol know help would appreci',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('selcurrentmanuf declar may inaccess due protect level drop list tri use id sql code behind get error declar may inaccess due protect level code behind function tri use select option',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('except prelud read pars haskel pars express recurs portion code read two number main io function omit give sum ration use later multipl oper second error pars function unabl handl look data ratio page web solut found would appreci help thank csjc',)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "('append caus ie error follow code work fine ff function caud error ie anyon abl tell anyth wrong code ie problem purpos add hidden field form differ depend user click submit form ie debugg give error text hightlight within jqueri debugg',)\n",
            "----------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IspyyegoU99N",
        "colab_type": "text"
      },
      "source": [
        "__ Saving Preprocessed data to a Database __"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3af6qQYP93Q",
        "colab_type": "code",
        "outputId": "1d2f0f8b-bcc2-4403-b594-d5c2403b8915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "i=0\n",
        "conn_r = create_connection(read_db)\n",
        "if conn_r is not None:\n",
        "    reader =conn_r.cursor()\n",
        "    # for selecting first 0.5M rows\n",
        "    reader.execute(\"SELECT Title, Body, Tags From no_dup_train LIMIT 2;\")\n",
        "\n",
        "for row in reader:\n",
        "    \n",
        "    is_code = 0\n",
        "    \n",
        "    title, question, tags = row[0], row[1], str(row[2])\n",
        "    \n",
        "    if '' in question:\n",
        "        questions_with_code+=1\n",
        "        is_code = 1\n",
        "    x = len(question)+len(title)\n",
        "    len_pre+=x\n",
        "    \n",
        "    code = str(re.findall(r'(.*?)', question, flags=re.DOTALL))\n",
        "    \n",
        "    question=re.sub('(.*?)', '', question, flags=re.MULTILINE|re.DOTALL)\n",
        "    question=striphtml(question.encode('utf-8'))\n",
        "    \n",
        "    title=title.encode('utf-8')\n",
        "    \n",
        "    # adding title three time to the data to increase its weight\n",
        "    # add tags string to the training data\n",
        "    print(\"title   \",title,\" title\")\n",
        "    question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
        "    print(question)\n",
        "#     if questions_proccesed<=train_datasize:\n",
        "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question+\" \"+str(tags)\n",
        "#     else:\n",
        "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
        "\n",
        "    question=re.sub(r'[^A-Za-z0-9#+.\\-]+',' ',question)\n",
        "    words=word_tokenize(str(question.lower()))\n",
        "    \n",
        "    #Removing all single letter and and stopwords from question exceptt for the letter 'c'\n",
        "    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))\n",
        "    \n",
        "    len_post+=len(question)\n",
        "    tup = (question,code,tags,x,len(question),is_code)\n",
        "    questions_proccesed += 1\n",
        "    print(\" Final Question: \",question)\n",
        "conn_r.commit()\n",
        "conn_r.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "title    b'     Implementing Boundary Value Analysis of Software Testing in a C++ program?'  title\n",
            "b'     Implementing Boundary Value Analysis of Software Testing in a C++ program?' b'     Implementing Boundary Value Analysis of Software Testing in a C++ program?' b'     Implementing Boundary Value Analysis of Software Testing in a C++ program?' b\"  \\n\\n The answer should come in the form of a table like \\n\\n  \\n\\n if the no of inputs is 3 and their ranges are\\n1,100\\n1,100\\n1,100\\n(could be varied too) \\n\\n The output is not coming,can anyone correct the code or tell me what's wrong? \\n\"\n",
            " Final Question:  implement boundari valu analysi softwar test c++ program implement boundari valu analysi softwar test c++ program implement boundari valu analysi softwar test c++ program answer come form tabl like input rang n1 100 n1 100 n1 100 could vari output come anyon correct code tell wrong\n",
            "title    b'    Dynamic Datagrid Binding in Silverlight?'  title\n",
            "b'    Dynamic Datagrid Binding in Silverlight?' b'    Dynamic Datagrid Binding in Silverlight?' b'    Dynamic Datagrid Binding in Silverlight?' b\" I should do binding for datagrid dynamically at code. I wrote the code as below. When I debug this code block, it seems that it does bindings correctly, but grid comes with no columns on form. \\n\\n  \\n\\n Why doesn't come grid with columns, although I did necessary bindings?\\nThanks for the replies in advance.. \\n\"\n",
            " Final Question:  dynam datagrid bind silverlight dynam datagrid bind silverlight dynam datagrid bind silverlight bind datagrid dynam code wrote code debug code block seem bind correct grid come column form come grid column although necessari bind nthank repli advance..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "69cf6ed2-4bf1-483c-8276-d902a48e5d2a",
        "id": "n-sveD7lU_lw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "preprocessed_old.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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questiontags
0chang cpu soni vaio pcg grx tri everywher find...cpu motherboard sony-vaio replacement disassembly
1display size grayscal qimag qt abl display ima...c++ qt qt4
2datagrid selecteditem set back null eventtocom...mvvm silverlight-4.0
3filter string collect base listview item resol...c# winforms string listview collections
4disabl home button without use type keyguard c...android android-layout android-manifest androi...
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              "
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            ],
            "text/plain": [
              "                                            question                                               tags\n",
              "0  chang cpu soni vaio pcg grx tri everywher find...  cpu motherboard sony-vaio replacement disassembly\n",
              "1  display size grayscal qimag qt abl display ima...                                         c++ qt qt4\n",
              "2  datagrid selecteditem set back null eventtocom...                               mvvm silverlight-4.0\n",
              "3  filter string collect base listview item resol...            c# winforms string listview collections\n",
              "4  disabl home button without use type keyguard c...  android android-layout android-manifest androi..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_x-ETQJU99P",
        "colab_type": "code",
        "outputId": "d272cc1d-65d1-4509-9d3a-f18db8c727fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "start = datetime.now()\n",
        "#check here\n",
        "#Taking 0.5 Million entries to a dataframe.\n",
        "write_db = 'drive/My Drive/Stackoverflow/MyTitlemoreweight.db'\n",
        "\n",
        "if os.path.isfile(write_db):\n",
        "    conn_r = create_connection(write_db)\n",
        "    print(type(conn_r))\n",
        "    if conn_r is not None:\n",
        "        print(type(conn_r),\"inside\")\n",
        "        preprocessed_data = pd.read_sql_query(\"\"\"SELECT question, Tags FROM QuestionsProcessed\"\"\", conn_r)\n",
        "        print(\"done\")\n",
        "    conn_r.commit()\n",
        "    conn_r.close()\n",
        "    \n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            " inside\n",
            "done\n",
            "Time taken to run this cell : 0:00:03.673262\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oyiu0vNJ99hm",
        "colab_type": "code",
        "outputId": "1e42d6cb-441d-4116-d93a-aa2cebd88ed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "checkTableExists(conn_r)\n",
        "      \n",
        "def checkTableExists(dbcon):\n",
        "    cursr = dbcon.cursor()\n",
        "    str = \"select name from sqlite_master where type='table'\"\n",
        "    table_names = cursr.execute(str)\n",
        "    print(\"Tables in the databse:\")\n",
        "    tables =table_names.fetchall() \n",
        "    print(tables[0][0])\n",
        "    return(len(tables))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tables in the databse:\n",
            "QuestionsProcessed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rFfnQbBJU5lC",
        "colab": {}
      },
      "source": [
        "cou = \"select name from sqlite_master where type='table'\"\n",
        "table_names = cursr.execute(str)\n",
        "print(\"Tables in the databse:\")\n",
        "tables =table_names.fetchall() \n",
        "\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bc7hwHjBU99U",
        "colab_type": "code",
        "outputId": "f98c2087-8c33-4979-a641-ae30e272f547",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "preprocessed_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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questiontags
0sql inject issu prevent correct form submiss p...php forms
1countabl subaddit lebesgu measur let lbrace rb...real-analysis measure-theory
2hql equival sql queri hql queri replac name cl...hibernate hql
3undefin symbol architectur objc class skpsmtpm...iphone email-integration
4java lang nosuchmethoderror javax servlet serv...java servlets jboss
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              "
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            ],
            "text/plain": [
              "                                            question                          tags\n",
              "0  sql inject issu prevent correct form submiss p...                     php forms\n",
              "1  countabl subaddit lebesgu measur let lbrace rb...  real-analysis measure-theory\n",
              "2  hql equival sql queri hql queri replac name cl...                 hibernate hql\n",
              "3  undefin symbol architectur objc class skpsmtpm...      iphone email-integration\n",
              "4  java lang nosuchmethoderror javax servlet serv...           java servlets jboss"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xk9V0azqU99X",
        "colab_type": "code",
        "outputId": "4f7c07fc-651a-4bde-e604-33470f1fbcd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"number of data points in sample :\", preprocessed_data.shape[0])\n",
        "print(\"number of dimensions :\", preprocessed_data.shape[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of data points in sample : 499992\n",
            "number of dimensions : 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUpccCSkU99Z",
        "colab_type": "text"
      },
      "source": [
        "__ Converting string Tags to multilable output variables __ "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWg_g1lNU99a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = CountVectorizer(tokenizer = lambda x: x.split(), binary='true')\n",
        "multilabel_y = vectorizer.fit_transform(preprocessed_data['tags'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6EONIrUcQyR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbtD0Hx8U99c",
        "colab_type": "text"
      },
      "source": [
        "__ Selecting 500 Tags __"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_nMDxAIU99d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "questions_explained = []\n",
        "total_tags=multilabel_y.shape[1]\n",
        "total_qs=preprocessed_data.shape[0]\n",
        "for i in range(500, total_tags, 100):\n",
        "    questions_explained.append(np.round(((total_qs-questions_explained_fn(i))/total_qs)*100,3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fggMk2IJU99f",
        "colab_type": "code",
        "outputId": "7c443492-b0c4-492d-afc2-16c59b8b954e",
        "colab": {}
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(questions_explained)\n",
        "xlabel = list(500+np.array(range(-50,450,50))*50)\n",
        "ax.set_xticklabels(xlabel)\n",
        "plt.xlabel(\"Number of tags\")\n",
        "plt.ylabel(\"Number Questions coverd partially\")\n",
        "plt.grid()\n",
        "plt.show()\n",
        "# you can choose any number of tags based on your computing power, minimun is 500(it covers 90% of the tags)\n",
        "print(\"with \",5500,\"tags we are covering \",questions_explained[50],\"% of questions\")\n",
        "print(\"with \",500,\"tags we are covering \",questions_explained[0],\"% of questions\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qgIJ46i7xFLrOQEREBm/Au7DM7GLgv4GfhaKpwB8yGVS2aCwQEZH0xbmN91Ki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\nD/jbbnuRxaXkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "with  5500 tags we are covering  99.157 % of questions\n",
            "with  500 tags we are covering  90.956 % of questions\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuJzfmNrU99i",
        "colab_type": "code",
        "outputId": "bdbef0b8-b416-4c1f-fa96-b190d1dddea2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# we will be taking 500 tags\n",
        "multilabel_yx = tags_to_choose(500)\n",
        "print(\"number of questions that are not covered :\", questions_explained_fn(500),\"out of 500000\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of questions that are not covered : 45221 out of 500000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UJ-krqxv7es0",
        "colab": {}
      },
      "source": [
        "sub_size = 200000\n",
        "preprocessed_data_sub = preprocessed_data.head(sub_size)\n",
        "total_size=preprocessed_data_sub.shape[0]\n",
        "train_size=int(0.80*total_size)\n",
        "\n",
        "x_train=preprocessed_data_sub.head(train_size)\n",
        "x_test=preprocessed_data_sub.tail(total_size - train_size)\n",
        "\n",
        "y_train = multilabel_yx[0:train_size,:]\n",
        "y_test = multilabel_yx[train_size:total_size,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f4ee9779-44a4-45b4-cc56-b060055b9b9a",
        "id": "dLgA-2pick7o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Number of data points in train data :\", y_train.shape)\n",
        "print(\"Number of data points in test data :\", y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of data points in train data : (160000, 500)\n",
            "Number of data points in test data : (40000, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "17zP0m1Nck7r"
      },
      "source": [
        "
 4.5.2 Featurizing data with TfIdf vectorizer 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2M66UYo8PYu",
        "colab_type": "code",
        "outputId": "8cca51c1-9e26-4ffd-89a1-ec9dd1393ce7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start = datetime.now()\n",
        "vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm=\"l2\", \\\n",
        "                             tokenizer = lambda x: x.split(), sublinear_tf=False, ngram_range=(1,3))\n",
        "x_train_multilabel = vectorizer.fit_transform(x_train['question'])\n",
        "x_test_multilabel = vectorizer.transform(x_test['question'])\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 0:01:53.148966\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsduwXTeU99k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datasize = 400000\n",
        "x_train=preprocessed_data.head(train_datasize)\n",
        "x_test=preprocessed_data.tail(preprocessed_data.shape[0] - 400000)\n",
        "\n",
        "y_train = multilabel_yx[0:train_datasize,:]\n",
        "y_test = multilabel_yx[train_datasize:preprocessed_data.shape[0],:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZZDSH_VU99m",
        "colab_type": "code",
        "outputId": "24ca865b-662a-4109-ca89-9c765b249a39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Number of data points in train data :\", y_train.shape)\n",
        "print(\"Number of data points in test data :\", y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of data points in train data : (400000, 500)\n",
            "Number of data points in test data : (99992, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDJ2PvnzU99o",
        "colab_type": "text"
      },
      "source": [
        "
 4.5.2 Featurizing data with TfIdf vectorizer 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "530e8tW9U99o",
        "colab_type": "code",
        "outputId": "5fa33878-3645-47e9-a839-6734c7520e80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start = datetime.now()\n",
        "vectorizer = CountVectorizer(min_df=0.00009, max_features=200000, \\\n",
        "                             tokenizer = lambda x: x.split(), ngram_range=(1,4))\n",
        "x_train_multilabel = vectorizer.fit_transform(x_train['question'])\n",
        "x_test_multilabel = vectorizer.transform(x_test['question'])\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 0:07:23.758624\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osDJBcX958wd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "joblib.dump(x_train_multilabel, 'lr_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9iDfzXIU99t",
        "colab_type": "code",
        "outputId": "8bdea536-6661-4aa9-cd66-68d1eadf83b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Dimensions of train data X:\",x_train_multilabel.shape, \"Y :\",y_train.shape)\n",
        "print(\"Dimensions of test data X:\",x_test_multilabel.shape,\"Y:\",y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of train data X: (400000, 95549) Y : (400000, 500)\n",
            "Dimensions of test data X: (99992, 95549) Y: (99992, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kmmnoy4XU99v",
        "colab_type": "text"
      },
      "source": [
        "
 4.5.3 Applying Logistic Regression with OneVsRest Classifier 
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnHoxl5DU99w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = datetime.now()\n",
        "classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'))\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "# clf.fit(x_train_multilabel.copy(),y_train)\n",
        "predictions = classifier.predict (x_test_multilabel)\n",
        "\n",
        "\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='micro')\n",
        "recall = recall_score(y_test, predictions, average='micro')\n",
        "f1 = f1_score(y_test, predictions, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='macro')\n",
        "recall = recall_score(y_test, predictions, average='macro')\n",
        "f1 = f1_score(y_test, predictions, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "kWQKiSJxU994",
        "colab_type": "code",
        "outputId": "23b678ac-0e49-4fa8-e8d1-1908ba40b8f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = datetime.now()\n",
        "classifier_2 = OneVsRestClassifier(LogisticRegression(penalty='l1'), n_jobs=-1)\n",
        "x_train_multilabel.sort_indices()\n",
        "classifier_2.fit(x_train_multilabel, y_train)\n",
        "predictions_2 = classifier_2.predict(x_test_multilabel)\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions_2))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions_2))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='micro')\n",
        "recall = recall_score(y_test, predictions_2, average='micro')\n",
        "f1 = f1_score(y_test, predictions_2, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='macro')\n",
        "recall = recall_score(y_test, predictions_2, average='macro')\n",
        "f1 = f1_score(y_test, predictions_2, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions_2))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.2101468117449396\n",
            "Hamming loss  0.0031686334906792543\n",
            "Micro-average quality numbers\n",
            "Precision: 0.5590, Recall: 0.4193, F1-measure: 0.4792\n",
            "Macro-average quality numbers\n",
            "Precision: 0.4452, Recall: 0.3448, F1-measure: 0.3852\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.74      0.81      5516\n",
            "           1       0.52      0.41      0.46      8189\n",
            "           2       0.63      0.47      0.54      6529\n",
            "           3       0.68      0.53      0.60      3229\n",
            "           4       0.65      0.50      0.56      6430\n",
            "           5       0.61      0.42      0.50      2878\n",
            "           6       0.74      0.57      0.64      5086\n",
            "           7       0.74      0.63      0.68      4533\n",
            "           8       0.33      0.19      0.24      2999\n",
            "           9       0.70      0.59      0.64      2765\n",
            "          10       0.43      0.30      0.35      3051\n",
            "          11       0.59      0.46      0.52      3009\n",
            "          12       0.48      0.37      0.42      2630\n",
            "          13       0.53      0.38      0.44      1425\n",
            "          14       0.79      0.62      0.69      2548\n",
            "          15       0.48      0.31      0.38      2371\n",
            "          16       0.54      0.30      0.39       873\n",
            "          17       0.78      0.66      0.72      2151\n",
            "          18       0.43      0.31      0.36      2204\n",
            "          19       0.52      0.46      0.49       831\n",
            "          20       0.68      0.50      0.58      1860\n",
            "          21       0.25      0.19      0.22      2023\n",
            "          22       0.39      0.31      0.34      1513\n",
            "          23       0.77      0.59      0.67      1207\n",
            "          24       0.43      0.34      0.38       506\n",
            "          25       0.51      0.39      0.44       425\n",
            "          26       0.56      0.45      0.50       793\n",
            "          27       0.53      0.43      0.47      1291\n",
            "          28       0.60      0.42      0.49      1208\n",
            "          29       0.27      0.17      0.21       406\n",
            "          30       0.46      0.24      0.32       504\n",
            "          31       0.20      0.15      0.17       732\n",
            "          32       0.47      0.34      0.40       441\n",
            "          33       0.51      0.39      0.44      1645\n",
            "          34       0.46      0.31      0.37      1058\n",
            "          35       0.73      0.59      0.65       946\n",
            "          36       0.45      0.30      0.36       644\n",
            "          37       0.87      0.68      0.77       136\n",
            "          38       0.52      0.39      0.45       570\n",
            "          39       0.61      0.34      0.43       766\n",
            "          40       0.51      0.44      0.47      1132\n",
            "          41       0.34      0.32      0.33       174\n",
            "          42       0.66      0.57      0.61       210\n",
            "          43       0.61      0.46      0.52       433\n",
            "          44       0.61      0.50      0.55       626\n",
            "          45       0.54      0.40      0.46       852\n",
            "          46       0.63      0.49      0.55       534\n",
            "          47       0.29      0.26      0.28       349\n",
            "          48       0.65      0.57      0.61       496\n",
            "          49       0.76      0.66      0.70       785\n",
            "          50       0.20      0.12      0.15       475\n",
            "          51       0.25      0.22      0.23       305\n",
            "          52       0.23      0.10      0.14       251\n",
            "          53       0.53      0.42      0.47       914\n",
            "          54       0.38      0.25      0.30       728\n",
            "          55       0.15      0.07      0.10       258\n",
            "          56       0.35      0.30      0.32       821\n",
            "          57       0.33      0.18      0.24       541\n",
            "          58       0.58      0.35      0.44       748\n",
            "          59       0.88      0.71      0.79       724\n",
            "          60       0.33      0.20      0.25       660\n",
            "          61       0.43      0.25      0.31       235\n",
            "          62       0.86      0.74      0.80       718\n",
            "          63       0.78      0.71      0.74       468\n",
            "          64       0.45      0.32      0.38       191\n",
            "          65       0.29      0.21      0.24       429\n",
            "          66       0.20      0.13      0.16       415\n",
            "          67       0.67      0.56      0.61       274\n",
            "          68       0.72      0.54      0.62       510\n",
            "          69       0.59      0.50      0.54       466\n",
            "          70       0.25      0.17      0.20       305\n",
            "          71       0.32      0.21      0.25       247\n",
            "          72       0.72      0.55      0.62       401\n",
            "          73       0.83      0.80      0.82        86\n",
            "          74       0.58      0.44      0.50       120\n",
            "          75       0.82      0.71      0.76       129\n",
            "          76       0.14      0.06      0.09       473\n",
            "          77       0.38      0.32      0.35       143\n",
            "          78       0.67      0.49      0.56       347\n",
            "          79       0.51      0.28      0.36       479\n",
            "          80       0.43      0.38      0.40       279\n",
            "          81       0.49      0.29      0.37       461\n",
            "          82       0.17      0.08      0.11       298\n",
            "          83       0.71      0.55      0.62       396\n",
            "          84       0.38      0.39      0.39       184\n",
            "          85       0.43      0.32      0.37       573\n",
            "          86       0.26      0.13      0.17       325\n",
            "          87       0.51      0.44      0.47       273\n",
            "          88       0.45      0.31      0.37       135\n",
            "          89       0.24      0.17      0.20       232\n",
            "          90       0.47      0.41      0.44       409\n",
            "          91       0.53      0.34      0.42       420\n",
            "          92       0.68      0.57      0.62       408\n",
            "          93       0.53      0.54      0.53       241\n",
            "          94       0.19      0.09      0.13       211\n",
            "          95       0.30      0.18      0.23       277\n",
            "          96       0.22      0.14      0.17       410\n",
            "          97       0.76      0.48      0.59       501\n",
            "          98       0.67      0.63      0.65       136\n",
            "          99       0.45      0.37      0.40       239\n",
            "         100       0.33      0.21      0.25       324\n",
            "         101       0.85      0.75      0.80       277\n",
            "         102       0.88      0.79      0.83       613\n",
            "         103       0.36      0.23      0.28       157\n",
            "         104       0.22      0.13      0.16       295\n",
            "         105       0.66      0.46      0.54       334\n",
            "         106       0.67      0.39      0.49       335\n",
            "         107       0.68      0.60      0.63       389\n",
            "         108       0.52      0.33      0.40       251\n",
            "         109       0.54      0.49      0.51       317\n",
            "         110       0.30      0.11      0.16       187\n",
            "         111       0.41      0.17      0.24       140\n",
            "         112       0.54      0.47      0.51       154\n",
            "         113       0.49      0.27      0.35       332\n",
            "         114       0.42      0.31      0.36       323\n",
            "         115       0.43      0.35      0.38       344\n",
            "         116       0.68      0.59      0.63       370\n",
            "         117       0.39      0.31      0.35       313\n",
            "         118       0.75      0.76      0.75       874\n",
            "         119       0.35      0.26      0.30       293\n",
            "         120       0.12      0.08      0.10       200\n",
            "         121       0.67      0.52      0.58       463\n",
            "         122       0.24      0.12      0.16       119\n",
            "         123       0.16      0.04      0.06       256\n",
            "         124       0.86      0.72      0.78       195\n",
            "         125       0.30      0.20      0.24       138\n",
            "         126       0.70      0.54      0.61       376\n",
            "         127       0.19      0.07      0.11       122\n",
            "         128       0.11      0.06      0.07       252\n",
            "         129       0.43      0.40      0.41       144\n",
            "         130       0.32      0.21      0.25       150\n",
            "         131       0.21      0.11      0.14       210\n",
            "         132       0.50      0.34      0.41       361\n",
            "         133       0.84      0.66      0.74       453\n",
            "         134       0.79      0.75      0.77       124\n",
            "         135       0.15      0.12      0.14        91\n",
            "         136       0.51      0.39      0.44       128\n",
            "         137       0.45      0.42      0.43       218\n",
            "         138       0.33      0.23      0.27       243\n",
            "         139       0.26      0.18      0.21       149\n",
            "         140       0.69      0.55      0.61       318\n",
            "         141       0.21      0.13      0.16       159\n",
            "         142       0.55      0.43      0.48       274\n",
            "         143       0.81      0.83      0.82       362\n",
            "         144       0.43      0.31      0.36       118\n",
            "         145       0.48      0.42      0.45       164\n",
            "         146       0.50      0.42      0.45       461\n",
            "         147       0.67      0.47      0.55       159\n",
            "         148       0.34      0.21      0.26       166\n",
            "         149       0.89      0.60      0.72       346\n",
            "         150       0.48      0.22      0.30       350\n",
            "         151       0.88      0.67      0.76        55\n",
            "         152       0.68      0.52      0.59       387\n",
            "         153       0.33      0.31      0.32       150\n",
            "         154       0.30      0.16      0.21       281\n",
            "         155       0.25      0.19      0.22       202\n",
            "         156       0.72      0.65      0.69       130\n",
            "         157       0.23      0.12      0.16       245\n",
            "         158       0.91      0.71      0.80       177\n",
            "         159       0.44      0.40      0.42       130\n",
            "         160       0.35      0.24      0.29       336\n",
            "         161       0.76      0.66      0.71       220\n",
            "         162       0.18      0.12      0.14       229\n",
            "         163       0.78      0.47      0.59       316\n",
            "         164       0.64      0.43      0.52       283\n",
            "         165       0.47      0.35      0.40       197\n",
            "         166       0.52      0.52      0.52       101\n",
            "         167       0.38      0.24      0.29       231\n",
            "         168       0.42      0.36      0.39       370\n",
            "         169       0.38      0.23      0.29       258\n",
            "         170       0.26      0.18      0.21       101\n",
            "         171       0.34      0.29      0.32        89\n",
            "         172       0.44      0.39      0.41       193\n",
            "         173       0.49      0.37      0.42       309\n",
            "         174       0.29      0.16      0.21       172\n",
            "         175       0.79      0.75      0.77        95\n",
            "         176       0.84      0.64      0.73       346\n",
            "         177       0.81      0.61      0.69       322\n",
            "         178       0.53      0.50      0.51       232\n",
            "         179       0.20      0.10      0.14       125\n",
            "         180       0.43      0.41      0.42       145\n",
            "         181       0.29      0.21      0.24        77\n",
            "         182       0.18      0.12      0.14       182\n",
            "         183       0.53      0.37      0.44       257\n",
            "         184       0.23      0.14      0.17       216\n",
            "         185       0.29      0.19      0.23       242\n",
            "         186       0.33      0.24      0.27       165\n",
            "         187       0.66      0.58      0.62       263\n",
            "         188       0.20      0.11      0.14       174\n",
            "         189       0.65      0.52      0.58       136\n",
            "         190       0.82      0.60      0.69       202\n",
            "         191       0.32      0.23      0.27       134\n",
            "         192       0.57      0.48      0.52       230\n",
            "         193       0.22      0.17      0.19        90\n",
            "         194       0.54      0.56      0.55       185\n",
            "         195       0.16      0.08      0.11       156\n",
            "         196       0.14      0.10      0.12       160\n",
            "         197       0.27      0.17      0.21       266\n",
            "         198       0.30      0.20      0.24       284\n",
            "         199       0.19      0.08      0.12       145\n",
            "         200       0.85      0.77      0.81       212\n",
            "         201       0.45      0.27      0.34       317\n",
            "         202       0.68      0.66      0.67       427\n",
            "         203       0.21      0.16      0.18       232\n",
            "         204       0.40      0.33      0.36       217\n",
            "         205       0.49      0.55      0.52       527\n",
            "         206       0.18      0.08      0.11       124\n",
            "         207       0.34      0.30      0.32       103\n",
            "         208       0.20      0.12      0.15       193\n",
            "         209       0.77      0.56      0.65       287\n",
            "         210       0.56      0.44      0.49       220\n",
            "         211       0.40      0.21      0.27       140\n",
            "         212       0.15      0.09      0.11       161\n",
            "         213       0.47      0.57      0.51        72\n",
            "         214       0.61      0.44      0.51       396\n",
            "         215       0.61      0.42      0.50       134\n",
            "         216       0.43      0.28      0.34       400\n",
            "         217       0.33      0.27      0.30        75\n",
            "         218       0.91      0.78      0.84       219\n",
            "         219       0.59      0.43      0.50       210\n",
            "         220       0.84      0.69      0.76       298\n",
            "         221       0.87      0.71      0.78       266\n",
            "         222       0.65      0.47      0.54       290\n",
            "         223       0.13      0.05      0.08       128\n",
            "         224       0.68      0.52      0.59       159\n",
            "         225       0.38      0.35      0.36       164\n",
            "         226       0.45      0.34      0.39       144\n",
            "         227       0.55      0.42      0.47       276\n",
            "         228       0.09      0.05      0.06       235\n",
            "         229       0.14      0.07      0.09       216\n",
            "         230       0.32      0.25      0.28       228\n",
            "         231       0.63      0.53      0.58        64\n",
            "         232       0.22      0.17      0.19       103\n",
            "         233       0.60      0.40      0.48       216\n",
            "         234       0.49      0.23      0.32       116\n",
            "         235       0.45      0.34      0.39        77\n",
            "         236       0.82      0.69      0.75        67\n",
            "         237       0.29      0.18      0.22       218\n",
            "         238       0.24      0.22      0.23       139\n",
            "         239       0.24      0.06      0.10        94\n",
            "         240       0.39      0.32      0.35        77\n",
            "         241       0.29      0.14      0.19       167\n",
            "         242       0.64      0.42      0.51        86\n",
            "         243       0.29      0.24      0.26        58\n",
            "         244       0.50      0.43      0.46       269\n",
            "         245       0.14      0.09      0.11       112\n",
            "         246       0.92      0.82      0.87       255\n",
            "         247       0.22      0.22      0.22        58\n",
            "         248       0.13      0.07      0.09        81\n",
            "         249       0.06      0.02      0.03       131\n",
            "         250       0.36      0.27      0.31        93\n",
            "         251       0.56      0.39      0.46       154\n",
            "         252       0.10      0.05      0.07       129\n",
            "         253       0.44      0.36      0.40        83\n",
            "         254       0.21      0.14      0.17       191\n",
            "         255       0.14      0.09      0.11       218\n",
            "         256       0.13      0.08      0.10       130\n",
            "         257       0.40      0.32      0.36        93\n",
            "         258       0.64      0.52      0.57       217\n",
            "         259       0.28      0.21      0.24       141\n",
            "         260       0.57      0.25      0.35       143\n",
            "         261       0.40      0.18      0.25       219\n",
            "         262       0.49      0.38      0.43       107\n",
            "         263       0.36      0.23      0.28       236\n",
            "         264       0.27      0.21      0.24       119\n",
            "         265       0.44      0.29      0.35        72\n",
            "         266       0.10      0.06      0.07        70\n",
            "         267       0.34      0.24      0.28       107\n",
            "         268       0.54      0.49      0.51       169\n",
            "         269       0.30      0.19      0.23       129\n",
            "         270       0.68      0.56      0.61       159\n",
            "         271       0.73      0.54      0.62       190\n",
            "         272       0.42      0.33      0.37       248\n",
            "         273       0.86      0.76      0.81       264\n",
            "         274       0.80      0.69      0.74       105\n",
            "         275       0.19      0.12      0.14       104\n",
            "         276       0.05      0.03      0.03       115\n",
            "         277       0.80      0.61      0.69       170\n",
            "         278       0.69      0.50      0.58       145\n",
            "         279       0.85      0.75      0.79       230\n",
            "         280       0.60      0.41      0.49        80\n",
            "         281       0.64      0.56      0.60       217\n",
            "         282       0.69      0.54      0.60       175\n",
            "         283       0.28      0.23      0.25       269\n",
            "         284       0.55      0.42      0.48        74\n",
            "         285       0.70      0.51      0.59       206\n",
            "         286       0.83      0.71      0.76       227\n",
            "         287       0.65      0.45      0.53       130\n",
            "         288       0.17      0.08      0.11       129\n",
            "         289       0.16      0.12      0.14        80\n",
            "         290       0.15      0.13      0.14        99\n",
            "         291       0.57      0.39      0.47       208\n",
            "         292       0.26      0.15      0.19        67\n",
            "         293       0.77      0.54      0.63       109\n",
            "         294       0.36      0.31      0.33       140\n",
            "         295       0.22      0.15      0.18       241\n",
            "         296       0.22      0.14      0.17        72\n",
            "         297       0.21      0.16      0.18       107\n",
            "         298       0.62      0.57      0.60        61\n",
            "         299       0.72      0.60      0.65        77\n",
            "         300       0.16      0.12      0.14       111\n",
            "         301       0.02      0.01      0.01       126\n",
            "         302       0.16      0.14      0.15        73\n",
            "         303       0.54      0.43      0.48       176\n",
            "         304       0.86      0.83      0.84       230\n",
            "         305       0.83      0.72      0.77       156\n",
            "         306       0.42      0.38      0.40       146\n",
            "         307       0.21      0.12      0.15        98\n",
            "         308       0.06      0.03      0.04        78\n",
            "         309       0.47      0.17      0.25        94\n",
            "         310       0.56      0.41      0.47       162\n",
            "         311       0.68      0.52      0.59       116\n",
            "         312       0.45      0.37      0.40        57\n",
            "         313       0.32      0.09      0.14        65\n",
            "         314       0.39      0.38      0.39       138\n",
            "         315       0.53      0.32      0.40       195\n",
            "         316       0.38      0.33      0.35        69\n",
            "         317       0.22      0.19      0.21       134\n",
            "         318       0.53      0.41      0.46       148\n",
            "         319       0.81      0.57      0.67       161\n",
            "         320       0.20      0.22      0.21       104\n",
            "         321       0.67      0.62      0.64       156\n",
            "         322       0.57      0.49      0.53       134\n",
            "         323       0.52      0.48      0.50       232\n",
            "         324       0.21      0.17      0.19        92\n",
            "         325       0.38      0.27      0.32       197\n",
            "         326       0.07      0.06      0.07       126\n",
            "         327       0.15      0.07      0.09       115\n",
            "         328       0.92      0.71      0.80       198\n",
            "         329       0.44      0.33      0.37       125\n",
            "         330       0.48      0.26      0.34        81\n",
            "         331       0.33      0.16      0.21        94\n",
            "         332       0.29      0.20      0.23        56\n",
            "         333       0.13      0.08      0.10       260\n",
            "         334       0.15      0.12      0.13        60\n",
            "         335       0.24      0.12      0.16       110\n",
            "         336       0.59      0.52      0.55        71\n",
            "         337       0.13      0.09      0.11        66\n",
            "         338       0.40      0.47      0.43       150\n",
            "         339       0.06      0.06      0.06        54\n",
            "         340       0.74      0.61      0.66       195\n",
            "         341       0.65      0.57      0.61        79\n",
            "         342       0.37      0.47      0.41        38\n",
            "         343       0.55      0.40      0.46        43\n",
            "         344       0.34      0.31      0.32        68\n",
            "         345       0.67      0.37      0.47        71\n",
            "         346       0.11      0.08      0.09       116\n",
            "         347       0.61      0.53      0.57       111\n",
            "         348       0.26      0.19      0.22        63\n",
            "         349       0.81      0.72      0.76       104\n",
            "         350       0.57      0.57      0.57        44\n",
            "         351       0.28      0.33      0.30        40\n",
            "         352       0.79      0.64      0.71       136\n",
            "         353       0.38      0.22      0.28        54\n",
            "         354       0.22      0.12      0.15       134\n",
            "         355       0.51      0.41      0.45       120\n",
            "         356       0.45      0.35      0.39       228\n",
            "         357       0.54      0.43      0.48       269\n",
            "         358       0.55      0.36      0.44        80\n",
            "         359       0.71      0.64      0.68       140\n",
            "         360       0.29      0.22      0.25       125\n",
            "         361       0.88      0.75      0.81       169\n",
            "         362       0.16      0.12      0.14        56\n",
            "         363       0.83      0.77      0.80       154\n",
            "         364       0.22      0.24      0.23        58\n",
            "         365       0.24      0.17      0.20        71\n",
            "         366       0.92      0.65      0.76        54\n",
            "         367       0.14      0.10      0.12       116\n",
            "         368       0.24      0.19      0.21        54\n",
            "         369       0.09      0.06      0.07        71\n",
            "         370       0.19      0.10      0.13        61\n",
            "         371       0.29      0.10      0.15        71\n",
            "         372       0.50      0.48      0.49        52\n",
            "         373       0.59      0.45      0.51       150\n",
            "         374       0.30      0.27      0.28        93\n",
            "         375       0.13      0.10      0.12        67\n",
            "         376       0.05      0.03      0.03        76\n",
            "         377       0.44      0.35      0.39       106\n",
            "         378       0.09      0.03      0.05        86\n",
            "         379       0.15      0.14      0.15        14\n",
            "         380       0.74      0.56      0.64       122\n",
            "         381       0.11      0.08      0.09       104\n",
            "         382       0.24      0.15      0.19        66\n",
            "         383       0.18      0.08      0.11       155\n",
            "         384       0.47      0.39      0.43       110\n",
            "         385       0.39      0.36      0.37        50\n",
            "         386       0.22      0.12      0.16        64\n",
            "         387       0.22      0.12      0.15        93\n",
            "         388       0.45      0.34      0.39       102\n",
            "         389       0.12      0.06      0.08       108\n",
            "         390       0.90      0.69      0.78       178\n",
            "         391       0.34      0.21      0.26       115\n",
            "         392       0.66      0.45      0.54        42\n",
            "         393       0.07      0.01      0.02       134\n",
            "         394       0.26      0.16      0.20       112\n",
            "         395       0.39      0.33      0.36       176\n",
            "         396       0.29      0.18      0.23       125\n",
            "         397       0.63      0.44      0.52       224\n",
            "         398       0.77      0.68      0.72        63\n",
            "         399       0.12      0.07      0.09        59\n",
            "         400       0.42      0.43      0.43        63\n",
            "         401       0.44      0.33      0.37        98\n",
            "         402       0.39      0.26      0.31       162\n",
            "         403       0.23      0.17      0.19        83\n",
            "         404       0.64      0.84      0.73        19\n",
            "         405       0.19      0.14      0.16        92\n",
            "         406       0.42      0.39      0.41        41\n",
            "         407       0.48      0.37      0.42        43\n",
            "         408       0.57      0.49      0.53       160\n",
            "         409       0.13      0.08      0.10        50\n",
            "         410       0.00      0.00      0.00        19\n",
            "         411       0.31      0.23      0.26       175\n",
            "         412       0.22      0.15      0.18        72\n",
            "         413       0.21      0.09      0.13        95\n",
            "         414       0.29      0.20      0.23        97\n",
            "         415       0.14      0.10      0.12        48\n",
            "         416       0.41      0.34      0.37        83\n",
            "         417       0.24      0.17      0.20        40\n",
            "         418       0.24      0.16      0.19        91\n",
            "         419       0.51      0.42      0.46        90\n",
            "         420       0.29      0.24      0.26        37\n",
            "         421       0.13      0.09      0.11        66\n",
            "         422       0.48      0.41      0.44        73\n",
            "         423       0.44      0.32      0.37        56\n",
            "         424       0.88      0.88      0.88        33\n",
            "         425       0.13      0.05      0.07        76\n",
            "         426       0.05      0.02      0.03        81\n",
            "         427       0.95      0.75      0.84       150\n",
            "         428       0.91      0.69      0.78        29\n",
            "         429       0.98      0.95      0.97       389\n",
            "         430       0.56      0.46      0.51       167\n",
            "         431       0.36      0.14      0.20       123\n",
            "         432       0.28      0.21      0.24        39\n",
            "         433       0.28      0.28      0.28        82\n",
            "         434       0.90      0.71      0.80        66\n",
            "         435       0.56      0.52      0.54        93\n",
            "         436       0.48      0.37      0.42        87\n",
            "         437       0.15      0.09      0.11        86\n",
            "         438       0.65      0.51      0.57       104\n",
            "         439       0.41      0.22      0.29       100\n",
            "         440       0.18      0.09      0.12       141\n",
            "         441       0.40      0.43      0.41       110\n",
            "         442       0.23      0.21      0.22       123\n",
            "         443       0.31      0.21      0.25        71\n",
            "         444       0.21      0.12      0.15       109\n",
            "         445       0.41      0.38      0.39        48\n",
            "         446       0.41      0.32      0.36        76\n",
            "         447       0.21      0.26      0.24        38\n",
            "         448       0.57      0.56      0.56        81\n",
            "         449       0.45      0.32      0.37       132\n",
            "         450       0.38      0.33      0.35        81\n",
            "         451       0.73      0.42      0.53        76\n",
            "         452       0.10      0.09      0.09        44\n",
            "         453       0.00      0.00      0.00        43\n",
            "         454       0.78      0.56      0.65        70\n",
            "         455       0.32      0.30      0.31       155\n",
            "         456       0.29      0.23      0.26        43\n",
            "         457       0.37      0.35      0.36        72\n",
            "         458       0.19      0.16      0.18        62\n",
            "         459       0.41      0.32      0.36        69\n",
            "         460       0.14      0.10      0.12       119\n",
            "         461       0.57      0.37      0.45        79\n",
            "         462       0.28      0.23      0.25        47\n",
            "         463       0.33      0.30      0.31       104\n",
            "         464       0.55      0.43      0.49       106\n",
            "         465       0.33      0.30      0.31        64\n",
            "         466       0.43      0.32      0.37       173\n",
            "         467       0.52      0.41      0.46       107\n",
            "         468       0.42      0.34      0.38       126\n",
            "         469       0.21      0.09      0.12       114\n",
            "         470       0.90      0.83      0.86       140\n",
            "         471       0.57      0.39      0.47        79\n",
            "         472       0.40      0.43      0.42       143\n",
            "         473       0.64      0.41      0.50       158\n",
            "         474       0.31      0.14      0.19       138\n",
            "         475       0.19      0.15      0.17        59\n",
            "         476       0.61      0.49      0.54        88\n",
            "         477       0.71      0.69      0.70       176\n",
            "         478       0.90      0.79      0.84        24\n",
            "         479       0.25      0.17      0.21        92\n",
            "         480       0.69      0.58      0.63       100\n",
            "         481       0.40      0.37      0.38       103\n",
            "         482       0.20      0.14      0.16        74\n",
            "         483       0.71      0.64      0.67       105\n",
            "         484       0.18      0.08      0.12        83\n",
            "         485       0.06      0.05      0.05        82\n",
            "         486       0.29      0.20      0.23        71\n",
            "         487       0.38      0.25      0.30       120\n",
            "         488       0.23      0.10      0.13       105\n",
            "         489       0.51      0.40      0.45        87\n",
            "         490       0.90      0.84      0.87        32\n",
            "         491       0.10      0.06      0.07        69\n",
            "         492       0.12      0.06      0.08        49\n",
            "         493       0.07      0.05      0.06       117\n",
            "         494       0.45      0.39      0.42        61\n",
            "         495       0.94      0.81      0.87       344\n",
            "         496       0.22      0.17      0.19        52\n",
            "         497       0.48      0.34      0.40       137\n",
            "         498       0.33      0.16      0.22        98\n",
            "         499       0.31      0.23      0.26        79\n",
            "\n",
            "   micro avg       0.56      0.42      0.48    173798\n",
            "   macro avg       0.45      0.34      0.39    173798\n",
            "weighted avg       0.54      0.42      0.47    173798\n",
            " samples avg       0.44      0.40      0.39    173798\n",
            "\n",
            "Time taken to run this cell : 2:25:11.518179\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qY1LzZPU991",
        "colab_type": "code",
        "outputId": "2f36f85a-0f04-46cf-a6c9-bbd0fb97b438",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier_2, 'drive/My Drive/Stackoverflow/Mylrnew_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/Mylrnew_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab_type": "code",
        "outputId": "7728dac3-cda2-495c-8a6f-dd08cdc1f5fc",
        "id": "__CIhvUb9WS2",
        "colab": {}
      },
      "source": [
        "\n",
        "start = datetime.now()\n",
        "classifier_2 = OneVsRestClassifier(LogisticRegression(penalty='l1'), n_jobs=-1)\n",
        "classifier_2.fit(x_train_multilabel, y_train)\n",
        "predictions_2 = classifier_2.predict(x_test_multilabel)\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions_2))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions_2))\n",
        "\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='micro')\n",
        "recall = recall_score(y_test, predictions_2, average='micro')\n",
        "f1 = f1_score(y_test, predictions_2, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='macro')\n",
        "recall = recall_score(y_test, predictions_2, average='macro')\n",
        "f1 = f1_score(y_test, predictions_2, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions_2))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.25108\n",
            "Hamming loss  0.00270302\n",
            "Micro-average quality numbers\n",
            "Precision: 0.7172, Recall: 0.3672, F1-measure: 0.4858\n",
            "Macro-average quality numbers\n",
            "Precision: 0.5570, Recall: 0.2950, F1-measure: 0.3710\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.94      0.72      0.82      5519\n",
            "          1       0.70      0.34      0.45      8190\n",
            "          2       0.80      0.42      0.55      6529\n",
            "          3       0.82      0.49      0.61      3231\n",
            "          4       0.80      0.44      0.57      6430\n",
            "          5       0.82      0.38      0.52      2879\n",
            "          6       0.86      0.53      0.66      5086\n",
            "          7       0.87      0.58      0.70      4533\n",
            "          8       0.60      0.13      0.22      3000\n",
            "          9       0.82      0.57      0.67      2765\n",
            "         10       0.60      0.20      0.30      3051\n",
            "         11       0.68      0.38      0.49      3009\n",
            "         12       0.62      0.29      0.40      2630\n",
            "         13       0.73      0.30      0.43      1426\n",
            "         14       0.89      0.57      0.70      2548\n",
            "         15       0.65      0.23      0.34      2371\n",
            "         16       0.65      0.25      0.37       873\n",
            "         17       0.89      0.63      0.74      2151\n",
            "         18       0.60      0.25      0.35      2204\n",
            "         19       0.71      0.41      0.52       831\n",
            "         20       0.76      0.47      0.58      1860\n",
            "         21       0.29      0.09      0.14      2023\n",
            "         22       0.52      0.24      0.33      1513\n",
            "         23       0.89      0.55      0.68      1207\n",
            "         24       0.56      0.28      0.38       506\n",
            "         25       0.69      0.34      0.45       425\n",
            "         26       0.65      0.43      0.52       793\n",
            "         27       0.62      0.38      0.47      1291\n",
            "         28       0.74      0.39      0.51      1208\n",
            "         29       0.46      0.10      0.17       406\n",
            "         30       0.76      0.21      0.33       504\n",
            "         31       0.26      0.08      0.12       732\n",
            "         32       0.60      0.29      0.39       441\n",
            "         33       0.60      0.27      0.38      1645\n",
            "         34       0.69      0.26      0.38      1058\n",
            "         35       0.83      0.58      0.68       946\n",
            "         36       0.65      0.24      0.35       644\n",
            "         37       0.98      0.65      0.78       136\n",
            "         38       0.62      0.38      0.47       570\n",
            "         39       0.84      0.31      0.45       766\n",
            "         40       0.59      0.35      0.44      1132\n",
            "         41       0.47      0.18      0.26       174\n",
            "         42       0.76      0.49      0.59       210\n",
            "         43       0.75      0.42      0.54       433\n",
            "         44       0.66      0.52      0.58       626\n",
            "         45       0.71      0.36      0.47       852\n",
            "         46       0.77      0.45      0.57       534\n",
            "         47       0.37      0.15      0.22       350\n",
            "         48       0.75      0.52      0.62       496\n",
            "         49       0.78      0.64      0.71       785\n",
            "         50       0.21      0.06      0.09       475\n",
            "         51       0.37      0.13      0.19       305\n",
            "         52       0.42      0.03      0.06       251\n",
            "         53       0.66      0.40      0.50       914\n",
            "         54       0.49      0.17      0.26       728\n",
            "         55       0.47      0.03      0.05       258\n",
            "         56       0.45      0.24      0.31       821\n",
            "         57       0.46      0.10      0.17       541\n",
            "         58       0.76      0.31      0.45       748\n",
            "         59       0.94      0.66      0.77       724\n",
            "         60       0.35      0.10      0.15       660\n",
            "         61       0.78      0.20      0.31       235\n",
            "         62       0.92      0.74      0.82       718\n",
            "         63       0.83      0.69      0.75       468\n",
            "         64       0.55      0.36      0.43       191\n",
            "         65       0.33      0.11      0.17       429\n",
            "         66       0.29      0.06      0.10       415\n",
            "         67       0.74      0.50      0.59       274\n",
            "         68       0.82      0.53      0.64       510\n",
            "         69       0.67      0.45      0.54       466\n",
            "         70       0.30      0.09      0.13       305\n",
            "         71       0.49      0.17      0.25       247\n",
            "         72       0.78      0.53      0.64       401\n",
            "         73       0.99      0.77      0.86        86\n",
            "         74       0.72      0.42      0.53       120\n",
            "         75       0.92      0.67      0.78       129\n",
            "         76       0.47      0.02      0.04       473\n",
            "         77       0.40      0.29      0.33       143\n",
            "         78       0.79      0.49      0.60       347\n",
            "         79       0.69      0.25      0.36       479\n",
            "         80       0.56      0.34      0.43       279\n",
            "         81       0.70      0.23      0.34       461\n",
            "         82       0.34      0.04      0.07       298\n",
            "         83       0.78      0.50      0.61       396\n",
            "         84       0.55      0.29      0.38       184\n",
            "         85       0.61      0.24      0.35       573\n",
            "         86       0.50      0.07      0.12       325\n",
            "         87       0.51      0.29      0.37       273\n",
            "         88       0.49      0.21      0.30       135\n",
            "         89       0.36      0.11      0.17       232\n",
            "         90       0.56      0.34      0.43       409\n",
            "         91       0.61      0.27      0.37       420\n",
            "         92       0.78      0.57      0.66       408\n",
            "         93       0.66      0.44      0.53       241\n",
            "         94       0.30      0.04      0.07       211\n",
            "         95       0.37      0.10      0.15       277\n",
            "         96       0.28      0.04      0.07       410\n",
            "         97       0.86      0.43      0.57       501\n",
            "         98       0.75      0.63      0.69       136\n",
            "         99       0.54      0.34      0.42       239\n",
            "        100       0.57      0.15      0.24       324\n",
            "        101       0.91      0.68      0.78       277\n",
            "        102       0.91      0.75      0.82       613\n",
            "        103       0.47      0.17      0.25       157\n",
            "        104       0.22      0.06      0.10       295\n",
            "        105       0.75      0.43      0.55       334\n",
            "        106       0.88      0.28      0.43       335\n",
            "        107       0.75      0.54      0.63       389\n",
            "        108       0.58      0.27      0.37       251\n",
            "        109       0.58      0.45      0.51       317\n",
            "        110       0.68      0.10      0.18       187\n",
            "        111       0.73      0.11      0.20       140\n",
            "        112       0.67      0.43      0.52       154\n",
            "        113       0.58      0.20      0.29       332\n",
            "        114       0.46      0.27      0.34       323\n",
            "        115       0.47      0.26      0.33       344\n",
            "        116       0.75      0.55      0.63       370\n",
            "        117       0.58      0.24      0.34       313\n",
            "        118       0.78      0.73      0.75       874\n",
            "        119       0.45      0.21      0.29       293\n",
            "        120       0.11      0.01      0.01       200\n",
            "        121       0.77      0.51      0.61       463\n",
            "        122       0.32      0.10      0.15       119\n",
            "        123       0.67      0.02      0.03       256\n",
            "        124       0.91      0.70      0.79       195\n",
            "        125       0.44      0.14      0.21       138\n",
            "        126       0.81      0.53      0.64       376\n",
            "        127       0.27      0.03      0.06       122\n",
            "        128       0.20      0.04      0.07       252\n",
            "        129       0.48      0.22      0.30       144\n",
            "        130       0.42      0.11      0.18       150\n",
            "        131       0.33      0.03      0.06       210\n",
            "        132       0.65      0.28      0.39       361\n",
            "        133       0.92      0.59      0.72       453\n",
            "        134       0.89      0.77      0.82       124\n",
            "        135       0.31      0.05      0.09        91\n",
            "        136       0.69      0.28      0.40       128\n",
            "        137       0.55      0.38      0.45       218\n",
            "        138       0.67      0.18      0.28       243\n",
            "        139       0.45      0.18      0.26       149\n",
            "        140       0.77      0.46      0.58       318\n",
            "        141       0.32      0.10      0.15       159\n",
            "        142       0.63      0.38      0.47       274\n",
            "        143       0.85      0.79      0.82       362\n",
            "        144       0.54      0.21      0.30       118\n",
            "        145       0.63      0.39      0.48       164\n",
            "        146       0.54      0.31      0.39       461\n",
            "        147       0.68      0.45      0.54       159\n",
            "        148       0.30      0.12      0.17       166\n",
            "        149       0.97      0.55      0.70       346\n",
            "        150       0.64      0.13      0.21       350\n",
            "        151       0.93      0.67      0.78        55\n",
            "        152       0.78      0.52      0.63       387\n",
            "        153       0.51      0.17      0.25       150\n",
            "        154       0.58      0.12      0.21       281\n",
            "        155       0.25      0.06      0.10       202\n",
            "        156       0.81      0.67      0.73       130\n",
            "        157       0.28      0.06      0.10       245\n",
            "        158       0.93      0.63      0.75       177\n",
            "        159       0.53      0.34      0.41       130\n",
            "        160       0.48      0.18      0.26       336\n",
            "        161       0.90      0.65      0.75       220\n",
            "        162       0.28      0.06      0.09       229\n",
            "        163       0.87      0.44      0.58       316\n",
            "        164       0.78      0.44      0.56       283\n",
            "        165       0.60      0.34      0.44       197\n",
            "        166       0.65      0.43      0.51       101\n",
            "        167       0.45      0.18      0.26       231\n",
            "        168       0.56      0.27      0.36       370\n",
            "        169       0.40      0.21      0.27       258\n",
            "        170       0.36      0.08      0.13       101\n",
            "        171       0.38      0.24      0.29        89\n",
            "        172       0.53      0.36      0.43       193\n",
            "        173       0.47      0.26      0.33       309\n",
            "        174       0.62      0.14      0.23       172\n",
            "        175       0.92      0.73      0.81        95\n",
            "        176       0.93      0.62      0.74       346\n",
            "        177       0.86      0.57      0.69       322\n",
            "        178       0.65      0.51      0.57       232\n",
            "        179       0.20      0.04      0.07       125\n",
            "        180       0.65      0.33      0.44       145\n",
            "        181       0.44      0.10      0.17        77\n",
            "        182       0.26      0.06      0.10       182\n",
            "        183       0.60      0.32      0.41       257\n",
            "        184       0.21      0.03      0.05       216\n",
            "        185       0.35      0.09      0.14       242\n",
            "        186       0.43      0.18      0.25       165\n",
            "        187       0.75      0.59      0.66       263\n",
            "        188       0.39      0.12      0.18       174\n",
            "        189       0.75      0.40      0.53       136\n",
            "        190       0.89      0.55      0.68       202\n",
            "        191       0.44      0.16      0.24       134\n",
            "        192       0.68      0.40      0.51       230\n",
            "        193       0.44      0.18      0.25        90\n",
            "        194       0.57      0.48      0.52       185\n",
            "        195       0.26      0.05      0.09       156\n",
            "        196       0.33      0.07      0.11       160\n",
            "        197       0.49      0.10      0.16       266\n",
            "        198       0.47      0.13      0.20       284\n",
            "        199       0.32      0.04      0.07       145\n",
            "        200       0.93      0.74      0.82       212\n",
            "        201       0.65      0.26      0.37       317\n",
            "        202       0.78      0.59      0.67       427\n",
            "        203       0.36      0.11      0.17       232\n",
            "        204       0.51      0.29      0.37       217\n",
            "        205       0.50      0.46      0.48       527\n",
            "        206       0.24      0.03      0.06       124\n",
            "        207       0.50      0.17      0.26       103\n",
            "        208       0.85      0.53      0.65       287\n",
            "        209       0.33      0.11      0.16       193\n",
            "        210       0.75      0.38      0.50       220\n",
            "        211       0.72      0.21      0.32       140\n",
            "        212       0.12      0.02      0.03       161\n",
            "        213       0.63      0.43      0.51        72\n",
            "        214       0.64      0.45      0.53       396\n",
            "        215       0.87      0.34      0.49       134\n",
            "        216       0.61      0.17      0.27       400\n",
            "        217       0.51      0.24      0.33        75\n",
            "        218       0.96      0.76      0.85       219\n",
            "        219       0.77      0.42      0.54       210\n",
            "        220       0.88      0.64      0.74       298\n",
            "        221       0.96      0.70      0.81       266\n",
            "        222       0.76      0.45      0.57       290\n",
            "        223       0.11      0.01      0.01       128\n",
            "        224       0.78      0.45      0.57       159\n",
            "        225       0.55      0.29      0.38       164\n",
            "        226       0.58      0.31      0.41       144\n",
            "        227       0.56      0.29      0.38       276\n",
            "        228       0.19      0.03      0.05       235\n",
            "        229       0.33      0.03      0.06       216\n",
            "        230       0.40      0.17      0.23       228\n",
            "        231       0.70      0.48      0.57        64\n",
            "        232       0.48      0.10      0.16       103\n",
            "        233       0.72      0.35      0.47       216\n",
            "        234       0.72      0.11      0.19       116\n",
            "        235       0.54      0.36      0.43        77\n",
            "        236       0.90      0.67      0.77        67\n",
            "        237       0.57      0.12      0.20       218\n",
            "        238       0.40      0.14      0.20       139\n",
            "        239       0.00      0.00      0.00        94\n",
            "        240       0.54      0.34      0.42        77\n",
            "        241       0.47      0.08      0.14       167\n",
            "        242       0.78      0.37      0.50        86\n",
            "        243       0.40      0.10      0.16        58\n",
            "        244       0.62      0.27      0.38       269\n",
            "        245       0.16      0.04      0.07       112\n",
            "        246       0.95      0.76      0.84       255\n",
            "        247       0.44      0.24      0.31        58\n",
            "        248       0.44      0.05      0.09        81\n",
            "        249       0.23      0.02      0.04       131\n",
            "        250       0.43      0.24      0.31        93\n",
            "        251       0.61      0.29      0.39       154\n",
            "        252       0.36      0.04      0.07       129\n",
            "        253       0.69      0.40      0.50        83\n",
            "        254       0.34      0.08      0.13       191\n",
            "        255       0.15      0.03      0.05       219\n",
            "        256       0.32      0.05      0.09       130\n",
            "        257       0.48      0.26      0.34        93\n",
            "        258       0.65      0.48      0.55       217\n",
            "        259       0.41      0.13      0.20       141\n",
            "        260       0.86      0.17      0.29       143\n",
            "        261       0.62      0.17      0.27       219\n",
            "        262       0.55      0.27      0.36       107\n",
            "        263       0.41      0.27      0.32       236\n",
            "        264       0.33      0.22      0.26       119\n",
            "        265       0.57      0.24      0.33        72\n",
            "        266       0.00      0.00      0.00        70\n",
            "        267       0.36      0.14      0.20       107\n",
            "        268       0.67      0.44      0.53       169\n",
            "        269       0.32      0.14      0.19       129\n",
            "        270       0.74      0.53      0.62       159\n",
            "        271       0.88      0.48      0.62       190\n",
            "        272       0.61      0.27      0.37       248\n",
            "        273       0.90      0.75      0.82       264\n",
            "        274       0.90      0.68      0.77       105\n",
            "        275       0.52      0.12      0.20       104\n",
            "        276       0.08      0.01      0.02       115\n",
            "        277       0.83      0.63      0.72       170\n",
            "        278       0.74      0.41      0.52       145\n",
            "        279       0.90      0.70      0.78       230\n",
            "        280       0.58      0.42      0.49        80\n",
            "        281       0.66      0.54      0.59       217\n",
            "        282       0.75      0.50      0.60       175\n",
            "        283       0.33      0.13      0.18       269\n",
            "        284       0.65      0.32      0.43        74\n",
            "        285       0.82      0.49      0.61       206\n",
            "        286       0.89      0.66      0.75       227\n",
            "        287       0.84      0.41      0.55       130\n",
            "        288       0.32      0.07      0.11       129\n",
            "        289       0.57      0.05      0.09        80\n",
            "        290       0.21      0.09      0.13        99\n",
            "        291       0.76      0.35      0.48       208\n",
            "        292       0.42      0.07      0.13        67\n",
            "        293       0.84      0.48      0.61       109\n",
            "        294       0.46      0.26      0.34       140\n",
            "        295       0.24      0.12      0.16       241\n",
            "        296       0.31      0.12      0.18        72\n",
            "        297       0.44      0.11      0.18       107\n",
            "        298       0.77      0.49      0.60        61\n",
            "        299       0.89      0.51      0.64        77\n",
            "        300       0.21      0.08      0.12       111\n",
            "        301       0.00      0.00      0.00       126\n",
            "        302       0.25      0.01      0.03        73\n",
            "        303       0.57      0.43      0.49       176\n",
            "        304       0.91      0.79      0.85       230\n",
            "        305       0.92      0.72      0.81       156\n",
            "        306       0.50      0.37      0.43       146\n",
            "        307       0.34      0.11      0.17        98\n",
            "        308       0.00      0.00      0.00        78\n",
            "        309       0.80      0.13      0.22        94\n",
            "        310       0.74      0.41      0.53       162\n",
            "        311       0.79      0.51      0.62       116\n",
            "        312       0.52      0.28      0.36        57\n",
            "        313       0.83      0.08      0.14        65\n",
            "        314       0.52      0.36      0.42       138\n",
            "        315       0.54      0.22      0.31       195\n",
            "        316       0.56      0.35      0.43        69\n",
            "        317       0.29      0.13      0.18       134\n",
            "        318       0.56      0.39      0.46       148\n",
            "        319       0.84      0.50      0.63       161\n",
            "        320       0.24      0.19      0.21       104\n",
            "        321       0.82      0.61      0.70       156\n",
            "        322       0.60      0.37      0.46       134\n",
            "        323       0.58      0.44      0.50       232\n",
            "        324       0.34      0.15      0.21        92\n",
            "        325       0.41      0.24      0.31       197\n",
            "        326       0.14      0.03      0.05       126\n",
            "        327       0.20      0.03      0.05       115\n",
            "        328       0.99      0.70      0.82       198\n",
            "        329       0.59      0.32      0.41       125\n",
            "        330       0.73      0.20      0.31        81\n",
            "        331       0.45      0.10      0.16        94\n",
            "        332       0.54      0.12      0.20        56\n",
            "        333       0.19      0.05      0.08       260\n",
            "        334       0.42      0.13      0.20        60\n",
            "        335       0.35      0.08      0.13       110\n",
            "        336       0.62      0.49      0.55        71\n",
            "        337       0.18      0.05      0.07        66\n",
            "        338       0.47      0.36      0.41       150\n",
            "        339       0.00      0.00      0.00        54\n",
            "        340       0.84      0.57      0.68       195\n",
            "        341       0.91      0.52      0.66        79\n",
            "        342       0.38      0.26      0.31        38\n",
            "        343       0.62      0.42      0.50        43\n",
            "        344       0.56      0.29      0.38        68\n",
            "        345       0.62      0.33      0.43        73\n",
            "        346       0.14      0.03      0.04       116\n",
            "        347       0.86      0.43      0.57       111\n",
            "        348       0.33      0.11      0.17        63\n",
            "        349       0.84      0.65      0.74       104\n",
            "        350       0.62      0.48      0.54        44\n",
            "        351       0.57      0.30      0.39        40\n",
            "        352       0.93      0.57      0.70       136\n",
            "        353       0.38      0.15      0.21        54\n",
            "        354       0.39      0.09      0.15       134\n",
            "        355       0.64      0.35      0.45       120\n",
            "        356       0.54      0.29      0.38       228\n",
            "        357       0.66      0.36      0.47       269\n",
            "        358       0.62      0.38      0.47        80\n",
            "        359       0.84      0.59      0.69       140\n",
            "        360       0.39      0.18      0.24       125\n",
            "        361       0.90      0.71      0.79       169\n",
            "        362       0.14      0.05      0.08        56\n",
            "        363       0.92      0.73      0.82       154\n",
            "        364       0.46      0.10      0.17        58\n",
            "        365       0.22      0.08      0.12        71\n",
            "        366       1.00      0.69      0.81        54\n",
            "        367       0.30      0.07      0.11       116\n",
            "        368       0.38      0.06      0.10        54\n",
            "        369       0.33      0.03      0.05        71\n",
            "        370       0.00      0.00      0.00        61\n",
            "        371       0.40      0.08      0.14        71\n",
            "        372       0.72      0.44      0.55        52\n",
            "        373       0.78      0.41      0.54       150\n",
            "        374       0.41      0.14      0.21        93\n",
            "        375       0.20      0.04      0.07        67\n",
            "        376       0.00      0.00      0.00        76\n",
            "        377       0.58      0.28      0.38       106\n",
            "        378       0.25      0.02      0.04        86\n",
            "        379       0.50      0.14      0.22        14\n",
            "        380       0.93      0.52      0.67       122\n",
            "        381       0.23      0.07      0.10       104\n",
            "        382       0.46      0.20      0.28        66\n",
            "        383       0.54      0.35      0.42       110\n",
            "        384       0.14      0.01      0.01       155\n",
            "        385       0.69      0.22      0.33        50\n",
            "        386       0.20      0.06      0.10        64\n",
            "        387       0.32      0.08      0.12        93\n",
            "        388       0.53      0.24      0.33       102\n",
            "        389       0.07      0.01      0.02       108\n",
            "        390       0.96      0.68      0.80       178\n",
            "        391       0.49      0.17      0.26       115\n",
            "        392       0.81      0.40      0.54        42\n",
            "        393       0.00      0.00      0.00       134\n",
            "        394       0.22      0.04      0.06       112\n",
            "        395       0.54      0.27      0.36       176\n",
            "        396       0.47      0.13      0.20       125\n",
            "        397       0.74      0.37      0.49       224\n",
            "        398       0.84      0.67      0.74        63\n",
            "        399       0.30      0.05      0.09        59\n",
            "        400       0.51      0.32      0.39        63\n",
            "        401       0.49      0.23      0.32        98\n",
            "        402       0.51      0.19      0.27       162\n",
            "        403       0.38      0.14      0.21        83\n",
            "        404       0.76      0.84      0.80        19\n",
            "        405       0.34      0.11      0.17        92\n",
            "        406       0.69      0.22      0.33        41\n",
            "        407       0.64      0.37      0.47        43\n",
            "        408       0.80      0.46      0.58       160\n",
            "        409       0.20      0.12      0.15        50\n",
            "        410       0.00      0.00      0.00        19\n",
            "        411       0.35      0.11      0.17       175\n",
            "        412       0.28      0.07      0.11        72\n",
            "        413       0.38      0.05      0.09        95\n",
            "        414       0.12      0.02      0.04        97\n",
            "        415       0.33      0.10      0.16        48\n",
            "        416       0.53      0.35      0.42        83\n",
            "        417       0.43      0.07      0.13        40\n",
            "        418       0.48      0.16      0.25        91\n",
            "        419       0.53      0.37      0.43        90\n",
            "        420       0.38      0.27      0.32        37\n",
            "        421       0.04      0.02      0.02        66\n",
            "        422       0.69      0.45      0.55        73\n",
            "        423       0.48      0.25      0.33        56\n",
            "        424       0.94      0.88      0.91        33\n",
            "        425       0.00      0.00      0.00        76\n",
            "        426       0.27      0.05      0.08        81\n",
            "        427       0.98      0.73      0.84       150\n",
            "        428       0.95      0.69      0.80        29\n",
            "        429       0.99      0.93      0.96       389\n",
            "        430       0.63      0.40      0.49       167\n",
            "        431       0.57      0.11      0.18       123\n",
            "        432       0.52      0.31      0.39        39\n",
            "        433       0.33      0.21      0.25        82\n",
            "        434       1.00      0.70      0.82        66\n",
            "        435       0.55      0.38      0.45        93\n",
            "        436       0.56      0.37      0.44        87\n",
            "        437       0.10      0.02      0.04        86\n",
            "        438       0.72      0.53      0.61       104\n",
            "        439       0.54      0.13      0.21       100\n",
            "        440       0.38      0.04      0.06       141\n",
            "        441       0.43      0.33      0.37       110\n",
            "        442       0.37      0.15      0.22       123\n",
            "        443       0.57      0.18      0.28        71\n",
            "        444       0.32      0.06      0.11       109\n",
            "        445       0.45      0.31      0.37        48\n",
            "        446       0.47      0.29      0.36        76\n",
            "        447       0.39      0.18      0.25        38\n",
            "        448       0.67      0.54      0.60        81\n",
            "        449       0.67      0.26      0.37       132\n",
            "        450       0.42      0.27      0.33        81\n",
            "        451       0.89      0.32      0.47        76\n",
            "        452       0.00      0.00      0.00        44\n",
            "        453       0.00      0.00      0.00        44\n",
            "        454       0.84      0.51      0.64        70\n",
            "        455       0.39      0.18      0.25       155\n",
            "        456       0.50      0.21      0.30        43\n",
            "        457       0.54      0.28      0.37        72\n",
            "        458       0.35      0.13      0.19        62\n",
            "        459       0.63      0.25      0.35        69\n",
            "        460       0.00      0.00      0.00       119\n",
            "        461       0.71      0.19      0.30        79\n",
            "        462       0.61      0.23      0.34        47\n",
            "        463       0.39      0.14      0.21       104\n",
            "        464       0.70      0.42      0.52       106\n",
            "        465       0.64      0.22      0.33        64\n",
            "        466       0.55      0.35      0.43       173\n",
            "        467       0.78      0.42      0.55       107\n",
            "        468       0.56      0.26      0.36       126\n",
            "        469       0.20      0.01      0.02       114\n",
            "        470       0.93      0.81      0.87       140\n",
            "        471       0.85      0.42      0.56        79\n",
            "        472       0.40      0.35      0.37       143\n",
            "        473       0.67      0.37      0.47       158\n",
            "        474       0.48      0.10      0.17       138\n",
            "        475       0.00      0.00      0.00        59\n",
            "        476       0.63      0.33      0.43        88\n",
            "        477       0.83      0.65      0.73       176\n",
            "        478       0.95      0.79      0.86        24\n",
            "        479       0.22      0.04      0.07        92\n",
            "        480       0.79      0.50      0.61       100\n",
            "        481       0.51      0.28      0.36       103\n",
            "        482       0.40      0.22      0.28        74\n",
            "        483       0.78      0.63      0.69       105\n",
            "        484       0.20      0.02      0.04        83\n",
            "        485       0.20      0.02      0.04        82\n",
            "        486       0.48      0.15      0.23        71\n",
            "        487       0.45      0.21      0.29       120\n",
            "        488       0.50      0.06      0.10       105\n",
            "        489       0.73      0.37      0.49        87\n",
            "        490       1.00      0.81      0.90        32\n",
            "        491       0.33      0.03      0.05        69\n",
            "        492       0.33      0.02      0.04        49\n",
            "        493       0.11      0.02      0.03       117\n",
            "        494       0.52      0.23      0.32        61\n",
            "        495       0.95      0.79      0.87       344\n",
            "        496       0.32      0.13      0.19        52\n",
            "        497       0.59      0.28      0.38       137\n",
            "        498       0.31      0.10      0.15        98\n",
            "        499       0.48      0.20      0.29        79\n",
            "\n",
            "avg / total       0.67      0.37      0.46    173812\n",
            "\n",
            "Time taken to run this cell : 1:09:41.236859\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cow4e_y4U997",
        "colab_type": "text"
      },
      "source": [
        "
 5. Assignments 
"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ry9fz7FmU998",
        "colab_type": "text"
      },
      "source": [
        "
    \n",
        "    
  1.  Use bag of words upto 4 grams and compute the micro f1 score with Logistic regression(OvR) 
    2. \n",
        "    
  2.  Perform hyperparam tuning on alpha (or lambda) for Logistic regression to improve the performance using GridSearch  
    3. \n",
        "    
  3.  Try OneVsRestClassifier  with Linear-SVM (SGDClassifier with loss-hinge)
    4. \n",
        "
"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDdnHZ0X4fz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Output\n",
        "# 1st - bag of words upto 4 grams SGDClassifier(loss='log') 0.5M data points\n",
        "# 2nd - bag of words upto 4 grams Logistic Regression 0.5M data points\n",
        "# 3rd - tfidf(1 to 3 grams) 200k data points Logistic Regression grid search hyperparameter: alpha  \n",
        "# 4th - tfidf(1 to 3 grams) 200k data points (SGDClassifier with loss-hinge) grid search hyperparameter: alpha  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ae60a64d-e780-449c-fe10-58fd1da43bd8",
        "id": "M2HJXhrv4zb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = datetime.now()\n",
        "classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'))\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "# clf.fit(x_train_multilabel.copy(),y_train)\n",
        "predictions = classifier.predict (x_test_multilabel)\n",
        "\n",
        "\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='micro')\n",
        "recall = recall_score(y_test, predictions, average='micro')\n",
        "f1 = f1_score(y_test, predictions, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='macro')\n",
        "recall = recall_score(y_test, predictions, average='macro')\n",
        "f1 = f1_score(y_test, predictions, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.09221737739019122\n",
            "Hamming loss  0.005975858068645492\n",
            "Micro-average quality numbers\n",
            "Precision: 0.2838, Recall: 0.4719, F1-measure: 0.3544\n",
            "Macro-average quality numbers\n",
            "Precision: 0.2023, Recall: 0.4142, F1-measure: 0.2634\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.79      0.76      5516\n",
            "           1       0.43      0.44      0.44      8189\n",
            "           2       0.52      0.49      0.50      6529\n",
            "           3       0.52      0.59      0.55      3229\n",
            "           4       0.55      0.51      0.53      6430\n",
            "           5       0.44      0.45      0.44      2878\n",
            "           6       0.59      0.60      0.59      5086\n",
            "           7       0.60      0.64      0.62      4533\n",
            "           8       0.24      0.21      0.22      2999\n",
            "           9       0.53      0.63      0.57      2765\n",
            "          10       0.32      0.33      0.32      3051\n",
            "          11       0.45      0.49      0.47      3009\n",
            "          12       0.38      0.41      0.39      2630\n",
            "          13       0.35      0.43      0.39      1425\n",
            "          14       0.59      0.69      0.64      2548\n",
            "          15       0.36      0.39      0.37      2371\n",
            "          16       0.27      0.37      0.31       873\n",
            "          17       0.56      0.68      0.61      2151\n",
            "          18       0.30      0.39      0.34      2204\n",
            "          19       0.30      0.47      0.36       831\n",
            "          20       0.52      0.57      0.55      1860\n",
            "          21       0.18      0.22      0.20      2023\n",
            "          22       0.29      0.36      0.32      1513\n",
            "          23       0.53      0.68      0.59      1207\n",
            "          24       0.26      0.41      0.32       506\n",
            "          25       0.22      0.45      0.30       425\n",
            "          26       0.37      0.47      0.41       793\n",
            "          27       0.36      0.48      0.41      1291\n",
            "          28       0.41      0.51      0.46      1208\n",
            "          29       0.10      0.25      0.14       406\n",
            "          30       0.22      0.34      0.27       504\n",
            "          31       0.13      0.21      0.16       732\n",
            "          32       0.18      0.39      0.24       441\n",
            "          33       0.34      0.48      0.40      1645\n",
            "          34       0.27      0.38      0.31      1058\n",
            "          35       0.49      0.62      0.55       946\n",
            "          36       0.22      0.36      0.28       644\n",
            "          37       0.20      0.74      0.32       136\n",
            "          38       0.31      0.48      0.37       570\n",
            "          39       0.28      0.42      0.33       766\n",
            "          40       0.33      0.48      0.39      1132\n",
            "          41       0.11      0.32      0.16       174\n",
            "          42       0.33      0.61      0.43       210\n",
            "          43       0.31      0.55      0.40       433\n",
            "          44       0.34      0.53      0.41       626\n",
            "          45       0.29      0.48      0.36       852\n",
            "          46       0.33      0.56      0.42       534\n",
            "          47       0.16      0.33      0.22       349\n",
            "          48       0.33      0.59      0.42       496\n",
            "          49       0.51      0.70      0.59       785\n",
            "          50       0.11      0.19      0.14       475\n",
            "          51       0.11      0.23      0.15       305\n",
            "          52       0.10      0.15      0.12       251\n",
            "          53       0.32      0.48      0.39       914\n",
            "          54       0.22      0.29      0.25       728\n",
            "          55       0.07      0.13      0.09       258\n",
            "          56       0.23      0.40      0.29       821\n",
            "          57       0.12      0.22      0.16       541\n",
            "          58       0.29      0.41      0.34       748\n",
            "          59       0.59      0.73      0.65       724\n",
            "          60       0.17      0.25      0.20       660\n",
            "          61       0.16      0.31      0.21       235\n",
            "          62       0.51      0.77      0.61       718\n",
            "          63       0.44      0.76      0.56       468\n",
            "          64       0.19      0.47      0.27       191\n",
            "          65       0.15      0.26      0.19       429\n",
            "          66       0.13      0.21      0.16       415\n",
            "          67       0.28      0.61      0.39       274\n",
            "          68       0.35      0.59      0.44       510\n",
            "          69       0.29      0.52      0.37       466\n",
            "          70       0.12      0.27      0.17       305\n",
            "          71       0.11      0.28      0.16       247\n",
            "          72       0.37      0.59      0.45       401\n",
            "          73       0.30      0.86      0.45        86\n",
            "          74       0.16      0.53      0.25       120\n",
            "          75       0.30      0.77      0.43       129\n",
            "          76       0.08      0.12      0.09       473\n",
            "          77       0.11      0.38      0.17       143\n",
            "          78       0.30      0.58      0.39       347\n",
            "          79       0.22      0.32      0.26       479\n",
            "          80       0.21      0.52      0.30       279\n",
            "          81       0.21      0.35      0.27       461\n",
            "          82       0.08      0.15      0.10       298\n",
            "          83       0.32      0.62      0.42       396\n",
            "          84       0.17      0.45      0.25       184\n",
            "          85       0.22      0.36      0.28       573\n",
            "          86       0.12      0.22      0.16       325\n",
            "          87       0.21      0.45      0.29       273\n",
            "          88       0.10      0.32      0.15       135\n",
            "          89       0.14      0.32      0.19       232\n",
            "          90       0.28      0.54      0.37       409\n",
            "          91       0.22      0.41      0.29       420\n",
            "          92       0.40      0.66      0.50       408\n",
            "          93       0.22      0.55      0.32       241\n",
            "          94       0.08      0.15      0.11       211\n",
            "          95       0.14      0.28      0.19       277\n",
            "          96       0.13      0.23      0.17       410\n",
            "          97       0.43      0.59      0.50       501\n",
            "          98       0.21      0.69      0.32       136\n",
            "          99       0.20      0.45      0.28       239\n",
            "         100       0.13      0.28      0.18       324\n",
            "         101       0.49      0.79      0.60       277\n",
            "         102       0.63      0.79      0.70       613\n",
            "         103       0.14      0.38      0.21       157\n",
            "         104       0.08      0.16      0.11       295\n",
            "         105       0.30      0.53      0.38       334\n",
            "         106       0.25      0.42      0.31       335\n",
            "         107       0.29      0.61      0.40       389\n",
            "         108       0.17      0.39      0.23       251\n",
            "         109       0.25      0.54      0.34       317\n",
            "         110       0.06      0.18      0.09       187\n",
            "         111       0.08      0.27      0.12       140\n",
            "         112       0.19      0.60      0.29       154\n",
            "         113       0.19      0.36      0.25       332\n",
            "         114       0.24      0.39      0.30       323\n",
            "         115       0.17      0.38      0.23       344\n",
            "         116       0.33      0.59      0.42       370\n",
            "         117       0.19      0.34      0.24       313\n",
            "         118       0.62      0.74      0.68       874\n",
            "         119       0.16      0.35      0.22       293\n",
            "         120       0.06      0.12      0.08       200\n",
            "         121       0.38      0.57      0.46       463\n",
            "         122       0.12      0.31      0.17       119\n",
            "         123       0.03      0.05      0.04       256\n",
            "         124       0.43      0.74      0.54       195\n",
            "         125       0.10      0.33      0.15       138\n",
            "         126       0.36      0.63      0.46       376\n",
            "         127       0.03      0.10      0.04       122\n",
            "         128       0.09      0.19      0.12       252\n",
            "         129       0.21      0.43      0.29       144\n",
            "         130       0.09      0.29      0.13       150\n",
            "         131       0.07      0.17      0.10       210\n",
            "         132       0.21      0.39      0.27       361\n",
            "         133       0.55      0.70      0.62       453\n",
            "         134       0.36      0.80      0.49       124\n",
            "         135       0.05      0.19      0.07        91\n",
            "         136       0.13      0.47      0.20       128\n",
            "         137       0.19      0.46      0.27       218\n",
            "         138       0.14      0.35      0.20       243\n",
            "         139       0.13      0.40      0.20       149\n",
            "         140       0.34      0.58      0.42       318\n",
            "         141       0.07      0.20      0.10       159\n",
            "         142       0.31      0.53      0.39       274\n",
            "         143       0.58      0.85      0.69       362\n",
            "         144       0.11      0.41      0.17       118\n",
            "         145       0.16      0.45      0.24       164\n",
            "         146       0.26      0.44      0.33       461\n",
            "         147       0.25      0.53      0.34       159\n",
            "         148       0.10      0.23      0.14       166\n",
            "         149       0.41      0.64      0.50       346\n",
            "         150       0.17      0.31      0.22       350\n",
            "         151       0.19      0.75      0.30        55\n",
            "         152       0.35      0.60      0.45       387\n",
            "         153       0.20      0.37      0.26       150\n",
            "         154       0.11      0.22      0.15       281\n",
            "         155       0.11      0.27      0.16       202\n",
            "         156       0.26      0.68      0.38       130\n",
            "         157       0.11      0.21      0.15       245\n",
            "         158       0.34      0.72      0.46       177\n",
            "         159       0.14      0.46      0.22       130\n",
            "         160       0.16      0.33      0.22       336\n",
            "         161       0.31      0.68      0.43       220\n",
            "         162       0.09      0.24      0.13       229\n",
            "         163       0.33      0.58      0.42       316\n",
            "         164       0.29      0.53      0.37       283\n",
            "         165       0.15      0.37      0.21       197\n",
            "         166       0.18      0.58      0.27       101\n",
            "         167       0.14      0.30      0.19       231\n",
            "         168       0.19      0.41      0.26       370\n",
            "         169       0.19      0.36      0.25       258\n",
            "         170       0.06      0.21      0.09       101\n",
            "         171       0.09      0.37      0.14        89\n",
            "         172       0.16      0.42      0.23       193\n",
            "         173       0.25      0.44      0.32       309\n",
            "         174       0.09      0.22      0.12       172\n",
            "         175       0.26      0.77      0.39        95\n",
            "         176       0.51      0.71      0.59       346\n",
            "         177       0.38      0.66      0.48       322\n",
            "         178       0.25      0.59      0.36       232\n",
            "         179       0.05      0.15      0.08       125\n",
            "         180       0.17      0.43      0.24       145\n",
            "         181       0.06      0.27      0.09        77\n",
            "         182       0.09      0.29      0.14       182\n",
            "         183       0.25      0.47      0.33       257\n",
            "         184       0.08      0.18      0.11       216\n",
            "         185       0.13      0.27      0.18       242\n",
            "         186       0.09      0.24      0.13       165\n",
            "         187       0.33      0.65      0.44       263\n",
            "         188       0.11      0.24      0.15       174\n",
            "         189       0.34      0.45      0.38       136\n",
            "         190       0.39      0.69      0.50       202\n",
            "         191       0.09      0.26      0.14       134\n",
            "         192       0.21      0.52      0.30       230\n",
            "         193       0.08      0.31      0.13        90\n",
            "         194       0.24      0.60      0.35       185\n",
            "         195       0.05      0.17      0.08       156\n",
            "         196       0.06      0.21      0.09       160\n",
            "         197       0.15      0.32      0.20       266\n",
            "         198       0.14      0.24      0.18       284\n",
            "         199       0.05      0.12      0.07       145\n",
            "         200       0.51      0.81      0.62       212\n",
            "         201       0.19      0.37      0.25       317\n",
            "         202       0.39      0.62      0.48       427\n",
            "         203       0.12      0.31      0.17       232\n",
            "         204       0.16      0.40      0.23       217\n",
            "         205       0.36      0.61      0.45       527\n",
            "         206       0.05      0.17      0.08       124\n",
            "         207       0.20      0.38      0.26       103\n",
            "         208       0.09      0.22      0.13       193\n",
            "         209       0.38      0.57      0.45       287\n",
            "         210       0.20      0.45      0.27       220\n",
            "         211       0.13      0.36      0.19       140\n",
            "         212       0.08      0.25      0.12       161\n",
            "         213       0.24      0.64      0.35        72\n",
            "         214       0.42      0.59      0.49       396\n",
            "         215       0.17      0.55      0.26       134\n",
            "         216       0.19      0.30      0.24       400\n",
            "         217       0.10      0.44      0.16        75\n",
            "         218       0.55      0.81      0.66       219\n",
            "         219       0.23      0.50      0.32       210\n",
            "         220       0.47      0.71      0.57       298\n",
            "         221       0.49      0.82      0.61       266\n",
            "         222       0.31      0.57      0.40       290\n",
            "         223       0.04      0.12      0.07       128\n",
            "         224       0.19      0.47      0.27       159\n",
            "         225       0.15      0.48      0.23       164\n",
            "         226       0.19      0.53      0.28       144\n",
            "         227       0.25      0.52      0.34       276\n",
            "         228       0.04      0.11      0.06       235\n",
            "         229       0.08      0.19      0.11       216\n",
            "         230       0.09      0.29      0.13       228\n",
            "         231       0.16      0.66      0.26        64\n",
            "         232       0.04      0.16      0.07       103\n",
            "         233       0.24      0.52      0.33       216\n",
            "         234       0.15      0.31      0.20       116\n",
            "         235       0.15      0.53      0.24        77\n",
            "         236       0.36      0.81      0.50        67\n",
            "         237       0.10      0.27      0.15       218\n",
            "         238       0.09      0.31      0.14       139\n",
            "         239       0.04      0.11      0.06        94\n",
            "         240       0.09      0.35      0.15        77\n",
            "         241       0.06      0.16      0.08       167\n",
            "         242       0.21      0.43      0.29        86\n",
            "         243       0.05      0.33      0.09        58\n",
            "         244       0.30      0.47      0.37       269\n",
            "         245       0.06      0.23      0.10       112\n",
            "         246       0.60      0.84      0.70       255\n",
            "         247       0.06      0.33      0.11        58\n",
            "         248       0.02      0.15      0.04        81\n",
            "         249       0.05      0.17      0.08       131\n",
            "         250       0.10      0.39      0.16        93\n",
            "         251       0.18      0.51      0.26       154\n",
            "         252       0.04      0.09      0.06       129\n",
            "         253       0.14      0.43      0.21        83\n",
            "         254       0.11      0.26      0.15       191\n",
            "         255       0.09      0.18      0.12       218\n",
            "         256       0.05      0.15      0.08       130\n",
            "         257       0.12      0.38      0.18        93\n",
            "         258       0.33      0.57      0.41       217\n",
            "         259       0.09      0.26      0.13       141\n",
            "         260       0.22      0.45      0.30       143\n",
            "         261       0.10      0.24      0.14       219\n",
            "         262       0.15      0.52      0.23       107\n",
            "         263       0.23      0.39      0.29       236\n",
            "         264       0.10      0.34      0.15       119\n",
            "         265       0.11      0.42      0.18        72\n",
            "         266       0.07      0.27      0.10        70\n",
            "         267       0.12      0.36      0.18       107\n",
            "         268       0.21      0.49      0.29       169\n",
            "         269       0.14      0.35      0.20       129\n",
            "         270       0.29      0.64      0.40       159\n",
            "         271       0.26      0.67      0.37       190\n",
            "         272       0.16      0.39      0.22       248\n",
            "         273       0.62      0.84      0.72       264\n",
            "         274       0.40      0.73      0.52       105\n",
            "         275       0.07      0.26      0.11       104\n",
            "         276       0.04      0.11      0.06       115\n",
            "         277       0.39      0.65      0.49       170\n",
            "         278       0.25      0.63      0.36       145\n",
            "         279       0.52      0.78      0.63       230\n",
            "         280       0.18      0.49      0.26        80\n",
            "         281       0.42      0.68      0.52       217\n",
            "         282       0.36      0.64      0.46       175\n",
            "         283       0.16      0.34      0.21       269\n",
            "         284       0.14      0.46      0.22        74\n",
            "         285       0.27      0.62      0.37       206\n",
            "         286       0.46      0.74      0.57       227\n",
            "         287       0.17      0.57      0.27       130\n",
            "         288       0.04      0.12      0.06       129\n",
            "         289       0.04      0.26      0.08        80\n",
            "         290       0.08      0.25      0.12        99\n",
            "         291       0.24      0.50      0.32       208\n",
            "         292       0.04      0.22      0.07        67\n",
            "         293       0.24      0.61      0.34       109\n",
            "         294       0.14      0.42      0.21       140\n",
            "         295       0.13      0.25      0.17       241\n",
            "         296       0.07      0.22      0.11        72\n",
            "         297       0.08      0.22      0.11       107\n",
            "         298       0.25      0.56      0.35        61\n",
            "         299       0.28      0.62      0.39        77\n",
            "         300       0.06      0.23      0.10       111\n",
            "         301       0.01      0.02      0.01       126\n",
            "         302       0.11      0.29      0.16        73\n",
            "         303       0.25      0.56      0.35       176\n",
            "         304       0.70      0.84      0.76       230\n",
            "         305       0.55      0.79      0.65       156\n",
            "         306       0.21      0.50      0.30       146\n",
            "         307       0.08      0.27      0.12        98\n",
            "         308       0.01      0.03      0.01        78\n",
            "         309       0.06      0.19      0.10        94\n",
            "         310       0.20      0.51      0.29       162\n",
            "         311       0.27      0.55      0.36       116\n",
            "         312       0.06      0.25      0.09        57\n",
            "         313       0.06      0.20      0.09        65\n",
            "         314       0.18      0.45      0.26       138\n",
            "         315       0.21      0.43      0.28       195\n",
            "         316       0.11      0.42      0.17        69\n",
            "         317       0.09      0.28      0.13       134\n",
            "         318       0.21      0.48      0.29       148\n",
            "         319       0.39      0.62      0.48       161\n",
            "         320       0.09      0.38      0.15       104\n",
            "         321       0.28      0.69      0.40       156\n",
            "         322       0.16      0.46      0.24       134\n",
            "         323       0.30      0.48      0.37       232\n",
            "         324       0.08      0.29      0.13        92\n",
            "         325       0.17      0.40      0.24       197\n",
            "         326       0.07      0.22      0.11       126\n",
            "         327       0.04      0.09      0.05       115\n",
            "         328       0.47      0.76      0.58       198\n",
            "         329       0.17      0.50      0.26       125\n",
            "         330       0.15      0.43      0.22        81\n",
            "         331       0.10      0.23      0.14        94\n",
            "         332       0.08      0.21      0.11        56\n",
            "         333       0.08      0.20      0.12       260\n",
            "         334       0.06      0.22      0.09        60\n",
            "         335       0.11      0.29      0.16       110\n",
            "         336       0.19      0.59      0.29        71\n",
            "         337       0.05      0.23      0.08        66\n",
            "         338       0.14      0.46      0.22       150\n",
            "         339       0.02      0.07      0.03        54\n",
            "         340       0.39      0.69      0.50       195\n",
            "         341       0.29      0.65      0.40        79\n",
            "         342       0.13      0.50      0.20        38\n",
            "         343       0.10      0.53      0.17        43\n",
            "         344       0.15      0.37      0.21        68\n",
            "         345       0.16      0.41      0.23        71\n",
            "         346       0.08      0.25      0.12       116\n",
            "         347       0.21      0.58      0.31       111\n",
            "         348       0.06      0.29      0.09        63\n",
            "         349       0.32      0.70      0.44       104\n",
            "         350       0.14      0.59      0.23        44\n",
            "         351       0.12      0.47      0.20        40\n",
            "         352       0.30      0.59      0.39       136\n",
            "         353       0.07      0.22      0.10        54\n",
            "         354       0.08      0.23      0.12       134\n",
            "         355       0.16      0.47      0.24       120\n",
            "         356       0.23      0.43      0.30       228\n",
            "         357       0.33      0.49      0.40       269\n",
            "         358       0.17      0.44      0.25        80\n",
            "         359       0.33      0.69      0.45       140\n",
            "         360       0.12      0.31      0.17       125\n",
            "         361       0.56      0.81      0.66       169\n",
            "         362       0.02      0.12      0.04        56\n",
            "         363       0.52      0.80      0.63       154\n",
            "         364       0.10      0.34      0.16        58\n",
            "         365       0.08      0.31      0.12        71\n",
            "         366       0.39      0.78      0.52        54\n",
            "         367       0.06      0.21      0.09       116\n",
            "         368       0.05      0.17      0.07        54\n",
            "         369       0.02      0.14      0.04        71\n",
            "         370       0.02      0.11      0.04        61\n",
            "         371       0.04      0.18      0.06        71\n",
            "         372       0.16      0.56      0.25        52\n",
            "         373       0.34      0.55      0.42       150\n",
            "         374       0.13      0.42      0.19        93\n",
            "         375       0.06      0.18      0.08        67\n",
            "         376       0.03      0.11      0.05        76\n",
            "         377       0.16      0.37      0.22       106\n",
            "         378       0.02      0.08      0.04        86\n",
            "         379       0.02      0.29      0.04        14\n",
            "         380       0.24      0.62      0.34       122\n",
            "         381       0.05      0.16      0.08       104\n",
            "         382       0.06      0.30      0.10        66\n",
            "         383       0.05      0.12      0.07       155\n",
            "         384       0.16      0.45      0.24       110\n",
            "         385       0.07      0.44      0.12        50\n",
            "         386       0.05      0.20      0.08        64\n",
            "         387       0.07      0.22      0.11        93\n",
            "         388       0.19      0.46      0.27       102\n",
            "         389       0.05      0.17      0.08       108\n",
            "         390       0.57      0.74      0.65       178\n",
            "         391       0.13      0.33      0.18       115\n",
            "         392       0.20      0.55      0.29        42\n",
            "         393       0.02      0.04      0.02       134\n",
            "         394       0.06      0.18      0.09       112\n",
            "         395       0.14      0.35      0.20       176\n",
            "         396       0.08      0.24      0.12       125\n",
            "         397       0.39      0.62      0.48       224\n",
            "         398       0.26      0.70      0.38        63\n",
            "         399       0.02      0.12      0.04        59\n",
            "         400       0.10      0.43      0.17        63\n",
            "         401       0.12      0.48      0.20        98\n",
            "         402       0.13      0.31      0.18       162\n",
            "         403       0.10      0.34      0.16        83\n",
            "         404       0.31      0.84      0.46        19\n",
            "         405       0.10      0.29      0.15        92\n",
            "         406       0.08      0.49      0.13        41\n",
            "         407       0.27      0.51      0.35        43\n",
            "         408       0.24      0.46      0.32       160\n",
            "         409       0.06      0.24      0.09        50\n",
            "         410       0.01      0.11      0.01        19\n",
            "         411       0.13      0.32      0.19       175\n",
            "         412       0.06      0.21      0.10        72\n",
            "         413       0.07      0.18      0.10        95\n",
            "         414       0.09      0.24      0.13        97\n",
            "         415       0.03      0.15      0.05        48\n",
            "         416       0.15      0.42      0.22        83\n",
            "         417       0.04      0.15      0.06        40\n",
            "         418       0.06      0.21      0.10        91\n",
            "         419       0.17      0.46      0.24        90\n",
            "         420       0.07      0.32      0.12        37\n",
            "         421       0.07      0.24      0.11        66\n",
            "         422       0.11      0.41      0.17        73\n",
            "         423       0.09      0.30      0.14        56\n",
            "         424       0.42      0.88      0.57        33\n",
            "         425       0.03      0.11      0.05        76\n",
            "         426       0.04      0.12      0.06        81\n",
            "         427       0.55      0.75      0.63       150\n",
            "         428       0.26      0.79      0.40        29\n",
            "         429       0.91      0.89      0.90       389\n",
            "         430       0.24      0.47      0.32       167\n",
            "         431       0.11      0.21      0.15       123\n",
            "         432       0.09      0.46      0.16        39\n",
            "         433       0.16      0.41      0.23        82\n",
            "         434       0.46      0.73      0.56        66\n",
            "         435       0.19      0.45      0.27        93\n",
            "         436       0.15      0.51      0.23        87\n",
            "         437       0.06      0.23      0.09        86\n",
            "         438       0.26      0.55      0.36       104\n",
            "         439       0.07      0.22      0.10       100\n",
            "         440       0.08      0.21      0.11       141\n",
            "         441       0.19      0.48      0.27       110\n",
            "         442       0.07      0.28      0.12       123\n",
            "         443       0.09      0.31      0.14        71\n",
            "         444       0.08      0.24      0.12       109\n",
            "         445       0.08      0.35      0.13        48\n",
            "         446       0.15      0.46      0.23        76\n",
            "         447       0.06      0.32      0.10        38\n",
            "         448       0.33      0.70      0.45        81\n",
            "         449       0.19      0.32      0.24       132\n",
            "         450       0.14      0.35      0.20        81\n",
            "         451       0.13      0.41      0.19        76\n",
            "         452       0.07      0.23      0.11        44\n",
            "         453       0.02      0.07      0.03        43\n",
            "         454       0.17      0.61      0.26        70\n",
            "         455       0.12      0.34      0.18       155\n",
            "         456       0.09      0.40      0.14        43\n",
            "         457       0.10      0.36      0.16        72\n",
            "         458       0.04      0.18      0.07        62\n",
            "         459       0.06      0.39      0.11        69\n",
            "         460       0.02      0.08      0.04       119\n",
            "         461       0.24      0.43      0.31        79\n",
            "         462       0.06      0.28      0.10        47\n",
            "         463       0.14      0.40      0.20       104\n",
            "         464       0.21      0.42      0.28       106\n",
            "         465       0.11      0.42      0.18        64\n",
            "         466       0.24      0.43      0.31       173\n",
            "         467       0.20      0.46      0.27       107\n",
            "         468       0.14      0.40      0.21       126\n",
            "         469       0.05      0.13      0.07       114\n",
            "         470       0.64      0.84      0.73       140\n",
            "         471       0.20      0.44      0.28        79\n",
            "         472       0.22      0.45      0.29       143\n",
            "         473       0.35      0.58      0.43       158\n",
            "         474       0.09      0.19      0.12       138\n",
            "         475       0.06      0.27      0.09        59\n",
            "         476       0.24      0.52      0.33        88\n",
            "         477       0.39      0.70      0.50       176\n",
            "         478       0.40      0.88      0.55        24\n",
            "         479       0.07      0.26      0.11        92\n",
            "         480       0.29      0.62      0.39       100\n",
            "         481       0.18      0.44      0.26       103\n",
            "         482       0.08      0.32      0.13        74\n",
            "         483       0.33      0.70      0.45       105\n",
            "         484       0.04      0.16      0.07        83\n",
            "         485       0.02      0.07      0.03        82\n",
            "         486       0.07      0.27      0.11        71\n",
            "         487       0.11      0.32      0.16       120\n",
            "         488       0.06      0.14      0.08       105\n",
            "         489       0.15      0.41      0.22        87\n",
            "         490       0.40      0.84      0.54        32\n",
            "         491       0.03      0.13      0.05        69\n",
            "         492       0.03      0.10      0.04        49\n",
            "         493       0.03      0.10      0.05       117\n",
            "         494       0.10      0.38      0.16        61\n",
            "         495       0.74      0.82      0.78       344\n",
            "         496       0.17      0.42      0.25        52\n",
            "         497       0.16      0.39      0.23       137\n",
            "         498       0.09      0.26      0.14        98\n",
            "         499       0.07      0.29      0.11        79\n",
            "\n",
            "   micro avg       0.28      0.47      0.35    173798\n",
            "   macro avg       0.20      0.41      0.26    173798\n",
            "weighted avg       0.35      0.47      0.39    173798\n",
            " samples avg       0.34      0.44      0.34    173798\n",
            "\n",
            "Time taken to run this cell : 4:10:40.362957\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "089ee65d-6aa5-4e3a-fbbf-1e94ec3aac29",
        "id": "oxMq1xrD4zcF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier, 'drive/My Drive/Stackoverflow/MySgd_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/MySgd_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22xC7M6j5__c",
        "colab_type": "text"
      },
      "source": [
        "### LR with 0.5M data points BOW "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab_type": "code",
        "outputId": "23b678ac-0e49-4fa8-e8d1-1908ba40b8f4",
        "id": "A0LPd00z6G7r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = datetime.now()\n",
        "classifier_2 = OneVsRestClassifier(LogisticRegression(penalty='l1'), n_jobs=-1)\n",
        "x_train_multilabel.sort_indices()\n",
        "classifier_2.fit(x_train_multilabel, y_train)\n",
        "predictions_2 = classifier_2.predict(x_test_multilabel)\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions_2))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions_2))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='micro')\n",
        "recall = recall_score(y_test, predictions_2, average='micro')\n",
        "f1 = f1_score(y_test, predictions_2, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions_2, average='macro')\n",
        "recall = recall_score(y_test, predictions_2, average='macro')\n",
        "f1 = f1_score(y_test, predictions_2, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions_2))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.2101468117449396\n",
            "Hamming loss  0.0031686334906792543\n",
            "Micro-average quality numbers\n",
            "Precision: 0.5590, Recall: 0.4193, F1-measure: 0.4792\n",
            "Macro-average quality numbers\n",
            "Precision: 0.4452, Recall: 0.3448, F1-measure: 0.3852\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.74      0.81      5516\n",
            "           1       0.52      0.41      0.46      8189\n",
            "           2       0.63      0.47      0.54      6529\n",
            "           3       0.68      0.53      0.60      3229\n",
            "           4       0.65      0.50      0.56      6430\n",
            "           5       0.61      0.42      0.50      2878\n",
            "           6       0.74      0.57      0.64      5086\n",
            "           7       0.74      0.63      0.68      4533\n",
            "           8       0.33      0.19      0.24      2999\n",
            "           9       0.70      0.59      0.64      2765\n",
            "          10       0.43      0.30      0.35      3051\n",
            "          11       0.59      0.46      0.52      3009\n",
            "          12       0.48      0.37      0.42      2630\n",
            "          13       0.53      0.38      0.44      1425\n",
            "          14       0.79      0.62      0.69      2548\n",
            "          15       0.48      0.31      0.38      2371\n",
            "          16       0.54      0.30      0.39       873\n",
            "          17       0.78      0.66      0.72      2151\n",
            "          18       0.43      0.31      0.36      2204\n",
            "          19       0.52      0.46      0.49       831\n",
            "          20       0.68      0.50      0.58      1860\n",
            "          21       0.25      0.19      0.22      2023\n",
            "          22       0.39      0.31      0.34      1513\n",
            "          23       0.77      0.59      0.67      1207\n",
            "          24       0.43      0.34      0.38       506\n",
            "          25       0.51      0.39      0.44       425\n",
            "          26       0.56      0.45      0.50       793\n",
            "          27       0.53      0.43      0.47      1291\n",
            "          28       0.60      0.42      0.49      1208\n",
            "          29       0.27      0.17      0.21       406\n",
            "          30       0.46      0.24      0.32       504\n",
            "          31       0.20      0.15      0.17       732\n",
            "          32       0.47      0.34      0.40       441\n",
            "          33       0.51      0.39      0.44      1645\n",
            "          34       0.46      0.31      0.37      1058\n",
            "          35       0.73      0.59      0.65       946\n",
            "          36       0.45      0.30      0.36       644\n",
            "          37       0.87      0.68      0.77       136\n",
            "          38       0.52      0.39      0.45       570\n",
            "          39       0.61      0.34      0.43       766\n",
            "          40       0.51      0.44      0.47      1132\n",
            "          41       0.34      0.32      0.33       174\n",
            "          42       0.66      0.57      0.61       210\n",
            "          43       0.61      0.46      0.52       433\n",
            "          44       0.61      0.50      0.55       626\n",
            "          45       0.54      0.40      0.46       852\n",
            "          46       0.63      0.49      0.55       534\n",
            "          47       0.29      0.26      0.28       349\n",
            "          48       0.65      0.57      0.61       496\n",
            "          49       0.76      0.66      0.70       785\n",
            "          50       0.20      0.12      0.15       475\n",
            "          51       0.25      0.22      0.23       305\n",
            "          52       0.23      0.10      0.14       251\n",
            "          53       0.53      0.42      0.47       914\n",
            "          54       0.38      0.25      0.30       728\n",
            "          55       0.15      0.07      0.10       258\n",
            "          56       0.35      0.30      0.32       821\n",
            "          57       0.33      0.18      0.24       541\n",
            "          58       0.58      0.35      0.44       748\n",
            "          59       0.88      0.71      0.79       724\n",
            "          60       0.33      0.20      0.25       660\n",
            "          61       0.43      0.25      0.31       235\n",
            "          62       0.86      0.74      0.80       718\n",
            "          63       0.78      0.71      0.74       468\n",
            "          64       0.45      0.32      0.38       191\n",
            "          65       0.29      0.21      0.24       429\n",
            "          66       0.20      0.13      0.16       415\n",
            "          67       0.67      0.56      0.61       274\n",
            "          68       0.72      0.54      0.62       510\n",
            "          69       0.59      0.50      0.54       466\n",
            "          70       0.25      0.17      0.20       305\n",
            "          71       0.32      0.21      0.25       247\n",
            "          72       0.72      0.55      0.62       401\n",
            "          73       0.83      0.80      0.82        86\n",
            "          74       0.58      0.44      0.50       120\n",
            "          75       0.82      0.71      0.76       129\n",
            "          76       0.14      0.06      0.09       473\n",
            "          77       0.38      0.32      0.35       143\n",
            "          78       0.67      0.49      0.56       347\n",
            "          79       0.51      0.28      0.36       479\n",
            "          80       0.43      0.38      0.40       279\n",
            "          81       0.49      0.29      0.37       461\n",
            "          82       0.17      0.08      0.11       298\n",
            "          83       0.71      0.55      0.62       396\n",
            "          84       0.38      0.39      0.39       184\n",
            "          85       0.43      0.32      0.37       573\n",
            "          86       0.26      0.13      0.17       325\n",
            "          87       0.51      0.44      0.47       273\n",
            "          88       0.45      0.31      0.37       135\n",
            "          89       0.24      0.17      0.20       232\n",
            "          90       0.47      0.41      0.44       409\n",
            "          91       0.53      0.34      0.42       420\n",
            "          92       0.68      0.57      0.62       408\n",
            "          93       0.53      0.54      0.53       241\n",
            "          94       0.19      0.09      0.13       211\n",
            "          95       0.30      0.18      0.23       277\n",
            "          96       0.22      0.14      0.17       410\n",
            "          97       0.76      0.48      0.59       501\n",
            "          98       0.67      0.63      0.65       136\n",
            "          99       0.45      0.37      0.40       239\n",
            "         100       0.33      0.21      0.25       324\n",
            "         101       0.85      0.75      0.80       277\n",
            "         102       0.88      0.79      0.83       613\n",
            "         103       0.36      0.23      0.28       157\n",
            "         104       0.22      0.13      0.16       295\n",
            "         105       0.66      0.46      0.54       334\n",
            "         106       0.67      0.39      0.49       335\n",
            "         107       0.68      0.60      0.63       389\n",
            "         108       0.52      0.33      0.40       251\n",
            "         109       0.54      0.49      0.51       317\n",
            "         110       0.30      0.11      0.16       187\n",
            "         111       0.41      0.17      0.24       140\n",
            "         112       0.54      0.47      0.51       154\n",
            "         113       0.49      0.27      0.35       332\n",
            "         114       0.42      0.31      0.36       323\n",
            "         115       0.43      0.35      0.38       344\n",
            "         116       0.68      0.59      0.63       370\n",
            "         117       0.39      0.31      0.35       313\n",
            "         118       0.75      0.76      0.75       874\n",
            "         119       0.35      0.26      0.30       293\n",
            "         120       0.12      0.08      0.10       200\n",
            "         121       0.67      0.52      0.58       463\n",
            "         122       0.24      0.12      0.16       119\n",
            "         123       0.16      0.04      0.06       256\n",
            "         124       0.86      0.72      0.78       195\n",
            "         125       0.30      0.20      0.24       138\n",
            "         126       0.70      0.54      0.61       376\n",
            "         127       0.19      0.07      0.11       122\n",
            "         128       0.11      0.06      0.07       252\n",
            "         129       0.43      0.40      0.41       144\n",
            "         130       0.32      0.21      0.25       150\n",
            "         131       0.21      0.11      0.14       210\n",
            "         132       0.50      0.34      0.41       361\n",
            "         133       0.84      0.66      0.74       453\n",
            "         134       0.79      0.75      0.77       124\n",
            "         135       0.15      0.12      0.14        91\n",
            "         136       0.51      0.39      0.44       128\n",
            "         137       0.45      0.42      0.43       218\n",
            "         138       0.33      0.23      0.27       243\n",
            "         139       0.26      0.18      0.21       149\n",
            "         140       0.69      0.55      0.61       318\n",
            "         141       0.21      0.13      0.16       159\n",
            "         142       0.55      0.43      0.48       274\n",
            "         143       0.81      0.83      0.82       362\n",
            "         144       0.43      0.31      0.36       118\n",
            "         145       0.48      0.42      0.45       164\n",
            "         146       0.50      0.42      0.45       461\n",
            "         147       0.67      0.47      0.55       159\n",
            "         148       0.34      0.21      0.26       166\n",
            "         149       0.89      0.60      0.72       346\n",
            "         150       0.48      0.22      0.30       350\n",
            "         151       0.88      0.67      0.76        55\n",
            "         152       0.68      0.52      0.59       387\n",
            "         153       0.33      0.31      0.32       150\n",
            "         154       0.30      0.16      0.21       281\n",
            "         155       0.25      0.19      0.22       202\n",
            "         156       0.72      0.65      0.69       130\n",
            "         157       0.23      0.12      0.16       245\n",
            "         158       0.91      0.71      0.80       177\n",
            "         159       0.44      0.40      0.42       130\n",
            "         160       0.35      0.24      0.29       336\n",
            "         161       0.76      0.66      0.71       220\n",
            "         162       0.18      0.12      0.14       229\n",
            "         163       0.78      0.47      0.59       316\n",
            "         164       0.64      0.43      0.52       283\n",
            "         165       0.47      0.35      0.40       197\n",
            "         166       0.52      0.52      0.52       101\n",
            "         167       0.38      0.24      0.29       231\n",
            "         168       0.42      0.36      0.39       370\n",
            "         169       0.38      0.23      0.29       258\n",
            "         170       0.26      0.18      0.21       101\n",
            "         171       0.34      0.29      0.32        89\n",
            "         172       0.44      0.39      0.41       193\n",
            "         173       0.49      0.37      0.42       309\n",
            "         174       0.29      0.16      0.21       172\n",
            "         175       0.79      0.75      0.77        95\n",
            "         176       0.84      0.64      0.73       346\n",
            "         177       0.81      0.61      0.69       322\n",
            "         178       0.53      0.50      0.51       232\n",
            "         179       0.20      0.10      0.14       125\n",
            "         180       0.43      0.41      0.42       145\n",
            "         181       0.29      0.21      0.24        77\n",
            "         182       0.18      0.12      0.14       182\n",
            "         183       0.53      0.37      0.44       257\n",
            "         184       0.23      0.14      0.17       216\n",
            "         185       0.29      0.19      0.23       242\n",
            "         186       0.33      0.24      0.27       165\n",
            "         187       0.66      0.58      0.62       263\n",
            "         188       0.20      0.11      0.14       174\n",
            "         189       0.65      0.52      0.58       136\n",
            "         190       0.82      0.60      0.69       202\n",
            "         191       0.32      0.23      0.27       134\n",
            "         192       0.57      0.48      0.52       230\n",
            "         193       0.22      0.17      0.19        90\n",
            "         194       0.54      0.56      0.55       185\n",
            "         195       0.16      0.08      0.11       156\n",
            "         196       0.14      0.10      0.12       160\n",
            "         197       0.27      0.17      0.21       266\n",
            "         198       0.30      0.20      0.24       284\n",
            "         199       0.19      0.08      0.12       145\n",
            "         200       0.85      0.77      0.81       212\n",
            "         201       0.45      0.27      0.34       317\n",
            "         202       0.68      0.66      0.67       427\n",
            "         203       0.21      0.16      0.18       232\n",
            "         204       0.40      0.33      0.36       217\n",
            "         205       0.49      0.55      0.52       527\n",
            "         206       0.18      0.08      0.11       124\n",
            "         207       0.34      0.30      0.32       103\n",
            "         208       0.20      0.12      0.15       193\n",
            "         209       0.77      0.56      0.65       287\n",
            "         210       0.56      0.44      0.49       220\n",
            "         211       0.40      0.21      0.27       140\n",
            "         212       0.15      0.09      0.11       161\n",
            "         213       0.47      0.57      0.51        72\n",
            "         214       0.61      0.44      0.51       396\n",
            "         215       0.61      0.42      0.50       134\n",
            "         216       0.43      0.28      0.34       400\n",
            "         217       0.33      0.27      0.30        75\n",
            "         218       0.91      0.78      0.84       219\n",
            "         219       0.59      0.43      0.50       210\n",
            "         220       0.84      0.69      0.76       298\n",
            "         221       0.87      0.71      0.78       266\n",
            "         222       0.65      0.47      0.54       290\n",
            "         223       0.13      0.05      0.08       128\n",
            "         224       0.68      0.52      0.59       159\n",
            "         225       0.38      0.35      0.36       164\n",
            "         226       0.45      0.34      0.39       144\n",
            "         227       0.55      0.42      0.47       276\n",
            "         228       0.09      0.05      0.06       235\n",
            "         229       0.14      0.07      0.09       216\n",
            "         230       0.32      0.25      0.28       228\n",
            "         231       0.63      0.53      0.58        64\n",
            "         232       0.22      0.17      0.19       103\n",
            "         233       0.60      0.40      0.48       216\n",
            "         234       0.49      0.23      0.32       116\n",
            "         235       0.45      0.34      0.39        77\n",
            "         236       0.82      0.69      0.75        67\n",
            "         237       0.29      0.18      0.22       218\n",
            "         238       0.24      0.22      0.23       139\n",
            "         239       0.24      0.06      0.10        94\n",
            "         240       0.39      0.32      0.35        77\n",
            "         241       0.29      0.14      0.19       167\n",
            "         242       0.64      0.42      0.51        86\n",
            "         243       0.29      0.24      0.26        58\n",
            "         244       0.50      0.43      0.46       269\n",
            "         245       0.14      0.09      0.11       112\n",
            "         246       0.92      0.82      0.87       255\n",
            "         247       0.22      0.22      0.22        58\n",
            "         248       0.13      0.07      0.09        81\n",
            "         249       0.06      0.02      0.03       131\n",
            "         250       0.36      0.27      0.31        93\n",
            "         251       0.56      0.39      0.46       154\n",
            "         252       0.10      0.05      0.07       129\n",
            "         253       0.44      0.36      0.40        83\n",
            "         254       0.21      0.14      0.17       191\n",
            "         255       0.14      0.09      0.11       218\n",
            "         256       0.13      0.08      0.10       130\n",
            "         257       0.40      0.32      0.36        93\n",
            "         258       0.64      0.52      0.57       217\n",
            "         259       0.28      0.21      0.24       141\n",
            "         260       0.57      0.25      0.35       143\n",
            "         261       0.40      0.18      0.25       219\n",
            "         262       0.49      0.38      0.43       107\n",
            "         263       0.36      0.23      0.28       236\n",
            "         264       0.27      0.21      0.24       119\n",
            "         265       0.44      0.29      0.35        72\n",
            "         266       0.10      0.06      0.07        70\n",
            "         267       0.34      0.24      0.28       107\n",
            "         268       0.54      0.49      0.51       169\n",
            "         269       0.30      0.19      0.23       129\n",
            "         270       0.68      0.56      0.61       159\n",
            "         271       0.73      0.54      0.62       190\n",
            "         272       0.42      0.33      0.37       248\n",
            "         273       0.86      0.76      0.81       264\n",
            "         274       0.80      0.69      0.74       105\n",
            "         275       0.19      0.12      0.14       104\n",
            "         276       0.05      0.03      0.03       115\n",
            "         277       0.80      0.61      0.69       170\n",
            "         278       0.69      0.50      0.58       145\n",
            "         279       0.85      0.75      0.79       230\n",
            "         280       0.60      0.41      0.49        80\n",
            "         281       0.64      0.56      0.60       217\n",
            "         282       0.69      0.54      0.60       175\n",
            "         283       0.28      0.23      0.25       269\n",
            "         284       0.55      0.42      0.48        74\n",
            "         285       0.70      0.51      0.59       206\n",
            "         286       0.83      0.71      0.76       227\n",
            "         287       0.65      0.45      0.53       130\n",
            "         288       0.17      0.08      0.11       129\n",
            "         289       0.16      0.12      0.14        80\n",
            "         290       0.15      0.13      0.14        99\n",
            "         291       0.57      0.39      0.47       208\n",
            "         292       0.26      0.15      0.19        67\n",
            "         293       0.77      0.54      0.63       109\n",
            "         294       0.36      0.31      0.33       140\n",
            "         295       0.22      0.15      0.18       241\n",
            "         296       0.22      0.14      0.17        72\n",
            "         297       0.21      0.16      0.18       107\n",
            "         298       0.62      0.57      0.60        61\n",
            "         299       0.72      0.60      0.65        77\n",
            "         300       0.16      0.12      0.14       111\n",
            "         301       0.02      0.01      0.01       126\n",
            "         302       0.16      0.14      0.15        73\n",
            "         303       0.54      0.43      0.48       176\n",
            "         304       0.86      0.83      0.84       230\n",
            "         305       0.83      0.72      0.77       156\n",
            "         306       0.42      0.38      0.40       146\n",
            "         307       0.21      0.12      0.15        98\n",
            "         308       0.06      0.03      0.04        78\n",
            "         309       0.47      0.17      0.25        94\n",
            "         310       0.56      0.41      0.47       162\n",
            "         311       0.68      0.52      0.59       116\n",
            "         312       0.45      0.37      0.40        57\n",
            "         313       0.32      0.09      0.14        65\n",
            "         314       0.39      0.38      0.39       138\n",
            "         315       0.53      0.32      0.40       195\n",
            "         316       0.38      0.33      0.35        69\n",
            "         317       0.22      0.19      0.21       134\n",
            "         318       0.53      0.41      0.46       148\n",
            "         319       0.81      0.57      0.67       161\n",
            "         320       0.20      0.22      0.21       104\n",
            "         321       0.67      0.62      0.64       156\n",
            "         322       0.57      0.49      0.53       134\n",
            "         323       0.52      0.48      0.50       232\n",
            "         324       0.21      0.17      0.19        92\n",
            "         325       0.38      0.27      0.32       197\n",
            "         326       0.07      0.06      0.07       126\n",
            "         327       0.15      0.07      0.09       115\n",
            "         328       0.92      0.71      0.80       198\n",
            "         329       0.44      0.33      0.37       125\n",
            "         330       0.48      0.26      0.34        81\n",
            "         331       0.33      0.16      0.21        94\n",
            "         332       0.29      0.20      0.23        56\n",
            "         333       0.13      0.08      0.10       260\n",
            "         334       0.15      0.12      0.13        60\n",
            "         335       0.24      0.12      0.16       110\n",
            "         336       0.59      0.52      0.55        71\n",
            "         337       0.13      0.09      0.11        66\n",
            "         338       0.40      0.47      0.43       150\n",
            "         339       0.06      0.06      0.06        54\n",
            "         340       0.74      0.61      0.66       195\n",
            "         341       0.65      0.57      0.61        79\n",
            "         342       0.37      0.47      0.41        38\n",
            "         343       0.55      0.40      0.46        43\n",
            "         344       0.34      0.31      0.32        68\n",
            "         345       0.67      0.37      0.47        71\n",
            "         346       0.11      0.08      0.09       116\n",
            "         347       0.61      0.53      0.57       111\n",
            "         348       0.26      0.19      0.22        63\n",
            "         349       0.81      0.72      0.76       104\n",
            "         350       0.57      0.57      0.57        44\n",
            "         351       0.28      0.33      0.30        40\n",
            "         352       0.79      0.64      0.71       136\n",
            "         353       0.38      0.22      0.28        54\n",
            "         354       0.22      0.12      0.15       134\n",
            "         355       0.51      0.41      0.45       120\n",
            "         356       0.45      0.35      0.39       228\n",
            "         357       0.54      0.43      0.48       269\n",
            "         358       0.55      0.36      0.44        80\n",
            "         359       0.71      0.64      0.68       140\n",
            "         360       0.29      0.22      0.25       125\n",
            "         361       0.88      0.75      0.81       169\n",
            "         362       0.16      0.12      0.14        56\n",
            "         363       0.83      0.77      0.80       154\n",
            "         364       0.22      0.24      0.23        58\n",
            "         365       0.24      0.17      0.20        71\n",
            "         366       0.92      0.65      0.76        54\n",
            "         367       0.14      0.10      0.12       116\n",
            "         368       0.24      0.19      0.21        54\n",
            "         369       0.09      0.06      0.07        71\n",
            "         370       0.19      0.10      0.13        61\n",
            "         371       0.29      0.10      0.15        71\n",
            "         372       0.50      0.48      0.49        52\n",
            "         373       0.59      0.45      0.51       150\n",
            "         374       0.30      0.27      0.28        93\n",
            "         375       0.13      0.10      0.12        67\n",
            "         376       0.05      0.03      0.03        76\n",
            "         377       0.44      0.35      0.39       106\n",
            "         378       0.09      0.03      0.05        86\n",
            "         379       0.15      0.14      0.15        14\n",
            "         380       0.74      0.56      0.64       122\n",
            "         381       0.11      0.08      0.09       104\n",
            "         382       0.24      0.15      0.19        66\n",
            "         383       0.18      0.08      0.11       155\n",
            "         384       0.47      0.39      0.43       110\n",
            "         385       0.39      0.36      0.37        50\n",
            "         386       0.22      0.12      0.16        64\n",
            "         387       0.22      0.12      0.15        93\n",
            "         388       0.45      0.34      0.39       102\n",
            "         389       0.12      0.06      0.08       108\n",
            "         390       0.90      0.69      0.78       178\n",
            "         391       0.34      0.21      0.26       115\n",
            "         392       0.66      0.45      0.54        42\n",
            "         393       0.07      0.01      0.02       134\n",
            "         394       0.26      0.16      0.20       112\n",
            "         395       0.39      0.33      0.36       176\n",
            "         396       0.29      0.18      0.23       125\n",
            "         397       0.63      0.44      0.52       224\n",
            "         398       0.77      0.68      0.72        63\n",
            "         399       0.12      0.07      0.09        59\n",
            "         400       0.42      0.43      0.43        63\n",
            "         401       0.44      0.33      0.37        98\n",
            "         402       0.39      0.26      0.31       162\n",
            "         403       0.23      0.17      0.19        83\n",
            "         404       0.64      0.84      0.73        19\n",
            "         405       0.19      0.14      0.16        92\n",
            "         406       0.42      0.39      0.41        41\n",
            "         407       0.48      0.37      0.42        43\n",
            "         408       0.57      0.49      0.53       160\n",
            "         409       0.13      0.08      0.10        50\n",
            "         410       0.00      0.00      0.00        19\n",
            "         411       0.31      0.23      0.26       175\n",
            "         412       0.22      0.15      0.18        72\n",
            "         413       0.21      0.09      0.13        95\n",
            "         414       0.29      0.20      0.23        97\n",
            "         415       0.14      0.10      0.12        48\n",
            "         416       0.41      0.34      0.37        83\n",
            "         417       0.24      0.17      0.20        40\n",
            "         418       0.24      0.16      0.19        91\n",
            "         419       0.51      0.42      0.46        90\n",
            "         420       0.29      0.24      0.26        37\n",
            "         421       0.13      0.09      0.11        66\n",
            "         422       0.48      0.41      0.44        73\n",
            "         423       0.44      0.32      0.37        56\n",
            "         424       0.88      0.88      0.88        33\n",
            "         425       0.13      0.05      0.07        76\n",
            "         426       0.05      0.02      0.03        81\n",
            "         427       0.95      0.75      0.84       150\n",
            "         428       0.91      0.69      0.78        29\n",
            "         429       0.98      0.95      0.97       389\n",
            "         430       0.56      0.46      0.51       167\n",
            "         431       0.36      0.14      0.20       123\n",
            "         432       0.28      0.21      0.24        39\n",
            "         433       0.28      0.28      0.28        82\n",
            "         434       0.90      0.71      0.80        66\n",
            "         435       0.56      0.52      0.54        93\n",
            "         436       0.48      0.37      0.42        87\n",
            "         437       0.15      0.09      0.11        86\n",
            "         438       0.65      0.51      0.57       104\n",
            "         439       0.41      0.22      0.29       100\n",
            "         440       0.18      0.09      0.12       141\n",
            "         441       0.40      0.43      0.41       110\n",
            "         442       0.23      0.21      0.22       123\n",
            "         443       0.31      0.21      0.25        71\n",
            "         444       0.21      0.12      0.15       109\n",
            "         445       0.41      0.38      0.39        48\n",
            "         446       0.41      0.32      0.36        76\n",
            "         447       0.21      0.26      0.24        38\n",
            "         448       0.57      0.56      0.56        81\n",
            "         449       0.45      0.32      0.37       132\n",
            "         450       0.38      0.33      0.35        81\n",
            "         451       0.73      0.42      0.53        76\n",
            "         452       0.10      0.09      0.09        44\n",
            "         453       0.00      0.00      0.00        43\n",
            "         454       0.78      0.56      0.65        70\n",
            "         455       0.32      0.30      0.31       155\n",
            "         456       0.29      0.23      0.26        43\n",
            "         457       0.37      0.35      0.36        72\n",
            "         458       0.19      0.16      0.18        62\n",
            "         459       0.41      0.32      0.36        69\n",
            "         460       0.14      0.10      0.12       119\n",
            "         461       0.57      0.37      0.45        79\n",
            "         462       0.28      0.23      0.25        47\n",
            "         463       0.33      0.30      0.31       104\n",
            "         464       0.55      0.43      0.49       106\n",
            "         465       0.33      0.30      0.31        64\n",
            "         466       0.43      0.32      0.37       173\n",
            "         467       0.52      0.41      0.46       107\n",
            "         468       0.42      0.34      0.38       126\n",
            "         469       0.21      0.09      0.12       114\n",
            "         470       0.90      0.83      0.86       140\n",
            "         471       0.57      0.39      0.47        79\n",
            "         472       0.40      0.43      0.42       143\n",
            "         473       0.64      0.41      0.50       158\n",
            "         474       0.31      0.14      0.19       138\n",
            "         475       0.19      0.15      0.17        59\n",
            "         476       0.61      0.49      0.54        88\n",
            "         477       0.71      0.69      0.70       176\n",
            "         478       0.90      0.79      0.84        24\n",
            "         479       0.25      0.17      0.21        92\n",
            "         480       0.69      0.58      0.63       100\n",
            "         481       0.40      0.37      0.38       103\n",
            "         482       0.20      0.14      0.16        74\n",
            "         483       0.71      0.64      0.67       105\n",
            "         484       0.18      0.08      0.12        83\n",
            "         485       0.06      0.05      0.05        82\n",
            "         486       0.29      0.20      0.23        71\n",
            "         487       0.38      0.25      0.30       120\n",
            "         488       0.23      0.10      0.13       105\n",
            "         489       0.51      0.40      0.45        87\n",
            "         490       0.90      0.84      0.87        32\n",
            "         491       0.10      0.06      0.07        69\n",
            "         492       0.12      0.06      0.08        49\n",
            "         493       0.07      0.05      0.06       117\n",
            "         494       0.45      0.39      0.42        61\n",
            "         495       0.94      0.81      0.87       344\n",
            "         496       0.22      0.17      0.19        52\n",
            "         497       0.48      0.34      0.40       137\n",
            "         498       0.33      0.16      0.22        98\n",
            "         499       0.31      0.23      0.26        79\n",
            "\n",
            "   micro avg       0.56      0.42      0.48    173798\n",
            "   macro avg       0.45      0.34      0.39    173798\n",
            "weighted avg       0.54      0.42      0.47    173798\n",
            " samples avg       0.44      0.40      0.39    173798\n",
            "\n",
            "Time taken to run this cell : 2:25:11.518179\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2f36f85a-0f04-46cf-a6c9-bbd0fb97b438",
        "id": "7SU7-V7-6G75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier_2, 'drive/My Drive/Stackoverflow/Mylrnew_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/Mylrnew_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCzPZ2sF62gG",
        "colab_type": "text"
      },
      "source": [
        "### Gridsearchcv on 200k data points Tfidf Vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2fff05b4-9920-4c77-a319-6d4535188a7c",
        "id": "IQHrjYIH61fP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "start = datetime.now()\n",
        "classifier_2 = OneVsRestClassifier(SGDClassifier(loss='log',penalty='l1'), n_jobs=-1)\n",
        "\n",
        "param={'estimator__alpha': [10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1]}\n",
        "\n",
        "\n",
        "grid_search_model = GridSearchCV(estimator = classifier_2, param_grid=param, cv=3, verbose=0,return_train_score=True,scoring='f1_micro',n_jobs=-1)\n",
        "x_train_multilabel.sort_indices()\n",
        "grid_search_model.fit(x_train_multilabel, y_train)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "value of alpha after hyperparameter tuning :  1e-05\n",
            "-------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZH8QLgKHP0b",
        "colab_type": "code",
        "outputId": "a24efb1e-0769-4a89-e4fd-aecdb715bec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 1:53:19.664826\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hugUUuiO61fb",
        "colab": {}
      },
      "source": [
        "alpha = [10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ouToPzsG61fi",
        "colab": {}
      },
      "source": [
        "log_alpha = np.log([10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BEkITqPP61fn",
        "colab": {}
      },
      "source": [
        "train_score = grid_search_model.cv_results_['mean_train_score']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V919Eyd861fq",
        "colab": {}
      },
      "source": [
        "test_score = grid_search_model.cv_results_['mean_test_score']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dff6529e-1963-4c17-b116-dbda4a13c1ac",
        "id": "z2nafKXi61fu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plt.scatter(log_alpha,train_score,label=\"Train Score\")\n",
        "plt.plot(log_alpha,train_score)\n",
        "plt.scatter(log_alpha,test_score,label=\"Test Score\")\n",
        "plt.plot(log_alpha,test_score)\n",
        "plt.xlabel(\"Alpha : Hyperparameter\")\n",
        "plt.ylabel(\"Micro F1 Score\")\n",
        "plt.xticks(log_alpha,alpha)\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GGBNmVhQAIiJg0EQoVx1mDIM/vDkW0KZuLP+4ph1rfkvljveWkZ5xwpO4xhgTKlYUTipz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\nTCYrCsYYYzJZUTDGGJPJioIxxphM/w+OElW16XjrVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "
"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a82fb4df-8934-43cf-8b69-705bcb299e89",
        "id": "pTqlQlXe61f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_alpha = grid_search_model.best_estimator_.get_params()['estimator__alpha']\n",
        "print('Best alpha: ',best_alpha)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best alpha:  1e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d634ac05-7937-4c05-d01d-e5ba769804b2",
        "id": "ATyBSJSs61f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier_2, 'drive/My Drive/Stackoverflow/gridlog_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/gridlog_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUAc_0e2ffrj",
        "colab_type": "code",
        "outputId": "e320ff6a-903b-4f13-a3f1-1ad29e8fa86b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = datetime.now()\n",
        "classifier = OneVsRestClassifier(SGDClassifier(loss='log', alpha=0.00001, penalty='l1'))\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "# clf.fit(x_train_multilabel.copy(),y_train)\n",
        "predictions = classifier.predict (x_test_multilabel)\n",
        "\n",
        "\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='micro')\n",
        "recall = recall_score(y_test, predictions, average='micro')\n",
        "f1 = f1_score(y_test, predictions, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='macro')\n",
        "recall = recall_score(y_test, predictions, average='macro')\n",
        "f1 = f1_score(y_test, predictions, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.348725\n",
            "Hamming loss  0.00205\n",
            "Micro-average quality numbers\n",
            "Precision: 0.8467, Recall: 0.5920, F1-measure: 0.6968\n",
            "Macro-average quality numbers\n",
            "Precision: 0.3854, Recall: 0.1986, F1-measure: 0.2415\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98     36910\n",
            "           1       0.58      0.08      0.14       141\n",
            "           2       0.46      0.12      0.19      4487\n",
            "           3       0.52      0.31      0.39       121\n",
            "           4       0.55      0.27      0.36       783\n",
            "           5       0.67      0.22      0.33        37\n",
            "           6       0.79      0.44      0.56       220\n",
            "           7       0.83      0.56      0.67       486\n",
            "           8       0.67      0.12      0.21        33\n",
            "           9       0.46      0.31      0.37       190\n",
            "          10       0.36      0.13      0.19       255\n",
            "          11       0.55      0.44      0.49       245\n",
            "          12       0.64      0.25      0.36       272\n",
            "          13       0.67      0.06      0.11        65\n",
            "          14       0.80      0.53      0.64        45\n",
            "          15       0.40      0.11      0.17       158\n",
            "          16       0.33      0.14      0.20         7\n",
            "          17       0.67      0.51      0.58       101\n",
            "          18       0.50      0.15      0.23        82\n",
            "          19       0.67      0.32      0.43        44\n",
            "          20       0.83      0.59      0.69        17\n",
            "          21       1.00      0.02      0.04        51\n",
            "          22       0.58      0.23      0.33       137\n",
            "          23       0.88      0.58      0.70        96\n",
            "          24       0.54      0.23      0.32       125\n",
            "          25       0.70      0.29      0.41        24\n",
            "          26       0.53      0.31      0.39       740\n",
            "          27       0.29      0.11      0.16        18\n",
            "          28       0.50      0.40      0.44         5\n",
            "          29       0.67      0.09      0.16        22\n",
            "          30       1.00      0.17      0.29         6\n",
            "          31       0.43      0.13      0.20       428\n",
            "          32       0.00      0.00      0.00         1\n",
            "          33       0.50      0.06      0.11        16\n",
            "          34       0.50      0.02      0.05        41\n",
            "          35       0.62      0.38      0.47       971\n",
            "          36       0.67      0.25      0.36         8\n",
            "          37       0.50      1.00      0.67         1\n",
            "          38       0.65      0.34      0.44       429\n",
            "          39       0.75      0.43      0.55         7\n",
            "          40       0.53      0.20      0.30        44\n",
            "          41       0.14      0.08      0.10        13\n",
            "          42       0.50      0.33      0.40         3\n",
            "          43       0.67      0.50      0.57         4\n",
            "          44       0.61      0.39      0.48       179\n",
            "          45       0.00      0.00      0.00         5\n",
            "          46       0.56      0.40      0.47        50\n",
            "          47       0.28      0.09      0.13       152\n",
            "          48       0.78      0.51      0.62       323\n",
            "          49       0.80      0.74      0.77       310\n",
            "          50       0.29      0.08      0.13       496\n",
            "          51       0.44      0.15      0.23       175\n",
            "          52       0.35      0.11      0.16      1654\n",
            "          53       0.50      0.31      0.39        35\n",
            "          54       0.67      0.22      0.33         9\n",
            "          55       0.00      0.00      0.00        17\n",
            "          56       0.12      0.05      0.07       104\n",
            "          57       0.41      0.12      0.19        88\n",
            "          58       0.83      0.33      0.47       146\n",
            "          59       1.00      0.58      0.74        12\n",
            "          60       0.00      0.00      0.00         4\n",
            "          61       0.67      0.33      0.44        55\n",
            "          62       0.89      0.68      0.77        37\n",
            "          63       0.89      0.59      0.71        29\n",
            "          64       0.55      0.44      0.49      1151\n",
            "          65       0.60      0.09      0.15        34\n",
            "          66       0.20      0.02      0.03        65\n",
            "          67       0.78      0.57      0.66       910\n",
            "          68       1.00      0.37      0.54        30\n",
            "          69       0.75      0.50      0.60         6\n",
            "          70       0.30      0.16      0.21       129\n",
            "          71       0.39      0.14      0.21        49\n",
            "          72       0.00      0.00      0.00         0\n",
            "          73       1.00      0.60      0.75         5\n",
            "          74       0.00      0.00      0.00         1\n",
            "          75       0.62      0.80      0.70        10\n",
            "          76       0.50      0.06      0.11        50\n",
            "          77       0.65      0.37      0.47        70\n",
            "          78       0.86      0.32      0.46        19\n",
            "          79       1.00      0.07      0.14        40\n",
            "          80       0.33      0.12      0.18        16\n",
            "          81       0.75      0.17      0.28        35\n",
            "          82       1.00      0.01      0.01       185\n",
            "          83       0.00      0.00      0.00         0\n",
            "          84       0.00      0.00      0.00         8\n",
            "          85       0.00      0.00      0.00         2\n",
            "          86       0.80      0.22      0.35        18\n",
            "          87       0.18      0.12      0.15        16\n",
            "          88       0.42      0.35      0.39       868\n",
            "          89       0.46      0.19      0.26       470\n",
            "          90       0.52      0.32      0.39        94\n",
            "          91       0.00      0.00      0.00         4\n",
            "          92       0.60      0.27      0.37        11\n",
            "          93       0.71      0.44      0.55       235\n",
            "          94       0.30      0.12      0.17       648\n",
            "          95       0.39      0.09      0.14        81\n",
            "          96       1.00      0.06      0.11        17\n",
            "          97       0.40      0.11      0.17        18\n",
            "          98       1.00      1.00      1.00         1\n",
            "          99       0.46      0.27      0.34       249\n",
            "         100       0.36      0.06      0.11       188\n",
            "         101       0.88      0.48      0.62        29\n",
            "         102       0.44      0.33      0.38        12\n",
            "         103       0.58      0.28      0.37       878\n",
            "         104       0.36      0.06      0.11       127\n",
            "         105       0.00      0.00      0.00         0\n",
            "         106       0.00      0.00      0.00         2\n",
            "         107       0.75      0.46      0.57        13\n",
            "         108       0.39      0.12      0.19       130\n",
            "         109       0.00      0.00      0.00         2\n",
            "         110       0.00      0.00      0.00        16\n",
            "         111       0.00      0.00      0.00         0\n",
            "         112       0.00      0.00      0.00         1\n",
            "         113       0.33      0.03      0.06        33\n",
            "         114       0.41      0.05      0.09       183\n",
            "         115       0.55      0.24      0.33       134\n",
            "         116       0.54      0.27      0.36        26\n",
            "         117       0.54      0.39      0.45        18\n",
            "         118       0.00      0.00      0.00         0\n",
            "         119       0.33      0.11      0.17         9\n",
            "         120       0.15      0.01      0.02       375\n",
            "         121       1.00      0.67      0.80         6\n",
            "         122       0.39      0.16      0.22       758\n",
            "         123       0.00      0.00      0.00         2\n",
            "         124       0.50      0.50      0.50         2\n",
            "         125       0.00      0.00      0.00        13\n",
            "         126       0.40      0.40      0.40         5\n",
            "         127       0.17      0.05      0.07        63\n",
            "         128       0.50      0.05      0.09        81\n",
            "         129       0.00      0.00      0.00         0\n",
            "         130       1.00      0.11      0.20        18\n",
            "         131       0.00      0.00      0.00         8\n",
            "         132       0.00      0.00      0.00         0\n",
            "         133       1.00      0.33      0.50         3\n",
            "         134       0.79      0.74      0.77        62\n",
            "         135       1.00      0.07      0.14        40\n",
            "         136       0.31      0.14      0.19        29\n",
            "         137       0.65      0.56      0.60        84\n",
            "         138       0.00      0.00      0.00         2\n",
            "         139       0.25      0.09      0.13        23\n",
            "         140       0.60      0.29      0.39        21\n",
            "         141       0.55      0.23      0.32       507\n",
            "         142       0.51      0.28      0.36       334\n",
            "         143       0.50      0.38      0.43         8\n",
            "         144       0.43      0.12      0.18        51\n",
            "         145       0.42      0.36      0.38        14\n",
            "         146       0.00      0.00      0.00         4\n",
            "         147       0.71      0.56      0.63       170\n",
            "         148       0.36      0.13      0.19       136\n",
            "         149       1.00      0.40      0.57         5\n",
            "         150       0.00      0.00      0.00         3\n",
            "         151       0.98      0.64      0.78       957\n",
            "         152       0.50      0.17      0.25         6\n",
            "         153       0.00      0.00      0.00         2\n",
            "         154       0.33      0.33      0.33         3\n",
            "         155       0.33      0.07      0.12        69\n",
            "         156       0.85      0.74      0.79        23\n",
            "         157       0.09      0.01      0.02        97\n",
            "         158       0.67      0.30      0.41        27\n",
            "         159       0.75      0.60      0.67         5\n",
            "         160       0.55      0.13      0.21        86\n",
            "         161       0.75      0.60      0.67         5\n",
            "         162       1.00      0.02      0.04        44\n",
            "         163       1.00      0.59      0.74        22\n",
            "         164       0.86      0.39      0.53        31\n",
            "         165       0.61      0.40      0.48        55\n",
            "         166       1.00      1.00      1.00         2\n",
            "         167       0.60      0.21      0.32        14\n",
            "         168       0.00      0.00      0.00         0\n",
            "         169       0.43      0.29      0.35       207\n",
            "         170       0.00      0.00      0.00        24\n",
            "         171       0.00      0.00      0.00         3\n",
            "         172       0.44      0.21      0.29        19\n",
            "         173       1.00      1.00      1.00         1\n",
            "         174       0.75      0.06      0.11       106\n",
            "         175       0.86      0.69      0.77       317\n",
            "         176       0.50      0.33      0.40         3\n",
            "         177       1.00      0.29      0.44        14\n",
            "         178       0.72      0.49      0.58        63\n",
            "         179       0.00      0.00      0.00        12\n",
            "         180       0.00      0.00      0.00         0\n",
            "         181       0.30      0.23      0.26        13\n",
            "         182       0.25      0.02      0.04        49\n",
            "         183       0.00      0.00      0.00         1\n",
            "         184       0.00      0.00      0.00        32\n",
            "         185       0.40      0.15      0.22        13\n",
            "         186       0.35      0.24      0.29        66\n",
            "         187       1.00      0.45      0.62        11\n",
            "         188       0.19      0.05      0.08       148\n",
            "         189       0.00      0.00      0.00         8\n",
            "         190       0.00      0.00      0.00         0\n",
            "         191       0.20      0.04      0.06        26\n",
            "         192       0.00      0.00      0.00         1\n",
            "         193       0.50      0.36      0.42       636\n",
            "         194       0.62      0.49      0.55        51\n",
            "         195       0.20      0.01      0.02        81\n",
            "         196       0.40      0.09      0.14        23\n",
            "         197       0.00      0.00      0.00         0\n",
            "         198       0.00      0.00      0.00        10\n",
            "         199       0.31      0.05      0.08       102\n",
            "         200       0.93      0.58      0.71       215\n",
            "         201       0.00      0.00      0.00         1\n",
            "         202       1.00      0.67      0.80         9\n",
            "         203       0.25      0.02      0.04       131\n",
            "         204       0.64      0.22      0.33        63\n",
            "         205       0.29      0.57      0.38         7\n",
            "         206       0.36      0.03      0.06       375\n",
            "         207       0.00      0.00      0.00         0\n",
            "         208       0.13      0.04      0.06        71\n",
            "         209       0.71      0.56      0.63         9\n",
            "         210       0.67      0.22      0.33        18\n",
            "         211       0.00      0.00      0.00         3\n",
            "         212       0.32      0.11      0.16       188\n",
            "         213       0.00      0.00      0.00         1\n",
            "         214       0.42      0.29      0.34       205\n",
            "         215       0.00      0.00      0.00         0\n",
            "         216       1.00      0.17      0.29        12\n",
            "         217       0.64      0.35      0.46       398\n",
            "         218       1.00      0.62      0.77         8\n",
            "         219       0.67      0.40      0.50         5\n",
            "         220       0.50      1.00      0.67         1\n",
            "         221       1.00      0.58      0.74        12\n",
            "         222       0.73      0.47      0.57        34\n",
            "         223       0.75      0.04      0.08       217\n",
            "         224       0.00      0.00      0.00         0\n",
            "         225       0.00      0.00      0.00         1\n",
            "         226       0.66      0.44      0.53        48\n",
            "         227       0.68      0.24      0.35        55\n",
            "         228       1.00      0.04      0.08        25\n",
            "         229       0.00      0.00      0.00        15\n",
            "         230       0.33      0.08      0.13        12\n",
            "         231       0.78      0.39      0.52       405\n",
            "         232       0.20      0.03      0.05        63\n",
            "         233       0.00      0.00      0.00         2\n",
            "         234       0.36      0.15      0.21        33\n",
            "         235       0.64      0.46      0.53       585\n",
            "         236       1.00      1.00      1.00         1\n",
            "         237       1.00      0.10      0.18        10\n",
            "         238       0.17      0.02      0.03        60\n",
            "         239       0.51      0.05      0.08       459\n",
            "         240       0.00      0.00      0.00         0\n",
            "         241       0.00      0.00      0.00         1\n",
            "         242       0.91      0.44      0.60       520\n",
            "         243       0.00      0.00      0.00         5\n",
            "         244       0.33      0.21      0.26        42\n",
            "         245       0.38      0.10      0.16        30\n",
            "         246       0.50      0.50      0.50         2\n",
            "         247       0.38      0.22      0.28        27\n",
            "         248       0.33      0.10      0.15        10\n",
            "         249       0.33      0.02      0.03       120\n",
            "         250       0.30      0.11      0.16        28\n",
            "         251       0.00      0.00      0.00        21\n",
            "         252       0.36      0.09      0.14        55\n",
            "         253       0.85      0.41      0.55       365\n",
            "         254       0.53      0.23      0.32        79\n",
            "         255       0.00      0.00      0.00        20\n",
            "         256       0.00      0.00      0.00         6\n",
            "         257       0.50      0.30      0.37        20\n",
            "         258       0.84      0.56      0.68        48\n",
            "         259       0.00      0.00      0.00        11\n",
            "         260       0.00      0.00      0.00         0\n",
            "         261       0.47      0.20      0.28        35\n",
            "         262       0.80      0.44      0.57         9\n",
            "         263       0.55      0.20      0.30       300\n",
            "         264       0.38      0.17      0.24        29\n",
            "         265       0.20      0.09      0.13        11\n",
            "         266       0.25      0.04      0.07       477\n",
            "         267       0.00      0.00      0.00        16\n",
            "         268       0.75      0.43      0.55        42\n",
            "         269       0.50      0.20      0.29        74\n",
            "         270       0.76      0.58      0.66        38\n",
            "         271       0.00      0.00      0.00         0\n",
            "         272       0.00      0.00      0.00         1\n",
            "         273       1.00      1.00      1.00         1\n",
            "         274       0.60      0.50      0.55        12\n",
            "         275       0.00      0.00      0.00        47\n",
            "         276       0.00      0.00      0.00        14\n",
            "         277       0.00      0.00      0.00         2\n",
            "         278       0.03      0.43      0.06         7\n",
            "         279       1.00      0.75      0.86         8\n",
            "         280       0.00      0.00      0.00         0\n",
            "         281       0.73      0.47      0.57        17\n",
            "         282       1.00      0.67      0.80         6\n",
            "         283       0.00      0.00      0.00         2\n",
            "         284       0.47      0.32      0.38        22\n",
            "         285       0.78      0.50      0.61        14\n",
            "         286       0.00      0.00      0.00         0\n",
            "         287       0.25      0.11      0.15         9\n",
            "         288       0.30      0.14      0.19       241\n",
            "         289       0.00      0.00      0.00         6\n",
            "         290       0.00      0.00      0.00        11\n",
            "         291       0.00      0.00      0.00         2\n",
            "         292       0.20      0.06      0.10       143\n",
            "         293       0.00      0.00      0.00         0\n",
            "         294       0.60      0.09      0.15        35\n",
            "         295       0.45      0.10      0.17        98\n",
            "         296       0.19      0.02      0.04       129\n",
            "         297       0.33      0.06      0.10        17\n",
            "         298       0.00      0.00      0.00         0\n",
            "         299       0.00      0.00      0.00         0\n",
            "         300       0.00      0.00      0.00         5\n",
            "         301       0.00      0.00      0.00        31\n",
            "         302       0.00      0.00      0.00         1\n",
            "         303       0.69      0.57      0.62       118\n",
            "         304       0.00      0.00      0.00         0\n",
            "         305       0.93      0.70      0.80        53\n",
            "         306       0.25      0.08      0.12        12\n",
            "         307       0.00      0.00      0.00         5\n",
            "         308       0.00      0.00      0.00        80\n",
            "         309       0.00      0.00      0.00         0\n",
            "         310       0.68      0.28      0.40        60\n",
            "         311       0.00      0.00      0.00         0\n",
            "         312       0.66      0.26      0.38        87\n",
            "         313       0.00      0.00      0.00         1\n",
            "         314       0.00      0.00      0.00         0\n",
            "         315       0.46      0.24      0.32       138\n",
            "         316       0.54      0.23      0.32        93\n",
            "         317       0.00      0.00      0.00        10\n",
            "         318       0.67      0.46      0.55        13\n",
            "         319       0.92      0.27      0.42        44\n",
            "         320       0.50      0.10      0.17        30\n",
            "         321       0.64      0.53      0.58        34\n",
            "         322       0.63      0.57      0.60        21\n",
            "         323       1.00      1.00      1.00         1\n",
            "         324       0.33      0.07      0.12       126\n",
            "         325       0.50      0.16      0.24        25\n",
            "         326       0.00      0.00      0.00        10\n",
            "         327       0.00      0.00      0.00         2\n",
            "         328       1.00      0.20      0.33         5\n",
            "         329       0.81      0.56      0.66        54\n",
            "         330       0.00      0.00      0.00         4\n",
            "         331       0.00      0.00      0.00         0\n",
            "         332       0.00      0.00      0.00         0\n",
            "         333       0.33      0.04      0.07        27\n",
            "         334       0.33      0.17      0.22         6\n",
            "         335       0.45      0.19      0.27       185\n",
            "         336       0.62      0.62      0.62         8\n",
            "         337       0.00      0.00      0.00         4\n",
            "         338       0.33      0.40      0.36        20\n",
            "         339       0.00      0.00      0.00         3\n",
            "         340       0.73      0.50      0.59        16\n",
            "         341       0.00      0.00      0.00         1\n",
            "         342       0.20      0.10      0.13        10\n",
            "         343       0.56      0.31      0.40       229\n",
            "         344       1.00      1.00      1.00         1\n",
            "         345       0.59      0.34      0.43       301\n",
            "         346       0.17      0.02      0.04        48\n",
            "         347       0.00      0.00      0.00         1\n",
            "         348       0.20      0.11      0.14        19\n",
            "         349       0.10      0.17      0.12         6\n",
            "         350       0.68      0.48      0.57       209\n",
            "         351       0.00      0.00      0.00         2\n",
            "         352       1.00      1.00      1.00         2\n",
            "         353       0.51      0.27      0.35       340\n",
            "         354       0.00      0.00      0.00        12\n",
            "         355       0.00      0.00      0.00         0\n",
            "         356       0.00      0.00      0.00         0\n",
            "         357       0.00      0.00      0.00         1\n",
            "         358       0.58      0.26      0.36        97\n",
            "         359       0.00      0.00      0.00         3\n",
            "         360       0.33      0.07      0.12        28\n",
            "         361       0.80      1.00      0.89         4\n",
            "         362       0.67      0.12      0.20        34\n",
            "         363       1.00      1.00      1.00         2\n",
            "         364       0.00      0.00      0.00         0\n",
            "         365       0.55      0.21      0.30       229\n",
            "         366       0.00      0.00      0.00         0\n",
            "         367       0.00      0.00      0.00         0\n",
            "         368       0.00      0.00      0.00         0\n",
            "         369       0.00      0.00      0.00        33\n",
            "         370       0.00      0.00      0.00         7\n",
            "         371       0.00      0.00      0.00         1\n",
            "         372       0.81      0.43      0.56       291\n",
            "         373       0.00      0.00      0.00         0\n",
            "         374       0.60      0.12      0.21        24\n",
            "         375       0.50      0.03      0.06        64\n",
            "         376       0.00      0.00      0.00        16\n",
            "         377       0.50      0.18      0.27        11\n",
            "         378       0.00      0.00      0.00         6\n",
            "         379       0.00      0.00      0.00         0\n",
            "         380       1.00      1.00      1.00         1\n",
            "         381       0.50      0.04      0.07        28\n",
            "         382       0.00      0.00      0.00        40\n",
            "         383       0.00      0.00      0.00         5\n",
            "         384       0.00      0.00      0.00         2\n",
            "         385       0.00      0.00      0.00         0\n",
            "         386       0.60      0.25      0.36       224\n",
            "         387       0.67      0.09      0.15        23\n",
            "         388       0.00      0.00      0.00         0\n",
            "         389       0.00      0.00      0.00        16\n",
            "         390       0.00      0.00      0.00         2\n",
            "         391       0.68      0.25      0.37        52\n",
            "         392       0.67      0.36      0.47       117\n",
            "         393       0.00      0.00      0.00         9\n",
            "         394       0.00      0.00      0.00        19\n",
            "         395       0.00      0.00      0.00         0\n",
            "         396       0.33      0.12      0.18         8\n",
            "         397       0.77      0.44      0.56        39\n",
            "         398       0.00      0.00      0.00         1\n",
            "         399       0.00      0.00      0.00        20\n",
            "         400       1.00      0.50      0.67         6\n",
            "         401       0.43      0.23      0.30        39\n",
            "         402       0.33      0.14      0.20         7\n",
            "         403       0.00      0.00      0.00         9\n",
            "         404       0.92      0.75      0.83       102\n",
            "         405       0.00      0.00      0.00        15\n",
            "         406       0.00      0.00      0.00         0\n",
            "         407       0.00      0.00      0.00         0\n",
            "         408       0.50      0.33      0.40         3\n",
            "         409       0.50      0.10      0.17       268\n",
            "         410       0.00      0.00      0.00         0\n",
            "         411       0.00      0.00      0.00        10\n",
            "         412       0.25      0.08      0.12        13\n",
            "         413       0.00      0.00      0.00         1\n",
            "         414       0.67      0.08      0.15        24\n",
            "         415       0.00      0.00      0.00         5\n",
            "         416       0.21      0.14      0.17        29\n",
            "         417       0.00      0.00      0.00         6\n",
            "         418       0.50      0.12      0.20        49\n",
            "         419       1.00      0.33      0.50         3\n",
            "         420       0.54      0.23      0.33       313\n",
            "         421       0.00      0.00      0.00        73\n",
            "         422       0.58      0.27      0.37        66\n",
            "         423       0.00      0.00      0.00         1\n",
            "         424       0.00      0.00      0.00         0\n",
            "         425       0.00      0.00      0.00        29\n",
            "         426       0.00      0.00      0.00        22\n",
            "         427       0.50      0.50      0.50         2\n",
            "         428       0.00      0.00      0.00         1\n",
            "         429       1.00      1.00      1.00         1\n",
            "         430       0.00      0.00      0.00         4\n",
            "         431       0.00      0.00      0.00         6\n",
            "         432       0.59      0.29      0.39       335\n",
            "         433       0.67      0.24      0.36       107\n",
            "         434       0.83      0.45      0.59        11\n",
            "         435       1.00      0.50      0.67         4\n",
            "         436       0.00      0.00      0.00         0\n",
            "         437       0.00      0.00      0.00         9\n",
            "         438       0.50      0.51      0.50        57\n",
            "         439       0.00      0.00      0.00        14\n",
            "         440       0.00      0.00      0.00        24\n",
            "         441       0.50      0.18      0.26        90\n",
            "         442       0.00      0.00      0.00         2\n",
            "         443       0.33      0.12      0.18        24\n",
            "         444       0.64      0.06      0.11       143\n",
            "         445       0.09      0.05      0.06        21\n",
            "         446       0.00      0.00      0.00         1\n",
            "         447       0.50      0.08      0.13        39\n",
            "         448       1.00      0.25      0.40         8\n",
            "         449       0.11      0.03      0.04        40\n",
            "         450       0.75      0.23      0.35        13\n",
            "         451       0.67      0.19      0.30        42\n",
            "         452       0.00      0.00      0.00         4\n",
            "         453       0.15      0.01      0.02       233\n",
            "         454       0.00      0.00      0.00         0\n",
            "         455       0.00      0.00      0.00         1\n",
            "         456       0.00      0.00      0.00         1\n",
            "         457       0.00      0.00      0.00         5\n",
            "         458       0.44      0.12      0.19        33\n",
            "         459       0.00      0.00      0.00        15\n",
            "         460       1.00      0.01      0.02        82\n",
            "         461       0.00      0.00      0.00         2\n",
            "         462       0.00      0.00      0.00         8\n",
            "         463       0.00      0.00      0.00         0\n",
            "         464       0.75      0.29      0.42        52\n",
            "         465       0.00      0.00      0.00         6\n",
            "         466       0.00      0.00      0.00         2\n",
            "         467       0.60      0.25      0.35        12\n",
            "         468       0.20      0.14      0.17         7\n",
            "         469       0.00      0.00      0.00         9\n",
            "         470       1.00      1.00      1.00         1\n",
            "         471       1.00      0.56      0.71         9\n",
            "         472       0.00      0.00      0.00         0\n",
            "         473       0.75      0.56      0.64        16\n",
            "         474       0.00      0.00      0.00         2\n",
            "         475       0.13      0.04      0.06        47\n",
            "         476       0.00      0.00      0.00         1\n",
            "         477       0.60      0.33      0.43         9\n",
            "         478       0.00      0.00      0.00         0\n",
            "         479       0.75      0.10      0.18        30\n",
            "         480       0.00      0.00      0.00         0\n",
            "         481       0.00      0.00      0.00         3\n",
            "         482       0.35      0.09      0.14        77\n",
            "         483       0.50      0.06      0.11        16\n",
            "         484       0.00      0.00      0.00       175\n",
            "         485       0.00      0.00      0.00        17\n",
            "         486       0.00      0.00      0.00         4\n",
            "         487       0.57      0.19      0.29        68\n",
            "         488       0.00      0.00      0.00         4\n",
            "         489       0.77      0.37      0.50        46\n",
            "         490       0.00      0.00      0.00         0\n",
            "         491       0.00      0.00      0.00        22\n",
            "         492       0.00      0.00      0.00         0\n",
            "         493       0.00      0.00      0.00         0\n",
            "         494       0.33      0.22      0.26        23\n",
            "         495       0.00      0.00      0.00         0\n",
            "         496       0.00      0.00      0.00         0\n",
            "         497       0.00      0.00      0.00         1\n",
            "         498       0.00      0.00      0.00         0\n",
            "         499       1.00      0.12      0.22         8\n",
            "\n",
            "   micro avg       0.85      0.59      0.70     79584\n",
            "   macro avg       0.39      0.20      0.24     79584\n",
            "weighted avg       0.74      0.59      0.63     79584\n",
            " samples avg       0.87      0.68      0.72     79584\n",
            "\n",
            "Time taken to run this cell : 0:10:46.186962\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dmmLPy8jrYY",
        "colab_type": "code",
        "outputId": "f74d32da-a737-46bb-bf3d-3438f5fba307",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier, 'drive/My Drive/Stackoverflow/bestlog200k_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/bestlog200k_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQNog0Mze1iO",
        "colab_type": "text"
      },
      "source": [
        "### SGD with hinge loss\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGivo-mtDx09",
        "colab_type": "code",
        "outputId": "f2b3a4cc-5379-4d70-d136-7d119dc55130",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "start = datetime.now()\n",
        "classifier_2 = OneVsRestClassifier(SGDClassifier(loss='hinge',penalty='l1'), n_jobs=-1)\n",
        "\n",
        "param={'estimator__alpha': [10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1]}\n",
        "\n",
        "\n",
        "grid_search_model = GridSearchCV(estimator = classifier_2, param_grid=param, cv=3, verbose=0,return_train_score=True,scoring='f1_micro',n_jobs=-1)\n",
        "x_train_multilabel.sort_indices()\n",
        "grid_search_model.fit(x_train_multilabel, y_train)\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 1:19:22.418007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nqz2W1wCuQm4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alpha = [10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8j8WNnRXtKN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_alpha = np.log([10**-5, 10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbkZdFwLsrCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_score = grid_search_model.cv_results_['mean_train_score']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_K3zcQ-8s2wP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_score = grid_search_model.cv_results_['mean_test_score']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dtlmfodtsNE",
        "colab_type": "code",
        "outputId": "92eaf7a0-ffb4-49c8-8f18-733648cb2765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plt.scatter(log_alpha,train_score,label=\"Train Score\")\n",
        "plt.plot(log_alpha,train_score)\n",
        "plt.scatter(log_alpha,test_score,label=\"Test Score\")\n",
        "plt.plot(log_alpha,test_score)\n",
        "plt.xlabel(\"Alpha : Hyperparameter\")\n",
        "plt.ylabel(\"Micro F1 Score\")\n",
        "plt.xticks(log_alpha,alpha)\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LFSrhFeD2GXAqHT4ZBueceeDsrg71qFExgkkLNuloqkoFiLd9ChXdlis4EYgq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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "
"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxIqafdfJAJ3",
        "colab_type": "code",
        "outputId": "0cf774e2-38ce-4cda-a73a-f429675c7feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_alpha = grid_search_model.best_estimator_.get_params()['estimator__alpha']\n",
        "print('Best alpha: ',best_alpha)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best alpha:  1e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pn7l6AWqb4Uq",
        "colab_type": "code",
        "outputId": "551ddfe4-1d49-41f0-9ec3-4a9bbac19979",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier_2, 'drive/My Drive/Stackoverflow/gridhinge_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/gridhinge_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plKwnGilXwCq",
        "colab_type": "code",
        "outputId": "a3db8b08-9848-44d7-96da-7eba64366550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!grep -c ^processor /proc/cpuinfo "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a46b7a28-a96f-40fd-a732-618d9c6afd0c",
        "id": "kIMoyVcgeiqH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = datetime.now()\n",
        "classifier = OneVsRestClassifier(SGDClassifier(loss='hinge', alpha=0.00001, penalty='l1'),n_jobs=-1)\n",
        "classifier.fit(x_train_multilabel, y_train)\n",
        "predictions = classifier.predict (x_test_multilabel)\n",
        "\n",
        "\n",
        "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
        "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
        "\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='micro')\n",
        "recall = recall_score(y_test, predictions, average='micro')\n",
        "f1 = f1_score(y_test, predictions, average='micro')\n",
        " \n",
        "print(\"Micro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "precision = precision_score(y_test, predictions, average='macro')\n",
        "recall = recall_score(y_test, predictions, average='macro')\n",
        "f1 = f1_score(y_test, predictions, average='macro')\n",
        " \n",
        "print(\"Macro-average quality numbers\")\n",
        "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
        "\n",
        "print (metrics.classification_report(y_test, predictions))\n",
        "print(\"Time taken to run this cell :\", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.364425\n",
            "Hamming loss  0.0019605\n",
            "Micro-average quality numbers\n",
            "Precision: 0.9146, Recall: 0.5596, F1-measure: 0.6943\n",
            "Macro-average quality numbers\n",
            "Precision: 0.2504, Recall: 0.1716, F1-measure: 0.1880\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99     36910\n",
            "           1       0.46      0.08      0.13       141\n",
            "           2       0.52      0.10      0.17      4487\n",
            "           3       0.45      0.31      0.36       121\n",
            "           4       0.66      0.20      0.31       783\n",
            "           5       0.57      0.35      0.43        37\n",
            "           6       0.70      0.50      0.58       220\n",
            "           7       0.85      0.55      0.67       486\n",
            "           8       0.50      0.30      0.38        33\n",
            "           9       0.55      0.35      0.43       190\n",
            "          10       0.38      0.01      0.02       255\n",
            "          11       0.59      0.32      0.41       245\n",
            "          12       0.60      0.19      0.29       272\n",
            "          13       0.70      0.11      0.19        65\n",
            "          14       0.74      0.69      0.71        45\n",
            "          15       0.65      0.07      0.13       158\n",
            "          16       0.50      0.29      0.36         7\n",
            "          17       0.66      0.56      0.61       101\n",
            "          18       0.50      0.20      0.28        82\n",
            "          19       0.64      0.48      0.55        44\n",
            "          20       0.72      0.76      0.74        17\n",
            "          21       0.00      0.00      0.00        51\n",
            "          22       0.67      0.07      0.13       137\n",
            "          23       0.88      0.53      0.66        96\n",
            "          24       0.00      0.00      0.00       125\n",
            "          25       0.58      0.29      0.39        24\n",
            "          26       0.58      0.21      0.31       740\n",
            "          27       0.62      0.28      0.38        18\n",
            "          28       0.50      0.40      0.44         5\n",
            "          29       0.00      0.00      0.00        22\n",
            "          30       0.00      0.00      0.00         6\n",
            "          31       0.00      0.00      0.00       428\n",
            "          32       0.00      0.00      0.00         1\n",
            "          33       0.00      0.00      0.00        16\n",
            "          34       0.50      0.07      0.13        41\n",
            "          35       0.65      0.35      0.45       971\n",
            "          36       1.00      0.38      0.55         8\n",
            "          37       0.50      1.00      0.67         1\n",
            "          38       0.70      0.35      0.47       429\n",
            "          39       0.29      0.29      0.29         7\n",
            "          40       0.60      0.07      0.12        44\n",
            "          41       0.21      0.23      0.22        13\n",
            "          42       0.33      0.33      0.33         3\n",
            "          43       0.50      0.50      0.50         4\n",
            "          44       0.59      0.44      0.50       179\n",
            "          45       0.00      0.00      0.00         5\n",
            "          46       0.57      0.52      0.54        50\n",
            "          47       0.00      0.00      0.00       152\n",
            "          48       0.75      0.59      0.66       323\n",
            "          49       0.77      0.85      0.81       310\n",
            "          50       0.00      0.00      0.00       496\n",
            "          51       0.00      0.00      0.00       175\n",
            "          52       0.00      0.00      0.00      1654\n",
            "          53       0.52      0.34      0.41        35\n",
            "          54       0.00      0.00      0.00         9\n",
            "          55       0.00      0.00      0.00        17\n",
            "          56       0.00      0.00      0.00       104\n",
            "          57       0.00      0.00      0.00        88\n",
            "          58       0.82      0.34      0.48       146\n",
            "          59       0.64      0.75      0.69        12\n",
            "          60       0.00      0.00      0.00         4\n",
            "          61       0.88      0.25      0.39        55\n",
            "          62       0.90      0.70      0.79        37\n",
            "          63       0.89      0.55      0.68        29\n",
            "          64       0.55      0.39      0.46      1151\n",
            "          65       0.00      0.00      0.00        34\n",
            "          66       0.00      0.00      0.00        65\n",
            "          67       0.80      0.57      0.67       910\n",
            "          68       1.00      0.53      0.70        30\n",
            "          69       0.75      0.50      0.60         6\n",
            "          70       0.00      0.00      0.00       129\n",
            "          71       0.00      0.00      0.00        49\n",
            "          72       0.00      0.00      0.00         0\n",
            "          73       1.00      0.80      0.89         5\n",
            "          74       1.00      1.00      1.00         1\n",
            "          75       0.57      0.80      0.67        10\n",
            "          76       0.00      0.00      0.00        50\n",
            "          77       0.64      0.30      0.41        70\n",
            "          78       0.88      0.37      0.52        19\n",
            "          79       0.00      0.00      0.00        40\n",
            "          80       0.00      0.00      0.00        16\n",
            "          81       0.93      0.37      0.53        35\n",
            "          82       0.00      0.00      0.00       185\n",
            "          83       0.00      0.00      0.00         0\n",
            "          84       0.00      0.00      0.00         8\n",
            "          85       0.00      0.00      0.00         2\n",
            "          86       1.00      0.22      0.36        18\n",
            "          87       0.50      0.06      0.11        16\n",
            "          88       0.53      0.10      0.17       868\n",
            "          89       0.00      0.00      0.00       470\n",
            "          90       0.00      0.00      0.00        94\n",
            "          91       0.00      0.00      0.00         4\n",
            "          92       0.57      0.36      0.44        11\n",
            "          93       0.69      0.46      0.56       235\n",
            "          94       0.00      0.00      0.00       648\n",
            "          95       0.00      0.00      0.00        81\n",
            "          96       0.00      0.00      0.00        17\n",
            "          97       0.54      0.39      0.45        18\n",
            "          98       0.50      1.00      0.67         1\n",
            "          99       1.00      0.00      0.01       249\n",
            "         100       0.25      0.01      0.01       188\n",
            "         101       0.80      0.69      0.74        29\n",
            "         102       0.42      0.42      0.42        12\n",
            "         103       0.73      0.01      0.02       878\n",
            "         104       0.00      0.00      0.00       127\n",
            "         105       0.00      0.00      0.00         0\n",
            "         106       0.00      0.00      0.00         2\n",
            "         107       0.67      0.31      0.42        13\n",
            "         108       0.00      0.00      0.00       130\n",
            "         109       0.50      1.00      0.67         2\n",
            "         110       0.00      0.00      0.00        16\n",
            "         111       0.00      0.00      0.00         0\n",
            "         112       0.00      0.00      0.00         1\n",
            "         113       0.00      0.00      0.00        33\n",
            "         114       0.00      0.00      0.00       183\n",
            "         115       0.00      0.00      0.00       134\n",
            "         116       0.54      0.27      0.36        26\n",
            "         117       0.43      0.17      0.24        18\n",
            "         118       0.00      0.00      0.00         0\n",
            "         119       0.00      0.00      0.00         9\n",
            "         120       0.00      0.00      0.00       375\n",
            "         121       1.00      0.83      0.91         6\n",
            "         122       0.00      0.00      0.00       758\n",
            "         123       0.00      0.00      0.00         2\n",
            "         124       0.50      0.50      0.50         2\n",
            "         125       0.00      0.00      0.00        13\n",
            "         126       0.33      0.20      0.25         5\n",
            "         127       0.00      0.00      0.00        63\n",
            "         128       0.00      0.00      0.00        81\n",
            "         129       0.00      0.00      0.00         0\n",
            "         130       0.00      0.00      0.00        18\n",
            "         131       0.00      0.00      0.00         8\n",
            "         132       0.00      0.00      0.00         0\n",
            "         133       0.50      0.33      0.40         3\n",
            "         134       0.78      0.74      0.76        62\n",
            "         135       0.00      0.00      0.00        40\n",
            "         136       0.38      0.10      0.16        29\n",
            "         137       0.71      0.46      0.56        84\n",
            "         138       0.00      0.00      0.00         2\n",
            "         139       0.00      0.00      0.00        23\n",
            "         140       0.67      0.38      0.48        21\n",
            "         141       0.00      0.00      0.00       507\n",
            "         142       0.51      0.39      0.44       334\n",
            "         143       0.38      0.38      0.38         8\n",
            "         144       0.00      0.00      0.00        51\n",
            "         145       0.38      0.21      0.27        14\n",
            "         146       0.00      0.00      0.00         4\n",
            "         147       0.70      0.60      0.65       170\n",
            "         148       0.00      0.00      0.00       136\n",
            "         149       1.00      0.60      0.75         5\n",
            "         150       0.00      0.00      0.00         3\n",
            "         151       0.98      0.94      0.96       957\n",
            "         152       0.60      0.50      0.55         6\n",
            "         153       0.00      0.00      0.00         2\n",
            "         154       0.00      0.00      0.00         3\n",
            "         155       0.00      0.00      0.00        69\n",
            "         156       0.69      0.78      0.73        23\n",
            "         157       0.00      0.00      0.00        97\n",
            "         158       0.53      0.30      0.38        27\n",
            "         159       0.80      0.80      0.80         5\n",
            "         160       0.00      0.00      0.00        86\n",
            "         161       0.80      0.80      0.80         5\n",
            "         162       0.00      0.00      0.00        44\n",
            "         163       1.00      0.64      0.78        22\n",
            "         164       0.87      0.42      0.57        31\n",
            "         165       0.73      0.15      0.24        55\n",
            "         166       1.00      1.00      1.00         2\n",
            "         167       0.00      0.00      0.00        14\n",
            "         168       0.00      0.00      0.00         0\n",
            "         169       0.00      0.00      0.00       207\n",
            "         170       0.00      0.00      0.00        24\n",
            "         171       0.00      0.00      0.00         3\n",
            "         172       0.00      0.00      0.00        19\n",
            "         173       0.00      0.00      0.00         1\n",
            "         174       0.00      0.00      0.00       106\n",
            "         175       0.85      0.76      0.80       317\n",
            "         176       0.50      0.33      0.40         3\n",
            "         177       0.67      0.71      0.69        14\n",
            "         178       0.71      0.56      0.63        63\n",
            "         179       0.00      0.00      0.00        12\n",
            "         180       0.00      0.00      0.00         0\n",
            "         181       0.00      0.00      0.00        13\n",
            "         182       0.00      0.00      0.00        49\n",
            "         183       0.00      0.00      0.00         1\n",
            "         184       0.00      0.00      0.00        32\n",
            "         185       0.00      0.00      0.00        13\n",
            "         186       0.00      0.00      0.00        66\n",
            "         187       1.00      0.55      0.71        11\n",
            "         188       0.00      0.00      0.00       148\n",
            "         189       1.00      0.12      0.22         8\n",
            "         190       0.00      0.00      0.00         0\n",
            "         191       0.00      0.00      0.00        26\n",
            "         192       0.00      0.00      0.00         1\n",
            "         193       0.50      0.02      0.03       636\n",
            "         194       0.59      0.53      0.56        51\n",
            "         195       0.00      0.00      0.00        81\n",
            "         196       0.00      0.00      0.00        23\n",
            "         197       0.00      0.00      0.00         0\n",
            "         198       0.00      0.00      0.00        10\n",
            "         199       0.00      0.00      0.00       102\n",
            "         200       0.90      0.71      0.79       215\n",
            "         201       0.00      0.00      0.00         1\n",
            "         202       1.00      0.67      0.80         9\n",
            "         203       0.00      0.00      0.00       131\n",
            "         204       0.00      0.00      0.00        63\n",
            "         205       0.00      0.00      0.00         7\n",
            "         206       0.00      0.00      0.00       375\n",
            "         207       0.00      0.00      0.00         0\n",
            "         208       0.00      0.00      0.00        71\n",
            "         209       0.67      0.67      0.67         9\n",
            "         210       0.60      0.17      0.26        18\n",
            "         211       0.00      0.00      0.00         3\n",
            "         212       0.00      0.00      0.00       188\n",
            "         213       0.00      0.00      0.00         1\n",
            "         214       0.42      0.19      0.26       205\n",
            "         215       0.00      0.00      0.00         0\n",
            "         216       0.00      0.00      0.00        12\n",
            "         217       0.00      0.00      0.00       398\n",
            "         218       0.83      0.62      0.71         8\n",
            "         219       0.50      0.40      0.44         5\n",
            "         220       0.50      1.00      0.67         1\n",
            "         221       0.91      0.83      0.87        12\n",
            "         222       0.89      0.50      0.64        34\n",
            "         223       0.00      0.00      0.00       217\n",
            "         224       0.00      0.00      0.00         0\n",
            "         225       0.00      0.00      0.00         1\n",
            "         226       0.67      0.38      0.48        48\n",
            "         227       0.00      0.00      0.00        55\n",
            "         228       0.00      0.00      0.00        25\n",
            "         229       0.00      0.00      0.00        15\n",
            "         230       0.00      0.00      0.00        12\n",
            "         231       0.77      0.45      0.57       405\n",
            "         232       0.00      0.00      0.00        63\n",
            "         233       0.00      0.00      0.00         2\n",
            "         234       0.50      0.03      0.06        33\n",
            "         235       0.64      0.48      0.55       585\n",
            "         236       1.00      1.00      1.00         1\n",
            "         237       0.40      0.20      0.27        10\n",
            "         238       0.00      0.00      0.00        60\n",
            "         239       0.00      0.00      0.00       459\n",
            "         240       0.00      0.00      0.00         0\n",
            "         241       0.00      0.00      0.00         1\n",
            "         242       0.92      0.54      0.68       520\n",
            "         243       0.00      0.00      0.00         5\n",
            "         244       0.47      0.19      0.27        42\n",
            "         245       0.00      0.00      0.00        30\n",
            "         246       0.50      0.50      0.50         2\n",
            "         247       0.42      0.19      0.26        27\n",
            "         248       0.00      0.00      0.00        10\n",
            "         249       0.00      0.00      0.00       120\n",
            "         250       0.00      0.00      0.00        28\n",
            "         251       0.00      0.00      0.00        21\n",
            "         252       0.00      0.00      0.00        55\n",
            "         253       0.81      0.29      0.43       365\n",
            "         254       0.00      0.00      0.00        79\n",
            "         255       0.00      0.00      0.00        20\n",
            "         256       0.00      0.00      0.00         6\n",
            "         257       0.00      0.00      0.00        20\n",
            "         258       0.77      0.62      0.69        48\n",
            "         259       0.00      0.00      0.00        11\n",
            "         260       0.00      0.00      0.00         0\n",
            "         261       0.00      0.00      0.00        35\n",
            "         262       0.00      0.00      0.00         9\n",
            "         263       0.00      0.00      0.00       300\n",
            "         264       0.00      0.00      0.00        29\n",
            "         265       1.00      0.09      0.17        11\n",
            "         266       0.00      0.00      0.00       477\n",
            "         267       0.00      0.00      0.00        16\n",
            "         268       0.78      0.50      0.61        42\n",
            "         269       0.00      0.00      0.00        74\n",
            "         270       0.67      0.63      0.65        38\n",
            "         271       0.00      0.00      0.00         0\n",
            "         272       0.00      0.00      0.00         1\n",
            "         273       1.00      1.00      1.00         1\n",
            "         274       0.39      0.58      0.47        12\n",
            "         275       0.33      0.02      0.04        47\n",
            "         276       0.00      0.00      0.00        14\n",
            "         277       0.50      0.50      0.50         2\n",
            "         278       0.05      0.43      0.10         7\n",
            "         279       1.00      0.75      0.86         8\n",
            "         280       0.00      0.00      0.00         0\n",
            "         281       0.75      0.53      0.62        17\n",
            "         282       1.00      0.83      0.91         6\n",
            "         283       0.00      0.00      0.00         2\n",
            "         284       0.00      0.00      0.00        22\n",
            "         285       0.73      0.79      0.76        14\n",
            "         286       0.00      0.00      0.00         0\n",
            "         287       0.20      0.11      0.14         9\n",
            "         288       0.00      0.00      0.00       241\n",
            "         289       0.00      0.00      0.00         6\n",
            "         290       0.00      0.00      0.00        11\n",
            "         291       0.00      0.00      0.00         2\n",
            "         292       0.00      0.00      0.00       143\n",
            "         293       0.00      0.00      0.00         0\n",
            "         294       0.00      0.00      0.00        35\n",
            "         295       0.00      0.00      0.00        98\n",
            "         296       0.00      0.00      0.00       129\n",
            "         297       0.00      0.00      0.00        17\n",
            "         298       0.00      0.00      0.00         0\n",
            "         299       0.00      0.00      0.00         0\n",
            "         300       0.00      0.00      0.00         5\n",
            "         301       0.00      0.00      0.00        31\n",
            "         302       0.00      0.00      0.00         1\n",
            "         303       0.69      0.58      0.63       118\n",
            "         304       0.00      0.00      0.00         0\n",
            "         305       0.88      0.81      0.84        53\n",
            "         306       0.00      0.00      0.00        12\n",
            "         307       0.00      0.00      0.00         5\n",
            "         308       0.00      0.00      0.00        80\n",
            "         309       0.00      0.00      0.00         0\n",
            "         310       0.71      0.20      0.31        60\n",
            "         311       0.00      0.00      0.00         0\n",
            "         312       0.69      0.25      0.37        87\n",
            "         313       0.00      0.00      0.00         1\n",
            "         314       0.00      0.00      0.00         0\n",
            "         315       0.00      0.00      0.00       138\n",
            "         316       0.00      0.00      0.00        93\n",
            "         317       0.00      0.00      0.00        10\n",
            "         318       0.00      0.00      0.00        13\n",
            "         319       0.94      0.36      0.52        44\n",
            "         320       0.00      0.00      0.00        30\n",
            "         321       0.65      0.59      0.62        34\n",
            "         322       0.91      0.48      0.62        21\n",
            "         323       0.00      0.00      0.00         1\n",
            "         324       0.00      0.00      0.00       126\n",
            "         325       0.50      0.08      0.14        25\n",
            "         326       0.00      0.00      0.00        10\n",
            "         327       0.00      0.00      0.00         2\n",
            "         328       0.40      0.40      0.40         5\n",
            "         329       1.00      0.07      0.14        54\n",
            "         330       0.33      0.25      0.29         4\n",
            "         331       0.00      0.00      0.00         0\n",
            "         332       0.00      0.00      0.00         0\n",
            "         333       0.00      0.00      0.00        27\n",
            "         334       0.00      0.00      0.00         6\n",
            "         335       0.00      0.00      0.00       185\n",
            "         336       0.67      0.50      0.57         8\n",
            "         337       0.00      0.00      0.00         4\n",
            "         338       0.00      0.00      0.00        20\n",
            "         339       0.00      0.00      0.00         3\n",
            "         340       0.62      0.50      0.55        16\n",
            "         341       0.00      0.00      0.00         1\n",
            "         342       0.00      0.00      0.00        10\n",
            "         343       0.59      0.35      0.44       229\n",
            "         344       0.00      0.00      0.00         1\n",
            "         345       0.57      0.24      0.34       301\n",
            "         346       0.00      0.00      0.00        48\n",
            "         347       0.00      0.00      0.00         1\n",
            "         348       0.00      0.00      0.00        19\n",
            "         349       0.25      0.50      0.33         6\n",
            "         350       0.70      0.69      0.70       209\n",
            "         351       0.00      0.00      0.00         2\n",
            "         352       1.00      1.00      1.00         2\n",
            "         353       0.00      0.00      0.00       340\n",
            "         354       0.00      0.00      0.00        12\n",
            "         355       0.00      0.00      0.00         0\n",
            "         356       0.00      0.00      0.00         0\n",
            "         357       0.00      0.00      0.00         1\n",
            "         358       0.62      0.22      0.32        97\n",
            "         359       0.50      0.67      0.57         3\n",
            "         360       0.00      0.00      0.00        28\n",
            "         361       0.80      1.00      0.89         4\n",
            "         362       0.00      0.00      0.00        34\n",
            "         363       1.00      1.00      1.00         2\n",
            "         364       0.00      0.00      0.00         0\n",
            "         365       0.00      0.00      0.00       229\n",
            "         366       0.00      0.00      0.00         0\n",
            "         367       0.00      0.00      0.00         0\n",
            "         368       0.00      0.00      0.00         0\n",
            "         369       0.00      0.00      0.00        33\n",
            "         370       0.00      0.00      0.00         7\n",
            "         371       0.00      0.00      0.00         1\n",
            "         372       0.82      0.43      0.57       291\n",
            "         373       0.00      0.00      0.00         0\n",
            "         374       0.00      0.00      0.00        24\n",
            "         375       0.00      0.00      0.00        64\n",
            "         376       0.00      0.00      0.00        16\n",
            "         377       0.00      0.00      0.00        11\n",
            "         378       0.00      0.00      0.00         6\n",
            "         379       0.00      0.00      0.00         0\n",
            "         380       1.00      1.00      1.00         1\n",
            "         381       0.00      0.00      0.00        28\n",
            "         382       0.00      0.00      0.00        40\n",
            "         383       0.00      0.00      0.00         5\n",
            "         384       0.00      0.00      0.00         2\n",
            "         385       0.00      0.00      0.00         0\n",
            "         386       1.00      0.00      0.01       224\n",
            "         387       0.00      0.00      0.00        23\n",
            "         388       0.00      0.00      0.00         0\n",
            "         389       0.00      0.00      0.00        16\n",
            "         390       0.00      0.00      0.00         2\n",
            "         391       0.00      0.00      0.00        52\n",
            "         392       0.79      0.39      0.53       117\n",
            "         393       0.00      0.00      0.00         9\n",
            "         394       0.00      0.00      0.00        19\n",
            "         395       0.00      0.00      0.00         0\n",
            "         396       0.00      0.00      0.00         8\n",
            "         397       0.56      0.13      0.21        39\n",
            "         398       0.25      1.00      0.40         1\n",
            "         399       0.00      0.00      0.00        20\n",
            "         400       0.00      0.00      0.00         6\n",
            "         401       0.00      0.00      0.00        39\n",
            "         402       0.00      0.00      0.00         7\n",
            "         403       0.00      0.00      0.00         9\n",
            "         404       0.90      0.85      0.87       102\n",
            "         405       0.00      0.00      0.00        15\n",
            "         406       0.00      0.00      0.00         0\n",
            "         407       0.00      0.00      0.00         0\n",
            "         408       0.50      0.33      0.40         3\n",
            "         409       0.00      0.00      0.00       268\n",
            "         410       0.00      0.00      0.00         0\n",
            "         411       0.00      0.00      0.00        10\n",
            "         412       0.00      0.00      0.00        13\n",
            "         413       0.00      0.00      0.00         1\n",
            "         414       0.00      0.00      0.00        24\n",
            "         415       0.00      0.00      0.00         5\n",
            "         416       0.00      0.00      0.00        29\n",
            "         417       0.00      0.00      0.00         6\n",
            "         418       0.00      0.00      0.00        49\n",
            "         419       0.00      0.00      0.00         3\n",
            "         420       0.00      0.00      0.00       313\n",
            "         421       0.00      0.00      0.00        73\n",
            "         422       0.00      0.00      0.00        66\n",
            "         423       0.00      0.00      0.00         1\n",
            "         424       0.00      0.00      0.00         0\n",
            "         425       0.00      0.00      0.00        29\n",
            "         426       0.00      0.00      0.00        22\n",
            "         427       0.50      0.50      0.50         2\n",
            "         428       0.00      0.00      0.00         1\n",
            "         429       1.00      1.00      1.00         1\n",
            "         430       0.00      0.00      0.00         4\n",
            "         431       0.00      0.00      0.00         6\n",
            "         432       0.00      0.00      0.00       335\n",
            "         433       0.00      0.00      0.00       107\n",
            "         434       0.88      0.64      0.74        11\n",
            "         435       1.00      1.00      1.00         4\n",
            "         436       0.00      0.00      0.00         0\n",
            "         437       0.00      0.00      0.00         9\n",
            "         438       0.54      0.63      0.58        57\n",
            "         439       0.00      0.00      0.00        14\n",
            "         440       0.00      0.00      0.00        24\n",
            "         441       0.00      0.00      0.00        90\n",
            "         442       0.00      0.00      0.00         2\n",
            "         443       0.86      0.25      0.39        24\n",
            "         444       0.00      0.00      0.00       143\n",
            "         445       0.00      0.00      0.00        21\n",
            "         446       0.00      0.00      0.00         1\n",
            "         447       0.00      0.00      0.00        39\n",
            "         448       0.80      0.50      0.62         8\n",
            "         449       0.00      0.00      0.00        40\n",
            "         450       0.00      0.00      0.00        13\n",
            "         451       0.69      0.21      0.33        42\n",
            "         452       0.00      0.00      0.00         4\n",
            "         453       0.00      0.00      0.00       233\n",
            "         454       0.00      0.00      0.00         0\n",
            "         455       0.00      0.00      0.00         1\n",
            "         456       0.00      0.00      0.00         1\n",
            "         457       0.00      0.00      0.00         5\n",
            "         458       0.00      0.00      0.00        33\n",
            "         459       0.00      0.00      0.00        15\n",
            "         460       0.00      0.00      0.00        82\n",
            "         461       0.00      0.00      0.00         2\n",
            "         462       0.00      0.00      0.00         8\n",
            "         463       0.00      0.00      0.00         0\n",
            "         464       0.58      0.13      0.22        52\n",
            "         465       0.00      0.00      0.00         6\n",
            "         466       0.00      0.00      0.00         2\n",
            "         467       0.60      0.25      0.35        12\n",
            "         468       0.50      0.29      0.36         7\n",
            "         469       0.00      0.00      0.00         9\n",
            "         470       0.50      1.00      0.67         1\n",
            "         471       0.71      0.56      0.63         9\n",
            "         472       0.00      0.00      0.00         0\n",
            "         473       0.89      0.50      0.64        16\n",
            "         474       0.00      0.00      0.00         2\n",
            "         475       0.00      0.00      0.00        47\n",
            "         476       0.00      0.00      0.00         1\n",
            "         477       0.57      0.44      0.50         9\n",
            "         478       0.00      0.00      0.00         0\n",
            "         479       0.00      0.00      0.00        30\n",
            "         480       0.00      0.00      0.00         0\n",
            "         481       0.00      0.00      0.00         3\n",
            "         482       0.00      0.00      0.00        77\n",
            "         483       0.50      0.19      0.27        16\n",
            "         484       0.00      0.00      0.00       175\n",
            "         485       0.00      0.00      0.00        17\n",
            "         486       0.00      0.00      0.00         4\n",
            "         487       0.00      0.00      0.00        68\n",
            "         488       0.00      0.00      0.00         4\n",
            "         489       0.77      0.43      0.56        46\n",
            "         490       0.00      0.00      0.00         0\n",
            "         491       0.00      0.00      0.00        22\n",
            "         492       0.00      0.00      0.00         0\n",
            "         493       0.00      0.00      0.00         0\n",
            "         494       0.29      0.17      0.22        23\n",
            "         495       0.00      0.00      0.00         0\n",
            "         496       0.00      0.00      0.00         0\n",
            "         497       0.00      0.00      0.00         1\n",
            "         498       0.00      0.00      0.00         0\n",
            "         499       0.00      0.00      0.00         8\n",
            "\n",
            "   micro avg       0.91      0.56      0.69     79584\n",
            "   macro avg       0.25      0.17      0.19     79584\n",
            "weighted avg       0.66      0.56      0.58     79584\n",
            " samples avg       0.92      0.65      0.72     79584\n",
            "\n",
            "Time taken to run this cell : 0:03:50.500025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "95d5e5b4-3c60-47c2-a203-9216b55d55b9",
        "id": "NMWUAiueeiqS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "joblib.dump(classifier, 'drive/My Drive/Stackoverflow/MySgdhinge_with_more_title_weight.pkl') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/My Drive/Stackoverflow/MySgdhinge_with_more_title_weight.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vi3ughLT8gh_",
        "colab_type": "text"
      },
      "source": [
        "## Output\n",
        "### 1st - bag of words upto 4 grams SGDClassifier(loss='log') 0.5M data points\n",
        "### 2nd - bag of words upto 4 grams Logistic Regression 0.5M data points\n",
        "### 3rd - tfidf(1 to 3 grams) 200k data points Logistic Regression grid search hyperparameter: alpha  \n",
        "### 4th - tfidf(1 to 3 grams) 200k data points (SGDClassifier with loss-hinge) grid search hyperparameter: alpha"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jC34yLLudS7x",
        "outputId": "96d12055-3cb9-42d2-8e5b-09d4ff159682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "# Please compare all your models using Prettytable library\n",
        "# http://zetcode.com/python/prettytable/\n",
        "\n",
        "from prettytable import PrettyTable\n",
        "\n",
        "\n",
        "\n",
        "#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable\n",
        "\n",
        "x = PrettyTable()\n",
        "x.field_names = [\"Data points\",\"Vectorizer\", \"Model\", \"Hyper Parameter\",\"Penalty\", \"Micro F1 Score\",\"Hamming Loss\",\"Macro F1 Score\",\"Accuracy\"]\n",
        "\n",
        "x.add_row([\"500k\",\"BOW(upto 4 grams)\", \"SGDClassifier(loss='log')\",\"Fixed : 10^-5\",\"L1\",0.3544 ,0.0059,0.2634,0.092 ])\n",
        "x.add_row([\"500k\",\"BOW(upto 4 grams)\", \"Logisitic Regression\",\"Fixed:10^-5\",\"L1\", 0.4792 ,0.0031,0.3852,0.2101])\n",
        "x.add_row([\"200k\",\"TFIDF(upto 3 grams)\", \"Logisitic Regression implementation using SGDclassifier(loss='log')\",\"Best Alpha:10^-5\" ,\"L1\", 0.6968,0.00205,0.2415,0.3487])\n",
        "x.add_row([\"200k\",\"TFIDF(upto 3 grams)\", \"SGDClassifier(loss='hinge')\",\"Best Alpha:10^-5\",\"L1\", 0.6943,0.0019605,0.1880,0.3644])\n",
        "\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------------+---------------------+---------------------------------------------------------------------+------------------+---------+----------------+--------------+----------------+----------+\n",
            "| Data points |      Vectorizer     |                                Model                                | Hyper Parameter  | Penalty | Micro F1 Score | Hamming Loss | Macro F1 Score | Accuracy |\n",
            "+-------------+---------------------+---------------------------------------------------------------------+------------------+---------+----------------+--------------+----------------+----------+\n",
            "|     500k    |  BOW(upto 4 grams)  |                      SGDClassifier(loss='log')                      |  Fixed : 10^-5   |    L1   |     0.3544     |    0.0059    |     0.2634     |  0.092   |\n",
            "|     500k    |  BOW(upto 4 grams)  |                         Logisitic Regression                        |   Fixed:10^-5    |    L1   |     0.4792     |    0.0031    |     0.3852     |  0.2101  |\n",
            "|     200k    | TFIDF(upto 3 grams) | Logisitic Regression implementation using SGDclassifier(loss='log') | Best Alpha:10^-5 |    L1   |     0.6968     |   0.00205    |     0.2415     |  0.3487  |\n",
            "|     200k    | TFIDF(upto 3 grams) |                     SGDClassifier(loss='hinge')                     | Best Alpha:10^-5 |    L1   |     0.6943     |  0.0019605   |     0.188      |  0.3644  |\n",
            "+-------------+---------------------+---------------------------------------------------------------------+------------------+---------+----------------+--------------+----------------+----------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-3m4nZ0fKjd",
        "colab_type": "text"
      },
      "source": [
        "### Conclusion:\n",
        "\n",
        "1. Due to high computation requirement , We have taken 0.5 M points for 2 models and 0.2 M for other two models and have considerd 90% tags i.e. 500 tags for all models. \n",
        "2. We have highest accuracy using  SGDClassifier(loss='hinge')                     | Best Alpha:10^-5 |    L1  \n",
        "3. Performance of SGDClassifier with hinge loss is better in terms of time required for execution and scores .\n",
        "4. It took almost 1/3 rd time for same data with SGDClassifier with hinge loss .\n",
        "5. Performance of SGDClassifier with log loss is better in terms of time required for execution Logistic Regression ."
      ]
    }
  ]
}