{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem Statement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whether one trades in Stocks, Index, Currencies, Commodities, a person would like to know questions like:\n", "\n", "- What is the market trend? Will it continue?\n", "- Whether market will close higher or lower compared to its opening levels? \n", "- What could be expected high, low, close levels?\n", "\n", "There could be many more such questions. The first challenge is to know the trend and direction of the market. If there was a crystal ball that could provide meaningful prediction in advance on the trend and direction of the markets that could help take correct trading position to make profitable trades.\n", "Predictive analytics based on historical price data using supervised machine learning approach can provide prediction in advance on whether the next day market will close higher or lower compared to its opening levels. \n", "\n", "We chose to investigate whether there is a connection between the sentiment in the news for a given day and the resulting market value changes for Apple, Inc on the same day. Particularly, we treated this as a supervised machine learning approach and discrete classification problem where stock price either went down or up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Munging and Feature Extraction\n", "\n", "- As stated, we limited our scope to examine the news related to tech giant Apple. We scaped the Motley Fool site for a three year's worth of relevant news articles based on searches by the company name.\n", "\n", "\n", "- Afterwards, we used the Sentiment Lexicon, a dictionary of positive/negative words released by the University of Pittsburgh described in this [paper](\"http://people.cs.pitt.edu/~wiebe/pubs/papers/emnlp05polarity.pdf\") and that can be downloaded [here](\"http://mpqa.cs.pitt.edu\"), to count the sentiment score for each article. A negative word received a \"-1\" score, whereas positive word scored \"+1\". If a word is not found the dictionary, its score is zero.\n", "\n", "\n", "- For the historical Apple stock prices, we used \"go-to\" Quandl website and their API. We calculated the difference between **opening** and **closing** prices to get the returns. If a return is negative and less than -0.001, then we classified it as **-1**. If a return is between **-0.001 and 0.001**, we classified it as **0**. Finally, if a return is greater than **0.001**, we classified it as **1**. This **\"diff\"** columns constituted the label.\n", "\n", "\n", "- In the next step, we merged two data sets based on the date. For the final version of the data set, we chose only the **sentiment score** as a feature and **diff** as a label to train all our models.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Note**: we commented out lines of codes where the scraping part is done. The scrapped data is available in github repo with name *\"mfool.csv\"*." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "#data munging and feature extraction packages\n", "import requests\n", "import requests_ftp\n", "import requests_cache\n", "import lxml\n", "import itertools\n", "import pandas as pd\n", "import re\n", "import numpy as np\n", "import seaborn as sns\n", "import string\n", "from bs4 import BeautifulSoup\n", "from collections import Counter\n", "from matplotlib import pyplot as plt\n", "from wordcloud import WordCloud\n", "plt.style.use('ggplot')\n", "plt.rcParams['figure.figsize'] = [10, 8]\n", "\n", "#machine learning from scikit-learn\n", "from sklearn.metrics import classification_report,confusion_matrix, precision_recall_curve, roc_curve, auc, accuracy_score\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "#Deep learning from Tensor Flow\n", "#feed forward neural network\n", "import tensorflow as tf\n", "from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier\n", "from tensorflow.contrib.layers import real_valued_column\n", "\n", "#recurrent neural nets\n", "from tensorflow.contrib.layers.python.layers.initializers import xavier_initializer\n", "from tensorflow.contrib import rnn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def motley_page_links(page):\n", " \"\"\"\n", " Given a page number, it returns all article links.\n", " \n", " Input: a page number (default = 1)\n", " Output: a list with links on the given page\n", " \"\"\"\n", " \n", " response = requests.get(\n", " 'https://www.fool.com/search/solr.aspx?page={}&q=apple&sort=date&source=isesitbut0000001'.format(page))\n", " response.raise_for_status()\n", " html = response.text\n", " parsed_html = BeautifulSoup(html, 'lxml')\n", "\n", " div_with_links = parsed_html.find_all(name = 'dl',\n", " attrs = {'class' : 'results'})\n", " links = []\n", " for link in div_with_links[0].find_all('a', href = True):\n", " links.append(link['href'])\n", " \n", " return links\n", "\n", "def motley_all_links(no_pages = 1):\n", " \"\"\"\n", " Given number of pages, it returns all the links \n", " from \"no_pages\"\n", " \n", " Input: number of pages (default = 1)\n", " Output: a list with links from the pages\n", " \"\"\"\n", " all_links = []\n", " for page in range(1, (no_pages + 1)):\n", " all_links.extend(motley_page_links(page))\n", " \n", " return all_links\n", "\n", "def motley_article_info(url):\n", " \"\"\"\n", " Given an article url, it returns title, date, content\n", " and url of that article.\n", " \n", " Input: article url\n", " Ouput: a dictionary with 'title', 'date',\n", " 'article', and 'url' as keys.\n", " \"\"\"\n", " \n", " response = requests.get(url)\n", " response.raise_for_status()\n", " html = response.text\n", " parsed_html = BeautifulSoup(html, 'lxml')\n", " content = parsed_html.find_all(name = 'div',\n", " attrs = {'class' : 'full_article'})\n", "\n", " date = parsed_html.find_all(name = 'div', attrs = {'class' : 'publication-date'})[0].text.strip()\n", " title = parsed_html.find_all('h1')[0].text\n", " article = ' '.join([t.text for t in content[0].find_all('p')])\n", " \n", " return {'title' : title,\n", " 'date' : date,\n", " 'article' : article,\n", " 'url' : url}\n", "\n", "def motley_df(no_pages):\n", " \"\"\"\n", " Creates DataFrame for the articles in url\n", " with author, text, title, and url as column\n", " names.\n", " \n", " Input: A url, number of pages\n", " Output: DataFrame with 4 columns: author,\n", " text, title, and url.\n", " \"\"\"\n", " \n", " #get all links in the specified number of pages\n", " #from url\n", " links = motley_all_links(no_pages)\n", " \n", " #create dataframe for each link and\n", " #combine them into one dataframe\n", " article_df = pd.DataFrame(index = [999999], columns=['article', 'date', 'title', 'url'])\n", " for i, link in enumerate(links):\n", " try:\n", " append_to = pd.DataFrame(motley_article_info(link), index = [i])\n", " article_df = article_df.append(append_to)\n", " except:\n", " pass\n", " \n", " article_df = article_df.drop(999999)\n", " return article_df\n", "\n", "#df = motley_df(1000)\n", "#convert_to_csv(df, \"mfool.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.\n", "sentiment scoring" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ew8ND/uVf/kWWL18uZWVl1dreOmF3/fp1GT9+vHI5npeXl4wdO1YuXryotP/222+la9eu\n4uTkVO0S3zvVdonv3r17pWvXrqLT6SQ4ONjqZKvIzatnunbtKi4uLjJw4ED57rvvrE6si4gsX75c\nfH19RavV3vMS31v1+vr6yvz582u8xPd2ixcvlo4dO961j++8xHfIkCHKJb632HJiPTIyUh555BFx\ncXERDw8Pefrpp+X06dPK65WVlbJ8+XLp0qWLODk5idFolCFDhsi2bduUNgDkyy+/tJpuaGioTJ48\nWRlOSkqSPn36iIuLyz0v8b01LFL9ogYRkZycHAEg//jHP5RxFy5ckOeff148PT1Fp9NJhw4dZMKE\nCfLLL7+IiG0XTIjUvF7vlJCQIMOGDRNPT09xdnaWgIAAef/998VisShtfvzxRwkNDRVXV1fR6/XS\nv39/+fHHH5XX1WwXIvdeH7bUdqd7XeIrYtuJ9YqKCpk6daoYDAZp2bKljB8/Xj755BO5/c9lTZ8R\nkZtX8D311FPi5uZ210t8KyoqZMGCBdKxY0dxcnKStm3bymuvvaa8fmf7+uivuqIR4ZMNiYhIHR7O\nIiIi1RgiRESkGkOEiIhUY4gQEZFqDBEiIlKtWdz2JDs7294lNAqenp6qvpPyoGJ/VMc+qa659smt\n2+ncC/dEiIhINYYIERGpxhAhIiLVGCJERKQaQ4SIiFRjiBARkWoMESIiUo0hQkREqjFEiIhItWbx\njXWqrurPz9T6msPabxqwEiJqyrgnQkREqjFEiIhINYYIERGpxhAhIiLVGCJERKQaQ4SIiFRjiBAR\nkWoMESIiUo0hQkREqjFEiIhINYYIERGpxhAhIiLVGCJERKQaQ4SIiFRjiBARkWoMESIiUo0hQkRE\nqjFEiIhItQZ5PG5hYSFWr16N3377DRqNBmFhYXj66adRVlaG6OhoFBQUwMvLCzNnzoRer4eIICYm\nBqmpqXB2dkZkZCT8/f0BAPv378fOnTsBAKNGjcLQoUMbYhGIiKgGDRIiDg4OeOGFF+Dv74/r169j\n7ty56NWrF/bv34+ePXti5MiRiI2NRWxsLCZOnIjU1FTk5ubi448/RkZGBtatW4dly5ahrKwMO3bs\nQFRUFABg7ty5CAoKgl6vb4jFICKiOzTI4SyDwaDsSbRo0QJt27aFyWRCUlISQkJCAAAhISFISkoC\nACQnJ2PIkCHQaDTo3Lkzrl27huLiYqSlpaFXr17Q6/XQ6/Xo1asX0tLSGmIRiIioBg2yJ3K7/Px8\nnD9/HgEBASgpKYHBYABwM2hKS0sBACaTCZ6ensp7jEYjTCYTTCYTjEajMt7DwwMmk6naPOLi4hAX\nFwcAiIqKsppWc+bo6Kj0Rd5d2jWX/rq9P+gm9kl17JO7a9AQKS8vx0cffYTw8HC4urrW2k5Eqo3T\naDQ1tq1pfFhYGMLCwpThwsJCFdU+eDw9PW3qi+bSX7b2R3PCPqmuufaJr6+vTe0a7OqsyspKfPTR\nRxg8eDAeffRRAIC7uzuKi4sBAMXFxXBzcwNwc8/j9pVWVFQEg8EADw8PFBUVKeNNJpOyJ0NERA2v\nQUJERPD555+jbdu2+Nd//VdlfFBQEBISEgAACQkJCA4OVsYnJiZCRHDu3Dm4urrCYDAgMDAQJ06c\nQFlZGcrKynDixAkEBgY2xCIQEVENGuRw1tmzZ5GYmIgOHTrgP/7jPwAA48ePx8iRIxEdHY34+Hh4\nenpi1qxZAIA+ffrg+PHjmDFjBnQ6HSIjIwEAer0ezz77LObNmwcAGD16NK/MIiKyI43UdALiAZOd\nnW3vEhqF24/tVv35mVrbOaz9pqFKsqvmeqz7btgn1TXXPml050SIiOjBwxAhIiLVGCJERKQaQ4SI\niFRjiBARkWoMESIiUo0hQkREqjFEiIhINYYIERGpxhAhIiLVGCJERKQaQ4SIiFRjiBARkWoMESIi\nUo0hQkREqjFEiIhINYYIERGpxhAhIiLVGCJERKQaQ4SIiFRjiBARkWoMESIiUo0hQkREqjFEiIhI\nNYYIERGp5mjvAqh+Vf35GeX3PDvWQUQPJu6JEBGRagwRIiJSjSFCRESqMUSIiEg1hggREanGECEi\nItUYIkREpBpDhIiIVGOIEBGRagwRIiJSjSFCRESqNci9sz777DMcP34c7u7u+OijjwAA27Ztww8/\n/AA3NzcAwPjx49G3b18AwK5duxAfHw+tVosXX3wRgYGBAIC0tDTExMTAYrEgNDQUI0eObIjyiYio\nFg0SIkOHDsWTTz6J1atXW40fMWIEnnnmGatxly9fxuHDh7FixQoUFxdj8eLFWLVqFQBg/fr1WLBg\nAYxGI+bNm4egoCC0a9euIRah0bv9RotERA2lQUKke/fuyM/Pt6ltUlISBg4cCCcnJ7Ru3Ro+Pj7I\nzMwEAPj4+MDb2xsAMHDgQCQlJTFEiIjsyK63gt+7dy8SExPh7++PSZMmQa/Xw2Qy4ZFHHlHaeHh4\nwGQyAQCMRqMy3mg0IiMjo8bpxsXFIS4uDgAQFRUFT0/PelyKxqEub/PeHPoLABwdHZvNstqKfVId\n++Tu7BYiTzzxBEaPHg0A2Lp1KzZt2oTIyEiISI3taxqv0WhqbBsWFoawsDBluLCwsA4qbj6aS395\neno2m2W1FfukuubaJ76+vja1s+nqLIvF8k8VU5NWrVpBq9VCq9UiNDQUWVlZAG7uYRQVFSntTCYT\nPDw8qo0vKiqCwWCo87qIiMh2NoXI1KlTERMTo/yhrwvFxcXK78eOHUP79u0BAEFBQTh8+DDMZjPy\n8/ORk5ODgIAAdOrUCTk5OcjPz0dlZSUOHz6MoKCgOquHiIjun02Hs+bPn48DBw5g+fLleOihhzB4\n8GAMGTLE5uOEK1euxE8//YSrV68iIiICY8eORXp6Oi5cuACNRgMvLy9MnToVANC+fXsMGDAAs2bN\nglarxcsvvwyt9mbWvfTSS1i6dCksFguGDRumBA8REdmHRmo7CVEDi8WCkydPIjExESkpKfD398fg\nwYMxcOBAuLi41Ged/5Ts7Gx7l1Dv6vISX4e139TZtBqz5nqs+27YJ9U11z6p03MiSmOtFr6+vmjb\nti3c3NxgMplw8OBBvPLKK0hMTFRVKBERNV02Hc4qKyvDkSNHkJiYiCtXrmDAgAGYPn06unTpAgDI\nzMzE0qVLMWTIkHotloiIGhebQuSVV15Bjx498NRTTyE4OBhOTk5WrwcEBPAkNxFRM2RTiHzyySdo\n1arVXdtMmzatTgoiIqKmw6ZzIvv371duPXJLZmYmdu/eXS9FERFR02BTiOzZs6faParatWuHPXv2\n1EtRRETUNNgUIpWVlXB0tD7y5ejoiIqKinopioiImgabQsTf3x979+61Gvf999/D39+/XooiIqKm\nwaYT65MnT8aSJUuQmJgIb29v5OXl4bfffsPbb79d3/UREVEjZlOItG/fHqtWrUJycjJMJhMeffRR\n9OvXr1F/S52IiOqfzbeCd3FxwaBBg+qzFiIiamJsCpH8/Hx8/fXXuHDhAsrLy61eW7NmTb0URkRE\njZ9NIbJq1Sp4e3tj0qRJcHZ2ru+aiIioibApRC5fvozFixcrt2QnIiICbLzEt1u3brhw4UI9l0JE\nRE2NTXsiXl5eWLp0Kfr371/tHlrjxo2rl8KIiKjxsylEbty4gX79+qGqqsrqOedERNS82RQikZGR\n9V0HERE1QTZ/T+Ty5cs4evQoSkpK8PLLLyM7OxtmsxkdO3asz/qIiKgRs+nE+pEjR7Bw4UKYTCbl\nMbjXr1/Hpk2b6rU4IiJq3GzaE9m2bRvefvtt+Pn54ciRIwCAjh078oqtB1TVn5+pcbzD2m8auBIi\nauxs2hMpKSmpdthKo9FAo9HUS1FERNQ02Hwr+FuHsW45dOgQAgIC6qUoIiJqGmw6nPXiiy9iyZIl\niI+Px40bN7B06VJkZ2djwYIF9V0fERE1YjaFSNu2bbFy5UqkpKSgX79+MBqNvBU8ERHZfomvs7Mz\nBg4cWJ+1EBFRE2NTiLzzzju1nkR/77336rQgIiJqOmwKkccff9xq+LfffsO+ffswePDgeimKiIia\nBptCZOjQodXGPfbYY/jss88wevTouq6JiIiaCNUPCPHw8MCvv/5al7UQEVETY9OeSHx8vNVwRUUF\nfvzxR3Tu3LleiiIioqbBphA5cOCA1bCzszO6dOmCESNG1EtRRETUNNgUIgsXLqzvOoiIqAmyKUTy\n8vJsmpi3t/c/VQwRETUtNoXIjBkzbJrY1q1b/6liiIioabEpRCIiInDq1CmMGTMGXl5eKCgowI4d\nO9CzZ88aL/+lBxNvEU9Ed7LpEt+tW7ciIiICbdq0gaOjI9q0aYOpU6diy5Yt9V0fERE1YjbtiYgI\n8vPz0a5dO2VcQUEBLBaLTTP57LPPcPz4cbi7u+Ojjz4CAJSVlSE6OhoFBQXw8vLCzJkzodfrISKI\niYlBamoqnJ2dERkZCX9/fwDA/v37sXPnTgDAqFGjuBdERGRnNoXIiBEjsGjRIgwdOhSenp4oLCxE\nQkKCzZf4Dh06FE8++SRWr16tjIuNjUXPnj0xcuRIxMbGIjY2FhMnTkRqaipyc3Px8ccfIyMjA+vW\nrcOyZctQVlaGHTt2ICoqCgAwd+5cBAUFQa/Xq1hsIiKqCzYdznrmmWcQGRmJkpISJCcn47fffsMr\nr7yCf/u3f7NpJt27d6/2xz4pKQkhISEAgJCQECQlJQEAkpOTMWTIEGg0GnTu3BnXrl1DcXEx0tLS\n0KtXL+j1euj1evTq1QtpaWn3s6xERFTHbL4VfGBgIAIDA+tsxiUlJTAYDAAAg8GA0tJSAIDJZIKn\np6fSzmg0wmQywWQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "motley = pd.read_csv('mfool.csv')\n", "\n", "negative = pd.read_csv('negative-words.txt', sep = ' ', header = None)\n", "positive = pd.read_csv('positive-words.txt', sep=' ', header=None)\n", "\n", "def score_word(word):\n", " \"\"\"\n", " returns -1 if negative meaning, +1 if positive meaning,\n", " else 0\n", " \n", " input: a word\n", " ouput: -1, 0, or + 1\n", " \"\"\"\n", " if word.lower() in negative.values:\n", " return -1\n", " elif word.lower() in positive.values:\n", " return +1\n", " return 0\n", "\n", "def get_scores(article):\n", " \"\"\"\n", " returns sentiment scores for a given article\n", " \n", " input: an article\n", " output: sentiment score\n", " \"\"\"\n", " wordsArticle = article.split(' ')\n", " scores = [score_word(word) for word in wordsArticle]\n", " return sum(scores)\n", "\n", "motley['sentiment'] = motley['article'].apply(get_scores)\n", "\n", "plt.hist(motley.sentiment, bins=50)\n", "plt.xlabel('sentiment scores')\n", "plt.ylabel('frequency')\n", "plt.title('Distribution of sentiment scores of articles');\n", "\n", "# motley.to_csv('motley_with_s_scores.csv', encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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lPHseJX87HYD6Y/+ViHeVrW+JfqW0J963eL8Awu+sz+hXvii592RACKH+v3wB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\n1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DYZVtXk6Feeh+HoIwEA4QUL097WdMHZ0mSE+yMKWsuLaZZ741QrXrnHiYo6eqEriops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\n3AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "most_positive_article = motley['article'][motley['sentiment'] == np.max(motley['sentiment'])].values[0]\n", "wc = WordCloud().generate(most_positive_article)\n", "plt.imshow(wc)\n", "plt.axis('off');" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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ILPDBnxZR8Y9HxXfZvHc1vxXHSSNxXTFWv55bQuhXERrveeoD/F/Ma3F/aaOt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\n7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rtcgKwCHp469fCGKK4eT8KwrKpZ3x9YH4HApIWkOjeHBWtpYZw/1o2gqCAh038YTGWr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\nIgDh5uUYpqx7qdCPndt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "most_negative_article = motley['article'][motley['sentiment'] == np.min(motley['sentiment'])].values[0]\n", "wc = WordCloud().generate(most_negative_article)\n", "plt.imshow(wc)\n", "plt.axis('off');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3.\n", "merging data sets\n", "\n", "APPLE stock data was obtained using **[Quandl](https://www.quandl.com)** API at \"https://www.quandl.com/api/v3/datasets/WIKI/AAPL.csv\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "path = \"../datasets/\"\n", "aapl = pd.read_csv(path+'WIKI_PRICES_AAPL.csv')\n", "fool = pd.read_csv(path+'motley_with_s_scores.csv')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def format_df(stock_df, news_df, word):\n", " \"\"\"\n", " merges stock_df and news_df on \"date\"\n", " column\n", " \n", " input: stock df, news df, word\n", " output: merged df\n", " \"\"\"\n", " \n", " stock_df['diff'] = stock_df['close']-stock_df['open']\n", " news_df['Count'] = news_df['article'].apply(lambda x: x.count(word))\n", " news_df.loc[news_df['Count'] <= 5, 'sentiment'] = 0\n", " news_df['date'] = pd.to_datetime(news_df['date'])\n", " news_df['date'] = news_df['date'].dt.strftime('%Y-%m-%d')\n", " news_df = news_df.groupby(['date'], as_index = False).sum()\n", " news_df = news_df[['date', 'sentiment', 'Count']]\n", " merged_df = pd.merge(news_df, stock_df)\n", " merged_df['bin_sentiment'] = pd.cut(merged_df['sentiment'], [-np.inf, -0.001, 0.001, np.inf], labels = [-1, 0, 1])\n", " merged_df['bin_diff'] = pd.cut(merged_df['diff'], [-np.inf, -0.001, 0.001, np.inf], labels = [-1, 0, 1])\n", " return merged_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "merged_df = format_df(aapl, fool, 'Apple')\n", "merged_df.head()\n", "#merged_df.to_csv('merged_df.csv', encoding='utf-8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Method Selection and Model Evaluation\n", "\n", "This problem lends itself to learning algorithms such as SVMs, logistic regression, and random forest classification trees. However, the difficulty with stock price prediction is that the observations are highly dependent to each other. Therefore, even a little knowledge whether about the outcome stock behaviour might give an edge in investement.\n", "\n", "Due to high correlation between stock prices of each day, generic learning algorithms usually perform very poorly on this type of problems and therefore we sought to use Deep Learning methods too. Particularly, we used basic 2-layer feedword neural network and recurrent neural networks with tensorflow.\n", "\n", "The reason behind using recurrent neural networks (RNNs) is that they have loops. These loops allow the network to use information from previous passes, which acts as memory and can store the dependency relationship between past and present time.\n", "\n", "\n", "\n", "**Note** SVMs and logistic regression were optimized using penalty, random forest with out-of-bad errors, and neural nets with cross validation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_ROC(y_test, scores, label, color):\n", " \"\"\"\n", " plots ROC curve\n", " input: y_test, scores, and title\n", " output: ROC curve\n", " \"\"\"\n", " false_pr, true_pr, _ = roc_curve(y_test, scores[:, 1])\n", " roc_auc = auc(false_pr, true_pr)\n", " plt.plot(false_pr, true_pr, lw = 3,\n", " label='{}: area={:10.4f})'.format(label, roc_auc), color = color)\n", " plt.plot([0, 1], [0, 1], color='black', lw=1, linestyle='--')\n", " plt.xlabel('False positive rate')\n", " plt.ylabel('True positive rate')\n", " plt.legend(loc=\"best\")\n", " plt.ylim([0.0, 1.05])\n", " plt.xlim([0.0, 1.0])\n", " plt.title('ROC')\n", "\n", "def plot_PR(y_test, scores, label, color):\n", " \"\"\"\n", " plots PR curve\n", " input: y_test, scores, title\n", " output: Precision-Recall curve\n", " \"\"\"\n", " precision, recall, _ = precision_recall_curve(y_test, scores[:, 1])\n", " plt.plot(recall, precision,lw = 2,\n", " label='{}'.format(label), color = color)\n", " plt.xlabel('Recall')\n", " plt.ylabel('Precision')\n", " \n", " plt.legend(loc=\"best\")\n", " plt.ylim([0.0, 1.05])\n", " plt.xlim([0.0, 1.0])\n", " plt.title('PR')\n", " \n", "def plot_confusionmatrix(ytrue, ypred):\n", " \"\"\"\n", " \n", " plots confusion matrix heatmap and prints out\n", " classification report\n", " \n", " input: ytrue (actual value), ypred(predicted value)\n", " output: confusion matrix heatmap and classification report\n", " \n", " \"\"\"\n", " \n", " print (classification_report(ytrue, ypred))\n", " \n", " print ('##################################################################')\n", " \n", " cnf_matrix = confusion_matrix(ytrue, ypred)\n", " sns.heatmap(cnf_matrix, cmap='coolwarm_r', annot = True, linewidths=.5, fmt = '.4g')\n", " plt.title('Confusion matrix')\n", " plt.xlabel('Prediction')\n", " plt.ylabel('Actual');" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(779, 20)\n" ] } ], "source": [ "apple = pd.read_csv(path + 'merged_df.csv')\n", "apple.head()\n", "print (apple.shape)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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AHqIM9x3WBreaZrT5uoCe5/s5Z7fjQw89hOuvvx4vfOELceKJJwIA/u7v/i4y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\n919T5SMGwgNkRyBv4SuVSuGCCy7A4sWLceWVV+Lwww/HoEGDsHLlSrz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Lr4Zx/+PhB+qQ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\nUTqQ8EUQBBFG8oUvY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apple.plot('date', 'diff');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is exterme fluctuation betweeen opening and closing prices of **Apple, Inc.** (as expected).\n", "\n", "Let's choose the features and label (bin_diff) and make the dataframe ready for machine learning and deep learning." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datesentimentbin_diff
02014-05-05531
12014-05-0615-1
22014-05-0740-1
32014-05-0831-1
42014-05-0931
\n", "
" ], "text/plain": [ " date sentiment bin_diff\n", "0 2014-05-05 53 1\n", "1 2014-05-06 15 -1\n", "2 2014-05-07 40 -1\n", "3 2014-05-08 31 -1\n", "4 2014-05-09 3 1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aapl = apple.copy()[['date', 'sentiment', 'bin_diff']]\n", "aapl.head()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(aapl['bin_diff']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's drop the observation with \"0\" and make it binary classification." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "aapl = aapl[aapl['bin_diff'] != 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, to make the models work properly, from now on, we **re-code** *loss* category from -1 to 0." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "label = aapl['bin_diff'] == 1\n", "label = label.astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "let's look at the features and standardize them." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "InputDF = aapl.copy().drop('bin_diff', axis = 1)\n", "InputDF = InputDF.set_index('date')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sentiment
date
2014-05-0553
2014-05-0615
2014-05-0740
2014-05-0831
2014-05-093
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" ], "text/plain": [ " sentiment\n", "date \n", "2014-05-05 53\n", "2014-05-06 15\n", "2014-05-07 40\n", "2014-05-08 31\n", "2014-05-09 3" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "InputDF.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "InputDF = InputDF.apply(lambda x:(x -x.mean())/x.std())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sentiment
date
2014-05-050.692653
2014-05-06-0.497135
2014-05-070.285620
2014-05-080.003828
2014-05-09-0.872857
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" ], "text/plain": [ " sentiment\n", "date \n", "2014-05-05 0.692653\n", "2014-05-06 -0.497135\n", "2014-05-07 0.285620\n", "2014-05-08 0.003828\n", "2014-05-09 -0.872857" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "InputDF.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_size = 600\n", "xtrain, xtest = InputDF.iloc[:test_size, :], InputDF.iloc[test_size:, :]\n", "ytrain, ytest = label[:test_size], label[test_size:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results\n", "\n", "As expected, SVMs, logistic regression, and random forest classifiers have worse/equal to performance than a coin flip based on ROC area calculated for the hold-out test set. The feed forward neural network has a slighly better performance with 0.53 ROC area while the RNN has the highest area of 0.56.\n", "\n", "| Method | ROC area |\n", "| ------ | ----------- |\n", "| Logistic Regression | 0.4373|\n", "| SVM | 0.4430|\n", "| Random Forets Classifier | 0.4735|\n", "| Feed forward neural network | 0.5323|\n", "| Random Forets Classifier | 0.5557 |\n", "\n", "The PR curves also look reasonable only for neural nets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic regression" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score is 0.4971751412429379 for penalty = 0.001\n", "Accuracy score is 0.4971751412429379 for penalty = 1\n", "Accuracy score is 0.4971751412429379 for penalty = 100\n", "\n", "confusion matrix for penalty = 100\n", " precision recall f1-score support\n", "\n", " 0 0.27 0.11 0.15 75\n", " 1 0.54 0.78 0.64 102\n", "\n", "avg / total 0.43 0.50 0.43 177\n", "\n", "##################################################################\n" ] }, { "data": { "image/png": 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eV8hVVyrt/yUFeklAlRYZ7tCV1Rx6+f1C1aru0KC7QvT0/z+tyDCpUbSTtAVlchhcufik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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for pen in [1e-3, 1, 100]:\n", " logreg = LogisticRegression()\n", " logreg_model = logreg.fit(xtrain, ytrain)\n", " logpred = logreg_model.predict(xtest)\n", " logscores = logreg_model.predict_proba(xtest)\n", " print (\"Accuracy score is {} for penalty = {}\".format(accuracy_score(ytest, logpred), pen))\n", "print ()\n", "print (\"confusion matrix for penalty = {}\".format(pen))\n", "plot_confusionmatrix(ytest, logpred)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "image/png": 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78fOKiaHAiBEErl3r2JZZogQxH3xAZpUq4Kbf/43+QOS0qcwKFSqwd+9eAA4d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\n91Iwk5sYhoHNZiP811+ZEBrK7JMnCfxjp56+FBERcRvdYyZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ROC(ytest, logscores, 'Logistic regression', 'r')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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6nR07rBo7Nll5eXYNGuTVkiX5XFwBAMARiFqYWa1WTZ48udrH2rdvH/75aaed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\nyf7Iqgqumm7zEymLxVBKSkA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_PR(ytest, logscores, 'Logistic regression', 'b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support Vector Machines" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score is 0.576271186440678 for penalty = 0.001\n", "Accuracy score is 0.5988700564971752 for penalty = 1\n", "Accuracy score is 0.5875706214689266 for penalty = 100\n", "\n", "confusion matrix for penalty = 1\n", " precision recall f1-score support\n", "\n", " 0 0.54 0.33 0.41 75\n", " 1 0.62 0.79 0.70 102\n", "\n", "avg / total 0.59 0.60 0.58 177\n", "\n", "##################################################################\n" ] }, { "data": { "image/png": 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JCwAAljE5caFwAQDAMi6D77JIqwgAABiDxAVXx+1Wi7HjFRHXRK7wcB1asUxlR4/o5umz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\nAJxKrhnGRlScAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for pen in [1e-3, 1, 100]:\n", " svm = SVC(probability=True, C = pen)\n", " svm_model = svm.fit(xtrain, ytrain)\n", " svmpred = svm_model.predict(xtest)\n", " svmscores = svm_model.predict_proba(xtest)\n", " print (\"Accuracy score is {} for penalty = {}\".format(accuracy_score(ytest, svmpred), pen))\n", "print ()\n", "pen = 1\n", "svm = SVC(probability=True, C = pen)\n", "svm_model = svm.fit(xtrain, ytrain)\n", "svmpred = svm_model.predict(xtest)\n", "svmscores = svm_model.predict_proba(xtest)\n", "print (\"confusion matrix for penalty = {}\".format(pen))\n", "plot_confusionmatrix(ytest, svmpred)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ROC curve for penalty = 1\n" ] }, { "data": { "image/png": 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Ejh07ULVqVWzbtg3ly5d/7jGy+/d1X6dFRkIoW1Zv43PGjIiIiEo1rVaLJUuWoFatWhgw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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"ROC curve for penalty = {}\".format(pen))\n", "plot_ROC(ytest, svmscores, 'SVM', 'r')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "image/png": 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Wf/QMAADACiyXXpo7xJw1ZgAAwMosl15crtBtIGBTINDwuUOHHPU+ttyXBgAAOjnLpReb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\nKu3dK/3wh8dp2zaX3O5ghwW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_PR(ytest, svmscores, 'SVM', 'b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest Tree Classifiers" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/admin/anaconda/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n" ] }, { "data": { "image/png": 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vZwsICNDkyZPrPXBFRYVvKfPo46qrq+V0OnX48GENHTpUI0aMkNfrVUZGhrp0\n6aLo6GglJCRo9erVtY516aWXasiQIQoLC9Ps2bO1bt069e7d+4Tnb+ilUZZa7cXc2Il5sRdzYyfm\nxV5NOTf1hlpMTIwWLFigb7/9Vk6nUy6XS4GB9T5MoaGhqqio8N02xsjpdEqquUo3bNgw399j6969\nu9xut6Kjo485jjFGw4cP90Vfr169VFxcXG+olZaW1jvGk+VyuRr0eGg4zI2dmBd7MTd2Yl7s1Rhz\nc6IQrPdTn2VlZcrJyVGnTp303//933r22WdVUlJS70nj4uKUk5MjSSosLFSHDh1820pLSzVt2jR5\nvV55PB4VFBSoU6dOdR6noqJCkydPVmVlpYwxys/P571qAACgWaj30tiCBQsUHx+v/Px85ebmavjw\n4frb3/6mjIyMEz4uKSlJeXl5Sk9PlzFGEyZM0MqVKxUZGak+ffqof//+SktLk9PpVHJystq3b1/n\nccLCwjRq1ChlZGQoMDBQPXr0UK9evX7bswUAADiF1BtqBw4c0IgRI7RkyRL169dPAwcO1AcffFDv\ngQMCAjRu3Lha90VFRfl+v/rqq3X11VfX+diBAwfWup2cnKzk5OR6zwkAAHA6OakvZfd4PMrNzVV8\nfLwOHz6sysrKxhgbAABAs1ZvqF100UW6++671apVK3Xu3FlTpkxRv379GmNsAAAAzVq9S5833HCD\nLrvsMrVp00aS9MADD9T56UwAAAA0rPr/zoaks846y/c7kQYAANA46l36BAAAQNMg1AAAACx1Ukuf\nBw4cUGFhoZxOp2JiYtSiRQt/jwsAAKDZqzfU1q9fr/nz56tdu3byer364Ycf9OCDD+rCCy9sjPEB\nAAA0W/WG2rJly/Tkk0/6vjlg27ZtWrhwoWbMmOH3wQEAADRnJ/UetZ9/vVPnzp1ljPHbgAAAAFDj\nuKF28OBBHTx4UF26dNG7776ryspKHT58WB988IG6devWmGMEAABolo679DlmzJhat1999dVat0eP\nHu2fEQEAAEDSCUJt2bJljTkOAAAA/EK9Hybwer167733lJubK4/Ho4SEBF177bVyOp2NMT4AAIBm\nq94PE7z22mvKz8/XFVdcoREjRmjz5s1asmRJY4wNAACgWav3itqGDRs0ffp0BQbW7NqrVy898sgj\nfh8YAABAc1fvFTWv1+uLNEkKCgpi2RMAAKAR1BtqHTt21Msvv6ydO3fqhx9+0CuvvKLo6OjGGBsA\nAECzVu9CPHrmAAATN0lEQVTS55gxY5Sdna2pU6fKGKP4+HjdddddjTE2AACAZq3eUAsLC9PEiRO1\na9cueb1eRUZGNsa4AAAAmr16Q+3777/X7NmztW/fPhlj1KpVKz366KNq165dY4wPAACg2ao31LKz\ns3XVVVdp4MCBkqRPPvlEixYt0hNPPOHvsQEAADRr9X6YoLy83BdpkpSSkqL9+/f7c0wAAADQSYRa\ndXW1Dh486Lu9f/9+ORwOvw4KAAAAJ7H0ecUVVygtLU2XXHKJHA6HPv/8cw0fPrwxxgYAANCs1Rtq\ngwcPVmRkpHJzc+X1ejVmzBjFx8c3xtgAAACatXpDTZK6d++u7t27+3ssAAAA+Jl636MGAACApkGo\nAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMA\nALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABg\nKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKE\nGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUA\nAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgqUB/Hdjr9SorK0tut1tBQUEaP368IiMj\nfduzs7O1efNmhYaGSpJSU1MVFhYmSVq1apXKysp0yy23SJK++uorLV++XAEBAUpJSdHgwYP9NeyT\nYsr2SLt2Sm0j5Yg4q0nHAgAATl9+C7W1a9eqqqpKmZmZKiws1OLFi5WamurbXlxcrLS0NIWHh/vu\nO3LkiF588UVt3bpVffv2lSR5PB698sormj59ukJCQjR16lT16dNHERER/hr6cZnKCnmznpNKtkj7\ny6TwCKljjALunixHSGijjwcAAJze/Lb0WVBQoMTERElSbGysioqKfNu8Xq927typhQsXaurUqfr4\n448l1YTagAEDNHLkSN++O3bsUGRkpFq2bKnAwEDFxcVp06ZN/hr2CXmznpM2rJHK90nG1PzcsKbm\nfgAAgAbmt1CrqKjwLWVKUkBAgKqrqyVJhw8f1tChQ3X//fdrypQp+uc//ym3262WLVsqISHhhMcJ\nDQ3VoUOH/DXs4zJle2qupNWlZEvNdgAAgAbkt6XP0NBQVVRU+G4bY+R0OiVJwcHBGjZsmIKDgyVJ\n3bt3l9vtVnR0dJ3Hqays9N2uqKhQixYt6j2/y+X6vU+hlrO9Hu3aX173xgPlOttUK7iBz4mT09Bz\njYbBvNiLubET82Kvppwbv4VaXFyc1q1bp0svvVSFhYXq0KGDb1tpaan+/d//XTNnzpTX61VBQYEG\nDBhQ53GioqL0/fff6+DBgwoJCdGmTZt01VVX1Xv+0tLSBnsuLpdLuwMCpfDWNcudv9SqtXY7nHI0\n4DlxclwuV4PONRoG82Iv5sZOzIu9GmNuThSCfgu1pKQk5eXlKT09XcYYTZgwQStXrlRkZKT69Omj\n/v37Ky0tTU6nU8nJyWrfvn3dAwwM1OjRo5WZmSmv16uUlBS1adPGX8M+LkfEWVLHmJr3qP1Sxxg+\n/QkAABqcwxhjmnoQ/tDQV9RKS0trf+rzQLnUqjWf+mxi/L9QOzEv9mJu7MS82Ou0vaJ2OnKEhMo5\nMb3mgwM//iCdcy5X0gAAgN8Qar+BI+IsiUADAAB+xldIAQAAWIpQAwAAsBShBgAAYClCDQAAwFKE\nGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUA\nAABLEWoAAACWItQAAAAsFdjUAwB+K1O2R4f3/iATEChHxFlNPRwAABocoYZTjqmskDfrOalki3bt\nL5fCW0sdYxRw92Q5QkKbengAADQYlj5xyvFmPSdtWCOV75OMt+bnhjU19wMAcBoh1HBKMWV7pJIt\ndW8s2VKzHQCA0wShhlPLrp3S/rK6tx0ol378oXHHAwCAHxFqOLW0jZTCI+re1qq1dM65jTseAAD8\niFDDKcURcZbUMabujR1j+PQnAOC0QqjhlBNw92QpIUlqfaYUEFDzMyGp5n4AAE4j/HkOnHIcIaFy\nTkyXKdujs021djucXEkDAJyWCDWcshwRZynY5ZKjtLSphwIAgF+w9AkAAGApQg0AAMBShBoAAICl\nCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFq\nAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAA\nACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABY\nilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBSh\nBgAAYClCDQAAwFKEGgAAgKUINQAAAEsF+uvAXq9XWVlZcrvdCgoK0vjx4xUZGenbnp2drc2bNys0\nNFSSlJqaKo/Ho7lz5+rIkSM688wzNWHCBAUHB9e5b1hYmL+GDgAAYAW/hdratWtVVVWlzMxMFRYW\navHixUpNTfVtLy4uVlpamsLDw333ZWdnq3///ho4cKDefvttffjhhxoxYkSd+wIAAJzu/Lb0WVBQ\noMTERElSbGysioqKfNu8Xq927typhQsXaurUqfr4448lSZs3b/Y9JjExUV9//fVx9wUAADjd+e2K\nWkVFRa3lyYCAAFVXV8vpdOrw4cMaOnSoRowYIa/Xq4yMDHXp0kWHDh3yPSY0NFSHDh067r7R0dEn\nPL/L5WrQ59PQx0PDYW7sxLzYi7mxE/Nir6acG7+FWmhoqCoqKny3jTFyOp2SpODgYA0bNkzBwcGS\npO7du8vtdissLEwVFRU644wzVFFRoRYtWhx33/pCrbS0tMGei8vlatDjoeEwN3ZiXuzF3NiJebFX\nY8zNiULQb0ufcXFxysnJkSQVFhaqQ4cOvm2lpaWaNm2avF6vPB6PCgoK1KlTp1qPyc3NVdeuXY+7\nLwAAwOnOb1fUkpKSlJeXp/T0dBljNGHCBK1cuVKRkZHq06eP+vfvr7S0NDmdTiUnJ6t9+/YaOXKk\n5s+fr48++kitWrXSAw88oJCQkDr3BQAAON05jDGmqQfhDyx9Ng/MjZ2YF3sxN3ZiXux12i59AgAA\n4Pch1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAs\nRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQ\nAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYA\nAGApQg0AAMBShBoAAIClCDU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "oobscores = []\n", "notrees = [10, 50, 100, 200, 500, 1000]\n", "for n in notrees:\n", " rf = RandomForestClassifier(n_estimators=n, oob_score=True, random_state=42)\n", " rf_model = rf.fit(xtrain, ytrain)\n", " oobscores.append(rf_model.oob_score_)\n", "\n", "plt.scatter(notrees, oobscores)\n", "plt.xlabel('no of trees')\n", "plt.ylabel('oob scores')\n", "plt.title('random forest classifiers');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit random forest with 500 trees" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.36 0.39 0.37 75\n", " 1 0.53 0.50 0.51 102\n", "\n", "avg / total 0.46 0.45 0.45 177\n", "\n", "##################################################################\n" ] }, { "data": { "image/png": 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16tRRjx49VLNmTV9cEgGSl3lRkuQqV1Yxf/u/2jN2hur1/4Oq/OZenf10q2o8/pBcZcMC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\nFwAAYIz/D9uD7tE5a0/kAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rf = RandomForestClassifier(n_estimators=500, oob_score=True, random_state=42)\n", "rf_model = rf.fit(xtrain, ytrain)\n", "rfpred = rf.predict(xtest)\n", "rfscores = rf.predict_proba(xtest)\n", "plot_confusionmatrix(ytest, rfpred)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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io6OZN28etWrVMjuOeBEVMxERkTzy22+/8eWXX9K5c2c2bdqEj4/+NyvXRlOZ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\nnC50uVyYzWZcLtcl17lcLpxOJ7fccgtTp04lNTWV77//nuHDhzN8+PB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ROC(ytest, rfscores, 'Random Forest', 'r')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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sb9++Wr16taZMmSLDMHTTTTdp3rx5ys7OVkFBgdasWaPJkyfLZrOpS5cu6t69e4Oev7EX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\nAJgEwQwAAMAkCGYAAAAmQTADAAAwCYI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_PR(ytest, rfscores, 'Random Forest', 'b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feed Forward Neural Network" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_features = len(InputDF.columns)\n", "dropout=0.2\n", "hidden_1_size = 25\n", "hidden_2_size = 5\n", "num_classes = label.nunique()\n", "NUM_EPOCHS=20\n", "BATCH_SIZE=1\n", "lr=0.0001\n", "np.random.RandomState(52);" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val = (InputDF[:-test_size].values, label[:-test_size].values)\n", "train = (InputDF[-test_size:].values, label[-test_size:].values)\n", "NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n", "NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Model():\n", " def __init__(self):\n", " global_step = tf.contrib.framework.get_or_create_global_step()\n", " self.input_data = tf.placeholder(dtype=tf.float32,shape=[None,num_features])\n", " self.target_data = tf.placeholder(dtype=tf.int32,shape=[None])\n", " self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n", " with tf.variable_scope(\"ff\"):\n", " droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n", " \n", " layer_1 = tf.contrib.layers.fully_connected(\n", " num_outputs=hidden_1_size,\n", " inputs=droped_input,\n", " )\n", " layer_2 = tf.contrib.layers.fully_connected(\n", " num_outputs=hidden_2_size,\n", " inputs=layer_1,\n", " )\n", " self.logits = tf.contrib.layers.fully_connected(\n", " num_outputs=num_classes,\n", " activation_fn =None,\n", " inputs=layer_2,\n", " )\n", " with tf.variable_scope(\"loss\"):\n", " \n", " self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = self.logits, \n", " labels = self.target_data)\n", " mask = (1-tf.sign(1-self.target_data)) #Don't give credit for flat days\n", " mask = tf.cast(mask,tf.float32)\n", " self.loss = tf.reduce_sum(self.losses)\n", " \n", " with tf.name_scope(\"train\"):\n", " opt = tf.train.AdamOptimizer(lr)\n", " gvs = opt.compute_gradients(self.loss)\n", " self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n", " \n", " with tf.name_scope(\"predictions\"):\n", " self.probs = tf.nn.softmax(self.logits)\n", " self.predictions = tf.argmax(self.probs, 1)\n", " correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n", " self.accuracy = tf.reduce_mean(correct_pred)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From :7: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", "Instructions for updating:\n", "Use `tf.global_variables_initializer` instead.\n", "################ done training ################\n", "################ done testing ################\n" ] } ], "source": [ "with tf.Graph().as_default():\n", " model = Model()\n", " input_ = train[0]\n", " target = train[1]\n", " losses = []\n", " with tf.Session() as sess:\n", " init = tf.initialize_all_variables()\n", " sess.run([init])\n", " epoch_loss =0\n", " for e in range(NUM_EPOCHS):\n", " if epoch_loss >0 and epoch_loss <1:\n", " break\n", " epoch_loss =0\n", " for batch in range(0,NUM_TRAIN_BATCHES):\n", " \n", " start = batch*BATCH_SIZE\n", " end = start + BATCH_SIZE \n", " feed = {\n", " model.input_data:input_[start:end],\n", " model.target_data:target[start:end],\n", " model.dropout_prob:0.9\n", " }\n", " \n", " _,loss,acc = sess.run(\n", " [\n", " model.train_op,\n", " model.loss,\n", " model.accuracy,\n", " ]\n", " ,feed_dict=feed\n", " )\n", " epoch_loss+=loss\n", " losses.append(epoch_loss)\n", " #print('step - {0} loss - {1} acc - {2}'.format((1+batch+NUM_TRAIN_BATCHES*e),epoch_loss,acc))\n", " \n", " \n", " print('################ done training ################')\n", " final_preds =np.array([])\n", " final_scores =None\n", " for batch in range(0,NUM_VAL_BATCHES):\n", " \n", " start = batch*BATCH_SIZE\n", " end = start + BATCH_SIZE \n", " feed = {\n", " model.input_data:val[0][start:end],\n", " model.target_data:val[1][start:end],\n", " model.dropout_prob:1\n", " }\n", " \n", " acc,preds,probs = sess.run(\n", " [\n", " model.accuracy,\n", " model.predictions,\n", " model.probs\n", " ]\n", " ,feed_dict=feed\n", " )\n", " #print(acc)\n", " final_preds = np.concatenate((final_preds,preds),axis=0)\n", " if final_scores is None:\n", " final_scores = probs\n", " else:\n", " final_scores = np.concatenate((final_scores,probs),axis=0)\n", " print ('################ done testing ################')\n", " prediction_conf = final_scores[np.argmax(final_scores, 1)]" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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Aofy9XUBTZ5UU68R3B2XZ/WULDfd2OQAAoBkhqJ0hq6JcjkVPSQW7dOhoqRTSRorpInvy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\nNNntdtntdlVVVSkgIMBbpfqEhvohSZ9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(np.linspace(0, 1, len(losses)), losses);\n", "plt.title('Validation loss with epoch')\n", "plt.ylabel('Validation Loss')\n", "plt.xlabel('epoch progression');" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.44 0.37 0.40 75\n", " 1 0.58 0.65 0.61 102\n", "\n", "avg / total 0.52 0.53 0.52 177\n", "\n", "##################################################################\n" ] }, { "data": { "image/png": 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RMjMz5XK55PF4FBMToz59+qhOnTr++EgEQfGpfEmSq1JFJfz1BWWMfV7yehX7wl/U5M/J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\n0LgAAABj0LgAAABj0LgAAABj/C+w06xI3x4g9gAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_confusionmatrix(ytest, final_preds)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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41q1bx5QpU0hMTCQiIkLBTERE5B+io6MJb9KEk0eP0oxroSyuRw/ie/Z0d2ni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\nZmYmp0+fplGjRjz88MN8+OGH5ObmcuHCBapUqcLAgQPZsGEDhw8fZu7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ROC(ytest, final_scores, 'Feed forward neural net', 'r')" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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1bFmUHA5DV19deUHZuXOj5fPZ1L+/p1Z6Kc45p1QfffSr+vXzKDfXrhtuSNak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\nUGMmImFv06ZN2Gw2GhsbiYqKYty4cSxfvhyAq1e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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_PR(ytest, final_probs, 'Feed forward neural net', 'b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Recursive Neural Nets" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RNN_HIDDEN_SIZE=4\n", "FIRST_LAYER_SIZE=50\n", "SECOND_LAYER_SIZE=10\n", "NUM_LAYERS=2\n", "BATCH_SIZE=1\n", "NUM_EPOCHS=25\n", "lr=0.0003\n", "NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n", "NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)\n", "ATTN_LENGTH=30\n", "beta=0\n", "np.random.RandomState(52);" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RNNModel():\n", " def __init__(self):\n", " global_step = tf.contrib.framework.get_or_create_global_step()\n", " self.input_data = tf.placeholder(dtype=tf.float32,shape=[BATCH_SIZE,num_features])\n", " self.target_data = tf.placeholder(dtype=tf.int32,shape=[BATCH_SIZE])\n", " self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n", " \n", " def makeGRUCells():\n", " base_cell = rnn.GRUCell(num_units=RNN_HIDDEN_SIZE,) \n", " layered_cell = rnn.MultiRNNCell([base_cell] * NUM_LAYERS,state_is_tuple=False) \n", " attn_cell =tf.contrib.rnn.AttentionCellWrapper(cell=layered_cell,attn_length=ATTN_LENGTH,state_is_tuple=False)\n", " return attn_cell\n", " \n", " self.gru_cell = makeGRUCells()\n", " self.zero_state = self.gru_cell.zero_state(1, tf.float32)\n", " \n", " self.start_state = tf.placeholder(dtype=tf.float32,shape=[1,self.gru_cell.state_size])\n", " \n", " \n", "\n", " with tf.variable_scope(\"ff\",initializer=xavier_initializer(uniform=False)):\n", " droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n", " \n", " layer_1 = tf.contrib.layers.fully_connected(\n", " num_outputs=FIRST_LAYER_SIZE,\n", " inputs=droped_input,\n", " \n", " )\n", " layer_2 = tf.contrib.layers.fully_connected(\n", " num_outputs=RNN_HIDDEN_SIZE,\n", " inputs=layer_1,\n", " \n", " )\n", " \n", " \n", " split_inputs = tf.reshape(droped_input,shape=[1,BATCH_SIZE,num_features],name=\"reshape_l1\") # Each item in the batch is a time step, iterate through them\n", " split_inputs = tf.unstack(split_inputs,axis=1,name=\"unpack_l1\")\n", " states =[]\n", " outputs =[]\n", " with tf.variable_scope(\"rnn\",initializer=xavier_initializer(uniform=False)) as scope:\n", " state = self.start_state\n", " for i, inp in enumerate(split_inputs):\n", " if i >0:\n", " scope.reuse_variables()\n", " \n", " output, state = self.gru_cell(inp, state)\n", " states.append(state)\n", " outputs.append(output)\n", " self.end_state = states[-1]\n", " outputs = tf.stack(outputs,axis=1) # Pack them back into a single tensor\n", " outputs = tf.reshape(outputs,shape=[BATCH_SIZE,RNN_HIDDEN_SIZE])\n", " self.logits = tf.contrib.layers.fully_connected(\n", " num_outputs=num_classes,\n", " inputs=outputs,\n", " activation_fn=None\n", " )\n", "\n", " \n", " with tf.variable_scope(\"loss\"):\n", " self.penalties = tf.reduce_sum([beta*tf.nn.l2_loss(var) for var in tf.trainable_variables()])\n", "\n", " \n", " self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = self.logits,\n", " labels = self.target_data)\n", " self.loss = tf.reduce_sum(self.losses + beta*self.penalties)\n", " \n", " with tf.name_scope(\"train_step\"):\n", " opt = tf.train.AdamOptimizer(lr)\n", " gvs = opt.compute_gradients(self.loss)\n", " self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n", " \n", " with tf.name_scope(\"predictions\"):\n", " self.probs = tf.nn.softmax(self.logits)\n", " self.predictions = tf.argmax(self.probs, 1)\n", " correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n", " self.accuracy = tf.reduce_mean(correct_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training the RNN" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", "################ done training ################\n", "################ done testing ################\n" ] } ], "source": [ "with tf.Graph().as_default():\n", " model = RNNModel()\n", " input_ = train[0]\n", " target = train[1]\n", " losses = []\n", " with tf.Session() as sess:\n", " init = tf.global_variables_initializer()\n", " sess.run([init])\n", " loss = 2000\n", " \n", " for e in range(NUM_EPOCHS):\n", " state = sess.run(model.zero_state)\n", " epoch_loss =0\n", " for batch in range(0,NUM_TRAIN_BATCHES):\n", " start = batch*BATCH_SIZE\n", " end = start + BATCH_SIZE \n", " feed = {\n", " model.input_data:input_[start:end],\n", " model.target_data:target[start:end],\n", " model.dropout_prob:0.5,\n", " model.start_state:state\n", " }\n", " _,loss,acc,state = sess.run(\n", " [\n", " model.train_op,\n", " model.loss,\n", " model.accuracy,\n", " model.end_state\n", " ]\n", " ,feed_dict=feed\n", " )\n", " epoch_loss+=loss\n", " losses.append(epoch_loss)\n", " #print('step - {0} loss - {1} acc - {2}'.format((e),epoch_loss,acc))\n", " print('################ done training ################')\n", " \n", " final_preds =np.array([])\n", " final_scores = None\n", " for batch in range(0,NUM_VAL_BATCHES):\n", " start = batch*BATCH_SIZE\n", " end = start + BATCH_SIZE \n", " feed = {\n", " model.input_data:val[0][start:end],\n", " model.target_data:val[1][start:end],\n", " model.dropout_prob:1,\n", " model.start_state:state\n", " }\n", " acc,preds,state, probs = sess.run(\n", " [\n", " model.accuracy,\n", " model.predictions,\n", " model.end_state,\n", " model.probs\n", " ]\n", " ,feed_dict=feed\n", " )\n", " #print(acc)\n", " assert len(preds) == BATCH_SIZE\n", " final_preds = np.concatenate((final_preds,preds),axis=0)\n", " if final_scores is None:\n", " final_scores = probs\n", " else:\n", " final_scores = np.concatenate((final_scores,probs),axis=0)\n", " print('################ done testing ################')" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "image/png": 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DAFCbV3vUysvL9eijj2rfvn3uY/Pnz9cHH3xQ4zqHw6Fp06bVOi5JhYWF+s1v\nfqPMzExlZmbqk08+8WbJ8BNWFgcAoDav9ahVV1crNzdXQUFBkqSKigrl5ORo//79stvtNa59/fXX\ndfTo0Trvs3v3bvXv318DBgzwVqnws4asLM6DwQCA5qhBPWoHDrhWdv7iiy+0ZMkSHTt2zOP3LFy4\nUH379lVERIQkqaqqSmlpaerZs2eN6z799FNZrVYlJSXVeZ/CwkJ98cUXevrppzVnzhxVVlY2pGQ0\nJqwsDgBAnTz2qOXm5kqS7rzzTs2dO1eJiYl66aWXNH78+HN+z5o1axQWFqakpCQtW7ZMkhQdHa3o\n6GgVFBS4r9u7d6/y8/M1duxYLVmypM57dezYUbfddps6dOigpUuX6s0339SwYcM8frCze+28zdfv\n15ScatlCJRGRcvzwfa1zVtvPFNM1UQGRUed9X9rELLSHeWgTs9Ae5jGhTTwGtcLCQk2bNk3Lli1T\n7969NXjwYE2cOLHe71m9erUkacuWLSoqKlJOTo4mTJggm63mNh9r167VDz/8oClTpujQoUMKDAxU\ndHR0jd61lJQUhYaGur+eN29egz5YcXFxg667GOx2u0/frylyxHaQ6ghqjtgOOnD8pHSeP1/axCy0\nh3loE7PQHubxZZvUFwg9BjWn0ymr1aotW7bo3nvvlSQdP3683u/Jyspyf52ZmalRo0bVCmmSNHTo\nUPfXixcvls1mqzUEmp2drREjRqhjx47asmWLOnTo4KlkNELW9HH/mfV5pNy1R9//zfoEAKC58hjU\nWrdurT/96U86cOCAEhIS9Pzzz6tdu3ZeLeq7777TypUrlZ6ervT0dM2bN0+BgYGy2WwaPXq0V98b\n/sHK4gAA1GZxOp3O+i6oqqrS+vXr1blzZ0VHR+uDDz5Q79691bJlS1/VeEEY+mzeaBOz0B7moU3M\nQnuYp9EMfbZq1UqdOnVSdHS0vvjiC1VUVOjUqVMXtcDGzFlWquM/HJDTGkgPEAAAuKi8MuuzOThz\nJf2DFeVSWDgr6QMAgIvK4zpqhYWFSk9P1/r169W7d2899thj+v772rPzmpuaK+k7WEkfAABcdB6D\n2pmzPrt27SrJ86zPpq4hK+n/1PuzMTkAADBy1qfxGrKS/gU8r8bG5AAA4Eweg9pjjz2m9evX6+qr\nr1ZgYKA6d+6s3r17+6I2c0XHuEJU+eHa5y4Nl6JaX9Bt3cOpp50xnBowJuMCiwUAAI1Vg2Z9Xn75\n5VqzZo3it7B+AAAgAElEQVROnTql7t27G780h7dZbJFSXHzNUHVaXPwFzf5kY3IAAHA2j8+orV27\nVn/5y1/0448/6tixY3r++ee1atUqX9RmNGv6OCkxRQqPkKxW12tiyoWvpM/G5AAA4Cwee9RWrFih\nadOmKSIiQpJ0zz33KDs7W3369PF6cSY7cyX9y5yn9L0l4Kf1eHlpOBUAADReDZr1eTqkSdLPfvYz\nWa0ev63ZsNgi1bJL0k8elnQPp9blAodTAQBA4+YxcV1yySX67LPP3H9ev369QkNDvVpUc3XRh1MB\nAECj5nHoc8SIEfrzn/+sefPmub4hMLDZ70rgLWxMDgAAzuQxqMXGxmr27NkqLi6Ww+FQmzZtFBAQ\n4Ivami2LLfKC1mEDAABNS4MeNrNarWrbtq2uuOIKBQQE6NFHH/V2XQAAAM3eBc0K+PHHHy92HQAA\nADjLBQU1i8VysesAAADAWVhnAxeMzeMBAPCuc04mePbZZ+vsOXM6nTpx4oRXi4LZ2DweAADfOGdQ\n69Gjxzm/qb5zaPrYPB4AAN84Z1C7+eabfVgGGgs2jwcAwHd4Rg3nh83jAQDwGYIazs/pzePr0ow2\nj2ciBQDAFzzuTACcyb15/JnPqJ3WDDaPZyIFAMCXPAa1srIy/fOf/9TRo0fldDrdx0eMGOHVwmAu\na/q4/4SVI+WunrT/CytNHRMpAAC+5DGovfDCC2rZsqXi4uJY6BaSmu/m8UykAAD4mseg9sMPP2jW\nrFm+qAWNTLPbPL4hEyma088DAOB1HicTXHbZZaqqqvJFLYDZmEgBAPAxjz1qERERevzxx5WQkKCg\noCD3cZ5RQ3PT3CdSAAB8z2NQi4qKUlRUlC9qAYzXnCdSAAB8z2NQS0tLU1VVlQoLC1VdXa34+HgF\nB7MMAZqn5jqRAgDgHx6D2s6dOzVjxgyFh4fL4XCotLRUEydOVKdOnXxRH2CkZjeRAgDgFx6D2sKF\nC/W73/1OXbt2lSRt3bpVCxYsUHZ2tteLAwAAaM48zvqsrKx0hzRJ6tq1q44fP+7VogBcHGx1BQCN\nm8ceNYvFokOHDrknFBw8eFBWK1uEAiZjqysAaBo8BrX7779fkydPVrdu3SRJmzdv1siRI71eGIAL\nx1ZXANA0eAxqKSkpatu2rbZu3SqHw6F7771Xbdu29UVtAC4AW10BQNNxzjHMrVu3SpLWrVunb7/9\nVuHh4YqIiNC+ffu0bt06nxUI4Dw1ZKsrAECjcM4etfz8fHXt2lUrV66s8/z111/vtaIA/ASnt7oq\nP1z7HFtdAUCjcs6g9sgjj0iShgwZoo4dO9Y4t3nzZu9WBeCCsdUVADQd5xz63L17twoLC/Xiiy+6\nvy4sLNSOHTuUl5fnyxoBnCdr+jgpMUUKj5CsVtdrYgpbXQFAI3POHrUPPvhAmzdv1uHDh/Xcc8+5\njwcEBCglJcUnxQG4MGx1BQBNwzmD2m9+8xtJ0uuvv65BgwZd0M3Ly8s1ceJEZWRkqE2bNpKk+fPn\ny263KzU11X2dw+HQs88+q+uuu67GcUkqKSnRiy++KIvFotjYWI0cOZJ13IAGYqsrAGjcPC7PMWjQ\nIO3evVtVVVVyOp1yOBwqKSlRnz596v2+6upq5ebmKigoSJJUUVGhnJwc7d+/X3a7vca1r7/+uo4e\nPVrnfV599VUNGjRIXbp0UW5urjZs2ECPHgAAaBY8BrWXX35ZGzZs0MmTJxUREaGSkhJ17tzZY1Bb\nuHCh+vbtq2XLlkmSqqqqlJaWpoKCghrXffrpp7JarUpKSqrzPoWFhUpISJAkJScna9OmTQQ1AADQ\nLHgMalu2bFFOTo7y8vL0wAMPqLS0VMuXL6/3e9asWaOwsDAlJSW5g1p0dLSio6NrBLW9e/cqPz9f\nY8eO1ZIlS855P4vFIkkKDg7WsWPHGvTBzu618zZfvx88o03MQnuYhzYxC+1hHhPaxGNQs9lsatWq\nldq0aaO9e/cqJSVFr7zySr3fs3r1akmukFdUVKScnBxNmDBBNputxnVr167VDz/8oClTpujQoUMK\nDAxUdHR0jd610yFNcm0QHxoa2qAPVlxc3KDrLga73e7T94NntIlZaA/z0CZmoT3M48s2qS8Qegxq\ngYGB+vLLL9W2bVsVFBSoa9euqqqqqvd7srKy3F9nZmZq1KhRtUKaJA0dOtT99eLFi2Wz2WoNgcbF\nxWnbtm3q0qWL+/0BAACaA4/TJ4cMGaJVq1YpOTlZe/bs0ciRI9WzZ0+vFvXdd9+512obNmyYFi9e\nrMmTJ6u6ulo9evTw6nsDAACYwuJ0Op3n8w3Hjh1TSEiIt+q5aBj6bN5oE7PQHuahTcxCe5jH+KHP\nl156qd6bPvbYYxdeEXzOWVbq2qw7OoaFTwEAaCTOGdRiY2MlSd98842+//573XTTTQoICNAnn3yi\n1q3Z1LmxcFZVypE3UyraIVWUuTbrjouXNX2cLK2C/V0eAACoxzmD2oABAyRJ69evV1ZWllq2bClJ\nuu2222pMFoDZHHkza27OXX5Y2rRejryZChiT4b/CAACARx4nE5SXl6tFixbuP1ssFh05csSrReHi\ncJaVunrS6lK0w3UeAAAYy+PyHN26dVN2drZuuukmOZ1OrV27Vtddd50vasNPdbDENdxZlyPlrs26\neV4NAABjeQxqI0aM0Pvvv6/1613DZzfccIPH7aNgiOgY1zNp5Ydrn7s0XIriWUMAAEx2zqB2ehmO\nyspK9erVS7169apx7pJLLvFJgbhwFlukFBdf8xm10+Limf2JJotZzgCainMGtaysLE2fPl0jR46s\n8/wbb7zhtaJw8VjTx/1n1ueRcldP2v/N+gSaGmY5A2hqznvB28aCBW9rcpaVup5Ji2rdLHoYGkOb\nNCe+ao9TOVPr7kFOTGGW81n4O2IW2sM8xi94u2LFinpv2r9//wuvCD5nsUUycQBNWkNmOTeHX1IA\nNC3nDGp79+71ZR0A8NMwyxlAE3TOoMYWUQAaFWY5A2iCPC7PsX37di1btkxVVVVyOp1yOBw6ePCg\n5syZ44v6AKBBmOUMXFzMnjaDx6D28ssvq1evXlq3bp369u2r9evX6/rrr/dFbQBwXpjlDPx0zJ42\ni8egZrFYdM899+jIkSOy2+0aO3asJk6c6IvaAOC8WFoFK2BMRrOb5QxcTOwRbRaPe322atVKktS6\ndWt9++23CgoKktXq8dsAwG8stkhZ4hMIacB5Yo9o83hMXPHx8Zo1a5a6du2qd955RwsWLFBAQIAv\nagMAAL7UkNnT8CmPQW348OG66667ZLfb9fDDD8vhcOj3v/+9L2oDAAC+dHr2dF2YPe0X5wxqM2bM\n0JYtW2SxWHTVVVdJkq655ho9/PDD9a6gCwAAGif37Om6MHvaL845maBz586aN2+eJCk1NVU333yz\ngoOZ7QEAQFPG7GmznDOoDRgwQAMGDNCXX36pVatW6a233lKPHj10++23KzY21pc1AgD8iPW0mhdm\nT5vF4/IcCQkJSkhI0NGjR7V27Vrl5OQoJCRETz/9tC/qAwD4CetpNW/sEW2GBq+zERgYqJYtWyok\nJERHjhzxZk0AAAO419MqPyw5nTXW0wLgGx571L7++mv961//0meffabu3bsrLS1NCQkJvqgNAOAn\nDVlPi+EwwPvOGdTefvttrV69WsePH9ett96qv/zlL4qIiPBlbQAAf2nIeloENcDrzhnUNm7cqEGD\nBiklJYWdCACguTm9nlb54drnWE8L8JlzBjUmCwBA8+VeT+vMPR9PYz0twGfoKgMA1MmaPk5KTJHC\nIySr1fWamMJ6WoAPeZxMAABonlhPC/A/ghoAoF6spwX4D0OfAAAAhiKoAQAAGIqgBgAAYCiCGgAA\n5+AsK5Vz+zbXhArAD5hMAADAWdiQHqagRw0AgLOwIT1MQVADAOAMDdmQHvAVghoAAGdqyIb0gI8Q\n1AAAONPpDenrwob08DGCGgAAZ3BvSF8XNqSHjxHUAAA4CxvSwxReXZ6jvLxcEydOVEZGhtq0aSNJ\nmj9/vux2u1JTUyVJK1eu1EcffSRJeuCBB3TttdfWuEdhYaGmT5+uyy+/XJKUmpqqG264wZtlAwCa\nOTakhym8FtSqq6uVm5uroKAgSVJFRYVycnK0f/9+2e1297EPPvhAf/7zn3Xy5EmNHTtW11xzjSwW\ni/s+u3fvVv/+/TVgwABvlQoAQJ3YkB7+5rWgtnDhQvXt21fLli2TJFVVVSktLU0FBQXua8LCwjRj\nxgwFBATo0KFDCgkJqRHSJFePWnFxsTZs2KCYmBg9/PDDCg5msUEAAND0eSWorVmzRmFhYUpKSnIH\ntejoaEVHR9cIapIUEBCglStXavHixbrjjjtq3atjx4667bbb1KFDBy1dulRvvvmmhg0b5rGG0712\nvuLr94NntIlZaA/z0CZmoT3MY0KbeCWorV69WpK0ZcsWFRUVKScnRxMmTJDNVvd05379+qlPnz6a\nNm2atm7dqq5du7rPpaSkKDQ01P31vHnzGlRDcXHxT/wUDWe32336fvCMNjEL7WEe2sQstId5fNkm\n9QVCr8z6zMrKUlZWljIzMxUXF6cxY8bUGdKKi4v13HPPyel0KiAgQC1atJDVWrOk7Oxs7dy5U5Ir\n+HXo0MEbJQMAABjHr5uy2+12tWvXThkZGZKk5ORkJSQk6LvvvtPKlSuVnp6u9PR0zZs3T4GBgbLZ\nbBo9erQ/SwYAAPAZi9PpdPq7CG9g6LN5a4xt4iwrdW1dEx3T5JYBaIzt0dTRJmahPcxjytCnX3vU\nAEjOqko58ma6NoGuKHNtXRMXL2v6OFlaMcMZAJozdiYA/MyRN1PatF4qPyw5na7XTetdxwEAzRpB\nDfAjZ1mpqyetLkU7XOcBAM0WQQ3wp4MlruHOuhwpd21dAwBotghqgD9Fx7ieSavLpeFSVGvf1gMA\nMApBDfAjiy1Siouv+2RcfJOb/YmanGWlcm7fxhA3gHNi1ifgZ9b0cf+Z9Xmk3NWT9n+zPtE0MdMX\nMJuzrFTHfzggpzXQ778wE9QAP7O0ClbAmAxXr8qhA1JUa7//hwHe5Z7pe9oZM30DxmT4rzCgmTvz\nl6iDFeVSWLjff4li6BMwhMUWKUt8AiGtiWOmL2CumsslOYxYLomgBgC+xExfwEim/hJFUAMAX2Km\nL2AmQ3+JIqgBgA8x0xcwlKG/RBHUAMDHrOnjpMQUKTxCslpdr4kpzPQF/MjUX6KY9QkAPsZMX8BM\nJi6XRFADAD+x2CIlAhpgjDN/ibrMeUrfWwL8/ksUQQ0AAOAMFlukWtrtshQX+7sUnlEDAAAwFUEN\nAADAUAQ1AADQaDnLSuXcvq3J7urBM2oAAKDROXNfTlWUudZA8/O+nN5AjxoAAGh0au7L6TRiX05v\nIKgBAIBGxdR9Ob2BoAYAABoXQ/fl9AaCGgAAaFwM3ZfTGwhqAACgUTF1X05vIKgBAIBGx5o+TkpM\nkcIjJKvV9ZqY4td9Ob2B5TkAAECjc+a+nDp0QIpq3aR60k4jqAEAgEbLYouUmmBAO42hTwAAAEMR\n1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAKABnGWl\ncm7f5tquBgB8hC2kAPiVs6xUOlgiRccYuU+fs6pSjryZUtEOqaJMCrNJcfGypo+TpVWwv8sD0MQR\n1AD4RWMJQI68mdKm9f85UH5Y2rRejryZChiT4b/CADQLXh36LC8v16OPPqp9+/a5j82fP18ffPCB\n+88rV67UE088oSeeeEKff/55rXuUlJToySef1FNPPaW//vWvcjgc3iwZgI+4A1D5YcnprBGATOEs\nK3UFyboU7WAYFIDXeS2oVVdXKzc3V0FBQZKkiooKTZs2rUYYq6io0AcffKBnnnlGTz31lPLy8uR0\nOmvc59VXX9WgQYM0ZcoUOZ1ObdiwwVslA/CRRhOADpa4evvqcqRcOnTAt/UAaHa8FtQWLlyovn37\nKiIiQpJUVVWltLQ09ezZ031NWFiYZsyYocDAQJWVlSkkJEQWi6XGfQoLC5WQkCBJSk5O1ubNm71V\nMgBfaSwBKDrGNSRbl0vDpajWvq0HQLPjlaC2Zs0ahYWFKSkpyX0sOjpa8fHxta4NCAjQypUrNXny\nZPXo0aPO+50Ob8HBwTp27Jg3SgbgS40kAFlskVJc7f9uSZLi4o2c/ACgafHKZILVq1dLkrZs2aKi\noiLl5ORowoQJstnq/g9zv3791KdPH02bNk1bt25V165d3efO7GGrrKxUaGhog2qw2+0/4ROcP1+/\nHzyjTcxSoz3sdh3q3E1V69bWuq5V526KSujmw8rq53jyOZXOyNCJHV/KUfaDrLafKSg+QZH/NVXW\n4BB/l/eT8HfELLSHeUxoE68EtaysLPfXmZmZGjVqVJ0hrbi4WH/72980btw4BQQEqEWLFrJaa3by\nxcXFadu2berSpYsKCgpqhLj6FBcX/7QPcR7sdrtP3w+e0SZmqas9nEN/K1VVuZ5VO1Lu6kmLi9eJ\nob81r+3Sx0tlpbIeOiBFtdZJW6RKDpdJh88xfNsI8HfELLSHeXzZJvUFQr8uz2G329WuXTtlZLim\nuCcnJyshIUHfffedVq5cqfT0dA0bNkxz585VdXW12rRpc87hUQCNi6VVsALGZLgmDvxfADJ5KNFi\ni5QMrg9A02Rxnj3NsomgR615o03MQnuYhzYxC+1hHlN61NhCCgAAwFAENQAAAEMR1AAAAAxFUAMA\nADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAA\nwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAA\nQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAM\nRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAU\nQQ0AAMBQgd68eXl5uSZOnKiMjAy1adNGkjR//nzZ7XalpqZKklasWKFPPvlEkpScnKy0tLQa9ygs\nLNT06dN1+eWXS5JSU1N1ww03eLNsAAAAI3gtqFVXVys3N1dBQUGSpIqKCuXk5Gj//v2y2+2SpAMH\nDig/P1/Tpk2TJD399NNKSUlRu3bt3PfZvXu3+vfvrwEDBnirVAAAACN5LagtXLhQffv21bJlyyRJ\nVVVVSktLU0FBgfuayMhITZo0SVarawS2urpaLVq0qHGfwsJCFRcXa8OGDYqJidHDDz+s4OBgb5UN\nAABgDK8EtTVr1igsLExJSUnuoBYdHa3o6OgaQS0wMFBhYWFyOp1auHCh2rdv7+5tO61jx4667bbb\n1KFDBy1dulRvvvmmhg0b5rGGs+/jbb5+P3hGm5iF9jAPbWIW2sM8JrSJV4La6tWrJUlbtmxRUVGR\ncnJyNGHCBNlstlrXnjhxQnPmzFFwcLDS09NrnU9JSVFoaKj763nz5jWohuLi4p/wCc6P3W736fvB\nM9rELLSHeWgTs9Ae5vFlm9QXCL0S1LKystxfZ2ZmatSoUXWGNKfTqRkzZqhLly6655576rxXdna2\nRowYoY4dO2rLli3q0KGDN0oGAAAwjldnfXry2Wef6csvv9TJkye1ceNGSdLgwYMVEhKilStXKj09\nXenp6Zo3b54CAwNls9k0evRof5YMAADgMxan0+n0dxHewNBn80abmIX2MA9tYhbawzymDH2y4C0A\nAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAA\nAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAA\nGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABg\nKIIaAACAoQhqAAAAhiKoAQAAGIqgBgCADznLSuXcvk3OslJ/l4JGINDfBQAA0Bw4qyrlyJspFe2Q\nKsqkMJsUFy9r+jh/lwaD0aMGAIAPOPJmSpvWS+WHJafT9bppves4cA4ENQAAvMxZVurqSatL0Q6d\nKj3k24LQaBDUAADwtoMlruHOuhwpV3XJPt/Wg0aDoAYAgLdFx7ieSavLpeEKjGnj23rQaBDUAADw\nMostUoqLr/tkXLwCIqN8WxAaDYIaAAA+YE0fJyWmSOERktXqek1MYdYn6sXyHAAA+IClVbACxmS4\nJhYcOiBFtXb1tAH1IKgBAOBDFlukREBDA3k1qJWXl2vixInKyMhQmzauByXnz58vu92u1NRUSdKK\nFSv0ySefSJKSk5OVlpZW4x4lJSV68cUXZbFYFBsbq5EjR8pqZcQWAAA0fV5LPNXV1crNzVVQUJAk\nqaKiQtOmTdPnn3/uvubAgQPKz8/X1KlTNXXqVG3evFl79uypcZ9XX31VgwYN0pQpU+R0OrVhwwZv\nlQwAAGAUrwW1hQsXqm/fvoqIiJAkVVVVKS0tTT179nRfExkZqUmTJslqtcpqtaq6ulotWrSocZ/C\nwkIlJCRIcvW4bd682VslAwAAGMUrQ59r1qxRWFiYkpKStGzZMklSdHS0oqOjVVBQ8J83DwxUWFiY\nnE6nFi5cqPbt28tut9e6n8VikSQFBwfr2LFjDaqhrvt4k6/fD57RJmahPcxDm5iF9jCPCW3ilaC2\nevVqSdKWLVtUVFSknJwcTZgwQTZb7cX+Tpw4oTlz5ig4OFjp6em1zp8OaZJUWVmp0NDQBtVQXFx8\ngdWfP7vd7tP3g2e0iVloD/PQJmahPczjyzapLxB6JahlZWW5v87MzNSoUaPqDGlOp1MzZsxQly5d\ndM8999R5r7i4OG3btk1dunRRQUGBunbt6o2SAQAAjOPX5Tk+++wzffnllzp58qQ2btwoSRo8eLBC\nQkK0cuVKpaena9iwYZo7d66qq6vVpk0b9ejRw58lAwAA+IzF6XQ6/V2ENzD02bzRJmahPcxDm5iF\n9jCPKUOfLEgGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIZqsuuoAQAA\nNHb0qAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgqEB/F9BYOBwO5eXlac+ePWrR\nooUeeeQRxcTEuM+vWrVKq1atUkBAgO677z5de+21fqy2efDUJitWrNAnn3wiSUpOTlZaWpq/Sm0W\nPLXH6WueffZZXXfddUpNTfVTpc2HpzYpKCjQkiVLJEnt27fXyJEjZbFY/FVus+CpTZYvX66PP/5Y\nVqtV9957r1JSUvxYbfOxY8cOvfbaa8rMzKxxfMOGDXrrrbdktVp1yy23qE+fPj6vjR61Bvrss890\n8uRJZWdna/DgwVqwYIH7XFlZmd577z0988wzmjx5sv72t7/p5MmTfqy2eaivTQ4cOKD8/HxNnTpV\nU6dO1ebNm7Vnzx4/Vtv01dcep73++us6evSoH6prnuprk8rKSi1atEgTJkxQdna2oqKidOTIET9W\n2zzU1yY//vij3nvvPWVnZ2vy5MmaP3++/wptRt5++229/PLLtf6/XV1drVdffVWTJ09WVlaWPvzw\nQ5WVlfm8PoJaA3399ddKSkqSJF111VXatWuX+9zOnTvVqVMntWjRQiEhIYqJiSEU+EB9bRIZGalJ\nkybJarXKarWqurpaLVq08FepzUJ97SFJn376qaxWq/saeF99bfLNN98oNjZWCxYs0FNPPaXw8HCF\nhYX5q9Rmo742admypaKiolRVVaXjx4/Tu+kjrVu31vjx42sd37dvn2JiYnTJJZcoMDBQnTp10ldf\nfeXz+ghqDVRZWamQkBD3n61Wq06dOiVJOnbsWI1zwcHBOnbsmM9rbG7qa5PAwECFhYXJ6XRqwYIF\nat++vex2u79KbRbqa4+9e/cqPz9fDz74oL/Ka5bqa5MjR45o27ZtGjp0qCZNmqR3331XxcXF/iq1\n2aivTSTXL5ljx47VhAkTdMcdd/ijxGanR48eCggIqHX87Lby1//beUatgYKDg1VZWen+s9PpdDds\nSEiIqqqq3OcqKysVGhrq8xqbm/raRJJOnDihOXPmKDg4WOnp6f4osVmprz3Wrl2rH374QVOmTNGh\nQ4cUGBio6Ohoete8rL42ufTSS3XllVfKZrNJkq6++moVFRXxC42X1dcmGzduVFlZmXJyciRJ2dnZ\n6ty5szp27OiXWpu74OBgI/7fTo9aA3Xq1EkFBQWSpO3bt+uKK65wn+vYsaO++uornThxQseOHdO+\nffsUGxvrr1KbjfraxOl0asaMGWrXrp1Gjx4tq5V/1b2tvvYYOnSopk2bpszMTPXu3Vt33XUXIc0H\n6muTDh066Ntvv1VFRYVOnTqlHTt2qG3btv4qtdmor01CQ0MVFBSkFi1aKCgoSKGhofrxxx/9VWqz\n16ZNG+3fv19Hjx5VdXW1vvrqK1111VU+r4NN2Rvo9EydvXv3yul06rHHHlNBQYFiYmJ03XXXadWq\nVfrwww/lcDh07733qkePHv4uucmrr00cDodmz56t+Ph49/WDBw/2y1+y5sLT35HTFi9eLJvNxqxP\nH/DUJh9//LGWL18uSfrFL36he+65x88VN32e2mTx4sXauHGjLBaLOnfurKFDh/Ksmg8cPHhQs2fP\nVnZ2tvLz81VVVaU+ffq4Z306HA7dcsst6tevn89rI6gBAAAYivEgAAAAQxHUAAAADEVQAwAAMBRB\nDQAAwFAENQAAAEMR1AA0Kdu2bdO4ceP8XYZXvfHGG/roo4/8XQYAH2BnAgBoZH75y1/6uwQAPkJQ\nAyA8528AAAaLSURBVOAzGzZs0NKlS1VdXa2WLVvqV7/6la666iotXrxYJSUlKi0tVdn/b+/+Qpr6\n3ziAv8/ZmjWxxKRiTVxeZIa6ygvtIpKBV0WFYf8QylVqKtRCi0AKl1Q3wUC9kNqWoPRHKCKDJHNd\nFKiRjOm0LHIp/mPWZH/MTd3zu+jn+da3/H77/sHffvC87vacc57znDMYD8/Z4TM1hcTERBQXF0Op\nVGJ4eBgWiwU+nw+CIGD37t3YuXMnAKC9vR0tLS0QRRExMTEoLS0FAMzMzMBkMmFkZASzs7MoKipC\nSkrKd7U4nU40NTUhPj4eo6OjUCgUKCkpgVqtRl1dHfx+PyYmJrBt2zbk5ubi5s2b+PjxIwBg69at\nOHz4MGQyGbq7u9HU1ARRFKHRaNDT0wOj0Yi+vj60t7cjGAxCqVTi0qVLaG9vR2trK4gIMTEx0Ov1\nWL9+Pd68eYOGhgaEw2EIgoB9+/YhKytr0XhdXR0SEhKwZ88e9Pf3o7GxEcFgEHK5HIcOHcKWLVvw\n/PlzdHV1QRAEjI+PQ6FQoLS0lFcfYOz/DTHG2BIYHR2ls2fPktfrJSKioaEhOnnyJH358oXu3r1L\nxcXF5PF4aH5+nkwmEzU0NNDc3ByVlZVRR0cHERF9+vSJioqK6O3btzQ4OEh6vZ7cbjcREbW0tFB9\nfT319vbSwYMHaWBggIiIHj16RFVVVT/U09vbSwcOHKC+vj4iImptbaXz588TEVFtbS0ZjUZp35qa\nGrJYLBQOhykUClF1dTU9ePCAvF4vFRQU0ODgIBER2Ww2ysvLo4mJCbLZbHTs2DEKBAJEROR0Ouni\nxYs0MzNDRER2u53OnDlDRERVVVX04sULIiJyuVx048aNP4zX1tbSw4cPyev10okTJ6RrHRoaIr1e\nL53/6NGjNDk5SUREZrOZampq/sE3yBj7X+CJGmNsSTgcDkxNTcFoNEqxhWkPAGRlZUkLhOt0Oty6\ndQs6nQ6hUAiZmZkAgLi4OGRmZsJut0OpVEKr1SI+Ph4AsGvXLgBfJ2Vr166Vlg/TaDSw2Ww/rUmj\n0UiTNp1OB7PZDJ/PB+DrmowL7HY7Ll++DEEQsGzZMuTk5ODx48dQqVRQq9XQaDQAgOzsbFitVum4\nxMREKJVKAEB3dzfGx8dRWVkpbff7/fD7/di+fTvMZjNev36NtLQ0HDlyBAAWjS949+4d1q1bJ11r\nQkICkpOT4XQ6IQgCkpKSsHr1agDAhg0b0NnZ+WdfE2MswnCjxhhbEuFwGKmpqTAYDFJscnIScXFx\n6Orqgkwmk+JEBFEUpUd+3yIizM3NQRTF77aFQiG43W4AgFz+20/bH62TKIq/vU9F/11NbyG2fPny\n77Z9myccDmN+fh6iKErH/SzntznC4TB27NiB/Px86bPH40F0dDRycnKQkZEBh8MBu92O5uZmmEym\nRePf5vy9hdrkcjkUCsUv3QfGWOTitz4ZY0siLS0NDocDIyMjAL5OmCoqKhAKhQAAr169wvT0NMLh\nMNra2pCRkQGVSgWZTCZNgj5//ozOzk6kp6cjNTUVPT098Hg8AICnT5+isbHxL9Xkcrmk/521tbUh\nOTkZ0dHRP+yn1Wrx5MkTEBFmZ2fx7NkzpKenY9OmTRgbG5NydHR0IBAI/LQp0mq1ePny5Xf1LkwX\nKysr4XK5kJ2djcLCQgQCAUxNTS0aX7Bx40aMjo7i/fv3AIDh4WH09/dj8+bNf+k+MMYiF0/UGGNL\nQq1Wo7CwUJoIiaKIc+fOSVOn2NhYXL16FV6vFykpKcjNzYVcLkdFRQWsViuam5sxPz+P/fv3IzU1\nFQCQn5+PK1euSMefOnUKY2Njv1xTbGwsbt++DbfbjVWrVqGsrOyn+xUUFMBisaC8vBxzc3PQarVS\nfadPn0ZtbS1EUURSUhJkMhmioqJ+yKHVarF3715UV1dDEASsWLEC5eXlEAQB+fn5sFqtuHPnDgRB\nQF5eHtasWbNofMHKlSthMBhgsVgQDAYhiiJKSkqgUqkwMDDwy/eBMRa5BPr93J4xxpbYvXv34PP5\ncPz48SU7p9PphMViwfXr1/92junpady/fx95eXmIiorChw8fcO3aNdTX1/OjRsbYv4Inaowx9jcp\nlUrI5XJcuHABMpkMcrkcBoOBmzTG2L+GJ2qMMcYYYxGKXyZgjDHGGItQ3KgxxhhjjEUobtQYY4wx\nxiIUN2qMMcYYYxGKGzXGGGOMsQjFjRpjjDHGWIT6Dxe65ebR4Qj7AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(np.linspace(0, 1, len(losses)), losses);\n", "plt.title('Validation loss with epoch')\n", "plt.ylabel('Validation Loss')\n", "plt.xlabel('epoch progression');" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.51 0.29 0.37 75\n", " 1 0.60 0.79 0.69 102\n", "\n", "avg / total 0.57 0.58 0.55 177\n", "\n", "##################################################################\n" ] }, { "data": { "image/png": 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YoHPnzvlfLygoUGxsbLnrULgAAGCbEFzHJSMjw//n9PR0DRo0SKtWrVJ2drYS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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_confusionmatrix(ytest, final_preds)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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b01SmiIjIvxiGwezZs6lbty43btzgP//5j0qZOIRGzERERG5x9epV+vTpw5Ur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\ny5YtCgwMpJQBAIA7QjG7Szt27FBsbKySkpI0Z84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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ROC(ytest, final_scores, 'Feed forward neural net', 'r')" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "image/png": 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G9flsKiy0ye0u+X22dKlLL7yQoAULDiS2nBy7wmERzACgBuEtvRzmzjU/DM8/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\ntbDjdbgFZGkxAwAAVlWuYHbDDTdo+vT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_PR(ytest, final_scores, 'Feed forward neural net', 'b')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Conclusion\n", "\n", "Although, the neural nets do not have extremely better performance than a coin flip, they can definitely give an edge during decision of investement in Apple, Inc stockes. The classical supervised machine learning classification methods perform even worse than a random guess. In this sense, the performance of deep learning methods, especially RNNs, are really satisfactory.\n", "\n", "## Future work\n", "\n", "There are definitely ways to improve the performance of deep learning approaches. First, we're not that confident whether we have used the best parameters such no layers, hidden units, etc.\n", "\n", "Second, it makes sense to collect news articles from different sources and use aggregate sentiment scores. Articles from only one source might be biased. Moreover, we could think of using most common words as features instead of sentiment scoring.\n", "\n", "All in all, better feature engineering might improve performance of deep learning methods." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }