Kyrkos_TA_2nd_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/konkyrkos/textAnalyticsSecondAssignment/blob/galanos_branch/Galanos_Kyrkos_Saltos_TA_2nd_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "liMSglMZkfVw"
   },
   "source": [
    "# 2nd Assignment: Sentiment Analysis\n",
    "\n",
    "---\n",
    ">Konstantinos Kyrkos <br>\n",
    ">Registration Number p3351907 <br>\n",
    ">kkyrkos@aueb.gr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4M05ONsfmKnZ"
   },
   "source": [
    "This notebook accompanied with the report given provides our solution to the 2nd assignment of the Text Analytics course at Data Science MSc at AUEB. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UG28TqlMaMBz"
   },
   "source": [
    "#### Install required packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ux-swtfpaOb7"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install numpy\n",
    "!pip install scikit-learn\n",
    "!pip install pandas\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "utS1TYaaa29j"
   },
   "source": [
    "#### Import all libraries for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "whMA-02VaRWF",
    "outputId": "5ab082f4-a831-40f0-e6cd-bb5c5562628d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.tokenize import WordPunctTokenizer\n",
    "from google.colab import files\n",
    "from sklearn.datasets import load_files\n",
    "from bs4 import BeautifulSoup \n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import re\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from collections import defaultdict\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from tabulate import tabulate\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import recall_score,f1_score\n",
    "from sklearn.utils import resample\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "import time\n",
    "\n",
    "nltk.download('wordnet',quiet=True)\n",
    "nltk.download('stopwords',quiet=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TnIYxnYcbHKH"
   },
   "source": [
    "#### Download a sentiment analysis dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "LlOAA9oo8tqL",
    "outputId": "5179105f-3e68-456d-cf2c-e127c1473680"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-05-27 13:44:28--  http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip\n",
      "Resolving cs.stanford.edu (cs.stanford.edu)... 171.64.64.64\n",
      "Connecting to cs.stanford.edu (cs.stanford.edu)|171.64.64.64|:80... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip [following]\n",
      "--2020-05-27 13:44:29--  https://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip\n",
      "Connecting to cs.stanford.edu (cs.stanford.edu)|171.64.64.64|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 81363704 (78M) [application/zip]\n",
      "Saving to: ‘trainingandtestdata.zip’\n",
      "\n",
      "trainingandtestdata 100%[===================>]  77.59M  21.1MB/s    in 4.5s    \n",
      "\n",
      "2020-05-27 13:44:34 (17.2 MB/s) - ‘trainingandtestdata.zip’ saved [81363704/81363704]\n",
      "\n",
      "Archive:  trainingandtestdata.zip\n",
      "  inflating: testdata.manual.2009.06.14.csv  \n",
      "  inflating: training.1600000.processed.noemoticon.csv  \n"
     ]
    }
   ],
   "source": [
    "!wget http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip\n",
    "!unzip \"trainingandtestdata.zip\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RzYXzMsBtInq"
   },
   "source": [
    "#### Load Data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "CSjcKvmraReg",
    "outputId": "3982682e-b09c-4bdb-961c-f7474bc13fdc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>query_string</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810672</td>\n",
       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>scotthamilton</td>\n",
       "      <td>is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810917</td>\n",
       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>mattycus</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Managed to save 50%  The rest go out of bounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811184</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ElleCTF</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811193</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Karoli</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all. i'm mad. why am i here? because I can't see you all over there.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment  ...                                                                                                                 text\n",
       "0          0  ...  @switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D\n",
       "1          0  ...      is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\n",
       "2          0  ...                            @Kenichan I dived many times for the ball. Managed to save 50%  The rest go out of bounds\n",
       "3          0  ...                                                                      my whole body feels itchy and like its on fire \n",
       "4          0  ...      @nationwideclass no, it's not behaving at all. i'm mad. why am i here? because I can't see you all over there. \n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_colwidth', 800)\n",
    "cols = ['sentiment','id','date','query_string','user','text']\n",
    "tweets_final = pd.read_csv(\"/content/training.1600000.processed.noemoticon.csv\",\n",
    "                 encoding = \"ISO-8859-1\", header=None, names=cols)\n",
    "tweets_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fF5yYfvftQGo"
   },
   "source": [
    "#### Remove useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "FaWss6jnmGPT",
    "outputId": "e4ce2c82-252c-42ec-87d1-4605cf069786"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Managed to save 50%  The rest go out of bounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all. i'm mad. why am i here? because I can't see you all over there.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment                                                                                                                 text\n",
       "0          0  @switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D\n",
       "1          0      is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\n",
       "2          0                            @Kenichan I dived many times for the ball. Managed to save 50%  The rest go out of bounds\n",
       "3          0                                                                      my whole body feels itchy and like its on fire \n",
       "4          0      @nationwideclass no, it's not behaving at all. i'm mad. why am i here? because I can't see you all over there. "
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_final.drop(['id','date','query_string','user'],axis=1,inplace=True)\n",
    "tweets_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "99sc24JrtV8V"
   },
   "source": [
    "#### Get a count of data to see that there are no neutral tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "fzufFrszmGSu",
    "outputId": "5e18eff1-fde6-42c4-e92d-f2ab416fcaf5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    800000\n",
       "0    800000\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_final.sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fNK-iSgvtksK"
   },
   "source": [
    "#### Data preprocessing/cleaning/preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XEY6VJ1WDNRX"
   },
   "outputs": [],
   "source": [
    "tok = WordPunctTokenizer()\n",
    "stemmer = WordNetLemmatizer()\n",
    "\n",
    "#remove references\n",
    "pat1 = r'@[A-Za-z0-9]+'\n",
    "#remove links\n",
    "pat2 = r'https?://[A-Za-z0-9./]+'\n",
    "combined_pat1 = r'|'.join((pat1, pat2))\n",
    "\n",
    "# remove all single characters\n",
    "pat3 = r'\\s+[a-zA-Z]\\s+'\n",
    "#remove hashtags, numbers\n",
    "pat4 = r'\\^[a-zA-Z!]\\s+' \n",
    "# Substituting multiple spaces with single space\n",
    "pat5 = r'\\s+'\n",
    "combined_pat2 = r'|'.join((pat4, pat5))\n",
    "\n",
    "def tweet_cleaner(text):\n",
    "    #remove html related tags\n",
    "    soup = BeautifulSoup(text, 'lxml')\n",
    "    souped = soup.get_text()\n",
    "    stripped = re.sub(combined_pat1, '', souped)\n",
    "    stripped = re.sub(combined_pat2, ' ', stripped)\n",
    "\n",
    "    try:\n",
    "        #replace possibe faulty characters\n",
    "        clean = stripped.replace(u\"\\ufffd\", \"?\")\n",
    "    except:\n",
    "        clean = stripped\n",
    "    #keep only letters\n",
    "    letters_only = re.sub(\"[^a-zA-Z!]\", \" \", clean)\n",
    "    lower_case = letters_only.lower()\n",
    "    lower_case = lower_case.split()\n",
    "    #lemmatize\n",
    "    lower_case = [stemmer.lemmatize(word) for word in lower_case]\n",
    "    lower_case = ' '.join(lower_case)\n",
    "    words = tok.tokenize(lower_case)\n",
    "    return (\" \".join(words)).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "colab_type": "code",
    "id": "QWIX2AuSTVIe",
    "outputId": "dfe7869c-ff8a-4eb5-dd8f-d77983c8e060"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time passed 38 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1220597</th>\n",
       "      <td>4</td>\n",
       "      <td>excellent good time to buy one great pricing co tax benefit hand me down to the kid in month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385323</th>\n",
       "      <td>4</td>\n",
       "      <td>hey ! you promised you d tweet me tonight hope to talk to you again soon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225205</th>\n",
       "      <td>4</td>\n",
       "      <td>who is this faceless avenger with good tidings !</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066567</th>\n",
       "      <td>4</td>\n",
       "      <td>they say love hurt lol better to learn it young</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sentiment                                                                                          text\n",
       "1220597          4  excellent good time to buy one great pricing co tax benefit hand me down to the kid in month\n",
       "1385323          4                      hey ! you promised you d tweet me tonight hope to talk to you again soon\n",
       "1225205          4                                              who is this faceless avenger with good tidings !\n",
       "1066567          4                                               they say love hurt lol better to learn it young"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set N for sample size\n",
    "N = 100000\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# shuffle\n",
    "tweets_final = tweets_final.sample(frac=1)\n",
    "\n",
    "# get a subspace of the original dataset and preproccess it\n",
    "tweets_final = tweets_final[:N]\n",
    "tweets_final['text'] = tweets_final['text'].apply(lambda x : tweet_cleaner(x))\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "print('Time passed %0.0f seconds' % elapsed_time)\n",
    "tweets_final.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ncOzCtAJFgp1"
   },
   "source": [
    "#### Change labels to (0,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZCbHQNIfShZf"
   },
   "outputs": [],
   "source": [
    "tweets_final.loc[tweets_final['sentiment'] == 4, \"sentiment\"] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "49FqdzrsFweT"
   },
   "source": [
    "#### Train, Dev & Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "5QLpSty20Uhb",
    "outputId": "b96a9ffe-ff87-4c56-bf18-b4abbc12148c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         sentiment                                                                                          text\n",
      "1220597          1  excellent good time to buy one great pricing co tax benefit hand me down to the kid in month\n",
      "1385323          1                      hey ! you promised you d tweet me tonight hope to talk to you again soon\n",
      "1225205          1                                              who is this faceless avenger with good tidings !\n",
      "         sentiment                                                                     text\n",
      "172019           0     is making cupcake and say i can t have any kelli you re a bad sister\n",
      "1405287          1                                                     you are a crackup !!\n",
      "630560           0  i tried to give you up but i m addicted muse time is running out iphone\n",
      "         sentiment                                                                                                                                   text\n",
      "424912           0                                                                                     phew ! short hair na ako anyway it okay lang naman\n",
      "1543017          1  peter and vandy a great film that truthfully portrays relationship better than any film i ve ever seen before check it out if you can\n",
      "417921           0                                                                                                                    is missing my mattt\n"
     ]
    }
   ],
   "source": [
    "total_length = len(tweets_final)\n",
    "train_set = tweets_final[0:int((total_length*7)/10)]\n",
    "print(train_set[0:3])\n",
    "dev_set = tweets_final[int((total_length*7)/10)+1:int((total_length*9)/10)]\n",
    "print(dev_set[0:3])\n",
    "test_set = tweets_final[int((total_length*9)/10)+1:total_length]\n",
    "print(test_set[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kL7jlmA0a4z"
   },
   "source": [
    "#### Some statistics on the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "7-J-LLGr0cbJ",
    "outputId": "fcc18d6b-67ff-4e1d-de1c-cb24f1af880b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average document length per sentiment is: \n",
      "sentiment\n",
      "0    13.804990\n",
      "1    12.851547\n",
      "Name: text, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "avg_doc_len = tweets_final.groupby('sentiment').text.apply(lambda x: x.str.split().str.len().mean())\n",
    "print(\"Average document length per sentiment is: \")\n",
    "print(avg_doc_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "v4y6bZAX3xa1",
    "outputId": "8cb175b1-127b-4036-8a45-fd5f6e5e23e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of documents is:  100000 .\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of documents is: \", total_length, \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "4HsYGM6e37d2",
    "outputId": "9194feb7-7e47-4970-ed6d-7069311cfd2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set contains:  70000  documents.\n",
      "Developement set contains:  19999  documents.\n",
      "Test set contains:  9999  documents.\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set contains: \", len(train_set),\" documents.\")\n",
    "print(\"Developement set contains: \", len(dev_set),\" documents.\")\n",
    "print(\"Test set contains: \", len(test_set),\" documents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DTLFdDd0EeMS"
   },
   "source": [
    "#### Macro Average - Precision, Recall & F1 scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pbD2s-j8IIFi"
   },
   "outputs": [],
   "source": [
    "def get_scores(y_true, y_scores, threshold):\n",
    "  \"\"\"\n",
    "  input:\n",
    "    y_true: array with gound truth\n",
    "    y_scores: array-like, shape = [n_samples, n_classes], the probability of the sample for each class in the model\n",
    "    threshold: classification threshold in order to classify to positive class\n",
    "\n",
    "  output:\n",
    "    precision: dictionary with scores for all classes + macro precision\n",
    "    recall: dictionary with scores for all classes + macro recall\n",
    "    f1: dictionary with scores for all classes + macro f1\n",
    "\n",
    "  \"\"\"\n",
    "  N_labels = y_scores.shape[1] # number of classes\n",
    "  \n",
    "  precision = {}\n",
    "  recall = {}\n",
    "  f1 = {}\n",
    "\n",
    "  for label in range(N_labels):\n",
    "    \n",
    "    # create array with predictions based on which class is considered positive and a threshold\n",
    "    y_pred = np.array([int(label if elem >= threshold else 1-label) for elem in y_scores[:,label]])\n",
    "  \n",
    "    # calculate True Positive, False Positive, True Negative, False Negative\n",
    "    TP = 0\n",
    "    FP = 0\n",
    "    TN = 0\n",
    "    FN = 0\n",
    "\n",
    "    for i in range(len(y_pred)): \n",
    "      if y_true[i]==y_pred[i]==label:\n",
    "        TP += 1\n",
    "      if y_pred[i]==label and y_true[i]!=y_pred[i]:\n",
    "        FP += 1\n",
    "      if y_pred[i]==1-label and y_true[i]!=y_pred[i]:\n",
    "        FN += 1\n",
    "    precision[label] = 0 if (TP + FP) == 0 else TP / (TP + FP) \n",
    "    recall[label] = 0 if (TP + FN) == 0 else TP / (TP + FN)\n",
    "    f1[label] = 0 if (precision[label] + recall[label]) ==0 else 2 * (precision[label] * recall[label]) / (precision[label] + recall[label]) \n",
    "\n",
    "  precision['macro'] = sum(precision.values())/N_labels\n",
    "  recall['macro'] = sum(recall.values())/N_labels\n",
    "  f1['macro'] = sum(f1.values())/N_labels\n",
    "  \n",
    "  return precision, recall, f1\n",
    "\n",
    "def print_scores(estimator, X, y_true, threshold):\n",
    "  y_pred = estimator.predict_proba(X)\n",
    "  prec_dict, rec_dict, f1_dict = get_scores(y_true, y_pred, threshold)\n",
    "  scores_df = pd.DataFrame.from_dict({'precision': prec_dict, 'recall': rec_dict, 'f1': f1_dict})\n",
    "  print(tabulate(scores_df.round(3), headers='keys', tablefmt='psql'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QktiYG6OXUx4"
   },
   "source": [
    "\n",
    "#### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "gtUStG3_VhWJ",
    "outputId": "784cf851-327a-4a01-8acd-37be1ffa1149"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.501 |      1   | 0.668 |\n",
      "| 1     |       0     |      0   | 0     |\n",
      "| macro |       0.251 |      0.5 | 0.334 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Baseline- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.499 |      1   | 0.666 |\n",
      "| 1     |       0     |      0   | 0     |\n",
      "| macro |       0.25  |      0.5 | 0.333 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Baseline- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.495 |      1   | 0.662 |\n",
      "| 1     |       0     |      0   | 0     |\n",
      "| macro |       0.247 |      0.5 | 0.331 |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "def print_classifier_scores(estimator, est_name, X_train, y_train\n",
    "                            ,X_dev, y_dev\n",
    "                            ,X_test,y_test\n",
    "                            , thres):\n",
    "\n",
    "  print(\"{}- Train scores:\".format(est_name))\n",
    "  print_scores(estimator, X_train, y_train, thres)\n",
    "\n",
    "  print(\"---------------------------------------------------------\")\n",
    "  print(\"{}- Dev scores:\".format(est_name))\n",
    "  print_scores(estimator, X_dev ,y_dev, thres)\n",
    "\n",
    "  print(\"---------------------------------------------------------\")\n",
    "  print(\"{}- Test scores:\".format(est_name))\n",
    "  print_scores(estimator, X_test, y_test, thres)\n",
    "\n",
    "# Create pipeline\n",
    "base_pipeline = Pipeline([\n",
    "    ('tfidf_vectorizer', TfidfVectorizer(ngram_range=(1, 2)\n",
    "                             ,max_features = 5000\n",
    "                             ,sublinear_tf=True\n",
    "                             ,stop_words=stopwords.words('english')\n",
    "    )),\n",
    "    ('clf',  DummyClassifier(strategy='most_frequent'))\n",
    "    ])\n",
    "\n",
    "# fit on training set and print scores\n",
    "base_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "\n",
    "\n",
    "\n",
    "print_classifier_scores(base_pipeline, 'Baseline', train_set.text, train_set.sentiment.to_list()\n",
    "  , dev_set.text, dev_set.sentiment.to_list()\n",
    "  , test_set.text, test_set.sentiment.to_list(),0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ezTBolOkVhyE"
   },
   "source": [
    "#### SGD Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oID8AejvaxMq"
   },
   "source": [
    "Pipeline + Gridsearch for SGD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "Y-61_FNnavnZ",
    "outputId": "870e9f47-693f-4e23-909c-079756c6df59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 131 seconds  Random GridSearch found: \n",
      "Best Score: 0.754\n",
      "Best Params:  {'tfidf_vectorizer__sublinear_tf': False, 'tfidf_vectorizer__ngram_range': (1, 3), 'clf__penalty': 'l2', 'clf__alpha': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "lr_pipeline = Pipeline([\n",
    "    ('tfidf_vectorizer', TfidfVectorizer(stop_words=stopwords.words('english'), max_features=5000)),\n",
    "    ('clf', SGDClassifier(loss=\"log\", max_iter=5000))\n",
    "    ])\n",
    "\n",
    "grid_params = {\n",
    "    'tfidf_vectorizer__ngram_range': [(1,1),(1,2),(1,3)],  \n",
    "    'tfidf_vectorizer__sublinear_tf': [True,False],\n",
    "    \"clf__alpha\" : [0.0001, 0.001, 0.01, 0.1],\n",
    "    \"clf__penalty\" : [\"l2\", \"l1\", \"none\"],\n",
    "}\n",
    "# start timing\n",
    "start_time = time.time()\n",
    "\n",
    "grid_sgd_clf = RandomizedSearchCV(lr_pipeline, grid_params, scoring='f1_macro',n_jobs=-1, random_state=94)\n",
    "\n",
    "# perform gridsearch on both training and dev set with 5-fold cross validation and save the best model\n",
    "grid_sgd_clf.fit(np.concatenate((train_set.text, dev_set.text), axis=0)\n",
    "                ,train_set.sentiment.tolist() + dev_set.sentiment.tolist()\n",
    "                )\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print('After {:.0f} seconds'.format(elapsed_time), ' Random GridSearch found: ')\n",
    "print(\"Best Score: {:.3f}\" .format(grid_sgd_clf.best_score_))\n",
    "print(\"Best Params: \", grid_sgd_clf.best_params_)\n",
    "\n",
    "# get best estimator\n",
    "sgd_pipeline = grid_sgd_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "KzCFRGtZ0I35",
    "outputId": "71de2218-9480-4bde-a409-9f48d486cb7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting ended after 5 seconds.\n",
      "SGD classifier- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.78  |    0.757 | 0.768 |\n",
      "| 1     |       0.762 |    0.785 | 0.773 |\n",
      "| macro |       0.771 |    0.771 | 0.771 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.76  |    0.74  | 0.75  |\n",
      "| 1     |       0.748 |    0.767 | 0.757 |\n",
      "| macro |       0.754 |    0.754 | 0.754 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.767 |    0.74  | 0.754 |\n",
      "| 1     |       0.744 |    0.771 | 0.757 |\n",
      "| macro |       0.756 |    0.756 | 0.756 |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "sgd_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print('Fitting ended after {:.0f} seconds.'.format(elapsed_time))\n",
    "\n",
    "print_classifier_scores(sgd_pipeline, 'SGD classifier', train_set.text, train_set.sentiment.to_list()\n",
    "  , dev_set.text, dev_set.sentiment.to_list()\n",
    "  , test_set.text, test_set.sentiment.to_list(),0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2gDnaas9c7DF"
   },
   "source": [
    "#### Random forest classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1OySd3CUh6Pa"
   },
   "source": [
    "Pipeline + Gridsearch for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "QaSeIH8sc-An",
    "outputId": "d1503eaa-f502-415e-efa3-bdfd962883b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 622 seconds  Random GridSearch found: \n",
      "Best Score: 0.711\n",
      "Best Params:  {'tfidf_vectorizer__sublinear_tf': False, 'tfidf_vectorizer__ngram_range': (1, 3), 'clf__n_estimators': 1000, 'clf__max_depth': 3}\n"
     ]
    }
   ],
   "source": [
    "rf_pipeline = Pipeline([\n",
    "    ('tfidf_vectorizer', TfidfVectorizer(stop_words=stopwords.words('english'), max_features=5000)),\n",
    "    ('clf', RandomForestClassifier(random_state=94))\n",
    "    ])\n",
    "\n",
    "grid_params = {\n",
    "    'tfidf_vectorizer__ngram_range': [(1,1),(1,2),(1,3)],  \n",
    "    'tfidf_vectorizer__sublinear_tf': [True,False],\n",
    "    'clf__n_estimators': [100, 500, 1000, 2000],\n",
    "    'clf__max_depth': [1, 2, 3, 4]\n",
    "}\n",
    "\n",
    "grid_rf_clf = RandomizedSearchCV(rf_pipeline, grid_params, scoring='f1_macro', n_jobs=-1)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "grid_rf_clf.fit(np.concatenate((train_set.text, dev_set.text), axis=0)\n",
    "                ,train_set.sentiment.tolist() + dev_set.sentiment.tolist()\n",
    "                )\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print('After {:.0f} seconds'.format(elapsed_time), ' Random GridSearch found: ')\n",
    "print(\"Best Score: {:.3f}\" .format(grid_rf_clf.best_score_))\n",
    "print(\"Best Params: \", grid_rf_clf.best_params_)\n",
    "\n",
    "rf_pipeline = grid_rf_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "-P07DZwfUDmO",
    "outputId": "2f5daeb4-8c01-4eed-81b4-2f80825953d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting ended after 16 seconds.\n",
      "Random Forest classifier- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.625 |    0.882 | 0.731 |\n",
      "| 1     |       0.797 |    0.466 | 0.588 |\n",
      "| macro |       0.711 |    0.674 | 0.66  |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Random Forest classifier- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.617 |    0.876 | 0.724 |\n",
      "| 1     |       0.789 |    0.459 | 0.58  |\n",
      "| macro |       0.703 |    0.668 | 0.652 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Random Forest classifier- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.62  |    0.883 | 0.729 |\n",
      "| 1     |       0.79  |    0.448 | 0.572 |\n",
      "| macro |       0.705 |    0.666 | 0.65  |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "rf_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print('Fitting ended after {:.0f} seconds.'.format(elapsed_time))\n",
    "\n",
    "print_classifier_scores(rf_pipeline, 'Random Forest classifier', train_set.text, train_set.sentiment.to_list()\n",
    "  , dev_set.text, dev_set.sentiment.to_list()\n",
    "  , test_set.text, test_set.sentiment.to_list(),0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rHH-h55vqkER"
   },
   "source": [
    "#### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "MgW8Zb6AjmDB",
    "outputId": "7fe1b454-510e-4b51-8cf4-7529eabcdfff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 134 seconds  Random GridSearch found: \n",
      "Best Score: 0.748\n",
      "Best Params:  {'tfidf_vectorizer__sublinear_tf': True, 'tfidf_vectorizer__ngram_range': (1, 3), 'clf__alpha': 1.0}\n"
     ]
    }
   ],
   "source": [
    "nb_pipeline = Pipeline([\n",
    "    ('tfidf_vectorizer', TfidfVectorizer(stop_words=stopwords.words('english'), max_features = 5000)),\n",
    "    ('clf', MultinomialNB())\n",
    "    ])\n",
    "\n",
    "grid_params = {\n",
    "    'tfidf_vectorizer__ngram_range': [(1,1),(1,2),(1,3)],  \n",
    "    'tfidf_vectorizer__sublinear_tf': [True,False],\n",
    "    \"clf__alpha\" : np.linspace(0, 1, 5)\n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "grid_nb_clf = RandomizedSearchCV(nb_pipeline, grid_params, scoring='f1_macro', n_jobs=-1)\n",
    "grid_nb_clf.fit(np.concatenate((train_set.text, dev_set.text), axis=0)\n",
    "                ,train_set.sentiment.tolist() + dev_set.sentiment.tolist()\n",
    "                )\n",
    "elapsed_time = time.time() - start_time\n",
    "print('After {:.0f} seconds'.format(elapsed_time), ' Random GridSearch found: ')\n",
    "print(\"Best Score: {:.3f}\" .format(grid_nb_clf.best_score_))\n",
    "print(\"Best Params: \", grid_nb_clf.best_params_)\n",
    "\n",
    "nb_pipeline = grid_nb_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "N3qUYonIuXT4",
    "outputId": "60841a3f-4822-4c86-e7f4-4abe978bd855"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting ended after 5 seconds.\n",
      "Naive Bayes classifier- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.766 |    0.786 | 0.776 |\n",
      "| 1     |       0.778 |    0.758 | 0.768 |\n",
      "| macro |       0.772 |    0.772 | 0.772 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Naive Bayes classifier- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.743 |    0.762 | 0.753 |\n",
      "| 1     |       0.758 |    0.738 | 0.748 |\n",
      "| macro |       0.75  |    0.75  | 0.75  |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "Naive Bayes classifier- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.74  |    0.767 | 0.753 |\n",
      "| 1     |       0.753 |    0.725 | 0.739 |\n",
      "| macro |       0.746 |    0.746 | 0.746 |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "nb_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print('Fitting ended after {:.0f} seconds.'.format(elapsed_time))\n",
    "\n",
    "print_classifier_scores(nb_pipeline, 'Naive Bayes classifier', train_set.text, train_set.sentiment.to_list()\n",
    "  , dev_set.text, dev_set.sentiment.to_list()\n",
    "  , test_set.text, test_set.sentiment.to_list(),0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vTXN29bhnAfM"
   },
   "source": [
    "#### Precision - Recall Curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "_cTzdA55Fkf5",
    "outputId": "5dd15500-2baf-46ca-88c1-eb25ea332f33"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_curve(estimator, estimator_name, X, y_true):\n",
    "  # predict using given estimator\n",
    "  y_pred = estimator.predict_proba(X)\n",
    "  precisions = []\n",
    "  recalls = []\n",
    "  \n",
    "  # thresholds list\n",
    "  thres = np.linspace(0,1,1000)\n",
    "\n",
    "  # keep scores for each threshold\n",
    "  for t in thres:\n",
    "    prec_dict, rec_dict, _ = get_scores(y_true, y_pred, t)\n",
    "\n",
    "    precisions.append(prec_dict['macro'])\n",
    "    recalls.append(rec_dict['macro'])\n",
    "\n",
    "  # calculate area-under-curve\n",
    "  area = auc(recalls, precisions)\n",
    "\n",
    "  # plot\n",
    "  fig, ax = plt.subplots(figsize=(12,8))\n",
    "  ax.set_title('Macro Precision-Recall Curve for %s: AUC=%0.3f' % (estimator_name,area))\n",
    "  ax.plot(recalls, precisions, marker='.', label=estimator_name)\n",
    "  ax.plot(thres,np.zeros(len(thres)), linestyle='--', label='No Skill')\n",
    "  ax.set_xlim(0,1)\n",
    "  ax.legend()\n",
    "  fig.savefig(str(estimator_name)+\"_PR_CURVE.pdf\")\n",
    "\n",
    "\n",
    "pipeline_list = [\n",
    "                [sgd_pipeline,'SGD']\n",
    "               ,[rf_pipeline,'Random Forest']\n",
    "               ,[nb_pipeline,'Naive Bayes']\n",
    "               ]\n",
    "\n",
    "for pipe in pipeline_list:\n",
    "  pipe[0].fit(train_set.text, train_set.sentiment.to_list())\n",
    "  plot_precision_recall_curve(pipe[0], pipe[1], test_set.text, test_set.sentiment.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ok-0yQWD-7n"
   },
   "source": [
    "#### Learning Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "37qeZKNGw_TH",
    "outputId": "e383b4a1-c43f-4851-b699-fba1d88055a8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lw8qOWQ/4M7AGMBjYLaV0TzvXOSSltFs7nzERmAgwYsSI7a644oqqfJeiPfPMIA48cHs+/OHnOeqox4ouZ7nmzp3LkCFDii5DPZD3hjrivaGOeG+oM0XdH7vssss9KaXx7e1r6O5i2jgAuCildHZE7AhcEhFbppRaACJiC+AMYI/2Tk4pTQGmAIwfPz5NmDChe6ouwD33wI9/vD6nnro+W21VdDWdmzZtGr35d6GV572hjnhvqCPeG+pMT7w/qtkt5Flgw7L1DUrbyh0EXAWQUroNGAgMB4iIDYDfAV9IKT1RxTprwkknwbBhcNRRDs0nSZLUU1UzXN8FbBwRYyOiP/mBxWvaHDMD2BUgIjYnh+uXImJ14DrguJTSP6pYY81Yc034znfgxhvhj38suhpJkiS1p2rhOqW0CDgMuAF4lDwqyMMRcUpE7FM67Cjg/0XEA8DlwJdS7gR+GLARcFJE3F96rVOtWmvFV78KG2+cW68XLiy6GkmSJLVV1T7XKaXrgevbbDupbPkRYOd2zvs+8P1q1laL+veHs86CffeFKVPg0EOLrkiSJEnlnKGxxuy9N+yyS+4i8tprRVcjSZKkcobrGhMBP/whvPpqnh5dkiRJPYfhugZtsw0ceCCcey48/njR1UiSJKmV4bpGff/7uQ/2sccWXYkkSZJaGa5r1HrrwXHHwW9/C//3f0VXI0mSJDBc17SjjoINN4Qjj4SWlqKrkSRJkuG6hg0aBKedlqdGv/TSoquRJEmS4brGHXAAbL89HH88zJtXdDWSJEl9m+G6xtXVwTnnwKxZeYIZSZIkFcdw3QvsvDPstx+ceSY8+2zR1UiSJPVdhute4owzYNEimDSp6EokSZL6LsN1LzF2LBx+OFx8cX7AUZIkSd3PcN2LnHACrL12HpovpaKrkSRJ6nsM173IsGFwyil5Upnf/77oaiRJkvoew3Uvc/DBsMUWcMwxsGBB0dVIkiT1LYbrXqahAc4+G554As47r+hqJEmS+hbDdS+0557w4Q/nLiIvv1x0NZIkSX2H4bqXOussmDsXTj656EokSZL6DsN1LzVuHEycCOefD48+WnQ1kiRJfYPhuhc7+WQYPDg/3ChJkqTqM1z3YmuvDSeeCNddBzfeWHQ1kiRJvZ/hupf7xjfy7I1HHgmLFxddjSRJUu9muO7lBgyAM8+Ehx6C//3foquRJEnq3QzXfcAnPwmbbAKHHAJ1dTBmDEydWnRVkiRJvU9D0QWo+i67DGbMgJaWvD59eh5JBKCpqbi6JEmSehtbrvuASZNg/vyltzU35+2SJEmqHMN1HzBjxoptlyRJ0soxXPcBo0a1v33ttbu3DkmSpN7OcN0HTJ4MjY1Lb4uA116Dm24qpiZJkqTeyHDdBzQ1wZQpMHp0DtWjR8NPfgKbbQZ77w1//nPRFUqSJPUOhus+oqkJnn46jxjy9NN5WL6bb84Be5994E9/KrpCSZKk2me47sOGD4e//AXGjYN9983TpEuSJGnlGa77uDXXzAF7q63g4x+HP/yh6IokSZJql+FarLFGfrBx223zbI6//33RFUmSJNUmw7UAWH31/GDjdtvBfvvBb35TdEWSJEm1x3Cttw0bBjfcADvsAJ/5DPzqV0VXJEmSVFsM11rK0KF55JAdd4QDDoArrii6IkmSpNphuNYyVlsN/vhH2HnnPITf1KlFVyRJklQbDNdq15AhcP318MEPwhe+AJdcUnRFkiRJPZ/hWh0aPBiuvRZ22QW++EW46KKiK5IkSerZDNfqVGNjHvt6993hy1+GCy8suiJJkqSey3Ct5Ro0CK6+GvbcEw4+GKZMKboiSZKknslwrS4ZOBB+9zv4yEfgK1+B888vuiJJkqSex3CtLhs4EH77W9h7b/ja1+B//qfoiiRJknoWw7VWyIAB8Otfw777wte/Dj/6UdEVSZIk9RyGa62w/v3z7I2f+AQcfjj88IdFVyRJktQzGK61Uvr1y7M37rcfHHUU/OAHRVckSZJUvIaiC1Dt6tcPLrsM6urgW9+CRYvg+OOLrkqSJKk4hmutkoYGuPRSqK+HE06AxYvhfe8ruipJkqRiGK61yhoa4Je/zAH729+GL31pNBMmFF2VJElS9zNcqyLq6+EXv8jvF100lg03hJNPhoiiK5MkSeo+hmtVTH19nh79hRee43vfW4/Fi+H73zdgS5KkvsNwrYqqq4Ojj/43G2ywHqeemvtgn3aaAVuSJPUNhmtVXF0d/PSnuS/2GWfkgH3mmQZsSZLU+xmuVRV1dXDeefn9rLNywD77bAO2JEnq3QzXqpoI+PGPc1/sc87JAfu//9uALUmSei/DtaoqIgfq8oD94x8bsCVJUu9kuFbVReQuIQ0NeZr0xYuXdBmRJEnqTQzX6hYR+eHG+no4/fQ8VfrPfmbAliRJvYvhWt0mAk49NQfsyZOhpQUuuMCALUmSeg/DtbpVBHzvezlgn3JK7iJy4YV5XZIkqdYZrtXtIvLU6PX18J3v5IB90UUGbEmSVPsM1yrMSSflQH3iiTlg//KX+aFHSZKkWlXV3q4RsVdE/DsiHo+I49rZPyoibomI+yLiwYj4SNm+40vn/Tsi9qxmnSrOpEl5evTLL4fPfS4/6ChJklSrqtZOGBH1wHnA7sBM4K6IuCal9EjZYScCV6WUzo+IccD1wJjS8v7AFsD6wE0RsUlKaXG16lVxjjsut1gfc0xuwb7sMujXr+iqJEmSVlw1/xF+B+DxlNKTABFxBbAvUB6uEzC0tDwMmFVa3he4IqW0AHgqIh4vXe+2KtarAh19dO4icuSROWBfcQX07190VZIkSSummuF6JPBM2fpM4D1tjvku8OeI+DowGNit7Nzb25w7su0HRMREYCLAiBEjmDZtWiXq1iqaO3fuSv0utt0WDj10JOedtzFbbvkGs2f356WXBrDOOgs4+OAn2W23FytfrLrVyt4b6v28N9QR7w11pifeH0U/PnYAcFFK6eyI2BG4JCK27OrJKaUpwBSA8ePHpwkTJlSnSq2QadOmsbK/iwkTYO5cuPjioW9ve+GFgZxzzjg233wcTU2VqVHFWJV7Q72b94Y64r2hzvTE+6OaDzQ+C2xYtr5BaVu5g4CrAFJKtwEDgeFdPFe9VHt/AW1uzg8/SpIk9WTVDNd3ARtHxNiI6E9+QPGaNsfMAHYFiIjNyeH6pdJx+0fEgIgYC2wM3FnFWtWDzJixYtslSZJ6iqqF65TSIuAw4AbgUfKoIA9HxCkRsU/psKOA/xcRDwCXA19K2cPkFu1HgD8BhzpSSN8xalT72+vr4cYbu7cWSZKkFVHVca5TStenlDZJKb0zpTS5tO2klNI1peVHUko7p5S2Tiltk1L6c9m5k0vnbZpS+mM161TPMnkyNDYuvW3AABg+HPbYA77wBXj55WJqkyRJ6kxVw7W0MpqaYMoUGD06T5U+ejRceCE89VSezfHyy2GzzeCSSyCloquVJElawnCtHqmpCZ5+Glpa8ntTEwwcCN/7Htx3H2yySW7B3mMPeOKJoquVJEnKDNeqOVtuCX//O5x3HtxxB2y1FZx5JixcWHRlkiSprzNcqybV1cHXvgaPPgp77gnHHgvbbw933VV0ZZIkqS8zXKumjRwJv/sd/Pa38NJL8N73wuGHw5w5RVcmSZL6IsO1eoWPfxweeQQOOQTOPRe22AKuu67oqiRJUl9juFavMWxY7of997/D0KHwX/8Fn/kMPP980ZVJkqS+wnCtXmenneDee/PIIr//PWy+OVxwQR55RJIkqZoM1+qV+vfPY2I/+CBsvTVMnAi77AL/+lfRlUmSpN7McK1ebdNN4ZZb8iQ0//xnDtqnnAILFhRdmSRJ6o0M1+r1IuDLX87D9n3iE/Cd78C22+a+2ZIkSZVkuFafMWJEnjr9uutg3jx4//vhq1+F118vujJJktRbGK7V53zkI/Dww3DEETBlCowbB7/5DaRUdGWSJKnWGa7VJw0ZAj/8YZ4+fcQI+NSn4GMfg2eeKboySZJUywzX6tPGj89Tpv/gB3DjjbkV+3/+BxYvLroySZJUiwzX6vMaGuDoo3NXkZ12gq9/HXbeOY8uIkmStCIM11LJ2LHwpz/BpZfCE0/Au98NJ5wAb75ZdGWSJKlWGK6lMhHQ1JQnm2lqgtNOg3e9C26+uejKJElSLTBcS+1Yay246CK46aY8isiuu8KBB8IrrxRdmSRJ6skM11Indt01970+7ji45BLYbDOYOtVh+yRJUvsM19JyDBqUu4fcey+84x3wuc/Bhz8MTz1VdGWSJKmnMVxLXfSud8Gtt8K558I//gFbbAFnnZVbtMeMgbq6/D51atGVSpKkojQUXYBUS+rr81B9H/sYHHooHHNMfgiytZvI9OkwcWJebmoqrk5JklQMW66llbDhhnD11TB8+LL9r5ubYdKkYuqSJEnFMlxLKymi49FDZszo3lokSVLPYLiWVsGoUe1vr6/PE9JIkqS+xXAtrYLJk6GxceltAwbAmmvmEUX22gseeqiY2iRJUvczXEuroKkJpkyB0aNzN5HRo+HCC+GZZ+CHP4Q77oCtt4ZDDoEXXyy6WkmSVG2Ga2kVNTXB009DS0t+b2qC/v3hiCPg8cfzqCI//zlstBGccQbMn190xZIkqVoM11IVrbVWHhf7oYdgwoQ80+Nmm8GVVzrLoyRJvZHhWuoGm20G11wDN90Ew4bB/vvDzjvnbiOSJKn3MFxL3WjXXfM06j//eZ4+/b3vhc9+Nk8+I0mSap/hWupm9fVw0EHw2GNw4onwu9/BppvCCSfAnDlFVydJklaF4VoqyGqrwfe+l0P2pz4Fp52WH3q84AJYvLjo6iRJ0sowXEsF23BDuPTS3P96441h4kTYdlu48caiK5MkSSvKcC31EDvsAH/7G/zqVzB3LuyxB3z0o/Doo0VXJkmSuspwLfUgEbmLyCOPwJlnwt//DlttBYcdBi+/XHR1kiRpeQzXUg80cCAcc0yehOYrX4Gf/jT3xz7rLFiwoOjqJElSR7oUriNiUERsWu1iJC1t7bXhvPPgwQfzuNjHHAPjxsFvfuMkNJIk9UTLDdcRsTdwP/Cn0vo2EXFNtQuTtMS4cXDddXDDDTBoUO468oEPwF13FV2ZJEkq15WW6+8COwCvA6SU7gfGVrEmSR3YYw+4/3742c/yEH477ACf/zzMnFl0ZZIkCboWrhemlGa32eY/SEsFaWjIw/X95z9w/PF5dJFNNoGTTsqjjEiSpOJ0JVw/HBGfBeojYuOI+DFwa5XrkrQcQ4fCqafCv/4F++6bJ6TZeGP43/91EhpJkorSlXD9dWALYAFwGTAbOLyaRUnqujFj4PLL4dZb8/JBB8H48XDzzUVXJklS39NpuI6IeuC6lNKklNL2pdeJKaX53VSfpC7accccsC+/HF59FXbdNbdoP/ZY0ZVJktR3dBquU0qLgZaIGNZN9UhaBRGw//65q8hpp8Ett8AWW8Dhh+fALUmSqqsr3ULmAv+MiAsj4tzWV7ULk7TyBg2C447LDz0edBD8+Md5Epr//m+4+OLcfaSuLr9PnVp0tZIk9R4NXTjmt6WXpBozYkSe3fHQQ+Goo+CII3LrdusENNOn55FHAJqaiqtTkqTeYrnhOqV0cUT0BzYpbfp3SmlhdcuSVElbbZUnoFl3XXjxxaX3NTfDCScYriVJqoSuzNA4AfgPcB7wE+CxiPhAleuSVGER8NJL7e+bMSP31Z461b7ZkiStiq50Czkb2COl9G+AiNgEuBzYrpqFSaq8UaNyV5C2Bg+GadPgyitzX+ydd4a9986vTTfNwVySJC1fVx5o7NcarAFSSo8B/apXkqRqmTwZGhuX3tbYmKdTnzUL7rgjdxF54w341rdg883z7I9HHJHHzV5ohzBJkjrVlXB9d0T8PCImlF4XAHdXuzBJldfUBFOmwOjRuTV69Oi83tSUW6x32CHP9Hj//bmryE9+kmd9PP/8PG728OHwmc/ApZfCK68U/W0kSep5utIt5KvAocA3Sut/I/e9llSDmpq69vDihhvCV7+aX/PmwU03wR/+ANdeC1ddlcP4Tjst6T6y2WZ2H5EkqSst1w3Aj1JKn0gpfQI4F6ivbnPmUgYAACAASURBVFmSepLBg/Nsjz//ee4+cuedMGkSzJ0Lxx4L48blcbQPPxz+8hd4662iK5YkqRhdCdd/AQaVrQ8CbqpOOZJ6uro62H57OOUUuO8+eOaZ3G1ks83ymNq77QZrrw2f/jRccondRyRJfUtXuoUMTCnNbV1JKc2NiMbOTpDUd2ywARxySH7Nm5dbrlu7j/zqVzmM77hj7joyYkQjKdl9RJLUe3Wl5XpeRLy7dSUitgPerF5JkmrV4MGwzz5wwQXw7LNw111w4ol5oprjjoMDD9yBjTaCb34z9+G2+4gkqbfpSrg+HPhVRPwtIv4OXAkcVt2yJNW6ujoYPx5OPhnuvTd3HzniiH+z+eZ5hJLdd8+jj+y3H/zyl/Dyy0VXLEnSqltuuE4p3QVsRh415BBg85TSPdUuTFLvssEGsM8+z3Httbkf9jXX5Fkh//EP+OIXYZ118uQ1p58ODz8MKeUZI8eMyUF9zJi8LklST9aV6c/3I/e7fgj4GHBleTcRSVpRjY25D/aUKTBzJtx9N5x0EsyfD8cfD1tumcP2l76UZ5RMKb9PnGjAliT1bF3pFvLtlNKciHgfsCtwIXB+dcuS1FfU1cF228F3vwv33JPD9s9+lh+OXLRo6WObm/MMkpIk9VRdCdeLS+8fBS5IKV0H9K9eSZL6spEjcwv1/Pnt758xIz8c+dBD3VuXJEld0ZVw/WxE/Az4DHB9RAzo4nlExF4R8e+IeDwijmtn/zkRcX/p9VhEvF6278yIeDgiHo2IcyMcvEvqS0aNan/7oEFw1lmw1VawzTZw9tl5YhtJknqCroTkTwM3AHumlF4H1gSOWd5JEVEPnAd8GBgHHBAR48qPSSkdkVLaJqW0DfBj4Lelc3cCdgbeBWwJbA98sKtfSlLtmzw5980u19iYh/mbNQvOPRcGDICjj84PS+6+O1x8McyZU0y9kiRB10YLaU4p/Tal9J+ImJhSei6l9OcuXHsH4PGU0pMppbeAK4B9Ozn+AODy1o8FBpK7nwwA+gEvdOEzJfUSTU35gcfRo/OkM6NH5/Wmpvyw49e/DnfcAf/+N3z72/Dkk/kByBEj4IAD4PrrYeHCor+FJKmv6VL3jjKHrMCxI4FnytZnlrYtIyJGA2OBmwFSSrcBtwDPlV43pJQeXcFaJdW4piZ4+mloacnvTU3LHrPJJnks7ccfh1tvhQMPhD//GT760dx/+xvfgDvvzCOOSJJUbZFW4P84EXFfSmnbLh77KWCvlNLBpfXPA+9JKS0zAU1EHAtskFL6eml9I+BH5H7eADcC30op/a3NeROBiQAjRozY7oorrujyd1H1zJ07lyFDhhRdhnqg7ro3Fi4M7rxzTW68cQS33jqchQvr2GCDZnbb7QV23/0F1l+/g6clVRj/3FBHvDfUmaLuj1122eWelNL49vataLjeIKU0s4vH7gh8N6W0Z2n9eICU0mntHHsfcGhK6dbS+jHksbW/V1o/CZifUjqzo88bP358uvvuu7v8XVQ906ZNY8KECUWXoR6oiHtj9mz4zW/g0kth2rTcgr3TTvC5z8GnPw1rrdWt5agD/rmhjnhvqDNF3R8R0WG4XqFuIa3BOiIO7MLhdwEbR8TYiOgP7A9c005xmwFrALeVbZ4BfDAiGiKiH/lhRruFSFphw4bBl78MN9+cJ6I5/fQcuL/2NVhvPdh3X/j1rzse+k+SpBWxon2uW528vANSSouAw8gjjTwKXJVSejgiTomIfcoO3R+4Ii3dhP5r4Angn8ADwAMppT+sZK2SBMCGG8Kxx8I//wn33w/f/GaeHXK//fKDkAcfnFu3W1qKrlSSVKsaOtoREQ92tAsY0ZWLp5SuB65vs+2kNuvfbee8xcBXuvIZkrSiImDrrfPr9NNzoL7kErjySrjwwhzCP/tZ+PznYYstiq5WklRLOmu5HgF8Adi7ndcr1S9Nkqqvvh523RUuugheeAEuvxze9a48Uc2WW8K22zpRjSSp6zoL19cCQ1JK09u8ngamdUt1ktSNGhth//3h2mtzmP7xj52oRpK0YjoM1ymlg1JKf+9g32erV5IkFW+ddeCww+D227s+Uc3UqTBmDNTV5fepUwsqXpJUmA7DdUR8omx5je4pR5J6nq5MVHPKKTBxYh6RJKX8PnGiAVuS+prOuoWcWLb8l2oXIkk9XQTsuCOcdx489xxcfTVMmJCnZf/Od6C5eenjm5th0qRCSpUkFaTD0ULIo4K0tyxJfV7//rDPPvk1ezasvnr7x02fDh/4QO4mMnZsfm99bbghNHT2p7AkqeZ09sf6oIjYlty6PbC0/HbITindW+3iJKkWDBsGo0fnIN3W4MG5xfuvf81dRMrH0K6vzw9Klgfu8gA+cqThW5JqTWd/bD8H/LC0/HzZMkACPlStoiSp1kyenPtYl3cNaWyEn/0Mmpry+ltvwcyZ8PTT+fXUU0uW//IXePbZ3F+7VX19bt1u2+Ldur7++vkYSVLP0WG4Tint0p2FSFItaw3QkybBjBkwalQO3K3bIXclecc78qs9CxbAM8+0H75vuGHZsbYbGvLntNflZOzYPL17XQdP1kyd2nmtkqSV4z84SlKFNDWtWkAdMAA22ii/2jN/fg7DrYG7PIBfdx08//zSx/fvvyR8lwfwxx6DM8+EN9/Mx7WObNL6HSRJK89wLUk1YuDAPCzgJpu0v//NN3NQbi98X3MNvPhix9duboavfS0/nLnBBktew4d33PotSVqW4VqSeolBg2CzzfKrPfPm5fC95ZZL9+1u9cYbcOihS2/r3z8/WFkeuNu+Royw77cktepSuI6IfYAPlFb/mlL6Q/VKkiRVw+DBMG5c7irS3sgmo0blGSlnzmz/dccd8Jvf5Aczy9XX54cr24bu8lC+/vrQr1/3fE9JKtJyw3VEnAbsALTOM/aNiNgxpXRCVSuTJFVFRyObnHpqfghyvfVg++3bPzcleOWVjgP4gw/m/t9tJ9SJyC3cHbV+P/vsQObPz11f2uMDmJJqRVdarj8KbJNSagGIiIuB+wDDtSTVoK6MbNKRiNwPe/hw2Gab9o9JKffd7iiA/+c/cMst+Zgl3svnPpev2zZ4z5gBF1+cR1MBH8CU1LN1tc/16sCrpeVhVapFktRNVnVkk85E5BkrV1899+/uyJw5eWzvmTPhppv+xZAhmy0Vwm+/HV5+uf1zm5vh4IPzEIUjR+bX+usvWV53XSfgkVSMrvzRcypwX0TcQp6h8QPAcVWtSpLU66222pIHMBsanmfChGWfxHzzzdxXvL0HMOfPzzNfzpoFixYtva+1G0rb0N12eY018rGSVCmdhuuIqANagPcCrT3wjk0pPd/xWZIkVcagQR0/gDl6dB5msKUlt3A/+2x+zZq19PL06XDrrbmveFsDBy4/gK+/fsd9wdtj/3Cpb+s0XKeUWiLiWymlq4BruqkmSZLe1tEDmJMn5+W6OlhnnfzadtuOrzN/Pjz33LLhu3X57rvh6quXTK5Tbq21Og/gI0fC2mvD5ZcvXav9w6W+pyvdQm6KiKOBK4F5rRtTSq92fIokSZWxKg9glhs4MM9SOXZsx8ekBK+/3nEAnzULHngAXnght5iXa2jI5y9evPT25mY4/PDcTaX1YdC11sqt8pJ6n66E68+U3sunFkjAOypfjiRJy6rmA5jlInI/7DXWgC226Pi4RYtywG4bvk87rf3jX34Zdt996W2NjUvCdmvgLl9vu22ttVase4qkYiw3XKeUOvk7viRJfU9Dw5JuIeUuu6z9/uHrrQdXXplD9ssv5/7frcutryefzO+vv97x5w4ZsvwQXr5trbVgwIDOv0st9RGvpVqhtuqtpVqhvN4P9rh6uzKJzKHA1JTS66X1NYADUko/qXZxkiTVko76h//gB/D+93ftGgsXwquvdhzCy7f95z/5/Y03Or7eaqt1HMKfeCKHlNZZN6dPh4MOgscegz32WPmfQyX9859D6dcP/vxnOOOMpcc772m1lqulemupVmhbb/S4ZxsitTe+UfkBEfenlLZps+2+lFInj410v/Hjx6e777676DIETJs2jQkTJhRdhnog7w11pDfdG0W0AL711pJA3lnrePn2OXOqW5PU3VpHEOoOEXFPSml8e/u60ue6PiIilVJ4RNQD/StZoCRJvUV39Q8v179/njhn3XW7fs6CBfmhyvba2CLyBD09wQMPPMDWW2/Nnnv2/FrL1VK9tVQrdFzvjBndX0t7uhKu/wRcGRE/K61/pbRNkiTVqAEDOh5DfNSoZR/ALEq/fq8xYUJt1FquluqtpVqh83p7grouHHMscAvw1dLrL8C3qlmUJEmqvsmTc5/wcuVjiPcktVQr1Fa9tVQr9Px6lxuuU0otKaXzU0qfKr1+llJavLzzJElSz9bUBFOm5L6qEfl9ypSe8VBYW7VUK9RWvbVUK7StN/W4ersyWsjGwGnAOODtETZTSo5zLUlSjSuij/jKqqVaobbqraVaYUm906b9tcc9DN2VbiG/AM4HFgG7AL8ELq1mUZIkSVIt6kq4HpRS+gt52L7pKaXvAh+tblmSJElS7enKaCELIqIO+E9EHAY8CwypblmSJElS7elKy/U3gUbgG8B2wOeBL1azKEmSJKkWLbflOqV0V2lxLnBgdcuRJEmSaleH4ToirunsxJTSPpUvR5IkSapdnbVc7wg8A1wO3AFEt1QkSZIk1ajOwvW6wO7AAcBngeuAy1NKD3dHYZIkSVKt6fCBxpTS4pTSn1JKXwTeCzwOTCuNGCJJkiSpjU4faIyIAeQxrQ8AxgDnAr+rflmSJElS7ensgcZfAlsC1wMnp5Qe6raqJEmSpBrUWcv154B55HGuvxHx9vOMAaSU0tAq1yZJkiTVlA7DdUqpKxPMSJIkSSoxQEuSJEkVYriWJEmSKsRwLUmSJFWI4VqSJEmqEMO1JEmSVCGGa0mSJKlCDNeSJElShRiuJUmSpAoxXEuSJEkVYriWJEmSKsRwLUmSJFWI4VqSJEmqEMO1JEmSVCGGa0mSJKlCDNeSJElShRiuJUmSpAoxXEuSJEkVYriWJEmSKsRwLUmSJFWI4VqSJEmqEMO1JEmSVCFVDdcRsVdE/DsiHo+I49rZf05E3F96PRYRr5ftGxURf46IRyPikYgYU81aJUmSpFXVUK0LR0Q9cB6wOzATuCsirkkpPdJ6TErpiLLjvw5sW3aJXwKTU0o3RsQQoKVatUqSJEmVUM2W6x2Ax1NKT6aU3gKuAPbt5PgDgMsBImIc0JBSuhEgpTQ3pdRcxVolSZKkVVbNcD0SeKZsfWZp2zIiYjQwFri5tGkT4PWI+G1E3BcRPyi1hEuSJEk9VtW6hayg/YFfp5QWl9YbgPeTu4nMAK4EvgRcWH5SREwEJgKMGDGCadOmdVO56szcuXP9Xahd3hvqiPeGOuK9oc70xPujmuH6WWDDsvUNStvasz9waNn6TOD+lNKTABHxe+C9tAnXKaUpwBSA8ePHpwkTJlSkcK2aadOm4e9C7fHeUEe8N9QR7w11pifeH9XsFnIXsHFEjI2I/uQAfU3bgyJiM2AN4LY2564eEWuX1j8EPNL2XEmSJKknqVq4TiktAg4DbgAeBa5KKT0cEadExD5lh+4PXJFSSmXnLgaOBv4SEf8EArigWrVKkiRJlVDVPtcppeuB69tsO6nN+nc7OPdG4F1VK06SJEmqMGdolCRJkirEcC1JkiRViOFakiRJqhDDtSRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JkiRViOFakiRJqhDDtSRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JkiRViOFakiRJqhDDtSRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JUiU9NRV+PwYuq8vvT00tuiJJ3aih6AIkSeo1npoKd06Exc15vXl6XgcY21RcXZK6jeFakqRKaJ4F9x6xJFi3WtwMd34FXr4N+g+DfmWv/u0sNwyBiO6r+6mp8MAkaJ4BjaNg68k99y8CtVQr1Fa9tVQrvF3vB5tnwO97Vr2Ga0mSVkRK8OYsePWepV/zn+/4nMXzYPplsHA2pJbOrx910DC0a0G8o30NQ/J1lqeWWtprqVaorXprqVZYqt6AHlev4VqSpI6kBG8+206QfiHvjzoYujmstwesuR08fOqSfeUaR8PHns7XWzQvh+yFs+Gt2UuW266XL785E2Y/vGQ9LV5O4QH9hi4/iD/6g/Zb2u89EgYMr8RPcJWtMf8BmLUg19TDa11KLdVbS7VCx/U+MMlwLUlSj5ESNM9cOkS/dg/MfzHvjzoYOg7W2ysH6TW3gzW2hobBS67Rf62lWwAB6hvzP1lD7u7Rb0h+MXLl61zc3HEQ72j9zVnwxr+WbGtZ2PFnLHgRpu21cvVV2NYA0zo5oAfV2iW1VG8t1Qq5S0sPYLiWJPU9KUHzM8u2SC94Ke+Pehg2Dtb/CKxRHqQbO79ua6tZNfuuRuRA3zAYWH/lrpESLJ4Pf9gkt4q3NXAEvP93q1Rmpdx73728e9t3w98+3v6/CvSgWpdSS/XWUq3Qcb2No7q/lnYYriWpUmrtgaC+IqX8O1kmSL+c97cG6ZH/taRFevV3LT9Id2RsU8//vUdAwyDY5vT2W9q3PRvW3rG4+sq80X9BrmXbs3t8rUuppXprqVbouN7WfyEqmOFakiqh1h4I6q1SgnnTl+3aseCVvD/qYdgWMHLvsiC9dQ6afVF3tLRXSi3VCrVVby3VCkvVm5pnED2sXsO1JK2KlsUw+yG45+vtP2Bzx0Hw1C9L/4zfmFtXWpcbBpfWG6G+nW1vL5f21Q2o3BBttdbK3l69Yz4L855etkX6rVfzOdFQCtL7tmmR7qNBuiO10NLeqpZqhdqqt5Zqhbfr/eu0aUyYMKHoapZiuJakFbFwDrx8O7x8K7z0j7y8aE7Hx7csKI32MCuH7UXNebSIxfOWPyRbW1G3bBjvKKyXB/O2216+Df51DrTMz9dtng53HgxvPg8b7N1JAalrdaYuHld2zcaF02H2o+0f8uy18M+Tch9hyPXe9oU8dvTieXlbNMDqW8KGHy/1j94O1ngX1A9cgVokadUZriWpI61dDFqD9Ev/gNn/LIXigNW3grGfg+E7w/3fygG6rcbRsOft7V+75a02gbvt8ry8Xr5cftzism3zX1j23MVvdv27Lp4P9x+dXwXYAeC6FTmjJbfib39+qUV6K4O0pB7BcC1JrVoWwmv3l1qkS4G6NTA3DIHh74UNvg1r75SX+w1d+vwVecAmAuoH5Ff/NarzfVJLDs1tw/oNO9BhK/ROU5dz0a52S1mB7isRPPzIw2wxbov29//jANqtd9E82PiQrn+OJHUDw7WkvmvBq7mLRGuQfuXOJa29g0fDOh+EtXfOr2FbQl0nf2T2xAeCoq7UHaTNqBeNo3LXirYaR+d+zAV46alpMHpC+zvvO7aDenvGsFuSVM5wLalvSAnm/GfpLh5vlPr4Rj2ssS1sNDEH6eE7QeNKTPBRKw8EbT25Rw9jtYxaq1dSn2a4ltQ7LZ6fR454u4vHrUsmCOm3eu7aMaYph+m1tl96lr3erie2snem1uqV1KcZriX1Dm++UDaCx605WLe8lfettnGeaa+1i8fQzXKXib6sVlrZW9VavZL6LMO1pJ6ro7GYUwvMfmTpLh5zn8jn1PWHNcfDpt8sdfHYEQauU+z3kCT1GYZrST1TezMe3n4gPHJmXl44O28fsHYO0Rt9Jb+vuV0egUOSpAIYriUVr2UhND+Tx5Se+3R+/9dZy854mBbmhxDf8aUlXTyGvLNysxZKkrSKDNeSqm/Rm9A8gzXm3wWPP5bD87zpeerqedOh+VmWHsc46HAc5rQI3jOl+jVLkrQSDNeSVt3COcsG5vLl+S8AsDXAneSh7xo3zGNJj9g1vw8eDYPH5PfGDeEPmzi2sSSp5hiupb6mo4cEO5ISLHx9SXeN9sLzW68ufU7dABg8KgflkXu/HZzve+xVtn3fx2DQ+p1PyAKObSxJqkmGa6kvae8hwTsn5ocD1xyf198O0WXhedGcpa/TMLjUwjw6TwNe3uo8eDQMHNHuUHezp0/LobsrHNtYklSDDNdSX/LApGUfElzcDHcfuvS2fqvnkDzkHTDiQ6XlMUsC9YC1uuchQsc2liTVGMO11BcsnAszrmy/D3OrD1yzpOW5/7Duq02SpF7EcC31VinBq3fDEz+Hpy+DRXMhGvJoG201joYN9u7+GiVJ6mUM11Jv89ZruW/1Ez+H1x/IDwGO/gy882CY+5QPCUqSVEWGa6k3SAle+hs8fgE882tYPD/PVLj9T2H0/ku6eay9U373IUFJkqrCcC3VsvkvwpMX51bqOY9Bv6Hwji/nVuo1t23/HB8SlCSpagzXUq1pWQzP3wRPXAAzr859qNd+H2wxCUZ9Choai65QkqQ+y3At1Yp5z8CTv4AnLsxdOgYMh02/Ce88CIZtXnR1kiQJw7XUs7UshGevy63Uz/0JUgusuzu8+ywYuQ/UDyi6QkmSVMZwLfVEcx7PLdRPXgTzn8/ThY87Ad75ZRgytujqJElSBwzXUk+xeD4889v8cOILt0DUw/ofhY3+H6y3F9T5n6skST2d/7eWivb6QzlQP3UJvPUqDB5bGh7vS9C4ftHVSZKkFWC4lorQOh354z+HV26Huv6wwcdzK/WIXSDqiq5QkiStBMO11F3am4586Obw7h/CmM/DwOFFVyhJklZRVcN1ROwF/AioB36eUjq9zf5zgF1Kq43AOiml1cv2DwUeAX6fUjqsmrVKVdPZdOTDd4SIoiuUJEkVUrVwHRH1wHnA7sBM4K6IuCal9EjrMSmlI8qO/zrQdkq57wH/V60apYp4auqy04mP+WwH05GfD6MPWDIduSRJ6lWq2XK9A/B4SulJgIi4AtiX3BLdngOA77SuRMR2wAjgT8D4KtYprbynpsKdE2Fxc15vng63Hwj3Hg0Lnu/adOSSJKnXqGa4Hgk8U7Y+E3hPewdGxGhgLHBzab0OOBv4HLBbFWuUVlzLIpj/Yh5/+t4jlwTrVmkhLHwN3nux05FLktTH9JQHGvcHfp1SWlxa/xpwfUppZnTSHzUiJgITAUaMGMG0adOqXae6YO7cubX3u0gt9GuZQ/+WV+nX8ir9F79K/5bXSu9l6y2v0q9lNkFazuXe4q8zRsGMO7vpC9SGmrw31C28N9QR7w11pifeH9UM188CG5atb1Da1p79gUPL1ncE3h8RXwOGAP0jYm5K6bjyk1JKU4ApAOPHj08TJkyoUOlaFdOmTWOVfxft9WMe27Ri10gJFs2BN5/PrczzX1iyvMy2FyAtWvYa9QNh4LrQuC4M2iovD1wXBpXe7zokn9tGNI5a9Z9BL1SRe0O9kveGOuK9oc70xPujmuH6LmDjiBhLDtX7A59te1BEbAasAdzWui2l1FS2/0vA+LbBWr1Ye/2Y75yYl8c2waI3c6BdbmB+Hha/uez1ox4GjlgSkld/19KBedC6S/b3G9r5aB6L5i1dK+TRQLaeXLmfhyRJqhlVC9cppUURcRhwA3kovv9NKT0cEacAd6eUrikduj9wRUqp839nV9/xwPHL9mNe3Ay3fwnuPhQWzm7/vAHDl4TjtXfOAXlQm5bmgSNgwFqVm6SltTV9VVvZJUlSr1DVPtcppeuB69tsO6nN+neXc42LgIsqXJp6ipZFMPsReOXOJa/mZ9o/Ni2CsV8oC8rloXltqOvXvbW3GttkmJYkSUDPeaBRfUFKMG/60kH61XuWtFL3XwPW2gHmPd1+63TjaBh/breWLEmStCIM16qeBa/AK3ctHaYXvJT31Q2ANbYtzVL4nhyqh7wz929u2+ca7McsSZJqguFalbHoTXjtPnjlTjZ/7Q9wzcEw94nSzoBhm8PIj+YQvdYOMGwrqO/f/rX+f3t3Hl1VdfZx/PsQYmIgBAUBAYXEUkAgA4QgaCACBYoWpJYXEUXAeSAqFWvFVlartU7FoVaIrVJoqoID2kKLtiQyIwEDEkEQiDRoKaaCQRrAZL9/3EsMmQjh3pwMv89arNyzzz7nPOdmkzzZd5+9NY5ZRERE6ikl13Lqiovgq60n9kgf+LBkKruoJudAh2T4zo2+RPrsPr5ZN06FxjGLiIhIPaTkWqrmnO8BwxPGSWf5pqADCI2CVn3hwnv9iXRf1r6/nZTkFE/DFhEREfGCkuvGorqLshz9svw46eOLpDQ5A86Kh5jJ3w7viOxSwbR224N+OyIiIiJ1kZLrxqCyRVncMWjR/cREuqBUYtyiG5w7/NtEumUshIR5cw8iIiIi9YCS68Zg04xKFmWZ/O32medCq34QM8k/vCMRzoiq1TBFRERE6jsl143B4T2V70t+w5dMR3SovXhEREREGqgArQEtdVrEeZWUd4LzxiixFhEREQkQJdcNnXMQ1aN8uRZlEREREQk4JdcN3dbH4fO/Qbvv+WYJwXw91klpmkdaREREJMA05roh2/kiZP8EOl0FA9IrmDJPRERERAJJ2VZDlfc2vH8jtBsGF/1RibWIiIhILVDG1RD9ZwWsGuebTi/5dQg5w+uIRERERBoFJdcNzZeb4b0fQLNOMGgxhDb3OiIRERGRRkPJdUNyaDdkDIemzeHSdyC8tdcRiYiIiDQqeqCxofjfPlg2DIqPwNAV0Ox8ryMSERERaXSUXDcEx76CzO/D//bC4H9CywrmtRYRERGRoFNyXd8VFcLyK+DAhzDobTinv9cRiYiIiDRaSq7rs+IiWH0N7MuA/vOh/fe9jkhERESkUdMDjfWVc5B1O/zrdej9G4i+xuuIRERERBo9Jdf11YcPwidz4ML7oNvdXkcj8uNoYAAAGY9JREFUIiIiIii5rp8+/i1s+SXETIG4X3kdjYiIiIj4Kbmub3JfgQ2p0HE0JM0BM68jEhERERE/Jdf1yefvwNqJ0CYZBrwMTfQ8qoiIiEhdouS6vvjifVjxQ2jRHQa+BU3P9DoiERERESlDyXV9cHAbvDcSwtrApX+HM1p6HZGIiIiIVEDJdV13OA8yhoOFwOB34MxzvY5IRERERCqhQbt12ZH/+hLro1/C0Pcg8jteRyQiIiIiVVByXVd98zW8dzkUfAKXLoWzE7yOSEREREROQsl1XVR8DFaMhfx1cMlCaJvidUQiIiIiUg1KrusaVwxrp8Dnf/PNY33eD72OSERERESqSQ801iXOwcZ7IPdPEPsQfOcmryMSERERkVOg5Lou2foYfDwLvpsKPe73OhoREREROUVKruuKnX+A7Pug03joM0vLmouIiIjUQ0qu64K8t+D9m+Dc4XDRXDB9W0RERETqI2VxXvvPclh1FZydCJe8BiFneB2RiIiIiNSQZgvx0peb4L1R0KwzDFoMoc29jkhERKTBOHbsGHl5eRQWFnodigRJVFQUW7duDdr5w8PD6dixI6GhodU+Rsm1Vw7tgowREBrpWyQmvLXXEYmIiDQoeXl5REZG0rlzZ0zPMjVIBQUFREZGBuXczjny8/PJy8sjOjq62sdpWIgX/rcPlg2D4qO+xLrZ+V5HJCIi0uAUFhbSqlUrJdZSI2ZGq1atTvmTD/Vc17ZjX0Hm9+F/n8OQf0LUhV5HJCIi0mApsZbTUZP2o57r2lRUCMuvgAMfQvLr0PoiryMSERGRIMnPzyc+Pp74+HjatWtHhw4dSraPHj1a5bFZWVmkpqae9BoDBgwIVLgSIOq5ri3FRbB6AuzLgP5/gvYjvI5IRERESklPhxkzYM8eOP98ePhhmDCh5udr1aoV2dnZAMycOZPmzZtzzz33lOz/5ptvaNq04lQsMTGRxMTEk15j9erVNQ8wiKq6t4ZOPde1wTnIug3+9Qb0fgqiT+N/qoiIiARcejrcdBN8+qnv1/ann/q209MDe51JkyZxyy230K9fP+69917ef/99+vfvT0JCAgMGDODjjz8GIDMzk8svvxzwJeZTpkwhJSWFmJgYnnnmmZLzNW/evKR+SkoKP/rRj+jWrRsTJkzAOQfAkiVL6NatG3369CE1NbXkvKXl5OSQlJREfHw8sbGx7NixA4B58+YRGxtLXFwc1157LQC5ubkMHjyY2NhYhgwZwp49eyq8t507dzJixAj69OlDcnIy27ZtC+ybWUc1zj8patvmn8Mnab4lzbvd6XU0IiIijc5dd4G/E7lCa9fCkSMnlh0+DNdfDy+8UPEx8fHw1FOnHkteXh6rV68mJCSEr776ihUrVtC0aVP+8Y9/cP/99/P666+XO2bbtm1kZGRQUFBA165dufXWW8tND/fBBx+Qk5ND+/btufjii1m1ahWJiYncfPPNLF++nOjoaMaPH19hTLNnz+bOO+9kwoQJHD16lKKiInJycnjooYdYvXo1rVu35r///S8AU6dO5brrruO6667jxRdfJDU1lUWLFpW7tyFDhjB79my6dOnCunXruO2221i2bNmpv2H1jJLrYPv4Wch5CC64AWIf8joaERERqUDZxPpk5adj7NixhISEAHDw4EGuu+46duzYgZlx7NixCo+57LLLCAsLIywsjDZt2rBv3z46dux4Qp2kpKSSsvj4eHJzc2nevDkxMTElU8mNHz+etLS0cufv378/Dz/8MHl5efzwhz+kS5cuLFu2jLFjx9K6tW+64LPPPhuANWvW8MYbbwBw7bXXcu+995a7t0OHDrF69WrGjh1bsu9IMN7MOkjJdTDlvgwbUqHjFdD3edATyyIiIp44WQ9z586+oSBldeoEmZmBjaVZs2Ylr3/2s59x6aWX8uabb5Kbm0tKSkqFx4SFhZW8DgkJ4ZtvvqlRncpcffXV9OvXj8WLFzNy5EjmzJlT7WNLO35vxcXFtGzZsmTMeWOiMdfB8tlSWDMR2gyCi1+GJvo7RkREpK56+GGIiDixLCLCVx5MBw8epEOHDgDMnTs34Ofv2rUru3btIjc3F4BXX321wnq7du0iJiaG1NRURo8ezebNmxk8eDALFy4kPz8foGRYyIABA3jllVcASE9PJzk5udz5WrRoQXR0NAsXLgR8C7Js2rQp0LdXJym5DoYv1sHKKyGqBwx8C0LCvY5IREREqjBhAqSl+XqqzXxf09JOb7aQ6rj33nv56U9/SkJCwin1NFfXmWeeye9+97uSBwsjIyOJiooqV2/BggX07NmT+Ph4tmzZwsSJE+nRowczZsxg0KBBxMXFMW3aNACeffZZXnrpJWJjY5k/fz5PP/10hddOT0/nD3/4A3FxcfTo0YO33nor4PdXF9nxJ0nru8TERJeVleV1GHBwG/zjEgiNgu+tgjPbeR1RrTv+xLJIWWobUhm1DanM6bSNrVu30r1798AGVA8dOnSI5s2b45zj9ttvp0uXLtx9991ehxUQwVz+/LiK2pGZbXDOVThXonquA+lwHmQMA2sKl77TKBNrERERqVteeOEF4uPj6dGjBwcPHuTmm2/2OqQGTQOBA+VIPiwbBscOwtD3IPICryMSERER4e67724wPdX1gZLr07E7HTbNgMN7oEmobxXGIf+As+K9jkxEREREPKDkuqZ2p8P7N0HRYd928VFoEgaH93obl4iIiIh4RmOua2rTjG8T6+OKj/jKRURERKRRUnJdU4f3nFq5iIiIiDR4Sq5rKuL8UysXERGRRuff//43V111FRdccAF9+vRh5MiRbN++3euwypk7dy533HEHALNnz2bevHnl6uTm5tKzZ88qz5Obm8uf//znku2srCxSU1MDG2wdp+S6puIehpAySzmFRPjKRUREpP7ZnQ6LOsOfm/i+7k4/rdM55xgzZgwpKSns3LmTDRs28Mgjj7Bv374T6gVj8ZjTccsttzBx4sQaHVs2uU5MTOSZZ54JVGgBE8z3XMl1TUVPgKQ0iOgEmO9rUpqvXEREROqX4xMVHP4UcL6v7990Wgl2RkYGoaGh3HLLLSVlcXFxJCcnk5mZSXJyMqNGjeLCCy+ksLCQyZMn06tXLxISEsjIyAAgJyeHpKQk4uPjiY2NZceOHXz99ddcdtllxMXF0bNnz3JLmhcXF9O5c2cOHDhQUtalSxf27dvHX/7yF/r160dCQgJDhw4tl+gDzJw5kyeeeAKADRs2EBcXR1xcHM8991xJndzcXJKTk+nduze9e/dm9erVANx3332sWLGC+Ph4Zs2aRWZmJpdffjngWz79iiuuIDY2losuuojNmzeXXG/KlCmkpKQQExNTYTJeVFTEpEmT6NmzJ7169WLWrFkA7Ny5k6FDhxIXF0fv3r3ZuXMnzjmmT59eUvf4+1P2PS8qKmL69On07duX2NhY5syZc4rf4YpptpDTET1BybSIiEh9sOEu+DK78v1frPVNTFBa0WFYdz3sfKHiY86Khz5PVXrKLVu20KdPn0r3b9y4kS1bthAdHc2TTz6JmfHhhx+ybds2hg0bxvbt25k9ezZ33nknEyZM4OjRoxQVFbFkyRLat2/P4sWLATh48OAJ523SpAmjR4/mzTffZPLkyaxbt45OnTrRtm1bLrnkEtauXYuZ8fvf/57HHnuMJ598stIYJ0+ezG9/+1sGDhzI9OnTS8rbtGnDu+++S3h4ODt27GD8+PFkZWXx61//mieeeIK//vWvgC+hPe7BBx8kISGBRYsWsWzZMiZOnEh2tu97sm3bNjIyMigoKKBr167ceuuthIaGlhybnZ3N3r172bJlC0DJHw433HADM2bMYMyYMRQWFlJcXMwbb7xBdnY2mzZt4osvvqBv374MHDiw3HuelpZGVFQU69ev58iRI1x88cUMGzaM6OjoSt+P6lDPtYiIiEjZxPpk5QGQlJRUksitXLmSa665BoBu3brRqVMntm/fTv/+/fnVr37Fo48+yqeffsqZZ55Jr169ePfdd/nJT37CihUriIqKKnfucePGlfTYvvLKK4wbNw6AvLw8hg8fTq9evXj88cfJycmpNL4DBw5w4MCBksT02muvLdl37NgxbrzxRnr16sXYsWP56KOPTnq/K1euLDnH4MGDyc/P56uvvgLgsssuIywsjNatW9OmTZtyPeoxMTHs2rWLqVOn8ve//50WLVpQUFDA559/zpgxYwAIDw8nIiKClStXMn78eEJCQmjbti2DBg1i/fr15d7zd955h3nz5hEfH0+/fv3Iz89nx44dJ72Pk1HPtYiIiDR8VfQwA74x1oc/LV8e0QmGZtbokj169OC1116rdH+zZs1Oeo6rr76afv36sXjxYkaOHMmcOXMYPHgwGzduZMmSJTzwwAMMGTKE4cOHlyxr/otf/IIf/OAHfPLJJ+zfv59FixbxwAMPADB16lSmTZvGqFGjyMzMZObMmTW6t1mzZtG2bVs2bdpEcXEx4eHhNTrPcWFhYSWvQ0JCyo2JPuuss9i0aRNLly5l9uzZLFiwgKeffvqUr1P6PXfO8eyzzzJ8+PCaB16BoPZcm9kIM/vYzD4xs/sq2D/LzLL9/7ab2QF/ebyZrTGzHDPbbGbjghmniIiINHJBmKhg8ODBHDlyhLS0tJKyzZs3s2LFinJ1k5OTSU/3je/evn07e/bsoWvXruzatYuYmBhSU1MZPXo0mzdv5rPPPiMiIoJrrrmG6dOns3HjRvr160d2djbZ2dmMGjUKM2PMmDFMmzaN7t2706pVK8A3hKRDhw4A/PGPf6wy/pYtW9KyZUtWrlwJUBLf8fOce+65NGnShPnz51NUVARAZGQkBQUFFZ6v9D1mZmbSunVrWrRoUa338osvvqC4uJgrr7yShx56iI0bNxIZGUn79u1ZtGgRAEeOHOHw4cMkJyfz6quvUlRUxP79+1m+fDlJSUnlzjl8+HCef/55jh07VvK+f/3119WKpypB67k2sxDgOeB7QB6w3szeds6VfG7gnLu7VP2pQIJ/8zAw0Tm3w8zaAxvMbKlz7tuR+SIiIiKBcvwZqk0zfGtWRJzvS6xP49kqM+PNN9/krrvu4tFHHyU8PJzOnTvz1FNPsXfviSs633bbbdx666306tWLpk2bMnfuXMLCwliwYAHz588nNDSUdu3acf/997N+/XqmT59OkyZNCA0N5fnnn6/w+uPGjaNv377MnTu3pGzmzJmMHTuWs846i8GDB7N79+4q7+Gll15iypQpmBnDhg07Id4rr7ySefPmMWLEiJIe4djYWEJCQoiLi2PSpEkkJCSUHHP8wcXY2FgiIiJOmtyXtnfvXiZPnkxxcTEAjzzyCABpaWn8+Mc/5uc//zmhoaEsXLiQMWPGsGbNGuLi4jAzHnvsMdq1a8e2bdtOOOcNN9xAbm4uvXv3xjnHOeecU5Konw5zzp32SSo8sVl/YKZzbrh/+6cAzrlHKqm/GnjQOfduBfs2AT9yzlU6ECYxMdFlZWUFJHY5PZmZmaSkpHgdhtRBahtSGbUNqczptI2tW7fSvXv3wAYkdUpBQQGRkZFBvUZF7cjMNjjnEiuqH8xhIR2Af5XazvOXlWNmnYBoYFkF+5KAM4CdQYhRRERERCRg6soDjVcBrznnikoXmtm5wHzgOudccdmDzOwm4CaAtm3bnjDdi3jn0KFD+l5IhdQ2pDJqG1KZ02kbUVFRlY7/lYahqKgo6N/jwsLCU2qDwUyu9wLnldru6C+ryFXA7aULzKwFsBiY4ZxbW9FBzrk0IA18w0L0kWLdoI93pTJqG1IZtQ2pzOkOCwn2kAHxVm0MCwkPDz9h7PjJBHNYyHqgi5lFm9kZ+BLot8tWMrNuwFnAmlJlZwBvAvOcc5XPYSMiIiJShWA9WyaNQ03aT9CSa+fcN8AdwFJgK7DAOZdjZr8ws1Glql4FvOJOjP7/gIHApFJT9cUHK1YRERFpeMLDw8nPz1eCLTXinCM/P/+U5/AO6phr59wSYEmZsp+X2Z5ZwXF/Av4UzNhERESkYevYsSN5eXns37/f61AkSAoLC097AZuqhIeH07Fjx1M6pq480CgiIiISUKGhoSVLXUvDlJmZeUrjoWtDUFdoFBERERFpTJRci4iIiIgEiJJrEREREZEACdry57XNzPYDn3odhwDQGvjC6yCkTlLbkMqobUhl1DakKl61j07OuXMq2tFgkmupO8wsyzmX6HUcUveobUhl1DakMmobUpW62D40LEREREREJECUXIuIiIiIBIiSawmGNK8DkDpLbUMqo7YhlVHbkKrUufahMdciIiIiIgGinmsRERERkQBRci01YmYjzOxjM/vEzO6rYP80M/vIzDab2T/NrJMXcYo3TtY+StW70sycmdWpJ70leKrTNszs//w/P3LM7M+1HaN4oxq/V843swwz+8D/u2WkF3FK7TOzF83sP2a2pZL9ZmbP+NvOZjPrXdsxlqbkWk6ZmYUAzwHfBy4ExpvZhWWqfQAkOudigdeAx2o3SvFKNdsHZhYJ3Amsq90IxSvVaRtm1gX4KXCxc64HcFetByq1rpo/Nx4AFjjnEoCrgN/VbpTiobnAiCr2fx/o4v93E/B8LcRUKSXXUhNJwCfOuV3OuaPAK8Do0hWccxnOucP+zbVAx1qOUbxz0vbh90vgUaCwNoMTT1WnbdwIPOec+xLAOfefWo5RvFGdtuGAFv7XUcBntRifeMg5txz4bxVVRgPznM9aoKWZnVs70ZWn5FpqogPwr1Lbef6yylwP/C2oEUldctL24f/I7jzn3OLaDEw8V52fHd8Fvmtmq8xsrZlV1VslDUd12sZM4BozywOWAFNrJzSpB041Lwmqpl5dWBoHM7sGSAQGeR2L1A1m1gT4DTDJ41CkbmqK76PdFHyfeC03s17OuQOeRiV1wXhgrnPuSTPrD8w3s57OuWKvAxMpTT3XUhN7gfNKbXf0l53AzIYCM4BRzrkjtRSbeO9k7SMS6AlkmlkucBHwth5qbBSq87MjD3jbOXfMObcb2I4v2ZaGrTpt43pgAYBzbg0QDrSuleikrqtWXlJblFxLTawHuphZtJmdge/BkrdLVzCzBGAOvsRaYyYblyrbh3PuoHOutXOus3OuM74x+aOcc1nehCu16KQ/O4BF+HqtMbPW+IaJ7KrNIMUT1Wkbe4AhAGbWHV9yvb9Wo5S66m1gon/WkIuAg865z70KRsNC5JQ5574xszuApUAI8KJzLsfMfgFkOefeBh4HmgMLzQxgj3NulGdBS62pZvuQRqiabWMpMMzMPgKKgOnOuXzvopbaUM228WPgBTO7G9/DjZOcVsJrFMzsZXx/dLf2j7l/EAgFcM7NxjcGfyTwCXAYmOxNpD5aoVFEREREJEA0LEREREREJECUXIuIiIiIBIiSaxERERGRAFFyLSIiIiISIEquRUREREQCRMm1iEiAmFkrM8v2//u3me0ttX3GSY5NNLNnqnGN1YGL2HtmNsnMfut1HCIigaJ5rkVEAsQ/H3M8gJnNBA455544vt/Mmjrnvqnk2CzgpAvpOOcGBCZaEREJBvVci4gEkZnNNbPZZrYOeMzMksxsjZl9YGarzayrv16Kmf3V/3qmmb1oZplmtsvMUkud71Cp+plm9pqZbTOzdPOv2GRmI/1lG8zsmePnLRNXiJk9bmbrzWyzmd3sL7/bzF70v+5lZlvMLKKKuCeZ2SIze9fMcs3sDjOb5q+31szO9tfLNLOn/b34W8wsqYKYzjGz1/0xrTezi/3lg0p9AvCBmUUG9JskIhJA6rkWEQm+jsAA51yRmbUAkv0r0g0FfgVcWcEx3YBLgUjgYzN73jl3rEydBKAH8BmwCrjYzLKAOcBA59xu/8pmFbke3xLBfc0sDFhlZu8ATwOZZjYGmAHc7Jw7bGbbqoi7pz+WcHwrpP3EOZdgZrOAicBT/noRzrl4MxsIvOg/rrSngVnOuZVmdj6+1fq6A/cAtzvnVplZc6CwknsSEfGckmsRkeBb6Jwr8r+OAv5oZl3wLeEcWskxi51zR4AjZvYfoC2QV6bO+865PAAzywY6A4eAXc653f46LwM3VXD+YUCsmf2oVFxd/An5JGAzMMc5t6oacWc45wqAAjM7CPzFX/4hEFuq3ssAzrnlZtbCzFqWiWkocKG/Ax6ghT+ZXgX8xszSgTeO37OISF2k5FpEJPi+LvX6l/iS0TFm1hnIrOSYI6VeF1Hxz+vq1KmMAVOdc0sr2NcFX5LevlRZVXGXjqO41HZxmZhcmeuU3W4CXOScK9sz/WszWwyMxNfDPtw5t62imxIR8ZrGXIuI1K4oYK//9aQgnP9jIMafAAOMq6TeUuBWMwsFMLPvmlkzM4sCngEGAq3K9Gyfbtzj/Ne6BN+QlINl9r8DTD2+YWbHHw69wDn3oXPuUWA9viEzIiJ1kpJrEZHa9RjwiJl9QBA+PXTO/Q+4Dfi7mW0ACoCySSzA74GPgI1mtgXfOO2mwCzgOefcdnzjsn9tZm0CFHeh//jZ/nOXlQok+h+w/Ai4xV9+l/8hyM3AMeBvNby+iEjQmXNlP5UTEZH6zMyaO+cO+WcPeQ7Y4Zyb5XFMmcA9/ikHRUQaLPVci4g0PDf6H3DMwTecY47H8YiINBrquRYRERERCRD1XIuIiIiIBIiSaxERERGRAFFyLSIiIiISIEquRUREREQCRMm1iIiIiEiAKLkWEREREQmQ/wfOFO5nIv5rnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, X_dev, y_dev, ylim=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "   \n",
    "  fig, ax = plt.subplots(figsize=(12,8))\n",
    "  ax.set_title(title)\n",
    "  if ylim is not None:\n",
    "    ax.set_ylim(*ylim)\n",
    "  ax.set_xlabel(\"Training examples\")\n",
    "  ax.set_ylabel(\"Macro F1-score\")\n",
    "\n",
    "  f1_train = []\n",
    "  f1_dev = []\n",
    "\n",
    "  # for each training subset, fit and predict\n",
    "  for t_sz in train_sizes:\n",
    "    X_subset = X[:int(t_sz * total_length)]\n",
    "    y_subset = y[:int(t_sz * total_length)]\n",
    "\n",
    "\n",
    "    estimator.fit(X_subset, y_subset)\n",
    "      \n",
    "    y_pred = estimator.predict_proba(X_subset)\n",
    "    y_pred_dev = estimator.predict_proba(X_dev)\n",
    "\n",
    "    _, _, f1_dict = get_scores(y_subset, y_pred, 0.5)\n",
    "    _, _, f1_dict_dev = get_scores(y_dev, y_pred_dev, 0.5)\n",
    "    f1_train.append(f1_dict['macro'])\n",
    "    f1_dev.append(f1_dict_dev['macro'])\n",
    "\n",
    "  ax.grid()\n",
    "  ax.plot(train_sizes, f1_train, 'o-', color=\"b\",\n",
    "            label=\"Training score\")\n",
    "  ax.plot(train_sizes, f1_dev, 'o-', color=\"orange\",\n",
    "            label=\"Cross-validation score\")\n",
    "\n",
    "  ax.legend(loc=\"lower right\")\n",
    "  fig.savefig(title+'.pdf')\n",
    "\n",
    "for pipe in pipeline_list:\n",
    "  title = \"Learning Curves (\"+str(pipe[1])+\")\"\n",
    "  plot_learning_curve(pipe[0], title\n",
    "                    , train_set.text, train_set.sentiment.to_list()\n",
    "                    , dev_set.text, dev_set.sentiment.to_list()\n",
    "                    , train_sizes=np.linspace(.1, 1.0, 15)\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TvaW8CjXHagM"
   },
   "source": [
    "#### Bootstrap Statistical Significance Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "tNlIgHPqHiNW",
    "outputId": "c2c6b014-26ef-4bc9-bd08-3c40f9fa25ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base macro-f1: 0.3356\n",
      "SGD macro-f1: 0.7559\n",
      "RF macro-f1: 0.6502\n",
      "NB macro-f1: 0.7460\n",
      "SGD vs BASE, p_value:  0.0000\n",
      "SGD vs Naive, p_value: 0.1820\n",
      "SGD vs Random Forest, p_value: 0.0000\n",
      "Elapsed time 300 seconds\n"
     ]
    }
   ],
   "source": [
    "# Initialize classifiers ##########################################\n",
    "# Base\n",
    "base_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "y_pred = base_pipeline.predict_proba(test_set.text)\n",
    "_, _, bs_f1 = get_scores(test_set.sentiment.to_list(), y_pred, 0.5)\n",
    "print('Base macro-f1: {:.4f}'.format( bs_f1['macro']))\n",
    "\n",
    "# SGD\n",
    "sgd_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "y_pred = sgd_pipeline.predict_proba(test_set.text)\n",
    "_, _, sgd_f1 = get_scores(test_set.sentiment.to_list(), y_pred, 0.5)\n",
    "print('SGD macro-f1: {:.4f}'.format(sgd_f1['macro']))\n",
    "\n",
    "# RF\n",
    "rf_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "y_pred = rf_pipeline.predict_proba(test_set.text)\n",
    "_, _, rf_f1 = get_scores(test_set.sentiment.to_list(), y_pred, 0.5)\n",
    "print('RF macro-f1: {:.4f}'.format( rf_f1['macro']))\n",
    "\n",
    "# NB\n",
    "nb_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "y_pred = nb_pipeline.predict_proba(test_set.text)\n",
    "_, _, nb_f1 = get_scores(test_set.sentiment.to_list(), y_pred, 0.5)\n",
    "print('NB macro-f1: {:.4f}'.format( nb_f1['macro']))\n",
    "\n",
    "\n",
    "# Bootstrapping significance test ###################################\n",
    "\n",
    "def bootstrap_sign_test(best_estimator, comp_estimator, dataset, b, N):\n",
    "  \"\"\"\n",
    "  Input:\n",
    "    best_estimator: best scoring pipeline in terms of macro-f1\n",
    "    comp_estimator: pipeline to be compared with the best\n",
    "    b: number of sampling datasets\n",
    "    N: size of sampling datasets\n",
    "\n",
    "  Output:\n",
    "    p_value: p-value for the bootstrapping significance test\n",
    "  \"\"\"\n",
    "  y1 = best_estimator.predict_proba(dataset.text)\n",
    "  _, _, e1_f1 = get_scores( dataset.sentiment.to_list(), y1, 0.5)\n",
    "\n",
    "  y2 = comp_estimator.predict_proba(dataset.text)\n",
    "  _, _, e2_f1 = get_scores( dataset.sentiment.to_list(), y2, 0.5)\n",
    "  \n",
    "  # calculate true difference\n",
    "  delta = e1_f1['macro'] - e2_f1['macro']\n",
    "\n",
    "  diffs = []\n",
    "\n",
    "  # calculate a diff for each simulated sample dataset\n",
    "  for i in range(b):\n",
    "    bootstrap = dataset.sample(replace = True, n = N)\n",
    "    y_best = best_estimator.predict_proba( bootstrap.text )\n",
    "    y_comp = comp_estimator.predict_proba( bootstrap.text )\n",
    "\n",
    "    _, _, best_f1 = get_scores(bootstrap.sentiment.to_list(), y_best, 0.5)\n",
    "    _, _, comp_f1 = get_scores(bootstrap.sentiment.to_list(), y_comp, 0.5)\n",
    "    diffs.append(best_f1['macro'] - comp_f1['macro'])\n",
    "  \n",
    "  # calculate p-value\n",
    "  s = 0\n",
    "  for i in range(b):\n",
    "    if diffs[i] > 2 * delta:\n",
    "      s += 1\n",
    "  p_value = s/b\n",
    "  \n",
    "  return p_value\n",
    "\n",
    "# print p-values\n",
    "start_time = time.time()\n",
    "\n",
    "print('SGD vs BASE, p_value:  {:.4f}' .format( bootstrap_sign_test(sgd_pipeline, base_pipeline, test_set, b = 1000, N = 1000) ))\n",
    "print('SGD vs Naive, p_value: {:.4f}' .format( bootstrap_sign_test(sgd_pipeline, nb_pipeline, test_set, b = 1000, N = 1000) ))\n",
    "print('SGD vs Random Forest, p_value: {:.4f}' .format( bootstrap_sign_test(sgd_pipeline, rf_pipeline, test_set, b = 1000, N = 1000) ))\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "print('Elapsed time {:.0f} seconds'.format(elapsed_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hZSJEUES8PKP"
   },
   "source": [
    "#### Appendix Α -  Dimensionality Reduction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "qZdC19RH78lq",
    "outputId": "1387a7f8-364f-4b87-ebaa-39070b1cd51b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting ended after 30 seconds.\n",
      "SGD classifier- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.694 |    0.813 | 0.749 |\n",
      "| 1     |       0.772 |    0.639 | 0.699 |\n",
      "| macro |       0.733 |    0.726 | 0.724 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.682 |    0.81  | 0.74  |\n",
      "| 1     |       0.768 |    0.625 | 0.689 |\n",
      "| macro |       0.725 |    0.717 | 0.715 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.688 |    0.81  | 0.744 |\n",
      "| 1     |       0.763 |    0.625 | 0.687 |\n",
      "| macro |       0.726 |    0.718 | 0.716 |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "# append SVD dimensionality reduction as a second step\n",
    "sgd_pipeline.steps.insert(1,('tfidf_svd', TruncatedSVD(n_components=500, random_state=94))) #insert as second step\n",
    "\n",
    "start_time = time.time()\n",
    "sgd_pipeline.fit(train_set.text, train_set.sentiment.to_list())\n",
    "elapsed_time = time.time() - start_time\n",
    "print('Fitting ended after {:.0f} seconds.'.format(elapsed_time))\n",
    "\n",
    "print_classifier_scores(sgd_pipeline, 'SGD classifier', train_set.text, train_set.sentiment.to_list()\n",
    "  , dev_set.text, dev_set.sentiment.to_list()\n",
    "  , test_set.text, test_set.sentiment.to_list(),0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 995
    },
    "colab_type": "code",
    "id": "J5yKoUz4R4Ez",
    "outputId": "2588a59b-2ee7-4357-88fa-2fbdfbe2d9cc"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "# Learning curve\n",
    "plot_learning_curve(sgd_pipeline, 'SGD - with SVD_LC'\n",
    "                    , train_set.text, train_set.sentiment.to_list()\n",
    "                    , dev_set.text, dev_set.sentiment.to_list()\n",
    "                    , train_sizes=np.linspace(.1, 1.0, 15)\n",
    "                    )\n",
    "# Precision Recall curve\n",
    "plot_precision_recall_curve(sgd_pipeline, 'SGD - with SVD_RC', test_set.text, test_set.sentiment.to_list())\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "print('Elapsed time {:.0f} seconds'.format(elapsed_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_F7GSfV_KN3"
   },
   "source": [
    "#### Appendix Β- Word Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "-Aqb94CTW8MB",
    "outputId": "6a9f6099-7859-476b-cf1f-e154f0b1e7ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-05-27 13:39:57--  https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.144.101\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.144.101|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1647046227 (1.5G) [application/x-gzip]\n",
      "Saving to: ‘GoogleNews-vectors-negative300.bin.gz’\n",
      "\n",
      "GoogleNews-vectors- 100%[===================>]   1.53G  16.5MB/s    in 98s     \n",
      "\n",
      "2020-05-27 13:41:36 (16.0 MB/s) - ‘GoogleNews-vectors-negative300.bin.gz’ saved [1647046227/1647046227]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -c \"https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ekk8EP4L-ful"
   },
   "outputs": [],
   "source": [
    "from gensim.models.keyedvectors import KeyedVectors\n",
    "import os\n",
    "import gzip\n",
    "import shutil, sys  \n",
    "\n",
    "#Centroids of Word Embeddings\n",
    "\n",
    "def extract_embeddings():\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        print('File \"GoogleNews-vectors-negative300.bin\" already exists!')\n",
    "        return\n",
    "    print('Extracting...')\n",
    "    with gzip.open('GoogleNews-vectors-negative300.bin.gz', 'r') as f_in, open('GoogleNews-vectors-negative300.bin', 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)\n",
    "    print('File extracted.')\n",
    "\n",
    "def load_embeddings(fname, vocab):\n",
    "    model = KeyedVectors.load_word2vec_format(fname, binary=True)\n",
    "    dim = model.vectors.shape[1]\n",
    "    embeddings = np.zeros((len(vocab), dim))\n",
    "\n",
    "    for word in vocab:\n",
    "        if word in model:\n",
    "            embeddings[vocab[word]] = model[word]\n",
    "        else:\n",
    "            embeddings[vocab[word]] = np.random.uniform(-0.25, 0.25, dim)\n",
    "    return embeddings\n",
    "\n",
    "def EmbeddingMeanVectorizer(docs, model):\n",
    "    dim = model.shape[1]\n",
    "    tweetvec = np.zeros((len(docs), dim))\n",
    "    for i, doc in enumerate(docs):\n",
    "        sent_vec = np.zeros(dim)\n",
    "        numw = 0\n",
    "        for w in doc.split():\n",
    "            try:\n",
    "                sent_vec = np.add(sent_vec, model[vectorizer.vocabulary_[w]])\n",
    "                numw += 1\n",
    "            except:\n",
    "                pass\n",
    "        tweetvec[i] = sent_vec/numw\n",
    "    return tweetvec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "NLjdn1YKXLAR",
    "outputId": "086ea03e-4f5f-4a9b-e512-3b5995c36849"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting...\n",
      "File extracted.\n"
     ]
    }
   ],
   "source": [
    "extract_embeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "_7iDWhUrPzyw",
    "outputId": "ba1e55e9-3155-4729-bea0-f10158cecdb7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:253: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
      "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
     ]
    }
   ],
   "source": [
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1, 1))\n",
    "vectorizer.fit(train_set.text)\n",
    "vectorizer.fit(dev_set.text)\n",
    "vectorizer.fit(test_set.text)\n",
    "embeddings = load_embeddings('GoogleNews-vectors-negative300.bin', vectorizer.vocabulary_)\n",
    "\n",
    "X_train = EmbeddingMeanVectorizer(train_set.text, embeddings)\n",
    "X_dev = EmbeddingMeanVectorizer(dev_set.text, embeddings)\n",
    "X_test = EmbeddingMeanVectorizer(test_set.text, embeddings)\n",
    "\n",
    "X_train = np.nan_to_num(X_train)\n",
    "X_dev = np.nan_to_num(X_dev)\n",
    "X_test = np.nan_to_num(X_test)\n",
    "\n",
    "y_train_true = train_set.sentiment.tolist()\n",
    "y_dev_true = dev_set.sentiment.tolist()\n",
    "y_test_true = test_set.sentiment.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "o83BUNqFD6ln",
    "outputId": "dbe7b992-8444-43c2-ca3d-02d63afbc88f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD classifier with word embeddings- Train scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.728 |    0.774 | 0.75  |\n",
      "| 1     |       0.757 |    0.71  | 0.733 |\n",
      "| macro |       0.743 |    0.742 | 0.741 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier with word embeddings- Dev scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.726 |    0.776 | 0.75  |\n",
      "| 1     |       0.76  |    0.707 | 0.733 |\n",
      "| macro |       0.743 |    0.742 | 0.741 |\n",
      "+-------+-------------+----------+-------+\n",
      "---------------------------------------------------------\n",
      "SGD classifier with word embeddings- Test scores:\n",
      "+-------+-------------+----------+-------+\n",
      "|       |   precision |   recall |    f1 |\n",
      "|-------+-------------+----------+-------|\n",
      "| 0     |       0.724 |    0.762 | 0.743 |\n",
      "| 1     |       0.755 |    0.716 | 0.735 |\n",
      "| macro |       0.739 |    0.739 | 0.739 |\n",
      "+-------+-------------+----------+-------+\n"
     ]
    }
   ],
   "source": [
    "grid_params = {\n",
    "    \"alpha\" : [0.0001, 0.001, 0.01, 0.1],\n",
    "    \"penalty\" : [\"l2\", \"l1\", \"none\"],\n",
    "}\n",
    "# start timing\n",
    "start_time = time.time()\n",
    "sgd_clf = SGDClassifier(loss=\"log\", penalty='l1', max_iter=5000, alpha=0.0001)\n",
    "grid_sgd_clf = RandomizedSearchCV(sgd_clf, grid_params, scoring='f1_macro',n_jobs=-1, random_state=94)\n",
    "\n",
    "# perform gridsearch on both training and dev set with 5-fold cross validation and save the best model\n",
    "grid_sgd_clf.fit(X_train, y_train_true)\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "sgd_clf = grid_sgd_clf.best_estimator_\n",
    "\n",
    "sgd_clf.fit(X_train, y_train_true)\n",
    "\n",
    "print_classifier_scores(sgd_clf, 'SGD classifier with word embeddings', X_train, y_train_true\n",
    "  , X_dev, y_dev_true\n",
    "  , X_test, y_test_true,0.5)"
   ]
  }
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