{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "HowToHandleBigFilesOnLowMemory-IncrementalLearning.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [
"45A-w8c2b3jk",
"GAEFn6w2cBAy",
"32g2_Q82nWAi",
"yJiOYauYePQO"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"metadata": {
"id": "45A-w8c2b3jk",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Initial"
]
},
{
"metadata": {
"id": "6WtyFZD6cbWp",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!pip -q install --upgrade --ignore-installed numpy pandas scipy sklearn"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "bR_7nQDHtt70",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!pip -q install catboost\n",
"!pip -q install lightgbm"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "P-IrGI84NjyG",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!pip -q install \"dask[complete]\"\n",
"!pip -q install \"dask-ml[complete]\""
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "JxR5tH77_BWS",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# https://stackoverflow.com/questions/49853303/how-to-install-pydot-graphviz-on-google-colab?rq=1\n",
"!pip -q install graphviz \n",
"!apt-get install graphviz -qq\n",
"!pip -q install pydot"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "HN9y6AADMe6E",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# After this restart your kernel using Ctrl+M+. to reset all loaded libraries to latest ones, that we just installed."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "32g2_Q82nWAi",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Import"
]
},
{
"metadata": {
"id": "Uu1Dzz0nnX8C",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from multiprocessing import Pool\n",
"from pandas.io.json import json_normalize\n",
"from sklearn.pipeline import make_pipeline, Pipeline\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.preprocessing import RobustScaler\n",
"from threading import Thread as trd\n",
"import queue\n",
"import json\n",
"import gc\n",
"gc.enable()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "GAEFn6w2cBAy",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Initial 2"
]
},
{
"metadata": {
"id": "L3mJPxElhG5Z",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from sklearn.datasets import fetch_california_housing"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "PSkXiaVsi5Iq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b0953791-26eb-42af-c5f0-b0130c2c8ba0"
},
"cell_type": "code",
"source": [
"dictionary = fetch_california_housing()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading Cal. housing from https://ndownloader.figshare.com/files/5976036 to /root/scikit_learn_data\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "1JjhSbxijckH",
"colab_type": "code",
"outputId": "a1075167-69d9-4f5a-e4b1-baacf7a07f42",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"dictionary.keys()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['data', 'target', 'feature_names', 'DESCR'])"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"metadata": {
"id": "Nb_4NqrThe9t",
"colab_type": "code",
"outputId": "d4687540-fe11-43f2-c85d-35030cfd36c1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"dictionary.data.shape"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(20640, 8)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"metadata": {
"id": "pEEodn3zj2_q",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"xtrain = pd.DataFrame(dictionary.data, index=np.arange(len(dictionary.data)), columns=dictionary.feature_names)\n",
"xtrain['Target'] = dictionary.target"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "2qGQUklxox0h",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"new_ = xtrain.iloc[:,:2].to_dict('list')\n",
"new_row = []\n",
"for i in range(len(xtrain)):\n",
" dic_ = {}\n",
" for key in new_.keys():\n",
" dic_[key] = new_[key][i]\n",
" new_row.append(str(dic_))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "lyQK4Y03p8Cc",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"xtrain['Dict'] = new_row"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "0-g28g4tqD4C",
"colab_type": "code",
"outputId": "ab7ceafb-b9fa-4d20-8b1c-ace72e42c81a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 106
}
},
"cell_type": "code",
"source": [
"xtrain.head(2)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" Target | \n",
" Dict | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.984127 | \n",
" 1.02381 | \n",
" 322.0 | \n",
" 2.555556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
" {'MedInc': 8.3252, 'HouseAge': 41.0} | \n",
"
\n",
" \n",
" 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.238137 | \n",
" 0.97188 | \n",
" 2401.0 | \n",
" 2.109842 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
" {'MedInc': 8.3014, 'HouseAge': 21.0} | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" MedInc ... Dict\n",
"0 8.3252 ... {'MedInc': 8.3252, 'HouseAge': 41.0}\n",
"1 8.3014 ... {'MedInc': 8.3014, 'HouseAge': 21.0}\n",
"\n",
"[2 rows x 10 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"metadata": {
"id": "ZgXXGAZOkaCi",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"for i in range(6):\n",
" xtrain = pd.concat([xtrain, xtrain], axis=0, sort=False).reset_index(drop=True)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "21IEArOZk3nJ",
"colab_type": "code",
"outputId": "a0d66401-1d36-4e84-8b59-5431219526f2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"xtrain.shape"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1320960, 10)"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"metadata": {
"id": "xEZbtEstk85V",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"xtrain.reset_index().to_csv(\"train.csv\", index=False)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "WwEFHMx8fbxZ",
"colab_type": "code",
"outputId": "f94087d3-9724-4c96-fdf5-123da5b38fa5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"del xtrain, dictionary, new_row, new_, dic_\n",
"gc.collect()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"143"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"metadata": {
"id": "5QU_6EBwdXjR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Incremental Learning"
]
},
{
"metadata": {
"id": "vhC1BguWtFcz",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Data Exploration:"
]
},
{
"metadata": {
"id": "mHy_qxCjmPuU",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!ls"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "code",
"id": "AqSBBruLtL9d",
"outputId": "30b315c7-f6d3-4c45-934c-e2e7fb631e38",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"part = pd.read_csv(\"train.csv\", nrows=10)\n",
"part.shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(10, 11)"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"metadata": {
"id": "gWiN2k8WuGDM",
"colab_type": "code",
"outputId": "559ea0e3-b25a-4358-b81a-8a4c57e9ae7e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 106
}
},
"cell_type": "code",
"source": [
"columns = part.columns\n",
"part.head(2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" index | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" Target | \n",
" Dict | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.984127 | \n",
" 1.02381 | \n",
" 322.0 | \n",
" 2.555556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
" {'MedInc': 8.3252, 'HouseAge': 41.0} | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.238137 | \n",
" 0.97188 | \n",
" 2401.0 | \n",
" 2.109842 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
" {'MedInc': 8.3014, 'HouseAge': 21.0} | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" index ... Dict\n",
"0 0 ... {'MedInc': 8.3252, 'HouseAge': 41.0}\n",
"1 1 ... {'MedInc': 8.3014, 'HouseAge': 21.0}\n",
"\n",
"[2 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"metadata": {
"id": "CsvUwefIu-xU",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# From above output you will have a basic understanding of type of columns. Divide them so you\n",
"# can use same function on similar columns (for exploration etc).\n",
"id_columns = [\"index\"]\n",
"num_columns = [\"MedInc\", \"HouseAge\", \"AveRooms\", \"AveBedrms\", \"Population\", \"AveOccup\", \"Latitude\", \"Longitude\", \"Target\"]\n",
"obj_columns = []\n",
"dict_columns = [\"Dict\"]\n",
"complex_columns = []"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "r3YlxA073ncT",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"index_col = \"index\"\n",
"columns_visited = [\"MedInc\", \"HouseAge\", \"AveRooms\"]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "IyL3EFXFAJyu",
"colab_type": "code",
"outputId": "ea58f036-972d-4a7a-a6c2-27050f8dceb5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
}
},
"cell_type": "code",
"source": [
"col = \"Dict\"\n",
"df = pd.read_csv(\"train.csv.zip\", usecols = [10])\n",
"\n",
"df[col] = df[col].map(lambda x: json.loads(x.replace(\"'\", '\"')))\n",
"\n",
"column_as_df = json_normalize(df[col])\n",
"column_as_df.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" HouseAge | \n",
" MedInc | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 41.0 | \n",
" 8.3252 | \n",
"
\n",
" \n",
" 1 | \n",
" 21.0 | \n",
" 8.3014 | \n",
"
\n",
" \n",
" 2 | \n",
" 52.0 | \n",
" 7.2574 | \n",
"
\n",
" \n",
" 3 | \n",
" 52.0 | \n",
" 5.6431 | \n",
"
\n",
" \n",
" 4 | \n",
" 52.0 | \n",
" 3.8462 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" HouseAge MedInc\n",
"0 41.0 8.3252\n",
"1 21.0 8.3014\n",
"2 52.0 7.2574\n",
"3 52.0 5.6431\n",
"4 52.0 3.8462"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"metadata": {
"id": "GeW_nDzpB4An",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Explore this column. Check if you want to keep it or not.\n",
"# If you want to scale this column or LableEncode this column, \n",
"# do it and maintain a dictionary with all columns as keys and\n",
"# corresponding Scalar or LabelEncoder or both as values."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "XE4ZA4q5l1q0",
"colab_type": "code",
"outputId": "06cb9816-3c67-4a40-d1e0-0ae74c1afe8f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67
}
},
"cell_type": "code",
"source": [
"# Calculate Standard Deviation, check if they are constant...\n",
"column_as_df.std()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"HouseAge 12.585257\n",
"MedInc 1.899776\n",
"dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"metadata": {
"id": "96nFdSBatrEc",
"colab_type": "code",
"outputId": "b6b2e9b0-0cfa-4dc0-f17b-53435398c74f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364
}
},
"cell_type": "code",
"source": [
"column_as_df.plot.kde()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 124
},
{
"output_type": "display_data",
"data": {
"image/png": 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iAObVUN+wYQMWL16MJUuW4OjRo22OGQwGPPzww7jttttcPoeCl9uj39lSJ6Ig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\nUYBgqBMREQUIhjoREVGA+P9QE8d9KbBFygAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "OM62b5QipQNP",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"columns_selected = ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'Target']"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Qb3ZWBRlosY0",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"preprocessing_pipeline = {col: [] for col in columns_selected}"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "44Gu-D6tpqbP",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Here I am doing all at once. But you can do it column by column.\n",
"data = pd.read_csv(\"train.csv\")\n",
"for col_ in columns_selected:\n",
" rsc = RobustScaler()\n",
" rsc.fit(data[col_].values.reshape(-1, 1)) # You have to do this for RobustScaler...\n",
" preprocessing_pipeline[col_].append(rsc)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "owZ7c_c7zl1-",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pickle\n",
"with open(\"pipeline.pickle\", \"wb\") as fle:\n",
" pickle.dump(preprocessing_pipeline, fle)\n",
"!ls"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "2A4Rvd5vwaaf",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Note:**\n",
"\n",
"If even one column of dataset is larger than your memory, then you can open that column incrementally (by using `chunksize` parameter in `pd.read_csv`), applying some transformation (maybe keeping it in a `np.float32` if its a number or maybe keeping only relevant part of a string and throwing all other away.)\n",
"\n",
"But if that column is not fitting into your memory, you can use `Dask`. (see last section)"
]
},
{
"metadata": {
"id": "iNVpOm4Vhwz_",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Preprocessing method:"
]
},
{
"metadata": {
"id": "5Cc0jrK9GCHC",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# After opening dictionary columns these will change, you can change it to reflect that.\n",
"id_columns = []\n",
"num_columns = [\"MedInc\", \"HouseAge\", \"AveRooms\", \"AveBedrms\", \"Population\", \"AveOccup\", \"Latitude\", \"Longitude\", \"Target\"]\n",
"obj_columns = []\n",
"dict_columns = []\n",
"complex_columns = []"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "vSim3GVjohCd",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"columns_to_drop_completely = [\"index\", \"Dict\"]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "xC9huFOkhwh_",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Now that you have a dictionary which has all the columns you need as keys\n",
"# and corresponding methods you want to apply to that column sequentially,\n",
"# you can make a preprocesing method which will be used to clean data at\n",
"# every incremental step.\n",
"\n",
"def preprocess(df):\n",
" df.reset_index(drop=True, inplace=True)\n",
" df = df.drop(columns_to_drop_completely, axis=1)\n",
" \n",
" # For dict columns:\n",
" for col in dict_columns:\n",
" if col not in df.columns: pass\n",
" col_df = json_normalize(df[col]) # You are already doing json.loads during pd.read_csv to convert string to dict\n",
" col_df.columns = [f\"{col}.{subcolumn}\" for subcolumn in col_df.columns]\n",
" selected_columns = [c for c in dictionary.keys() if c in col_df.columns()] # Select all columns from this dict-column which we selected before\n",
" to_drop = [c for c in col_df.columns if c not in selected_columns]\n",
" col_df = col_df.drop(to_drop, axis=1) # Drop all previously unselected sub-columns from this dict_column\n",
" \n",
" df = df.drop([col], axis=1).merge(col_df, right_index=True, left_index=True)\n",
" \n",
" # And so on...\n",
" \n",
" # And then apply all Scalars, LabelEncoders to all columns selected...\n",
" for col_ in columns_selected:\n",
" # RobustScaler at position 0\n",
" rsc = preprocessing_pipeline[col_][0]\n",
" df[col_] = rsc.transform(df[col_].values.reshape(-1, 1)).reshape(1, -1)[0] # Because of RobustScaler\n",
" \n",
" return df"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Cs1Id8TeEQ-E",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Note:**\n",
"\n",
"One thing to notice here is that we fitted methods (like LabelEncoder's, Scalars's etc.) during exploration to **whole** data column and we will use that to transform data at every incremental step here. Because, in each batch, there might be some data missing and if we had used different LabelEncoder's, Scaler's etc. for each batch, these methods wouldn't have given same result for same category (say). That's why to be on safe side, we already have fitted to whole columns during exploration."
]
},
{
"metadata": {
"id": "yJiOYauYePQO",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Method 1: Using Pandas"
]
},
{
"metadata": {
"id": "PEgsCVSZuefl",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import lightgbm as lgb\n",
"import xgboost as xgb\n",
"import catboost as cb # CatBoost is currently making its incremental learner: https://github.com/catboost/catboost/issues/464\n",
"from sklearn.model_selection import train_test_split"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "u_iS928pdouO",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"incremental_dataframe = pd.read_csv(\"train.csv.zip\", chunksize=100000) # Number of lines to read.\n",
"# This method will return a kind of sequential file reader reading 'chunksize' lines every time.\n",
"# To read file from starting again, you will have to call this method again."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "orP3rs6c8lVY",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"lgb_params = {\n",
" 'objective': 'regression',\n",
" 'verbosity': 0,\n",
"}\n",
"# First three are necessary for incremental learning.\n",
"xgb_params = {\n",
" 'update':'refresh',\n",
" 'process_type': 'update',\n",
" 'refresh_leaf': True,\n",
" 'silent': True,\n",
" }"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Ck6sR_Rl50WQ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"For intro on Parallel programming in Python read my post [here](https://towardsdatascience.com/speed-up-your-algorithms-part-3-parallelization-4d95c0888748)."
]
},
{
"metadata": {
"id": "vLA0b2cNe1Ph",
"colab_type": "code",
"outputId": "4b432342-af02-4b03-bcdd-165bc345bd10",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1042
}
},
"cell_type": "code",
"source": [
"# For saving regressor for next use.\n",
"lgb_estimator = None\n",
"xgb_estimator = None\n",
"\n",
"for df in incremental_dataframe:\n",
" df = preprocess(df)\n",
" \n",
" xtrain, xvalid, ytrain, yvalid = train_test_split(df.drop(['Target'], axis=1), df['Target'] )\n",
"\n",
" lgb_estimator = lgb.train(lgb_params,\n",
" init_model=lgb_estimator, # Pass partially trained model\n",
" train_set=lgb.Dataset(xtrain, ytrain),\n",
" valid_sets=[lgb.Dataset(xvalid, yvalid)],\n",
" valid_names=[\"Valid\"],\n",
" early_stopping_rounds = 50,\n",
" keep_training_booster=True, # For incremental learning\n",
" num_boost_round=70,\n",
" verbose_eval=50) # Output after each of 50th times\n",
" \n",
" #xgb_model = xgb.train(xgb_params, dtrain=(xtrain, ytrain),\n",
" # evals = (xgb.DMatrix(x_tr, y_tr), \"Valid\"),\n",
" # xgb_model = xgb_estimator) # Pass partially trained model\n",
" \n",
" del df, xtrain, ytrain, xvalid, yvalid\n",
" gc.collect()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Training until validation scores don't improve for 50 rounds.\n",
"[50]\tValid's l2: 0.0963618\n",
"Did not meet early stopping. Best iteration is:\n",
"[70]\tValid's l2: 0.0864367\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[100]\tValid's l2: 0.0743662\n",
"Did not meet early stopping. Best iteration is:\n",
"[140]\tValid's l2: 0.0663257\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[150]\tValid's l2: 0.0630322\n",
"[200]\tValid's l2: 0.0560808\n",
"Did not meet early stopping. Best iteration is:\n",
"[210]\tValid's l2: 0.054903\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[250]\tValid's l2: 0.0475982\n",
"Did not meet early stopping. Best iteration is:\n",
"[280]\tValid's l2: 0.0452189\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[300]\tValid's l2: 0.0434777\n",
"[350]\tValid's l2: 0.0399569\n",
"Did not meet early stopping. Best iteration is:\n",
"[350]\tValid's l2: 0.0399569\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[400]\tValid's l2: 0.0358432\n",
"Did not meet early stopping. Best iteration is:\n",
"[420]\tValid's l2: 0.034916\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[450]\tValid's l2: 0.0324789\n",
"Did not meet early stopping. Best iteration is:\n",
"[490]\tValid's l2: 0.0307512\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[500]\tValid's l2: 0.0302787\n",
"[550]\tValid's l2: 0.0282038\n",
"Did not meet early stopping. Best iteration is:\n",
"[560]\tValid's l2: 0.0278374\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[600]\tValid's l2: 0.025847\n",
"Did not meet early stopping. Best iteration is:\n",
"[630]\tValid's l2: 0.0249167\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[650]\tValid's l2: 0.0234998\n",
"[700]\tValid's l2: 0.022146\n",
"Did not meet early stopping. Best iteration is:\n",
"[700]\tValid's l2: 0.022146\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[750]\tValid's l2: 0.0203596\n",
"Did not meet early stopping. Best iteration is:\n",
"[770]\tValid's l2: 0.019881\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[800]\tValid's l2: 0.0188786\n",
"Did not meet early stopping. Best iteration is:\n",
"[840]\tValid's l2: 0.0180671\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[850]\tValid's l2: 0.0173369\n",
"[900]\tValid's l2: 0.016562\n",
"Did not meet early stopping. Best iteration is:\n",
"[910]\tValid's l2: 0.016357\n",
"Training until validation scores don't improve for 50 rounds.\n",
"[950]\tValid's l2: 0.0173281\n",
"Early stopping, best iteration is:\n",
"[911]\tValid's l2: 0.0170706\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "y9cC-f2z3U5j",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"test = pd.read_csv(\"train.csv\", nrows = 10000)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "H1gnks4g1VPB",
"colab_type": "code",
"outputId": "cbb2bba9-7e09-46c0-9da3-4565c945341f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364
}
},
"cell_type": "code",
"source": [
"preds = lgb_estimator.predict(preprocess(test).drop(['Target'], axis=1))\n",
"preds = preprocessing_pipeline['Target'][0].inverse_transform(preds.reshape(-1, 1)).reshape(1, -1)[0]\n",
"true = test['Target']\n",
"plt.scatter(preds, true)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 76
},
{
"output_type": "display_data",
"data": {
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IGwiDLFGJlKJ+sNqHs1Yt4ZNnJiHFZNisZs09Wyqv1Anhcb/6F52JqTB7vTYY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\nIEtERFQmDLJERERl8v8DJOfrwNUhJbYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "zv11-9iJ5asg",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Method 2: Using Dask:\n",
"\n",
"\n",
"\n",
"For intro on Dask read my post [here](https://towardsdatascience.com/speeding-up-your-algorithms-part-4-dask-7c6ed79994ef).\n",
"\n",
"**Note:**\n",
"You should only use `Dask` in case of Big Data, where it is not able to fit in your memory."
]
},
{
"metadata": {
"id": "rctiXDgnZ9tg",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import dask"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "v0Hcj6ih5aNv",
"colab_type": "code",
"outputId": "ed426b21-6dee-4874-a921-b6c14bf5c823",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 123
}
},
"cell_type": "code",
"source": [
"import dask.dataframe as dd\n",
"from dask.distributed import Client\n",
"client = Client(processes=False, threads_per_worker=4, n_workers=4, memory_limit='8GB')\n",
"client"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"\n",
"Client\n",
"\n",
" - Scheduler: inproc://172.28.0.2/805/1\n",
"
\n",
" | \n",
"\n",
"Cluster\n",
"\n",
" - Workers: 4
\n",
" - Cores: 16
\n",
" - Memory: 32.00 GB
\n",
" \n",
" | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"metadata": {
"id": "rPLfUVQU38ip",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"df = dd.read_csv(\"train.csv\", blocksize=25e6)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "GxrLksfT8_od",
"colab_type": "code",
"outputId": "00db78e6-a29f-4a46-d516-ec0c8b23ed8b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"df.npartitions"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"metadata": {
"id": "_p9Uudi--uNO",
"colab_type": "code",
"outputId": "139e59c9-b8f7-4f98-efa4-69782e31bc82",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351
}
},
"cell_type": "code",
"source": [
"df.visualize(size=\"7,5!\")"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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lypSvv/5a7WvxoqKi6OhoiUSydetWsJ/TBdbANzQ0XLhwYciQIW5ubiYmJkBi5uTkhIeH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LCwSElJKS8vnzx5cmNj48iRIx88eFBZWeno6Ojk5HThwoXk5GRJ4YiICNn7mcjtZ4FBEr4SsTc0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\nkZyiPXHix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"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"metadata": {
"id": "JPX2QoiN--MC",
"colab_type": "code",
"outputId": "dd50e404-8f0b-4a15-9798-618b8d62f854",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
}
},
"cell_type": "code",
"source": [
"df.head()"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" index | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" Target | \n",
" Dict | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.984127 | \n",
" 1.023810 | \n",
" 322.0 | \n",
" 2.555556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
" {'MedInc': 8.3252, 'HouseAge': 41.0} | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.238137 | \n",
" 0.971880 | \n",
" 2401.0 | \n",
" 2.109842 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
" {'MedInc': 8.3014, 'HouseAge': 21.0} | \n",
"
\n",
" \n",
" 2 | \n",
" 2 | \n",
" 7.2574 | \n",
" 52.0 | \n",
" 8.288136 | \n",
" 1.073446 | \n",
" 496.0 | \n",
" 2.802260 | \n",
" 37.85 | \n",
" -122.24 | \n",
" 3.521 | \n",
" {'MedInc': 7.2574, 'HouseAge': 52.0} | \n",
"
\n",
" \n",
" 3 | \n",
" 3 | \n",
" 5.6431 | \n",
" 52.0 | \n",
" 5.817352 | \n",
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" 558.0 | \n",
" 2.547945 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.413 | \n",
" {'MedInc': 5.6431, 'HouseAge': 52.0} | \n",
"
\n",
" \n",
" 4 | \n",
" 4 | \n",
" 3.8462 | \n",
" 52.0 | \n",
" 6.281853 | \n",
" 1.081081 | \n",
" 565.0 | \n",
" 2.181467 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.422 | \n",
" {'MedInc': 3.8462, 'HouseAge': 52.0} | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" index ... Dict\n",
"0 0 ... {'MedInc': 8.3252, 'HouseAge': 41.0}\n",
"1 1 ... {'MedInc': 8.3014, 'HouseAge': 21.0}\n",
"2 2 ... {'MedInc': 7.2574, 'HouseAge': 52.0}\n",
"3 3 ... {'MedInc': 5.6431, 'HouseAge': 52.0}\n",
"4 4 ... {'MedInc': 3.8462, 'HouseAge': 52.0}\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"metadata": {
"id": "83MkYBUUOXhc",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**NOTE:**\n",
"\n",
"`Dask` doesn't have equivalent fucntion of `pandas`'s `json_normalize`. But we can use `Dask`'s `to_bag` function and `bag`'s capability to handle JSON to our advantage."
]
},
{
"metadata": {
"id": "5MRTDdZnMd2n",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This method is taken from a great kernel by [Misha Lisovyi](https://www.kaggle.com/mlisovyi), [here](https://www.kaggle.com/mlisovyi/bigdata-dask-pandas-flat-json-trim-data-upd), on Kaggle."
]
},
{
"metadata": {
"id": "81rpOyD1_cBO",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Convert string Series to dictionary Series\n",
"df['Dict'] = df['Dict'].apply(lambda x: json.loads(x.replace(\"'\", '\"')), meta=('Dict', 'f8'))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "aGsdK3ddNjJa",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"dict_col_keys = {\n",
" 'Dict': ['MedInc', 'HouseAge']\n",
"}"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "5l6gQLUiOLdd",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"for dic_col in dict_col_keys:\n",
" for key in dict_col_keys[dic_col]:\n",
" df[f'{dic_col}.{key}'] = df[dic_col].to_bag().pluck(key).to_dataframe().iloc[:,0]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "TpDNWPiPPa8X",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"outputId": "9d3caa05-78a6-4ae2-92fa-b202da17a36b"
},
"cell_type": "code",
"source": [
"df.head()"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" index | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" Target | \n",
" Dict | \n",
" Dict.MedInc | \n",
" Dict.HouseAge | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.984127 | \n",
" 1.023810 | \n",
" 322.0 | \n",
" 2.555556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
" {'MedInc': 8.3252, 'HouseAge': 41.0} | \n",
" 8.3252 | \n",
" 41.0 | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.238137 | \n",
" 0.971880 | \n",
" 2401.0 | \n",
" 2.109842 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
" {'MedInc': 8.3014, 'HouseAge': 21.0} | \n",
" 8.3014 | \n",
" 21.0 | \n",
"
\n",
" \n",
" 2 | \n",
" 2 | \n",
" 7.2574 | \n",
" 52.0 | \n",
" 8.288136 | \n",
" 1.073446 | \n",
" 496.0 | \n",
" 2.802260 | \n",
" 37.85 | \n",
" -122.24 | \n",
" 3.521 | \n",
" {'MedInc': 7.2574, 'HouseAge': 52.0} | \n",
" 7.2574 | \n",
" 52.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 3 | \n",
" 5.6431 | \n",
" 52.0 | \n",
" 5.817352 | \n",
" 1.073059 | \n",
" 558.0 | \n",
" 2.547945 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.413 | \n",
" {'MedInc': 5.6431, 'HouseAge': 52.0} | \n",
" 5.6431 | \n",
" 52.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 4 | \n",
" 3.8462 | \n",
" 52.0 | \n",
" 6.281853 | \n",
" 1.081081 | \n",
" 565.0 | \n",
" 2.181467 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.422 | \n",
" {'MedInc': 3.8462, 'HouseAge': 52.0} | \n",
" 3.8462 | \n",
" 52.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" index MedInc ... Dict.MedInc Dict.HouseAge\n",
"0 0 8.3252 ... 8.3252 41.0\n",
"1 1 8.3014 ... 8.3014 21.0\n",
"2 2 7.2574 ... 7.2574 52.0\n",
"3 3 5.6431 ... 5.6431 52.0\n",
"4 4 3.8462 ... 3.8462 52.0\n",
"\n",
"[5 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"metadata": {
"id": "HoXkaG8bBrxs",
"colab_type": "code",
"outputId": "97298161-0e51-42fa-bc7c-933a17f2b504",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 539
}
},
"cell_type": "code",
"source": [
"df.visualize(size=\"20,10!\")"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
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A6BMnJyeYiRuh4uJi+c/PJOsQent7ayqRfiooKJj841wqe3v7gYEB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\n0Q5rTk5OPT09np6eaoo0E/T39ytUgIYe0LqCTqf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MAxuiiAkV5/HabWfDGwqKB7FYPOrR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\nd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LKCgoK8vLyZM2cuX76cqEj/SV9fX3p6uomJSWxs7OTR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\nIAo0Gm1suK2xsfHly5ccDkcsFr9//97JyWnatGk0Gs3Ozs7f33/lypXkh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0WFgYvC8JgiB1dXVFRUXJdWkAIYiMjPT09IT/rqqqqqOjQyAQ0JW0aElL\nS5veq7hy5UoGgzE4OIiuJIDo9Pf34/H46b2BR48effnyJbqS5I+QkBAfH5/pf549ezYiIgJFPQAu\nyGTyxMTE6tWr4X96e3tHR0ejK0nOiIqKglfeQRCkqKior68PunLkhIWFeXl5wX9XUVFZs2ZNU1MT\nupKkhPHx8Vu3bu3YseP06dMKCgriNb558+bPP/88Ozs7OztbvJYhCEpISGhtbb1+/brYIyCrqqpe\nuHBBT08vMDCQTqeL13hXV5e/v7+npye8clm87Nu379y5cyEhIfX19eK1zGKxnj59isPhLl++jDw2\nC0JWrlz5+eef9/f3h4aGitcyBEEVFRWxsbFXrlwR+64pDAbj6enp7Ox869at/v5+8RqfmJj45Zdf\ntm7deubMGbHn4tu4ceONGzdKSkokMcuSnp5OIBBu3Lgh9uSHKioqn376qYGBgb+/P41GE6/x7u5u\nPz8/d3f3Q4cOidcyBEEODg7nz58PCQmpq6sTu3FeMBiMvr6+zs7OwcHB7u5uMpm8YD/Nh7GxsY6O\njqGhIfhPtOVAEARRqVQikUgkEnt6eohEothvLSHgcDjTTdTZ2Tk+Po62IgiCoKmpqe7ublhYf3//\ndL5TFGEymQMDA3BDdXV1Sckcz/j4+Lt37+CGIpFIkpuhRA6NRuvt7e3q6urt7f3w4QOVShXFGvoh\nOLq6uvT19Wduhzlx4kRAQMC1a9dQVAWYpqioyNraeuZHxaFDh/z8/ODQWoCFJDk52c3NbeaRU6dO\nBQYGirLJCyANREZG3rhxY+YRV1fX169fS+LzcnFSUVGxZcuWmbGD1NXV9fT0GhsbN27ciKIwwDTh\n4eFnz56d/icejzc1Na2vr5f0fvBFwtDQkKKi4szUZC4uLnfu3AFdORIqKyutra1nRodzcnK6ffu2\nubn5Is8G3NLSkpWV9cUXX4jdwzUNBoM5ffp0aWlpaGjozFeEKLBYrAcPHnz00UcSDcK7YcMGAwOD\ne/fu+fr6zpsVECH5+fl9fX1ff/21WKzNibKy8uXLl5OTk3t7e8UV63x8fPzx48fiWmbOiwMHDnR1\ndd25c+fq1aviygYZExOzatUqiQ6zdXR0vvrqq6CgIBsbG3Fl+evo6EhLS7t+/bpEY1p6eHhUVlY+\ne/bs448/FotBNpv98OFDJycniXZMGzZsMDIyun///pkzZ1asWCEWm0VFRV1dXRJ9MJWUlK5evZqa\nmvrhwwfkYdMFpbOzs7CwkM1mczgcJSUlbW3tpUuX2tratrS0jI2NTU1Nwac2bNhgY2OzMN0fhULJ\nysoaHBzEYDBYLHb58uVqamoODg5EIrGlpWVwcJDD4bDZbA0NDScnJ+TxzUWktra2urqazWYrKCio\nqKgsW7ZMT0+Pw+E0NjaOjIxQKBT4lJWV1YJ9ZYyMjGRmZk5OTmKxWCwWq6OjAzdUV1dXU1MTiUSC\nIIjNZq9cudLJyWlhUg2x2ezi4uLW1lYMBoPBYNTV1dXV1R0cHAYGBiYnJwcGBuCtqHg83t7eXuwT\nTrzo6enJz8+n0+kYDEZBQWHlypVwQ7W1tb19+3ZsbIzD4XA4HGNjYzs7u4WJdEqn07Oysvr6+uCf\nm77Je3t729ra4OlJFou1ZMkSJyenBUsy0dzcXF5ezmKxsFissrKylpaWurq6sbFxU1PT6OgohUJh\nsVgYDGbr1q2WlpYCWUbfAZ2YmMjlecFgMLa2tgUFBfb29mipAsAwmcyGhoZLly5xHT927FhKSgqX\nMxQgUSYmJoaHh7m2/mGxWGtr6/z8/D179qAlDCAihYWFe/fu5erhTExMcnNzJycn4XArAFFgMpnV\n1dUXL17kOu7s7Ay7kEAgddTp6OgwMDDgyi7g6Ojo5+cHHNBiISoqarYP5fDhw2Cia15YLFZ5efmV\nK1e4jp84cSIpKenYsWOoqJIGqqqqGhoaZreMJNi5c+fKlSvv3bt39epVEd/YNBrN39///PnzC/AV\nt3Tp0i+//PLJkycuLi6if12np6fj8fjplfgSBU4GGxcXJ3os/v7+/vDw8Bs3bixAgh8jI6OLFy/e\nvXv3s88+Ez3G/fPnzy0tLcXlFOYDFou9cOFCUlLS5OTkrl27RLTW0NBQXV39+eefi0Ubf6ysrHR1\ndR8+fHjp0iURH0w6ne7v7+/r67sAqQKXLFny5ZdfPnr0SCypINPT01kslre3t1i08efQoUOVlZWx\nsbHTW5PFAv6b+GAAACAASURBVIPBSEpKGh0dNTY29vb2nndCsampKTg4GIKgw4cPS+561dTUVFRU\nqKqqHjhwYN45vPHx8fT09LGxMXNzczs7OwlJmpiYSExMpNFo27Zt8/X15e+Ch7P7PH36VFlZ+fjx\n46qqqhJSlZOT097erqWldfDgwXk/G/v7+2NjY6lU6q5duyTnHO/r60tNTcVgMPb29vNeDgaDkZub\nm5GRsXz58iNHjoh9KxUMi8V6+fLl0NCQgYHBiRMn5p2kbG9vDwkJYbPZBw8ehOPQSgICgVBQUKCk\npOTs7DxvPoCpqak3b96QSKS1a9c6OTlJSBKFQklISKBQKBs2bDhz5gz/FzuHw6mpqXny5Akejz9+\n/DjSbpcjSZ49e8a/QFZWVm1t7Zyn7t27x2Qy+dRNSUmBZ70AwtHT0/PmzRv+ZZ49ezY8PDznqadP\nn46Ojs5bXUhxi4zS0tKmpib+Ze7du0elUnmdotPp/KuDa4EK8zY7jUZ79OjRnKeoVOqTJ0/4V6+p\nqXn79q2Q4uQCJDd2YGAgr5fV4OBgVFSUWH4FwId5G/DOnTtzHoeT+YhoXO6ZtwUqKysLCgrmPPXg\nwYOpqSk+dcvKyhobG4UXJ9309/enpaXxLxMSEsJrtMlnjDSzjJDipJu3b9/GxcUt8I/29/ffu3dP\nFAt0Ov3nn38mk8nikoSQR48e9fT0iGIhJSWltLRUXHoQUlNT8+LFC1EsDA4O+vn5wasmFwwajSb6\nVX78+HFXV5e4JCEkLS2tuLhYFAt1dXXR0dHi0oMQEonk5+cnigUGg/Hzzz9PTk6KSxJCnjx58v79\ne1EsvH79urCwUFx6EFJfX49k7IoENpsdGxv75MkTeKe/QDAYjBcvXgQGBk4vGhUXTU1N9+/fF+5Z\nqKurCwgIKC8vF68kKpUaHBwcFhYmxItlcnIyJCQkNDR03q91QSksLHzw4EFzc7MQdfPy8u7fv9/e\n3i5eScPDww8fPkxISODvx5uT/v7+x48fJyYmilcSh8NJTk4ODAzs7e0VtCKLxUpKSnrw4IEQDwh/\nOjs779+/n52dLUTd1tbWBw8e5Ofni1cSg8EICwt79uzZ+Pi4oHUpFEp4eHhQUBCFQpm3MJrLvuh0\nent7O6+JZW9vb5AhB126u7s1NDR4bZc78//YO++wqLI0/9+qIuecBURQySiooAKmVlql1Tbbom1E\n7WDP9rM78+zszvTss+GZeaZ3t00YGpEcRAQFlAwiiOSkIDlTpAKqoIrKvz/ub2tqqKpbt6puAurz\nF9577jlfzw117znv+b5ffaVOwoMZLS0tXl5estbLnD17Vm2WukSJj48XOeAvQltb29nZGb2kOiuE\n9vZ2R0dHWYvyLCwsNDU1e3p6MFalRpzS0lJZM/kuLi4DAwMqeo2tcAQCwfv372VFoERERKgzP0NA\npVINDAxkxV6t2LzZ3d3dHz58OHLkCMbtWllZffHFF3FxccodLhQK7969e/HiRV1dXWSFyeXKlSvZ\n2dk0Gk25w9++faurq7t582ZkVcnFx8dn1apVr1+/Vu5wJpOZnJx848YNjJ1qtLS0vv3224cPHyrt\nMZqWlrZt27ZVq1YhK0wuYWFh4+PjTU1Nyh0+ODjY3NyMTYy8OObm5l9++WV0dLRyh4M35oULF8QN\nObHh4sWLL1++HB8fV+7wt2/f8vn8rVu3IqtKLp6enmvWrMnNzVWxno6Ojjt37mzdulW5ZI9gasdz\n5849f/48Pz9fRTEgPB7v8ePHg4OD169fVy6Ro5eX17Vr13g8XlRUFFKWvu/evYuNjT169OiZM2eU\n+PnQ19ePiIg4cOBAdHR0XV0dIpLodPrdu3e1tbUjIyOVs6wJDg6+fv16e3t7bGwsn89HRFVOTk5O\nTs7FixcPHTqkRCCzlZXVpUuX/Pz8bt261dfXh4ikwcHBW7dueXl5Xb58WW6IsSRkMjk8PPzy5ct5\neXkvXrxARJJAIEhISGhpabl+/fqOHTuUqMHV1TUyMtLAwODOnTszMzOIqGpsbHz48GFYWNi5c+eU\nyNOgo6Nz+vTp48ePx8fHv337FrowngPQSUlJEL+RZmZmPB4PNKxRgwvp6elffPGFrL1aWlpeXl6N\njY1YSlqZCASC8vLykJAQWQWMjIz09PSQelKrwYz+/n5bW1sIw7Jdu3aVlpYKCZB8YIkiEAhKSkr2\n7t0LUebIkSPZ2dnqTsYLLpfb0dEB4bNx5syZ9PR0LCUtM549e3b48GFZe/X09MzMzHp7e7GUtIRI\nS0uD6D0NDQ0vL6/q6mosJeHO/Px8Xl7eV199hUvrDg4Ofn5+yg2JxsXFnT592sTEBHFVcLh8+XJs\nbKxAIFD0wO7u7rGxsZ07d6KhSi6bNm3i8/kfPnxQ4tiHDx9GRkbiYnKlpaV1/vz5x48fK3FsaWmp\ns7Pz+vXrEVcFhy+++KK6unp6elrRA8F10+LJlrHEzs5u69atL1++VOLYhISEEydOoOoPDsH169cT\nExOVGIAbGBgYGhr6/PPP0VAlF39/fwqFovRcBQAA2dnZbW1t3333na2trSpKwHvN1tY2KioKtPRV\nmqGhoTt37hw9elR19/nAwMCLFy/GxsaqmC5YKBSCz+2rV6+qaIpoYmJy7do1Op2uegr0pqam5OTk\nq1ev+vv7q1jV/v37Dx48eOvWLRXzoILeVq6urmfPnlUxJ4Sjo+P3339fW1ur+qxGUVFRZWXl999/\n7+zsrEo9FArlzJkzHh4et2/fZrFYqlQ1OTn5yy+/7Nu3Lzw8XJV6AADw8/O7du1aenp6fX29ilWl\npqZOTk7euHHDzMxMlXr09fWvXLmioaERExMD8WWN2wA0lUo1NTWFtoo/efKk+rMTL4qLi8PCwqBj\nFrZu3frmzRslXqbVKERqaqrcOCNwEA0bPWqQ4sWLF3KTZZ84cSIzMxMbPcuPpKSko0ePyi129OhR\n5b6a1KhOSkqKrEUAIHp6evr6+gMDA5hJWk7Q6XQejwf9eRkeHq56INWypLi4eMeOHXJfhMAkLZip\nwp3Hjx9funQJRwE+Pj5zc3OKrlypqKhwdna2trZGSZVcKBTK2bNnExISFDqKy+VmZ2fD+SFDjwMH\nDhQXFzMYDIWOSkpKOnLkCFLJAJXAxMQkMDBQ0SGMsbGx0dFR7IPNxbl06RJo3aPQUY8ePZLMdYEl\n7u7uJBKpvb1doaNqa2sdHR1VHANVBTKZ/PXXX8fHxyt0FI/He/78OTa+z7LYt29fZWUlnU5X4tj4\n+HgnJycEcxh4e3ufPXv21q1bSgcdt7W1FRQU/PDDD0glEtTW1r5x40ZbW1tVVZVyNfB4vNu3b+/b\ntw9BU+mdO3du27bt3r17So+fvHnzZnBwMDIyEilXfXNz8x9++CErK6u7u1u5GsBw7IsXLyKYPvTY\nsWPm5uaqrLZPT083NDQ8efIkUpJcXV2vXLkSFRWlxAQhSH9//9OnT2/evImUc7qGhsbly5fHxsaK\ni4uVq0EoFN6/f3/z5s179uxBRBIAAIGBgQcOHLh165asGSncBqCho2tBKBSKr69vbW0tNpLUiGCx\nWH19fe7u7nJLqicJ0GZqakpDQwPOm9m+ffuQWgClBgOys7Ph5P6ysbGZm5tTrwVRgv7+fhMTEzgp\nzu3t7ZlMpoqT/2qUYHx8XF9fX25A4uHDh1+9eoWNpGVGUlISnFRFO3fuLCoqwkDPEoLNZvf19fn4\n+MgteerUqZXzIpSTk/P555/LMgTDjGPHjim0cmV+fr6trS00NBRVVXKxtLR0dHRUKGgxLi4O31FF\nkKtXryYmJsIv39nZaWJi4uTkhJ4kOPj4+IyNjSnkfJKamorvqCIAAGQy+fjx4woZUebn53/22WfY\ne8ss4uDBgwUFBfBH1lgsVn19Pe43pqmpqZubm0IDDvHx8RcuXEBPEkwuX76s6JwWAAAxMTHbt29H\nPLumoaHhd9999+DBAyVCRNva2hobG9Ho0i+//HJmZqayslLRAwUCwZ07d86fP6+EbwM0jo6OJ0+e\nvHPnjhKLL0tLS3k83sGDB5GVRCKRrl69+vbtWyXGoOfm5qKjo7///nvEsyz6+/v7+fkp9NMjIjU1\n1d3dHfGpRB0dnZs3b8bGxs7Ozip67MDAQGFhoeqJlCX5/PPPNTU1CwsLlTj23r17R44cWb16NbKS\nrKysLl68ePv2banxGfgMQFdWVgYFBcFxBAsKCqqoqFDH2GJMXFwczPkia2trgUCgtHmWGrkkJyfD\nTEHu5ubW19eHlN2VGlRhMpnT09Nr166FU1htQaAcWVlZ8F/RTpw4oe5k7IH2NxBBIpG2bNki11NM\nzSI+fPiwdu1aODEyHh4enz59YrPZGKhaKoCrwuGUNDc3J5FIIyMjaEvCHSaTSaPRXF1d8RYCAABw\n4sSJrKwsmIWTkpLOnj2Lqh6Y7Nixo6SkBOZ3TWdnp6OjI/bGuJJoa2v7+PjU1NTALP/69Wu5C7yw\n4fTp0/CN2nNzc+Wu/sQGe3v7hYUFmMEHLBZreHgYTtgQBij0NpWYmIiXmc8itm3bVlVVBXMtS3d3\nt42NjZGREdqq5KKpqbl582aFbKDS09ODgoIQH28C0dLSunHjRlRUlEJDN8PDw1VVVadPn0ZDEvB/\n1uqtra0KHXX//v2IiAikwrEXYW5ufuLECUVt0+vr65lMpqykKapz/vz5wsJChUZ1uFzugwcPvvnm\nGxVtN2Sxbt06b29vRc2Xc3Nz3dzcPD090ZBEoVC+/fbb6OhoDocD/6ipqans7Gz0Vo8FBwcLBAJF\nHeFiYmIOHTqE0sowQ0PDixcvRkVFSe7CYQCaz+e3tLTA96w5efIkUp7fauDQ0dHh4uIC/333xIkT\nGRkZqEpasVRWVm7ZsgW+i39ERERKSgqqktQgQnx8PPwsMRQKZdOmTe/fv0dV0jIjPT1doQRZZDL5\ns88+UzrVkholqK+v37hxI8xAAD8/v9bW1hVldKA6ZWVl8D9Uzp49q56DEdHV1eXg4ADf8PH48ePP\nnz9HVRIRSE5OhjkojwE2NjZ0On1ubk5uya6uLicnJx0dHQxUweHkyZMwh84LCgpU90JFCtBtBk7J\n169fIx6gpzQaGhre3t5wos45HM7o6CjMyAAMOH36NMzPKyJEbYuwtrbmcrlwwgP7+/vt7OyIML8C\ncvLkSZgdnpeXh5f1syQBAQF1dXUwY2nr6uosLCxQ9TfX0dG5cOEC/LhsHo+Xnp6Odjj54cOHy8vL\n4duVZGdn7969W4nEjPCxsbEJCgqC/90xPT3d3NyM9sTe1atXk5KS4L9sx8TEXL58GVWrJR8fHz09\nPfhZxzo6Ovh8/saNG9GTpKGhERkZGRMTA7O8UCiMj4+PjIxETxIAAHv37m1paaFSqTDLFxcX+/n5\nOTg4oCfJxMTkiy++kHyu4jAAnZKSopCXGfiKiVSGRzVyKSwsVOh9l0wmb9u2rby8HD1JKxMej/fx\n48dNmzbBP0RXV9fFxUXFfAtq0Ka1tXXNmjUKfQz7+/vX1NSomNxj5UClUoVCoaL569euXTs6Oqq0\nsZcahRAKhe/fv1cod/yJEydWwhgfUmRlZSn0oWJkZKSjo7MSwnjh8OrVKzgWSSJIJFJoaGhZWRl6\nknBndnbWxMQEd/MNcU6cOAEnQgV0J8BAD0xsbW3Hx8flfuE3NDRs2bIFG0kw2bt3L5yLfHBwEKX4\nSuXYunUrHBPY58+fw1xxiA0UCsXW1nZ4eBi6GJ1O19HRwd18Q5xjx47BSV7y+vVr4gzjAgBgaWkJ\nZk2ALtbU1OTn54eNJJjs27cPjonW/Px8fX39jh070NYDWprAdF6Oj4//+uuvUVYEAABw+fLluLg4\nOCV7enoEAgGCdsay8PT0pNPpcu9xkPj4+IiICLQlkUiks2fPJiUlwSlcVlYWEBCAUpC4OHv27Hn3\n7t3CwoLcklwut6CgQPX8fnLR19ffunUrTNeLlJSUU6dOwQ8oVJoLFy7AdM0eHx+nUqkbNmxAW5Kj\no6OOjs6nT5/EN2I9AD0+Pq6pqWlhYaHQUadPn05LS0NJkhpxMjIylJhb8/b2/vDhg3p0DFkSExPh\neHcuYufOnfn5+Uq4SqnBBqFQWFFRoYTT/1dffQV/GekK5+nTp0rcOwAAREREqH9rsOHZs2eKZr8x\nMzNjs9lTU1MoSVpOMJlMOp2uaNbvL7/8Up3MFgCAFy9eKBG/6eXl1dbWBucDaYmSkZGBwUedQujo\n6HC5XOg+HxoacnZ2JoKpgjiHDh3KycmBLlNTUwN/tSg2uLq6LvqMlKSoqAi9FeJK4+7uDh2cwefz\n5+bmTE1NMZMEhwMHDsjNf5CZmQnHyQpLtLW1KRQKk8mEKEOlUm1tbQl4Y8qd01J07hwDXFxc4CRl\nTUxMxGAEEyQoKKi2tlbuyEBnZ6e9vT0GI5gAAGhqaoaGhsIJmMvJyZGbqAwpjh8/DicLeklJyWef\nfYbBCCYAABYWFoaGhn19fdDFOBxOZ2cnqoHG4pw/fx7OB1pSUhJmrj7e3t49PT1yvU+pVKqOjg7i\nTuJSIZPJBw8ehJM1B8t1M/v371+UpQzrAeinT58qkcpZU1Nz7dq1LS0taEhSI4JOpy8sLCj6yQpy\n5swZVRKVqlnEwMCAmZmZ3NxcUjl69Ch8Y0Q1GJOenq7cN7ypqamOjg7MefKVDBjsptxXjYaGRlBQ\nUGlpKdKi1PwdDAaDzWbb2dkpeuCpU6fUjk9wSExMhG/yI4JEIm3atGmFe20zGIzZ2Vnl4jfPnTu3\nXKcJBQIBmUxGdZmtcoSHh+fl5UEUyMvLUyiYHRtsbGwmJiYgCoyMjDg6OmKmBz4eHh7t7e0QBfr6\n+tasWYOZHpiEhIRAx2MWFxcTKkwehEwma2trQ7jzC4VCPp9PHHsZEV988UVubi5EgVevXhHEJVwc\nCwsL6GVwExMTcNLCY4+vry/0OElfXx8YioiZpDNnzsh9YSsqKtq7dy82eoD/C5iDDtIqKioKCwvD\nTBKJRAoKCnr37h1EGT6f39nZiaXJ++HDh+V6g6SlpWHp/KOnp2dkZATtLzE5Oamnp6fc4IlywDGv\ny8rKwnKOcM2aNcPDw9BzP3V1dYGBgYjnQoTg4MGD4lcUpgPQdXV1fn5+ys3e7Nixo6ioSB3XiSoJ\nCQlKfLKCGBkZGRoa9vb2IitpxfLs2TOlHfQcHBzm5ubUTgIEhEajCQQCJcbdQI4cOaI2xIdmdnZ2\nYmJCFXs7Hx+f9vZ2dTJPVElISFAuRJ1Coaxbt665uRlxScuJrq6u1atXK/eduWHDhsbGRrlrkJcx\ncXFxStsc6+npmZubK5FEnviUlJSEhITgrUIKZmZm0KsiNDQ0sIkaUxRHR0eIELOCgoLdu3djKAcu\nYK42WXtpNJqlpSWWeuBDIpEgnmyDg4PEHPHfv39/QUGBrL0VFRVEi8YFMTIygvbbJZPJKGUtUxFX\nV9eOjg5Ze/Py8rAcMIXPli1bamtrIQpgPxVnZmY2NzcHcdN9/PjRx8cHS0mAvBsKAICuri43NzfM\n9AAA4OvrC/1am52djb2rvpubW2dnp6y9bDabz+fDz5OBCOHh4dArh54/f47xchA9PT0AACByUQwO\nDjo5OWG81OPIkSPQYfW1tbUKWbyqzurVq/v7+0X/xG4AWiAQ1NTUbNu2Tekajh49Cj2bqkYV6urq\nvL29NTU1la7h0KFD6tW7iFBUVPT555+r8rQ6derUco3DWtKkpKQoN+4GAtqMQr85rXBiY2NVz5EF\n30JLjRJ0dnauXbtWaSfZkJCQkpIS9Ww0BDk5OUqY/Ig4c+bMs2fPENSzhGhpafHx8VHF5nj//v3L\n8k2VaK6+4piZmcnKE1NbW4vZAmFF2bVrF8RicKFQqMoLOXpAv5rm5+cTc3gOAIDdu3fLMrBms9mE\n8lAWx9zcfHJyUtberq4uDJxqlcPa2lpWmH9zc7OXlxfGemASEhICEY7K4/EI5YMvDsS9SafTzczM\nsDc8WRT2uIh3795hP33i6Og4ODgoa29jY2NAQACWekDc3d0hRnsnJyeVDl1Smt27d79580bW3uzs\nbMxcSkRQKBQdHR0WiyV1L4fD0dbWxv538+jRoxDD4gUFBdivwTI3N4cIQxwYGHBxccFSD4j47LWq\nc4+zs7MQt/HQ0FBrayv4d1FREZjFXrTXxMQEOvEij8dbtM6roaHB1tYWXAPY399vaGgoy65LU1OT\nsD/JWNLe3i5r7nF8fLyvrw88I0KhMCsr68SJE+InyN3dXW7YyOjoqHjsiYuLS3R0tChrivgFIImd\nnZ2ZmRn8/8tSB6Iruru7DQwMwDPF5XIrKiq+/PJLUXkymezh4SG3/oGBAfFwA319/adPn4oW7ECf\nCycnJ0NDQ5j/ETXi0On0gYEBWXvFu721tdXc3FzchdDY2BhOorxF68UqKystLS3ByJGuri6BQCAr\nioREInl6esL8jxCWkZERGo0ma694Dzc0NNjb24s7VDo4OMBcijU1NTU6Oir6p6am5rNnz0Q/ItC3\nj6WlpbW1NZxWljHDw8MQrzviHZiWlrbot2bVqlXQDoB9fX3i8QXu7u4PHjzYvn27ZOWSuLi4gBEK\nS5r5+XmIBUbiPVBVVeXm5ibeIYaGhk5OTtD1L+rAgYGBd+/egT8K3d3denp6shKmwfx5wpdPnz7J\nWo1Io9FEL0IAAKSlpZ08eVLRFyEqlSo+QuTm5vbgwQNRvMXyeBEimlWrOCEhIeXl5VK9rVpaWrBJ\nb6UEFApFIBBI3UWj0czNzTHWAx9nZ+e+vj6pfn0LCwsEtIMAWbVqVXFxsdRdlZWVQUFBGOuBD5lM\nBj1w8BaiGCEhIYWFhUeOHJHc1dDQgJkZsaJAPOsYDAY2bsXKsXbt2s7OTqnRu7m5udiH0AIAYG1t\nPT4+LnXXwsICXu9m9vb2Q0NDUsegamtrL1++jL2k4ODguLg4qecO+4hsERQKhcfjSf3GnJmZwcUx\n//PPP8/Pz5eaQgav6U8DAwNZq2YFAgGFQsHl9cnT07O1tVXqPF9RUdG5c+ewl+Tl5fX48ePAwEBA\n9Qjo3NxcJpPJkUFYWJjo7+3btxsaGorvlWtTOz4+/vbtW/FDwGy54N9btmzR19eX1TSc9LsrgefP\nn8vqIhMTkw0bNoB/c7ncQ4cOie8tLi6WFVEiTlZWlvhRtra2fn5+Ui8ASVaUmQCXy3358qWsrli3\nbp29vT34t1AoPHDggPhemJ6ni86Fu7u7i4sLnHNBo9FKSkrQ7oHlSn5+Pp1Ol9W34t3u5ubm5uYm\nvhemVXdGRob4UQcPHhQIBODfzs7O4md5EcvDLXfRhQ3Rw+7u7qtXrxbfC/8hs6gVT09PJycnqa1I\norZcBxQ5TYt+a9hsttwELJmZmeKHWFhYbNq0Cc7ZGRgYqKmpwaYHUOXdu3cjIyNwunfDhg02Njbi\ne+W+Dkn+PO3atUtbWxv8e+3atQ4ODrKaXhIPGYgXIQMDg82bN4v+efjwYfG9ZWVlELGHIhZd/FZW\nVgEBAVLPjiRL4unR29tL2PBnANKFg0QiEXnonEKhcDgcye1v3rwRTbARkKCgIKkuHEKhkOCDpLKW\nzvT39yuX/wYbfHx8pK7QHx0dtbe3x14PTAwNDWdnZ6XuIviloq2tLTXKsry8nJiGJyCbN2+W9cLD\nZrMxdksQoa+vLzVPbElJCV4uQ3v27JG1GAKvdSckEknW06mysjI4OBhjPSChoaEVFRWS22dnZ/Ga\nIjUzM5MV7DI1NYVNoj9JrK2tpb4r1tTUiIIyMSYwMLCurk7qLjKZjJcvGYVCAa9zBNyXNmzYoNzt\nChESIsLV1VW51XNwKl8J2NvbK9eB4sGAEOjp6Sm9vHGlnSNnZ2dUL2ZjY2Pl6p+ZmYFYZaNGLn5+\nfspN48M8s05OTiv5Maivr4/BQ0ZDQ0P9KFMFAwMD5TpQIBCILwuQipmZmXKV9/f3y03kvVTw8vJS\nbg0mnOsT7Z8nfLGzs1PufwedJk6Erq7u8n561NXVgfEfhEXWYBaRB7kAAPDy8mpvb5d0QaXT6bgE\nl8EEnJ2S3E5YG2URWlpaUgP6CH6d+Pj4JCUl+fn5LdpeU1NDTGd2EUv0xvT19W1tbZX0SJ2cnCTy\ncjcNDQ2pi55lBbFiw9atWysrK3ft2rVo+9jYmJWVFS6SNDQ0pC6K6unpwcWaAMTa2ppKpUoOoQoE\nArymUVevXl1WVhYaGrpoe3l5OY5TpOBgPaGmlkNCQoqKiiTtp9va2nBcgyV1SgOv0HWQ9evXt7e3\nu7u7E/oHQI0aNWrUqFGjRo0aNXjBZDL19fXxVgGF1A+tiYkJwibEAwGXzOOtAjE+ffqkSgZgDFi9\nevVSnJIELTgkt8/MzMA0GcMLqXGddDqdyEYWAACsWbOmp6cHbxWI8eHDBxwdt1etWiXVrBXfAUSp\nrdfU1GCcmU2coKCg9+/fS27Hd7ZGakdNTU1ZWFhgLwbEzc1NMtXz4OAgHE9LlJC12gPfi1yqzdf7\n9+/xCsoGAMDf37++vh7AMgmhGjVq1KhRo0aNGjVqlhCECjWSiqWlpaQLR0dHB8GTwejp6TGZTMnt\nxO9wqQqlhu8RinXr1olniQDhcDiETSsnQmqHE/86sbOzk1xQ29HRsXbtWlz0wERLS0tqjD/xO1yq\nQtw7XKoqfMdVjYyMxNMmgbDZbBwt7E1MTCQHMalUqq2tLS56QAj45Fm/fr3kY/zTp0+irFe4QMCO\ncnJykpz7oVKpOC7j0NDQADPKoHjz02i0lpaWpVj5CkEoFA4NDS3d+pcT7e3tDQ0NS7d+NVJpb2//\n+PHjUm+C4EBkwV1yrSxXPn78+Pr1a1npcIlc+ZJgYWHh3bt3stKCE79+fGlsbHz9+jVMzzEC1k9k\nWltbIfLHql5eHAsLC0kHxsnJSeUioBVSoopsZMGywxEES9mmpqaS/qHT09PKJQJVXydyUd+YAOY3\npmTI3l30OAAAIABJREFU+cLCgq6uruoCRkdHGxsblZAkOQxHp9ONjIwUqkSqKqUlubi4SC6GUHS4\nEIOOUiIBA7IdBdoWLdqISEcpLcnMzEyyNkVH6jE4d4DisyyIX+SSKzkQkaSKKrCjULQEevbs2bp1\n6z5+/IhGhnRUK18hjI6Opqen//DDD0u0/uWEmZlZfn6+lZUVSulE0K5fjVTAbjc1NUVv+hqDJogM\ni8X6wx/+EBMTswxaWcaMjY35+fmhlPIC1cqXBDExMZcuXWKz2Uu0fnzR19fX1taWlfyH+PUTgfr6\nejqdvmrVqoWFBR6PNzExsXv37uHh4YqKitDQ0JmZGRaLpaGhQSKRRFF4ra2tQ0NDtra2vr6+PT09\nLBbL2NgYLA8OBSYmJoaHh8/OztJoND6fb2ZmlpeXd/r0aVljFoaGhgwGY9HGubk5COcQUHZoaGhv\nby+LxVqkfNWqVU5OTpKySSSSkZERjUYzNDSUlA0q9/Pz4/F4oGwLC4usrKzw8HBZyhW9NqBly+1w\nULanp+fQ0NCiDgdl+/r61tfXy+1w0ERVoQxA4teJp6dnU1PTxMTEunXroGVDd7jkdQLd4SQSSXJJ\nMoPBgMjPpkSHE+E6AQhzY0pOv6nS4eCN6enp+enTJ0J1OEFuTD09PRaLBT8tTWJi4saNGxkMxubN\nm8EtIgGg4MbGxvDw8MnJSRaLlZ2d7e3trampqaury2KxuFzu2NiYgYEB6PxOo9F8fX1htgsxAC0p\nSaRKdPYTExM9PT01NTWzs7NPnz7d2tra29vr4uLi6enZ2NgIR5WRkdHw8DD8XgoPD2cwGMPDwxAd\nBUry9PSMi4tDsKPWrFmjXEctOnfiHSUuCfRIkaXKwMCAwWDAdA1WqKNEkkgkkuodBXHD4nWRs1gs\nWQH1EOcOwY6SGuYP0Utwzh1SHYViBDSTybS2tu7v719yla8Q7OzsUE1KgHb9ywkrKyuBQKBcgiki\n1K9GKgYGBvPz86jeBRg0QWQKCgo8PDykrpRccq0sY+zt7UtLSyUt24hf+ZJgcnLy6dOnXV1dS7R+\nfHFzc2Oz2ej9OKJdPzZAj8VUV1fr6+vn5ORkZma+e/eOy+X29vZmZGSA9hcMBiMzMzMnJ2d+fl50\nSGlpaWlpaVFREY/HAwuIyoMwGAwjI6P4+HiwTE5OjlAohIiY09fXF68fhM/nQwT7gLLfvHkDClik\nXJbsVatWgaqmp6clZYPKs7KyRLLBr3dFY/0ggJYNyOtwUDYAAJIdLpINAIByHQ7/OgEAAFT+888/\nQ8uG7nDJ60SJDmcymRDxoUp0OGbXyRK9MRcWFiCMDuDcmAAAEK3DiXNjzs3Nwf8fMRiM4uJiHR0d\nUS76RYLBXgL/NjY27unpWbVqFfjPqqoqEolkYWHx4sUL0SUBs98gbjpJSSJVorPPYDBIJBIoydHR\nsbS01MTEBNwFU5XUKxOil4yMjDIzM6E7CpQEAACCHcVisZTuqEXnTryjxCUBAIBLR4kkIdJR0KqW\n0EWOYEehce6Q6igUB6AtLS3fv38vmbSX+JWvEMbHx0dGRtALa0K7/uXEX/7yl4WFBcl1akulfjVS\nqaqqMjU1pVKpS7oJImNoaNjX14f2fx+bVpYxo6OjZDIZpRF8VCtfEri6uhobG4Ouakuxfnz5+PEj\nquaYaNePDdDrbVksVmdnZ319PZvNHh8fZ7FYc3NzAoGgs7OTyWS+fPmSzWb7+/uXlZWJDqHT6ebm\n5qGhoRoaGmABNpsNlgcLsNlsGo2mp6cXGho6OTnJYDCGhoYglqVLDfqTlbpNXLa9vT0oYJHy1NRU\nqbJ1dXVBVa9evZKUDSpfWFgQyZ6YmAD/L7JkKLqWGVq23A4HZXO5XFH5RbI1NDSoVKpyHQ7/OuFy\nudPT0ywWi06nQ8uG7nDJ60SJDtfV1V1YWICWrVCHY3adLNEbU1tbG+LbEM6NyeVyAwMDCdXhxLkx\nFUoYy2azmUxmZ2enyL19keC5uTkajQb+PTk5OTAwoKurC/5zbGyMyWROTEzMzc2BnS8rGkahm05S\nkkiV6Oyz2ezY2FhQ0szMDJ1OLywsBHfRaDQ4qhSKEwcvFR0dHeiOAiVxudzZ2VmkOkpHR0fpjlp0\n7sQ7SlwSAAC4dJRIEiIdBa1qCV3kCHYUGucOsY4SqkZSUhKHw5G1l8/nQxwbGxsLXfnw8HBBQQFK\nla8QlO6H7OzsyclJ9OpX8dglB4fDSUpKUu5YmB2ldH9OT09nZWUpd6yap0+fzs/PS93F5/N5PB7E\nsaqfWegmlscths1DRv0oUxGITuDxeBBXKZ/Pj4+PR6nyvr6+0tJS6MqXBAUFBcPDw7L2qvickfvz\ntNQfMkqLfP36NZVKRa9+FY/FjLi4OIi9f/3rX8E1/ou2g2/p4HYul8vlcktKSkpKSrq6usACXC5X\nKHZ1cTgcUQE+ny8QCERlwNrALVKpqalpa2tbtPH58+cMBgNatlDa5S36YZUqG9wuOgosvEi5SLZc\n5VKffhAdDi1bKK/DxY+S7HD0ZAtVuE6Esju8o6Nj0XWihPLR0VGIj02kOhzj60RIjBuzubm5sbFx\n0cbc3FyID0w4N6ZQKFxYWCguLlbfmJKyJfdCyAZrA9+jxBsVCRYVED9K6iuBuEi5Aqanp1+8eAFf\nkvDvzz6fz1/UnKy3FFmqmpubGxoa5OoUlyQUCtls9iJJQrGOkmxL9Y6qqKjo7u6GUAXRUfDPHYSq\nlJQUNpstV6e4JCEeHUXAi1wgEMj6uoE4d0LkOmp4eLiwsFCuTnFJwv/7qkK7o9BduI1qhlN806eq\nUaNGDTQYPKPUj0E1xAdVg+aV7P4sAu1OUHeyGgiuXLmiqakp+WMEbgEvHjAWZseOHeIFwI2iq0tT\nU3NRAVEZQN6P3dTUFOg8KI6ZmdnU1JQst1lQNiDt8ha1JVW2uCpRYan/NTjKhQpazcqVLbfDRUh2\nOHqyARWuE6nClFM+OzsraV9gYmIimZlwkWxA5Q7H+DoBCHNjSjrYgjemubk5hGwA8sYEAEBbW3vn\nzp2yVAEr+MaUjMTU0tJis9na2tqS5cW1LWoU3CheYNGuRShkBmhiYjI7Oyt1lyxJ4v9xMpm8qB9k\nvaXIUtXb2xsSEgJTragtLS0tSUmijpI8Nap3lJOT06dPn1xcXGSpgugo+OcOQhWHw9HS0oKpFpuO\nmp2dNTY2XrTRyspqfHzcyspKlirsL3ISiSTrMQJ97pDqKIUyWIrawqaj1IMXatSoUaNGjRo1atSo\nkQL0WIyRkRHuU6FjY2OSX55ubm4dHR2yDiGCbDabLdUMF6LDiSAbkKHQwsICwuqNCMo7OjokLXF0\ndHRYLJasQ4ggG5DR4cS/MQcHByWTn7u5uXV2dso6hAiyeTye1AGUJXpjurq64pvFQaoqCH8kDJiZ\nmTExMVm0EcytioseQEbWXDs7u6GhIVz0gCjx5EGbT58+ST7G161b197ejoseEAJ2VF9fn5OT06KN\n4PwfLnoAABAIBOAMGf7PSjVq1KhRo0aNGjVq1BAQbW1tCJNcwmJra0tw7/6Ojg5XV1e8VSAG7kMA\ncunq6pIaS0hwhEKh1MFNAwMDOp2OvR74SFWO7wgIHHp6euBHDhIfb2/vlpYWvFofGxuztraW3I7v\nALTU1jds2FBfX4+9GJCqqqrNmzcv2kgikQjYUYaGhjMzM9iLAWlra3Nzc1u00cnJqa+vDw85AAAA\nTCZTqtsyvtlTuFyuZHjypk2b3r9/j4seAACampq8vb0BAEDAgmNsbEzqPCGDwTA0NIQ4EE7WoOnp\naYjXR4gmFEoCu4xhMBiSHSj31AAAAPPJwuVyod/vIdrCd14Ie2ZnZ2X1FfQZgfl+yWazlbtZCP7+\nSnzGxsakJrqVe6PBTNEJceVAtyJrgdvSAuIhA+dRBhOhUCh3qEJWc7JS/a4oOByOZAeCGXWkLvwU\nAeeHgMViKfdDMzExIbfypcLExISsACvoGwHO0CH0QwaiiSXx8zE3N6fcMwRiYb44PB5P6afHkngR\n8vPza2hoCAoKwluITGR9ohM8eeaHDx8OHz4suV1fXx/BXzfEkfpZCwCAs7Pz27dvt2/fjr0kmLDZ\nbKnLyfEd4pFLe3u75IALAAAbN26sr6+X9McgDrI6luAd3tzcHBYWJrkddGsxNTXFXhIc+Hy+1ORs\noAUH9npAysvLP//8c8ntZmZmNBrNzMwMe0kCgUDqE2z9+vWxsbFbtmzBXhIAAMPDw3v27JHcrmii\nWgQZHh52cHCQ3B4SEvL27duDBw9iLwlE8vRB+F1gwNu3b7dt2ya53dXVtaenB69ZT6lXjpWVFY5f\nRi0tLREREYDqA9A7d+5sbm6W3M5gMG7duvX73/8e4tjAwEDoys3NzY2NjaXWD/LHP/7xT3/6k9Rd\nXl5e0JWvELy8vBZ1oFAo/N3vfvfnP/8Z+kBLS0tJozRJtm3bBnGCAAD4z//8z++++07qa/Tu3bvl\n1r9s0NDQcHFxkdpXHA7nz3/+87/+67/KOtbd3R1OE5s3b4Y4F0lJSevWrfP395e6l8gflgQnJCSk\nsbFRcjubzf6v//qvn376CeJYmG8569evhzizf/rTn37/+99LnQVcv349nPoJTlBQkKz//s8//3z+\n/HkLCwtZx0p9mZPKjh07oB9lAAA8efJk3bp1kjdLcHAwzFaWMVJP008//XTz5k25X2tyfwsCAgKg\nz85PP/30L//yL1Lvgk2bNkFXviTYuHFjbW3t2NiY5K7379/39fWdPHlS1rEBAQHQlUP8PIFMT08/\nfPjwt7/9reQumD9P+CL5IiTiD3/4w7/927/JOtDMzAzOWIPcFyEAAEpKSkZGRr766qtF2yVtTAnI\nunXr4uLiCPuewGAwJBdTLwlkWXAEBwdXVFRIHQIjAjU1NZKRegAAkMlkgg8syhrKsbOzGx4eljSL\nIAh1dXWnT5+W3O7s7FxaWoq5HLgwmUxJSwEQMBsVjiNr0MzPz0v1jg8JCSkvLw8PD8deEhwaGho2\nbtwodReFQmGxWFJjZdCGTqdLvQx279796tWrY8eOYS+prKxM6nAhgOucpayH56ZNm6qrq6U+ctGm\npKTk+PHjktvNzc3Hx8ex1wMAAIPBkPVUMTQ0hDCXR5WhoaG9e/dKbg8KCkpJScFlAFoUaywJmDYQ\nl8cvj8f7/+3KylGoIpcuXdq6dStKlYOMjY1ZWFhkZGSg2sry44cffnB2dsasua1bt169ehWz5pYi\nf/nLX9asWSMrLy1SHDt27PDhw6g2oUacf/zHf/Ty8kK7FS6X6+zs/PPPP6PdEAGZn59fs2YNlv/3\nH3/80dXVtbe3F7MWlzR//OMfQUtQDNpav3797373OwwaIiAHDx48efIkqk1kZWWZmZk1NTWh2gr2\nvHnzxtTUtKOjA4O2UlJSTE1Nl+4ra0xMDN4SZJKdnT0+Pi51V0VFxadPnzDWAxOBQPDkyRNZe4nc\n4RDaEhISOBwOhloUgEql5ubmSt01Pz+flpaGsR74QHQ4ka+T/Pz84eFhqbtqampaWlow1gOfJdrh\nT548EQgEUndNTk4+f/4cYz1CoZBGoz179kzW3l9//RVLMXDarays/PDhA5ZiQKqrq5ubm2XtJWBH\nJScnz83NYSkGJDU1lU6nS93FZDITExMx1iMUClksVkJCgqy9BDx3HR0db968wVIMSFdXV0lJCfg3\nKh7Qra2t4OsgqjPhaWlpk5OTjx8/Rq+J5UdMTEx0dDRmeRIEAgGVSs3IyGhqasKmxSWHUCjMyMgY\nGBh49+4dqq20t7eXlZV9/PgRvVbUiKBSqc+ePZuYmEDbBKO0tHRoaAh8vUO1IQLyl7/8pbu7u66u\nDrMW+Xx+V1fXhQsX4PhHrXDS09Pv3r1LJpMVyhmtHDQabXJyMjk5eXh4GO22iEZjY2N5efmnT59Q\nbaW6uppGo/3hD39AtRXsuX379vT0dGpqKgZt6enpTU9P//jjj62trRg0hzhWVlaEvb+oVKqlpaXU\nXYGBgZWVlRjrgUl5eTnESlCBQEBw/xCp7Nmzp7CwEG8V0snPz9+1a5fUXXp6eoR1E6LT6RALUu3t\n7Xt7e7HUA5+BgQE7Ozupu/z9/WtqajDWA5P379/LWi0KQljXtb9FF0pgbm4+OjqKsR4AADIzM6X6\nb4B4e3tj77ksNWOtiKCgoIqKCiz1gNTX18uKVwUAwMDAACK/K0q8f/8eYgVheHj4ixcvsNQDAmFO\npaurOzc3h/0qnMzMzAMHDsjaGxoaWlZWhqUeQPayAxA3NzdchoMKCgpCQkLAv1EZiExLS7O1tZ2f\nnx8cHESjfpDKykonJ6fe3l5804MuLZhMpp+fH2bN9fX1MZlMBweHjIwMzBpdWpSXl8/MzFhbW6Pa\nRS0tLVNTUxYWFk+ePEGvFTUiUlJSjI2NhUKhVHcOBMnKyrK1taXRaNXV1ag2REAqKipcXFw+fPiA\nWYscDmf9+vU2NjZ4LT1bQvB4PC8vLx0dHU1NTbTbamxspFAoZmZmz549Q7stohEbGwsauqGa476/\nv3/16tUTExMjIyPotYIx09PTHR0dzs7O2Dw8dXR0HBwc3N3dl6g1/969e0tKSvBWIQXo4Tkymczn\n84k5QdvZ2blu3TpZe3fv3v3mzRss9cCktrZ2w4YNsvZaW1sT9inB4XAgEhJYW1sTM2VlTk6O1MXd\nILt37y4vL8dSD0xYLJZUFwsQ0LCVmIYtra2tEEOBe/fuLSoqwlIPTD58+ODj4wNRYOfOnRgbtszN\nzWlqakL4fmzevBn7j5cXL17s378fooCdnR2q41eSdHR0SDV5F/Hll19iP9rb0NAAcUXp6+uz2WyM\nsxO/evUK2lnx4MGD2dnZmOkBAIDD4TCZTAgTMFdX17a2NiwlAQCQkZEhNb2ECE9PT4yDIUZHR21t\nbf8WAotSlPWTJ09mZ2eZTCZK9QuFwvn5+aSkJC6Xi14Tyw8ejxcbGzsyMoJNc0wmk06nQ6wxVCMU\nCjs7O9+9ezc/P49eExwOh8PhxMfHo9eEmkU8efJkfn4e7QVK8/PzpaWlAwMDqLZCWOLj41G9cRZB\npVLpdDqRF+oSBz6f//jx47GxMXD0B1UYDMb8/PyK/aGJi4tjs9moLnufnJzMz88fGhpCrwm8iI+P\nR/VNVQSdTmcwGI8ePcKgLZT49ddf0fYKU4LExEQGgwFRoLOzs6ioCDM9MJmamnr69Cl0mQcPHmAj\nRiHkqsrOzibgs6Kmpqaurg6iAJvNJqa7gtwOj46OJqDtSUpKyszMDESB3t7e169fY6YHJrOzs8nJ\nydBlluiNKRQK79y5g+Wlcv/+fRaLBV2mubkZSzeAjo6O/Px86DICgSAqKgobPSC//PKL3DKZmZk9\nPT0YiAEpKipqa2uDLjM/Px8bG4uNHqFQyGaz4bxBPXjwAEtvkJiYmOnpaegyXV1dstyf0GB0dBSO\n385///d/y3LsQYP//d//Ff8eRNGKwcjICFW3ez09PQsLi6mpKfSaWH6UlJSEhITY2tpi05yuri5h\ns3gTh/HxcSsrKz09PfSa0NTUxCAOUc0i9PT0IJbAINWElZXVSg7IRfXGWYS1tbWhoSGDwcCsxaVL\naWlpSEiIlZUVBo5PBgYGWF4GRINEImlpaaH6hDc3N/fz82tpaUGvCVzg8XgaGhrY5GUyNDQ0MDCw\nsLCQmklySXDw4MGcnBy8VfwdXC6Xx+NBBFoCAODq6trZ2YmZJJg8f/78iy++gC6zbt06LJf4wGFw\ncNDR0RG6zP79+/Py8rDRA5+6ujpZ+dlAtLS0KBTK3NwcZpLgAGEbIuLQoUMvX77ERg9M+Hw+i8Uy\nNjaGKOPs7NzT04OZJJg8e/bs0KFD0GV8fX2xN46AhkqlwkmheebMmaSkJAz0AADQ1NS0du1aqUlW\nxfH29v748SOLxcJAkkAgePXq1WeffQZdjEQibd++HTMroZcvX0JHZIMcOnToxYsXQkxW8zAYjN7e\nXrl57MEv0Pb2dgwkAQAQFxd36tQpucUiIiISEhIw0AMAQHd3t7m5udwcyGvWrBkbG5uZmcFGVWpq\nqtyHGAAAR48eff78OQZ6AAAoKysLCQkR/x5E5ctQKBRi4zJsaWm5kkdelGBgYMDZ2RlvFWr+jvHx\ncVn2hciCze+WGoxZyY9BXC5pY2Pj6elp7NtdWvT09KxZswZvFWoQw9LScumOnMpiamrKzMwMyxb3\n799PWIdcuYAeBYTyP3327Fl4eLjcYtu2bSOUTQGNRtPX19fS0oIuFhoaSjQXjpycnH379kGXIZFI\nZmZmuBjOyqKhocHX11dusaNHjxLKx0koFPb09Li6ukIXMzc3p9FoXC4XG1VweP78OYQpqoidO3cS\nytiHTqdraWnJnZLcsmXL+/fvsZEEk6ysLAirZRGmpqZr1qzBQPzc3Fx1dfXOnTvhFD5//jw2/pBx\ncXFnzpyBU9LLy2t8fByDtAcDAwN8Pl/uPQ5y6tSptLQ0tCUBABATExMREQGnZFhYWEFBAZPJRFtS\nZWWlp6cn9GQziK6ubkBAAAYPFi6Xm5OTA+cNBACAiIgIcJkm2qpSUlIOHTokywteHEdHR21tbQzm\nD8bHx4eGhhY5d6EyTNzb24vNKOcKD/1TFDabLXcqEg3UpwkaCEN9NUsULpcLYTWILObm5itzPBSz\nmc5FhIWFEdMBkDjMz89DuLKihK6uLgYvwQQEs2kYOG+0SwswNQKWLWppaWET6oUSx44dw+YDGA40\nGg0AAFNTU7klvby8WlpaiDNCl5SUdPToUTglAwMDiTMGXV9f7+npCec5AAbrYSAJDkKh8N27dxD5\nHkXo6ekZGhoSJ9kmzPkVAABOnDiBTTJVONDpdA6HAyewZv369W1tbWw2GwNVcIiPjz927BickiEh\nIcSZSmxtbXV1dYX5Prx9+/b29nZUPY75fP6jR4++/vprmOV1dHT27NmD9i9LYWGhh4cH/F/806dP\nP336dH5+Hj1Js7OzL1++hPbqFcfa2trOzg7tjHYJCQmHDx+WOz8q4vLlyw8ePEDVz727u3twcDAo\nKAhmeX9//6mpKVSzcwuFwqioqEuXLsEsT6FQjh8/jvZES2VlpY2NDfwx2AMHDpSUlICvUijBYrGS\nkpIkJ35Q+Xpvbm6GNsJHCktLS+yzgi5d8vPzd+/ejX273t7ey2/pLoJg9lW//IYPCEtbW5vctUtI\nQSKRiJnIBW1mZmbkrntCA319fTqdjn27S4js7Gy5UXKI4+3tTbTl6moIzuTkJMYD0AAAeHt7Nzc3\nY9woUpiZmWlpaREky1xycvLx48dhFj59+nRKSgqqemBSU1Pj7+8P0zNnw4YNHz9+5HA4aKuSC5/P\nf/fuXXBwMJzCJBIpMDAQ44xnskhPTz9y5AjMwkeOHCFI1vSpqamFhQU4vgoAABgZGRkYGPT396Ot\nCg6JiYnwb8yzZ88mJyejqgcmjY2N3t7eMGNHPD09e3p6MM7AJhWBQFBaWqrQ1/358+dfvHgxMTGB\nkp67d+9evHhRIVswNzc3FxcX9NLHVVZWCoXCzZs3wz+ERCLduHHj/v37KJ1lJpMZHR0dGRmp0FHB\nwcFzc3M1NTVoSAIAICMjw8fHR67Pkji6uroRERGgazYakkZGRoqKik6ePKnQUceOHXv79u3AwAAa\nkgAAePDgwcmTJxWy2bS3t9+yZQt6Ey2NjY3j4+M7duxQ6KjIyMjY2FiUvCU5HE5UVNS1a9ckB6BQ\nGYCenZ2FNn5CCk1NTeJENBCfiYkJa2tr7Nt1cHAgyOeKGjXYgOUA9IoFNE/HpWkjI6OVGXUOEwaD\ngc07gDhr167t6OjAuFEigNnMooaGBhEGwhBkcnLS3Nwc40a3bNmC3tcjBhw9ejQtLY3P5+Mro6Cg\nYPv27RQKBWZ5U1NTOzu7hoYGVFXJZW5urrm5GX4kFwAAERERMTEx6EmCyePHj+H4b4rw9fXt7e3F\nPUios7NTS0sLfuYbEom0f/9+zJwxZSEUCuPj40+fPg3/kMOHD2dmZuL+UVxUVBQYGAh/8NHIyGjN\nmjXV1dWoqpLL/Pz8+/fvQ0JC4B9y7ty52NhY9CTBJDY2VqEbE+TGjRvPnj3r6upCVgyHw/nll19O\nnz6txBtgQECAra1teno6spIAAMjPz5+enpZr/SyJlpbWt99+++jRo9nZWWQl0Wi0qKioGzduaGho\nKHrsgQMHpqen0ZjeS01NdXFxUSKE1MLC4tixY7/88gviz5/u7u7c3NwrV64oceylS5dKS0sRn+/n\n8/m3b98+cOCAEkNqHh4eXl5e0dHRyEoCAKC8vLyrqwt+NL0IMpn83XffJSQkIO5VMDc3d+vWrStX\nrkh1X8Bh/TKyqG1tYYLLmmgRKzNCEybqa3j5gZfdzYoCM/N0SdQuHBD09/fjkmmAQqEQyp0WG9hs\nNmZuP+vXr19mQ/zz8/No54mVColEWrrXKolEOnfuHDaWnbLo6emh0+lwXH3F2b17d3V1NY7rV4RC\n4a+//nru3DmFjtLX19+6devr169RUgWHsrIyX19fRWdrzp8/Hx8fj+P7P4vFysvLg5OOSZw1a9Zo\na2vju54mMTHx5MmT8OdXQL7++mt85yoGBgYmJycXeX3KJTg4uLm5Gd9J/ejo6AsXLih0iI6OTkhI\nCHpBu3B4+/bt+vXrlVjHQyKRrl27Vltbi+A4Zn9/f1RU1NWrV5V+M/f39/fy8rp37x5SriwCgSAm\nJsbExASOI7lUtLW1r1+/npyc3NraiogkAADq6uoyMjJ+85vfKP2duHfvXg0NjYSEBKQGEJhM5u3b\ntzdt2uTn56dcDdbW1hcuXLhz5w6CEYf5+flNTU2XL19WOszi3LlzPT09r169QkrS2NjYrVu3zp49\nu2rVKuVq8PDw2LNnz61bt5BydxEKhUlJSUKhEKZ9kCQaGhrXrl3Lzs5GcCLw48ePcXFxN2/elOWP\nSNvtAAAgAElEQVQxu+QHoNXABE7iVzXLG01NzWUWv6YGZGXOYeAYAW1gYKB24ZBFUVERzMwzalSH\nSqVitrDJw8Pj48eP2LSFDXjZUu3bt6+4uBiXphHBzMwsICAAL5PfycnJ169fw/RQXsTFixd//fVX\nvDxno6OjT5w4odDKdBBvb2+BQFBbW4uGKrm0trZOTU0ptHodhEwmnzt37v79+2iokgufz4+Kirp8\n+bISx+7fv7+qqgovM+i8vDw3Nzf4UdsijI2Nt23bhpeFyPT09IsXLxRdKQ9y4cKFmJgYvBwtYmJi\njhw5At/0VoS7u7uWllZVVRUaquTS1tY2Ojqq0HKKRZw6dcrAwOD+/fsqvtAKhcL09PTq6uqbN2+q\nOKe7fv368+fPR0dHq75OqL29/c6dO+Hh4Uo8u8QBh+eGhobi4+NVjPBls9kxMTGzs7OXL19WMYfN\n9u3bwXHM7u5uVeoBAKCysjI+Pv7KlSsuLi6q1GNsbPzDDz+UlZVlZmaqKGl6evrevXs2NjZffvml\nilUdPnzY0dHxzp07U1NTKlb18uXLgoKCH374AU7mCQicnJwiIyMTExNVz43c19d369at0NBQhVZv\nSEIikS5evMhisaKjo1V8DvN4vISEhL6+vhs3bkC87SA/AD0zM6PiiVEIta0tTOh0OvZrokWQyWTc\nF2wSFsyuYUtLS5Q8v9SowR4Wi6Wnp4dX68bGxogvylsekMlkReO2kIJEIq20yRgqlWpjY4NNWzo6\nOkSwvFwG2Nvbo2dNiA2+vr5mZmbYLwRhMBgJCQnXrl1T7nBNTc1r167du3cP+wj05OTkkJAQOzs7\n5Q7fv39/T09PW1sbsqrk0tfXV19fr/QogLm5+cGDB7EPyxUKhaARrdIxhpcuXcrIyEA1O5NUKioq\nKBTKli1blDvc09PTzs4uLy8PWVVyYTKZT548uX79unKHUyiU69ev37t3D3sLkadPn27ZskXpeMa9\ne/eOjIxgn+hocHCwqqoKvte2LAICAi5evJiZmZmcnKzc73txcfH9+/e3bdumuhgQfX39Gzdu8Pn8\n+/fvK2cSMjIy8vDhw8HBwe+//x6pNA9hYWHh4eExMTEvX75UYlUHn89//vx5fHz88ePHd+3ahYgk\nGxubmzdvfvr06ddff1XOP6GtrS0qKkpHRycyMhKRZbskEun06dP+/v5RUVHKja4ymczExMTc3Nyr\nV68ilU/O09Pz+vXr+fn58fHxysUdV1RU3Lt3z9vb++zZs4gM2mhra1+9etXQ0DAqKkq54PrJycno\n6OiWlpabN2/CTBUgl9DQ0FOnTiUmJmZkZCjxjiQQCHJycqKjow8cOLB//37owpSffvpJSZkyqK6u\nXrt2LWa5oTBLeLikmZ6enpycXLduHV4CWCzWwsIC9maLSwLMruH5+fm5uTnMRitWLODttnbtWsxa\nbGlpWYGPQXwf/o6Ojvn5+R4eHngJICYVFRUuLi54RaYzGAwej2dmZoZL67hQX1/v5eWFmQtHU1OT\nor4HRAbHZ0hPT4+dnd2SdmpydHQER14w+7GbmppKTEy8fv26Er6ZIjQ1Nb29vR8+fOjj46NEMLJy\npKWleXh4qPh74enpWVhYyGQyHRwckBIGzcePH9+8eaOoNcEijI2NTUxMEhMTN23ahE28BY/He/jw\n4YkTJ1T86AgICHj8+LGdnR1m4TsFBQXz8/NhYWGqVOLg4EClUqurqzF7P5menn706NGNGzeUCCIW\nAd6Yd+/ehZ8JUHVSU1PXrl3r7e2tSiXu7u5VVVUzMzOY3ZhtbW15eXlXr15FpDYKheLr6+vk5JSe\nnl5fX6+trQ3HQ2NycvL58+fV1dU+Pj779u2TtdBeaRwcHAICAhoaGkpKSkZGRpydneVGNvD5/JKS\nkpKSEjqdfurUKVdXV2Ql6ejo+Pv7GxoaPn36tKWlxcLCAo61aV9fX2ZmZmNj4549e4KDg1W5R6Ti\n5ubm6+tbWFhYWVnJYDAcHR3lPmYXFhZyc3PLy8t1dXWPHTumxGILaIyNjTdt2jQ/P5+ZmdnR0eHg\n4KCrqyv3qObm5pycnI6OjsOHDwcEBKgYIb4IMpns7e3t5uaWmZlZU1NDJpPhDIbMzMxkZWVVVVW5\nuroeOHAA8bFNGxubTZs2dXZ25ufnDwwMODk5yX0nEQgEb9++LSoqolKpJ0+edHd3R1aSlpbWxo0b\nLS0tnz592tjYaGxsDCeqeGRk5Pnz57W1tdu3b9+1axec0418uFBcXFxERARmQZ3x8fERERHYtLV0\nSU5OPnz4MJwLAiXm5+fz8vJUX0mx/BAKhcnJyWfOnMGgrfHx8cbGxr1792LQ1kqmrKzM2dnZyckJ\nsxYTEhLOnj2LWXMEAfeH/+PHjy9evIijAALy66+/KrfkGREYDEZhYeGRI0fwEoA9GN8FcXFxijrY\nEhkcnyFMJjMrK0uhJGPEpL6+/sOHD0iFBUHQ1tb25s2bq1evItLQwsJCVFTUmTNn0Haw4XA4Dx8+\nDA8PR+qVIDc3V0NDA4MXubKysqmpKaTe26lUakpKSmRkJNpfIjQa7cmTJ1evXjUwMECkwpiYmI0b\nN2Iw8Zaamrpq1aqtW7ciUltTU1NDQ8P58+fRvjE7OzsLCwsjIyMRGTBis9lRUVEnTpxQeq0ATLhc\n7qNHj8LCwlS0HRCRl5fH5/Plxv2pztu3b0dHR5EKN5bk/fv37e3tAoFAQ0PDxsbG1tZWX19/YWGB\nTCZPTk729vbyeDwSiWRubr5nzx5s5lDHx8eLiorYbDaJRNLX13d0dDQ3N+dyuVpaWjMzM/39/QwG\nQyAQUCiUkJAQbD6++Hx+cXHx8PAwiUQik8mOjo6WlpYaGhpkMpnD4YyNjQ0NDfH5fBKJ5OjoGBoa\niuxwqix6enoqKirAdk1MTBwdHY2NjTkcjqam5tTUVH9/Pxj/q6Oj89lnn2ETFMhisfLz82dmZoRC\noZaWlrOzs7m5Ofj3/Pz8yMjI2NgYGFHu7e3t7++PgSQAABoaGpqamgAAIJPJ1tbWtra2BgYGHA6H\nTCZPTU319fWBF5uRkdG+ffuwWWtLo9EKCgpYLBYAAKKLnMfjaWlp0en0gYGB6elpoVBIoVACAwPd\n3NwwkCQQCMrLy/v6+sB27e3tra2tdXR0QDfXiYmJ/v5+gUAgFArt7Ox27dqlUGSA8kEEshAKhVja\nYqgtOODAYrFwHH0GAEBfX5/BYOAogLDQaDTMLGvMzc1Vt0BSI5e+vj4VzZgUxcDAgMFgIB6AQHBw\nf/iDLhw4WhsRjYWFBRxNUQAAMDQ0VHtzo4qJicnMzAxmS9yWMXp6ekiloMGXjRs3Ojg43L1798yZ\nM+gtPsjMzCSTyZGRkUhVqKOj88MPP6SkpNjY2KDnWT8wMPD06dPLly8j+DOxf//+xsbGO3fuXL16\nFfFgOhAwMMLFxQXBqBEbG5tr167FxsZu377d09MTqWoXUVdXV1tbe/PmTQRtoC5cuFBQUIDqNP/c\n3NyjR48OHTqE1GAoAAC+vr52dnY///xzREQEerMsr169mp+fV9p5QxJtbe2bN28mJyfb2Ngg5VQg\nydDQUGpq6qVLlxD8Ldu3b19bW9vt27evXr2KUgS3UCjMyMiwt7dHb/QZAIAtW7aIHGDGxsaGh4er\nqqpyc3N///vf29raBgYGYu+xZmVlJZqsZbFYPT09ExMTv/nNb/76178aGRnt2bMH+88fCoUiSqwl\nFAoHBwcnJibi4+PBmGI3NzdcUqG4uLiIniGzs7P9/f0jIyP/9E//9D//8z+Ghobh4eHYL7rS1dUV\npYHl8Xh9fX2zs7M//fTTqVOnPDw8NmzYgMuKyQ0bNohypU5MTAwNDTU0NDx8+PA//uM/LCwsAgIC\nMFsdJcLMzExkoM9ms7u7u2k02j/8wz/8+7//u5mZWUhICPYv3mQyOTQ0NDQ0FPzn8PAwlUrNyMgY\nGhq6evWqk5PT9u3blZ5ZWfJJCLW1tfFKJ7JUGB0dRcodRg3iYJlIjUKhYO97uAIhkUgYj41aWVkp\n5/+lRhX27t1bUFCAtwoCkZubq15gsbzx9vbG3uwSPfCdxHJzc/v06ROOApDCysrq+vXrL168yMnJ\nQbzywcHB27dve3p6fvHFF8jWDLpV6unp3b9/H3FDfz6fn5SUVFdX9+OPPyI+Senn53fu3Llff/31\n/fv3yNYMAEBjY+Pdu3fDwsICAwORrRl0Gu3r64uLi0Pc6ndubu7hw4dMJjMyMhLxAbLPPvssODj4\n9u3bPT09yNYMAEBBQUFqauo333yD4OgziKWl5W9+85v8/Hw08oWOjIzcuXNn9erVx44dQ7ZmEol0\n5swZIyOjqKio6elpZCsXCAQpKSlVVVU//vgj4gM67u7uYDbFyspKZGsGAKC5ufnu3buhoaGI35gQ\nWFtbb9y4saGhYWxszN/f38XFBa8MHyJ0dXU9PT0dHBw6OztnZ2e9vb1xD74Bw5z9/f37+vq6u7v9\n/f0xM2OBwNjY2MfHZ3R0tLOz09nZ2cPDA3fLLw0NDVdXV39/fyqVWl9fj9fo8yIsLS03bNhQW1s7\nOjq6ceNGNzc37EefF6Gtre3h4bF+/fre3t6enh4fHx8ihH3Y29v7+/t/+vRpcHDQ39/fyclJlbh+\nhAegeTwexqdNnVdNLoWFhehNI6tRESwHoAG8v7fVoISVlZX6MYg96njbRdBoNKTyvaiBCcaPdGdn\n5/7+fixbXMaEhISgMU6BCxQK5euvv3Z3d793715tbS0idc7OzsbExNTV1X377bfoLTjdsmXLpUuX\nXrx4kZKSglQ4S0FBwaNHj/bt24eeHZCRkdGNGzdIJFJUVBRSo6KDg4P379+fm5v79ttv0QtmP3Dg\nwKFDhx4/fpybm4uICSSXy01PT09PTz9//nxwcLDqFUrFycnpu+++a2tri46ORmopYVNT07179xwd\nHS9duoRSMDuFQomIiPD19b137x5S0xUMBuPJkydVVVXffPPN+vXrEalTkoCAgCtXruTm5iYlJSGV\n/LaoqOjBgwe7d+9GfNBchIGBwbVr17S1te/du6dcAj1JhoeHHzx4MDMz8+233+LyilV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Tc3Z2Zm+vn5TfnFNDU1ld+k7+vru3Hjxpw5c9S5jaomgUCQ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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"metadata": {
"id": "3vOGY453FVIh",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"columns_to_drop = ['index', 'Dict', 'Dict.MedInc', 'Dict.HouseAge']"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "gEgToiVFJM9s",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"df = df.drop(columns_to_drop, axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "x_iTycAUQWRb",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"outputId": "51f83023-b717-414c-900c-1ecba7b6d5fd"
},
"cell_type": "code",
"source": [
"df.head()"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MedInc | \n",
" HouseAge | \n",
" AveRooms | \n",
" AveBedrms | \n",
" Population | \n",
" AveOccup | \n",
" Latitude | \n",
" Longitude | \n",
" Target | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 8.3252 | \n",
" 41.0 | \n",
" 6.984127 | \n",
" 1.023810 | \n",
" 322.0 | \n",
" 2.555556 | \n",
" 37.88 | \n",
" -122.23 | \n",
" 4.526 | \n",
"
\n",
" \n",
" 1 | \n",
" 8.3014 | \n",
" 21.0 | \n",
" 6.238137 | \n",
" 0.971880 | \n",
" 2401.0 | \n",
" 2.109842 | \n",
" 37.86 | \n",
" -122.22 | \n",
" 3.585 | \n",
"
\n",
" \n",
" 2 | \n",
" 7.2574 | \n",
" 52.0 | \n",
" 8.288136 | \n",
" 1.073446 | \n",
" 496.0 | \n",
" 2.802260 | \n",
" 37.85 | \n",
" -122.24 | \n",
" 3.521 | \n",
"
\n",
" \n",
" 3 | \n",
" 5.6431 | \n",
" 52.0 | \n",
" 5.817352 | \n",
" 1.073059 | \n",
" 558.0 | \n",
" 2.547945 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.413 | \n",
"
\n",
" \n",
" 4 | \n",
" 3.8462 | \n",
" 52.0 | \n",
" 6.281853 | \n",
" 1.081081 | \n",
" 565.0 | \n",
" 2.181467 | \n",
" 37.85 | \n",
" -122.25 | \n",
" 3.422 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" MedInc HouseAge AveRooms ... Latitude Longitude Target\n",
"0 8.3252 41.0 6.984127 ... 37.88 -122.23 4.526\n",
"1 8.3014 21.0 6.238137 ... 37.86 -122.22 3.585\n",
"2 7.2574 52.0 8.288136 ... 37.85 -122.24 3.521\n",
"3 5.6431 52.0 5.817352 ... 37.85 -122.25 3.413\n",
"4 3.8462 52.0 6.281853 ... 37.85 -122.25 3.422\n",
"\n",
"[5 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"metadata": {
"id": "qmv2aWo1VB98",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 185
},
"outputId": "53d539bf-2d75-426a-d5d1-b9ce00b59400"
},
"cell_type": "code",
"source": [
"df.isnull().sum().compute()"
],
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MedInc 0\n",
"HouseAge 0\n",
"AveRooms 0\n",
"AveBedrms 0\n",
"Population 0\n",
"AveOccup 0\n",
"Latitude 0\n",
"Longitude 0\n",
"Target 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "W3vqiTp6m_LV"
},
"cell_type": "markdown",
"source": [
"You cannot re-input scaled values from your dataframe directly into its columns (It has to be a series). So, we will Scale Array and directly use it to train our model."
]
},
{
"metadata": {
"colab_type": "code",
"id": "tJoUglPUm_Kh",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 151
},
"outputId": "4d771f3c-2e18-4f4c-c4c2-185a373343fb"
},
"cell_type": "code",
"source": [
"# Necessary for converting dataframe to array. Takes specified length from each block.\n",
"lengths = []\n",
"for part in df.partitions:\n",
" l = part.shape[0].compute()\n",
" lengths.append(l)\n",
" print(l, part.shape[1])"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"182557 9\n",
"181760 9\n",
"181750 9\n",
"181756 9\n",
"181750 9\n",
"181087 9\n",
"180446 9\n",
"49854 9\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"colab_type": "code",
"id": "hJsEAVBqm_KW",
"colab": {}
},
"cell_type": "code",
"source": [
"X, y = df.drop(['Target'], axis=1).to_dask_array(lengths=lengths) , df['Target'].to_dask_array(lengths=lengths)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "code",
"id": "ZCfR7qEtm_KD",
"colab": {}
},
"cell_type": "code",
"source": [
"Xo = dask.array.zeros((X.shape[0],1), chunks=(200000,1))\n",
"from dask_ml.preprocessing import RobustScaler\n",
"for i, col_ in enumerate(df.columns):\n",
" if col_ == \"Target\":\n",
" rsc = RobustScaler()\n",
" y = rsc.fit_transform(y.reshape(-1, 1)).reshape(1, -1)[0]\n",
" else:\n",
" rsc = RobustScaler()\n",
" temp = rsc.fit_transform(X[:,i].reshape(-1, 1))\n",
" Xo = dask.array.concatenate([Xo, temp], axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "code",
"id": "j9aL44LLm_Jc",
"colab": {}
},
"cell_type": "code",
"source": [
"Xo = Xo[:, 1:]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "V58KhMi3okdL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "c5aa082b-ab7b-44d4-f256-b07c19ad5957"
},
"cell_type": "code",
"source": [
"Xo[-5:].compute()"
],
"execution_count": 144,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-9.11033660e-01, -2.10526316e-01, -1.32135031e-01,\n",
" 8.99459515e-01, -3.45010616e-01, -3.05382018e-01,\n",
" 1.32631579e+00, -7.09677419e-01],\n",
" [-4.54049344e-01, -5.78947368e-01, 5.33792434e-01,\n",
" 2.84432167e+00, -8.64118896e-01, 3.53090353e-01,\n",
" 1.32894737e+00, -7.41935484e-01],\n",
" [-8.46968724e-01, -6.31578947e-01, -3.23698645e-02,\n",
" 7.58319669e-01, -1.73036093e-01, -5.80589452e-01,\n",
" 1.31315789e+00, -7.44623656e-01],\n",
" [-7.70292580e-01, -5.78947368e-01, 4.48869608e-02,\n",
" 1.31076550e+00, -4.55414013e-01, -8.17678848e-01,\n",
" 1.31315789e+00, -7.71505376e-01],\n",
" [-5.31184078e-01, -6.84210526e-01, -1.72501711e-03,\n",
" 1.20784296e+00, 2.30360934e-01, -2.39353267e-01,\n",
" 1.29736842e+00, -7.50000000e-01]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 144
}
]
},
{
"metadata": {
"id": "P71Q5JOkTKX3",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"To make blocks for both of equal size. Otherwise you might get broadcast error.\n",
"\n"
]
},
{
"metadata": {
"id": "41fspI44pEZB",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"Xo = Xo.rechunk({1: Xo.shape[1]})\n",
"Xo = Xo.rechunk({0: 200000})\n",
"y = y.rechunk({0: 200000})"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "7R7k9yXLQXw9",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b4709f0b-7daf-445f-9029-bffdf59552aa"
},
"cell_type": "code",
"source": [
"tr_len = 0.8*Xo.shape[0]\n",
"xtrain, ytrain = Xo[:tr_len], y[:tr_len]\n",
"xvalid, yvalid = Xo[tr_len:], y[tr_len:]\n",
"xtrain.shape, ytrain.shape, xvalid.shape, yvalid.shape"
],
"execution_count": 179,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1056768, 8), (1056768,), (264192, 8), (264192,))"
]
},
"metadata": {
"tags": []
},
"execution_count": 179
}
]
},
{
"metadata": {
"id": "W1khyX7fn3pj",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from dask_ml.linear_model import LinearRegression"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-S9OVZV-oKPz",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"est = LinearRegression()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Vvl4-6Lho-88",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "31227917-7150-475f-a011-1513edeb7471"
},
"cell_type": "code",
"source": [
"est.fit(xtrain, y=ytrain)"
],
"execution_count": 182,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1.0, max_iter=100, multiclass='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='admm',\n",
" solver_kwargs=None, tol=0.0001, verbose=0, warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 182
}
]
},
{
"metadata": {
"id": "OnxIlTzdpEqg",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"preds = est.predict(xvalid)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "1JR3D7TjqvUP",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
},
"outputId": "091d0aea-1bce-4bad-e569-2312f16878c8"
},
"cell_type": "code",
"source": [
"preds[0:10].compute()"
],
"execution_count": 184,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 0.35169143, 0.46621322, 0.41237802, 0.28285737, 0.24595807,\n",
" 0.08746062, 0.24150728, 0.26845929, -0.11211374, 0.40954053])"
]
},
"metadata": {
"tags": []
},
"execution_count": 184
}
]
},
{
"metadata": {
"id": "FWfFG7MArlIM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364
},
"outputId": "5a0d3c38-21a7-4a30-b625-76f77a63415b"
},
"cell_type": "code",
"source": [
"plt.scatter(preds.compute(), yvalid.compute())"
],
"execution_count": 185,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 185
},
{
"output_type": "display_data",
"data": {
"image/png": 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XVQ3WVfmxYthyjiJF+dmyY4R6//Jnt6f5vnV9RsC/tbzHz67bw80vBHCZGRmF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\ntrGi3EyUl7kxPW9QWK7XfUF+che3QiS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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
}
]
}