quora_vectorizing_and_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fIin1_l-TdG_"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics.classification import accuracy_score, log_loss\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rSI-gsUlUXJY"
   },
   "source": [
    "## Reading the saved data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "colab_type": "code",
    "id": "-4c2PnhvT4A5",
    "outputId": "490befc8-680b-4b6f-fb95-6b606f33413e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Extracted Features is:  (404287, 30)\n",
      "Number row which contain NaN:  0\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_word</th>\n",
       "      <th>q2_n_word</th>\n",
       "      <th>...</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>token_set_ratio</th>\n",
       "      <th>token_sort_ratio</th>\n",
       "      <th>fuzz_ratio</th>\n",
       "      <th>fuzz_partial_ratio</th>\n",
       "      <th>longest_substr_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>what is the step by step guide to invest in sh...</td>\n",
       "      <td>what is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.714281</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>98</td>\n",
       "      <td>0.965517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>what is the story of kohinoor (koh-i-noor) dia...</td>\n",
       "      <td>what would happen if the indian government sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307690</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>86</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  what is the step by step guide to invest in sh...   \n",
       "1   1     3     4  what is the story of kohinoor (koh-i-noor) dia...   \n",
       "\n",
       "                                           question2  is_duplicate  q1len  \\\n",
       "0  what is the step by step guide to invest in sh...             0     66   \n",
       "1  what would happen if the indian government sto...             0     51   \n",
       "\n",
       "   q2len  q1_n_word  q2_n_word  ...   ctc_max  last_word_eq  first_word_eq  \\\n",
       "0     57         14         12  ...  0.714281             0              1   \n",
       "1     88          8         13  ...  0.307690             0              1   \n",
       "\n",
       "   abs_len_diff  mean_len  token_set_ratio  token_sort_ratio  fuzz_ratio  \\\n",
       "0             2      13.0              100                93          93   \n",
       "1             5      10.5               86                63          66   \n",
       "\n",
       "   fuzz_partial_ratio  longest_substr_ratio  \n",
       "0                  98              0.965517  \n",
       "1                  75              0.576923  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = './Data/'\n",
    "if os.path.isfile(d+'extracted_features.csv'):\n",
    "    df = pd.read_csv(d+'extracted_features.csv', encoding = 'latin-1')\n",
    "    print(\"Shape of Extracted Features is: \", df.shape)\n",
    "else:\n",
    "    df = extract_features(df)\n",
    "    df.to_csv('extracted_features.csv', index = False)\n",
    "    print('After Extraction shape of data is: ', df.shape)\n",
    "print('Number row which contain NaN: ', df.isnull().values.sum())\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "49EN2pUyXlFZ"
   },
   "source": [
    "* __Splitting__ the data into __70-30%__ ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zpMMVgS2Wr1z",
    "outputId": "240c18a1-7fed-44fe-a532-b290bd8b52fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datapoints in data is: 404287 and after splitting datapoints in train is: 283000 and in test is: 121287\n"
     ]
    }
   ],
   "source": [
    "# split the data\n",
    "cols = [i for i in df.columns if i not in ['qid1', 'qid2', 'is_duplicate']]\n",
    "X = df.loc[:,cols]\n",
    "Y = df.loc[:, 'is_duplicate']\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, stratify = Y, random_state=42)\n",
    "print('Datapoints in data is: {} and after splitting datapoints in train is: {} and in test is: {}'\n",
    "      .format(df.shape[0], x_train.shape[0], x_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QS-VBtkLdpSh"
   },
   "source": [
    "## Tf-IDF weighted W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FOldPqC9dzOb"
   },
   "source": [
    "- After we find TF-IDF scores, we convert each question to a weighted average of word2vec vectors by these scores.\n",
    "- here we use a pre-trained GLOVE model which comes free with <a href = ' https://spacy.io/usage/vectors-similarity'> \"Spacy\"</a>. \n",
    "- It is trained on Wikipedia and therefore, it is stronger in terms of word semantics. \n",
    "- I'm using 'en_web_core_sm' becasue it is smaller so faster to train, dataset is large (404k datapoints) and I don't have enogh RAM.\n",
    "- Getting IDF value of each word by fitting the TF-IDF with both question1 and question2 of triaing data.\n",
    " _ If the some words of testing data is not in the dictionary of TF-IDF features, I'll simply return IDF = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kScOykNaaLa3",
    "outputId": "0ca13f9b-d8a3-4451-aee3-352a099457d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lenght of features of tf-idf vector:  74462\n"
     ]
    }
   ],
   "source": [
    "# Getting the IDF score of each word\n",
    "questions = list(x_train['question1']) + list(x_train['question2'])\n",
    "tfidf = TfidfVectorizer(lowercase=True)\n",
    "tfidf.fit(questions)\n",
    "# dict key:word and value:tf-idf score\n",
    "word_to_idf_score = dict(zip(tfidf.get_feature_names(), tfidf.idf_))\n",
    "print('Lenght of features of tf-idf vector: ', len(tfidf.get_feature_names()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Js0r4ltM2YZ0"
   },
   "outputs": [],
   "source": [
    "# for TF-IDF W2V featurization:\n",
    "# importing the 'en_core_web_sm' (of 96 dimensions) to convert tfidf-w2v\n",
    "try:\n",
    "    nlp = spacy.load('en_core_web_sm')\n",
    "except:\n",
    "    import en_core_web_sm\n",
    "    nlp = en_core_web_sm.load()\n",
    "\n",
    "def tfidf_w2v(col):\n",
    "    vecs1 = []\n",
    "    for q1 in tqdm(col.values):\n",
    "        doc1 = nlp(q1)\n",
    "        # 96 is the number of dimensions of vectors\n",
    "        mean_vec1 = np.zeros([len(doc1), len(doc1[0].vector)])\n",
    "        for word1 in doc1:\n",
    "            # word2vec\n",
    "            vec1 = word1.vector\n",
    "            # fetch idf score\n",
    "            try:\n",
    "                idf = word_to_idf_score[str(word1)]\n",
    "            except:\n",
    "                idf = 0\n",
    "            # compute final vec\n",
    "            mean_vec1 += vec1 * idf\n",
    "        mean_vec1 = mean_vec1.mean(axis=0)\n",
    "        vecs1.append(mean_vec1)\n",
    "    return vecs1\n",
    "\n",
    "def vectorize_question(col):\n",
    "    new_df = pd.DataFrame()\n",
    "    new_df['q'] = tfidf_w2v(col)\n",
    "    new_df[[i for i in range(96)]] = pd.DataFrame(new_df['q'].values.tolist(), index = new_df.index)\n",
    "    new_df.drop(['q'], axis =1,inplace = True)\n",
    "\n",
    "    return new_df\n",
    "\n",
    "def get_TFIDF_W2V(x_train, x_test):\n",
    "    if os.path.isfile(d+\"tfidf_w2v_q1_train.csv\") and os.path.isfile(d+\"tfidf_w2v_q2_train.csv\"):\n",
    "        tfidf_w2v_q1_train = pd.read_csv(d+\"tfidf_w2v_q1_train.csv\")\n",
    "        tfidf_w2v_q2_train = pd.read_csv(d+\"tfidf_w2v_q2_train.csv\")\n",
    "    else:\n",
    "        print('\\nTraining data is Vectorizing... \\n')\n",
    "        tfidf_w2v_q1_train = vectorize_question(x_train['question1'])\n",
    "        tfidf_w2v_q2_train = vectorize_question(x_train['question2'])\n",
    "    #     saving Vectrized features for future use\n",
    "        tfidf_w2v_q1_train.to_csv(d+\"tfidf_w2v_q1_train.csv\", index=False)\n",
    "        tfidf_w2v_q2_train.to_csv(d+\"tfidf_w2v_q2_train.csv\", index=False)\n",
    "        \n",
    "    if os.path.isfile(d+\"tfidf_w2v_q1_test.csv\") and os.path.isfile(d+\"tfidf_w2v_q2_test.csv\"):\n",
    "        tfidf_w2v_q1_test = pd.read_csv(d+\"tfidf_w2v_q1_test.csv\")\n",
    "        tfidf_w2v_q2_test = pd.read_csv(d+\"tfidf_w2v_q2_test.csv\")\n",
    "    else:\n",
    "        print('\\nTesting data is Vectorizing...\\n')\n",
    "        tfidf_w2v_q1_test = vectorize_question(x_test['question1'])\n",
    "        tfidf_w2v_q2_test = vectorize_question(x_test['question2'])\n",
    "    #     saving Vectrized features for future use\n",
    "        tfidf_w2v_q1_test.to_csv(d+\"tfidf_w2v_q1_test.csv\", index=False)\n",
    "        tfidf_w2v_q2_test.to_csv(d+\"tfidf_w2v_q2_test.csv\", index=False)\n",
    "\n",
    "    return tfidf_w2v_q1_train, tfidf_w2v_q2_train, tfidf_w2v_q1_test, tfidf_w2v_q2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WhP47TUQNoRr"
   },
   "outputs": [],
   "source": [
    "tfidf_w2v_q1_train, tfidf_w2v_q2_train, tfidf_w2v_q1_test, tfidf_w2v_q2_test = get_TFIDF_W2V(x_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "W2J-MifxntaC",
    "outputId": "d8494892-3da8-4639-eea7-5586ffc6107d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tfidf_w2v_q1_train is: (283000, 96)\n",
      "Number row which contain NaN:  0\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102.121707</td>\n",
       "      <td>-10.229564</td>\n",
       "      <td>-17.095489</td>\n",
       "      <td>-117.130373</td>\n",
       "      <td>32.309742</td>\n",
       "      <td>37.485594</td>\n",
       "      <td>90.280606</td>\n",
       "      <td>139.685723</td>\n",
       "      <td>-64.669678</td>\n",
       "      <td>98.552540</td>\n",
       "      <td>...</td>\n",
       "      <td>25.390127</td>\n",
       "      <td>-2.982139</td>\n",
       "      <td>45.051484</td>\n",
       "      <td>-37.749759</td>\n",
       "      <td>-41.085997</td>\n",
       "      <td>64.828821</td>\n",
       "      <td>-71.679641</td>\n",
       "      <td>-30.878717</td>\n",
       "      <td>8.066583</td>\n",
       "      <td>-64.005891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.149618</td>\n",
       "      <td>0.832300</td>\n",
       "      <td>-14.565274</td>\n",
       "      <td>-27.421711</td>\n",
       "      <td>-6.537139</td>\n",
       "      <td>24.970962</td>\n",
       "      <td>39.960418</td>\n",
       "      <td>48.246943</td>\n",
       "      <td>-24.967099</td>\n",
       "      <td>37.386857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.369008</td>\n",
       "      <td>-9.461654</td>\n",
       "      <td>61.290202</td>\n",
       "      <td>1.991991</td>\n",
       "      <td>-14.415560</td>\n",
       "      <td>-7.720726</td>\n",
       "      <td>-25.894139</td>\n",
       "      <td>14.203012</td>\n",
       "      <td>-6.935930</td>\n",
       "      <td>3.516629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2           3          4          5  \\\n",
       "0  102.121707 -10.229564 -17.095489 -117.130373  32.309742  37.485594   \n",
       "1   27.149618   0.832300 -14.565274  -27.421711  -6.537139  24.970962   \n",
       "\n",
       "           6           7          8          9  ...         86        87  \\\n",
       "0  90.280606  139.685723 -64.669678  98.552540  ...  25.390127 -2.982139   \n",
       "1  39.960418   48.246943 -24.967099  37.386857  ...   1.369008 -9.461654   \n",
       "\n",
       "          88         89         90         91         92         93        94  \\\n",
       "0  45.051484 -37.749759 -41.085997  64.828821 -71.679641 -30.878717  8.066583   \n",
       "1  61.290202   1.991991 -14.415560  -7.720726 -25.894139  14.203012 -6.935930   \n",
       "\n",
       "          95  \n",
       "0 -64.005891  \n",
       "1   3.516629  \n",
       "\n",
       "[2 rows x 96 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Shape of tfidf_w2v_q1_train is:', tfidf_w2v_q1_train.shape)\n",
    "print('Number row which contain NaN: ', tfidf_w2v_q1_train.isnull().values.sum())\n",
    "tfidf_w2v_q1_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "AZ_V4JNFsd36",
    "outputId": "0dcfebbe-14bf-430e-dab5-3423f035687e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tfidf_w2v_q1_train is: (283000, 96)\n",
      "Number row which contain NaN:  0\n"
     ]
    },
    {
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       "\n",
       "[2 rows x 96 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Shape of tfidf_w2v_q1_train is:', tfidf_w2v_q2_train.shape)\n",
    "print('Number row which contain NaN: ', tfidf_w2v_q2_train.isnull().values.sum())\n",
    "tfidf_w2v_q2_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
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    "colab_type": "code",
    "id": "aAQ8YRCusdn0",
    "outputId": "41aaf7e8-59d5-45ca-83ea-fdf597bf671e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tfidf_w2v_q1_train is: (121287, 96)\n",
      "Number row which contain NaN:  0\n"
     ]
    },
    {
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Shape of tfidf_w2v_q1_train is:', tfidf_w2v_q1_test.shape)\n",
    "print('Number row which contain NaN: ', tfidf_w2v_q1_test.isnull().values.sum())\n",
    "tfidf_w2v_q1_test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "XxSdFu7DsdeZ",
    "outputId": "06f7be36-07d7-415c-cb74-5f74b98471fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tfidf_w2v_q1_train is: (121287, 96)\n",
      "Number row which contain NaN:  0\n"
     ]
    },
    {
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       "           0          1          2           3           4          5  \\\n",
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       "\n",
       "            6          7          8          9  ...         86          87  \\\n",
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       "\n",
       "           88         89         90          91         92         93  \\\n",
       "0  209.118499  58.331057  13.213237 -115.653385  56.113592  49.264578   \n",
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       "\n",
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       "\n",
       "[2 rows x 96 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Shape of tfidf_w2v_q1_train is:', tfidf_w2v_q2_test.shape)\n",
    "print('Number row which contain NaN: ', tfidf_w2v_q2_test.isnull().values.sum())\n",
    "tfidf_w2v_q2_test.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NVk-CCtxKa1e"
   },
   "source": [
    "## Tf-IDF Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0qfEndZZKqwl",
    "outputId": "46564468-8970-47b9-d7d0-2382427f3f9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train, CV and test data are (226400, 27), (56600, 27), and (121287, 27)\n"
     ]
    }
   ],
   "source": [
    "#I'll split the x_train into tfidf_train, tfidf_CV\n",
    "tfidf_y_test = y_test\n",
    "tfidf_x_test = x_test\n",
    "\n",
    "tfidf_x_train, tfidf_cv, tfidf_y_train, tfidf_y_cv = train_test_split(x_train, y_train, test_size = 0.2, \n",
    "                                                                      stratify = y_train, random_state = 42)\n",
    "print('Shape of train, CV and test data are {}, {}, and {}'.format(tfidf_x_train.shape, tfidf_cv.shape, tfidf_x_test.shape))\n",
    "\n",
    "# I'll fit TF-IDF vectorizer with combined question1 and question2 of tfidf_x_train\n",
    "# then cv and test data will be transformed using above dictionary\n",
    "questions = list(tfidf_x_train['question1']) + list(tfidf_x_train['question2'])\n",
    "\n",
    "tfidf = TfidfVectorizer(max_features = 5000, lowercase=True)\n",
    "tfidf.fit(questions)\n",
    "\n",
    "#for train data\n",
    "tfidf_train_q1, tfidf_train_q2 = tfidf.transform(tfidf_x_train['question1']), tfidf.transform(tfidf_x_train['question2'])\n",
    "tfidf_cv_q1, tfidf_cv_q2 = tfidf.transform(tfidf_cv['question1']), tfidf.transform(tfidf_cv['question2'])\n",
    "tfidf_test_q1, tfidf_test_q2 = tfidf.transform(tfidf_x_test['question1']), tfidf.transform(tfidf_x_test['question2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 216
    },
    "colab_type": "code",
    "id": "3le2cKJttMTY",
    "outputId": "4eec61f8-976d-4738-ff0f-bd1576ed79e3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>q1len</th>\n",
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       "      <th>q1_n_word</th>\n",
       "      <th>q2_n_word</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>...</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>token_set_ratio</th>\n",
       "      <th>token_sort_ratio</th>\n",
       "      <th>fuzz_ratio</th>\n",
       "      <th>fuzz_partial_ratio</th>\n",
       "      <th>longest_substr_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155514</th>\n",
       "      <td>155515</td>\n",
       "      <td>which are the best books for iit jam mathematics?</td>\n",
       "      <td>what are the best books for iit-jam mathematics?</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666659</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>0.551020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400577</th>\n",
       "      <td>400580</td>\n",
       "      <td>what are the best photo startups?</td>\n",
       "      <td>what is a good photo?</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166664</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>67</td>\n",
       "      <td>54</td>\n",
       "      <td>50</td>\n",
       "      <td>48</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                          question1  \\\n",
       "155514  155515  which are the best books for iit jam mathematics?   \n",
       "400577  400580                  what are the best photo startups?   \n",
       "\n",
       "                                               question2  q1len  q2len  \\\n",
       "155514  what are the best books for iit-jam mathematics?     49     48   \n",
       "400577                             what is a good photo?     33     21   \n",
       "\n",
       "        q1_n_word  q2_n_word  word_Common  word_total  word_share  ...  \\\n",
       "155514          9          8          6.0        17.0    0.352941  ...   \n",
       "400577          6          5          1.0        11.0    0.090909  ...   \n",
       "\n",
       "         ctc_max  last_word_eq  first_word_eq  abs_len_diff  mean_len  \\\n",
       "155514  0.666659             1              0             1       8.5   \n",
       "400577  0.166664             0              1             1       5.5   \n",
       "\n",
       "        token_set_ratio  token_sort_ratio  fuzz_ratio  fuzz_partial_ratio  \\\n",
       "155514               95                95          95                  92   \n",
       "400577               67                54          50                  48   \n",
       "\n",
       "        longest_substr_ratio  \n",
       "155514              0.551020  \n",
       "400577              0.272727  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ofc9Bik6tDvr"
   },
   "source": [
    "## Merging & Standarizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dy7LMN2CyBCQ"
   },
   "source": [
    "* We need to drop the unwanted column of x_train/test.\n",
    "* Then we'll convert each dataframe into numpy ndarray and then we'll merge them into one numpy ndarray using numpy hstack().\n",
    "* We'll standarize the data using training dataset (x_train).\n",
    "* I'm again splitting the X_train into train and CV dataset. (with small __data leakage which can be ignored__).\n",
    "\n",
    "__Note:__ Only use x_train to fit the data and tranform both x_train and x_test to transform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4dMsJB9jR4az"
   },
   "source": [
    "**For TF-IDF W2V**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "gnsXEqLiK7NI",
    "outputId": "d48cb960-4728-483c-c5c9-e13a9497a216"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train: (226400, 216), CV: (56600, 216), and test: (121287, 216)\n"
     ]
    }
   ],
   "source": [
    "# #Now let's merge or stack the data using np.hstack(): https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html\n",
    "cols = [i for i in x_train.columns if i not in ['id', 'qid1', 'qid2','question1', 'question2']]\n",
    "x_train = x_train.loc[:,cols].values\n",
    "x_test = x_test.loc[:, cols].values\n",
    "tfidf_w2v_q1_train, tfidf_w2v_q2_train = tfidf_w2v_q1_train.values, tfidf_w2v_q2_train.values\n",
    "tfidf_w2v_q1_test, tfidf_w2v_q2_test = tfidf_w2v_q1_test.values, tfidf_w2v_q2_test.values\n",
    "\n",
    "X_train = np.hstack((x_train, tfidf_w2v_q1_train, tfidf_w2v_q2_train))\n",
    "X_test = np.hstack((x_test, tfidf_w2v_q1_test, tfidf_w2v_q2_test))\n",
    "\n",
    "#let's split the X_train into train and CV datasets \n",
    "\n",
    "X_train, X_cv , Y_train, Y_cv = train_test_split(X_train, y_train, stratify = y_train, test_size=0.2, random_state = 42)\n",
    "std_data1 = StandardScaler()\n",
    "std_data1.fit(X_train)\n",
    "\n",
    "X_train = std_data1.transform(X_train)\n",
    "X_cv = std_data1.transform(X_cv)\n",
    "X_test = std_data1.transform(X_test)\n",
    "\n",
    "print('Shape of train: {}, CV: {}, and test: {}'.format(X_train.shape, X_cv.shape, X_test.shape))     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_5jU-wbR1h_"
   },
   "source": [
    "**For TF-IDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rw4sJIRaRy7X",
    "outputId": "7616c7af-d127-4969-fb53-860745ce04fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train: (226400, 10024), CV: (56600, 10024), and test: (121287, 10024)\n"
     ]
    }
   ],
   "source": [
    "from scipy.sparse import hstack\n",
    "tfidf_x_train = hstack((tfidf_train_q1, tfidf_train_q2, tfidf_x_train.iloc[:,3:])).tocsr()\n",
    "tfidf_x_cv = hstack((tfidf_cv_q1, tfidf_cv_q2, tfidf_cv.iloc[:,3:])).tocsr()\n",
    "tfidf_x_test = hstack((tfidf_test_q1, tfidf_test_q2, tfidf_x_test.iloc[:,3:])).tocsr()\n",
    "\n",
    "std_data2 = StandardScaler(with_mean=False)\n",
    "std_data2.fit(tfidf_x_train)\n",
    "\n",
    "tfidf_x_train = std_data2.transform(tfidf_x_train)\n",
    "tfidf_x_cv = std_data2.transform(tfidf_x_cv)\n",
    "tfidf_x_test = std_data2.transform(tfidf_x_test)\n",
    "\n",
    "print('Shape of train: {}, CV: {}, and test: {}'.format(tfidf_x_train.shape, tfidf_x_cv.shape, tfidf_x_test.shape))     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the vectorizer and standardizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the path for model saving\n",
    "d = \"./Models/\"\n",
    "import pickle\n",
    "with open(d+\"std_w2v.pkl\", \"wb\") as f:\n",
    "    pickle.dump(std_data1, f)\n",
    "\n",
    "with open(d+\"std_tfidf.pkl\", \"wb\") as f:\n",
    "    pickle.dump(std_data2, f)\n",
    "    \n",
    "with open(d+\"tfidf_vectorizer.pkl\", \"wb\") as f:\n",
    "    pickle.dump(tfidf, f)\n",
    "\n",
    "with open(d+\"idf_score.pkl\", \"wb\") as f:\n",
    "    pickle.dump(word_to_idf_score, f)\n",
    "\n",
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zcXXY_tbGVML"
   },
   "source": [
    "## Random Model for training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oIjaa6iENnX-"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrices(y_true, y_pred):\n",
    "    C = confusion_matrix(y_true, y_pred) #for confusion matrix\n",
    "    # for presion matrix: take the sum column wise, i.e. column normalization\n",
    "    P = C/C.sum(axis = 0)\n",
    "    #for recall matrix: take the sum row wise, i.e. row normalization\n",
    "    R = (C.T/C.sum(axis = 1)).T\n",
    "\n",
    "    fig = plt.figure(figsize=(18,4))\n",
    "    \n",
    "    for i, j in enumerate(zip([C,P,R], ['Confusion', 'Precison', 'Recall'])):\n",
    "        ax = fig.add_subplot(1,3,i+1)\n",
    "        if i == 0:\n",
    "            fmts = 'd'\n",
    "        else:\n",
    "            fmts = '0.3f'\n",
    "        #https://seaborn.pydata.org/generated/seaborn.heatmap.html\n",
    "        sns.heatmap(j[0], annot = True, fmt = fmts, annot_kws={\"size\":15})\n",
    "        plt.xlabel('Predicted Class', fontsize = 12)\n",
    "        plt.ylabel('Original Class', fontsize = 12)\n",
    "        plt.title(str(j[1])+' '+'Matrix', fontsize = 15) \n",
    "    plt.show()\n",
    "                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ba-B33oxw5bE"
   },
   "outputs": [],
   "source": [
    "y_true_cv, y_true_test = Y_cv, y_test\n",
    "# adding two columns: one for probability of class '0' and for probability of class '1'\n",
    "# i.e. P(y_train_random_pred[0] == 0), P(y_train_random_pred[1] == 1)\n",
    "y_cv_random_pred = np.zeros((len(y_true_cv), 2)) \n",
    "y_test_random_pred = np.zeros((len(y_true_test), 2)) \n",
    "\n",
    "for i in range(len(y_true_cv)):\n",
    "    random_value = np.random.rand(1,2)\n",
    "    y_cv_random_pred[i] = (random_value/np.sum(random_value))[0]\n",
    "\n",
    "for i in range(len(y_true_test)):\n",
    "    random_value = np.random.rand(1,2)\n",
    "    y_test_random_pred[i] = (random_value/np.sum(random_value))[0]    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6bdMpRmpMh7k"
   },
   "source": [
    "**For Training data**\n",
    "* Worst log-loss value for train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "QVMY9rzzL7FU",
    "outputId": "df108070-10c3-4414-b2c9-88c566bc0902"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss for CV Data using Random Model 0.8864\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss for CV Data using Random Model\",round(log_loss(y_true_cv, y_cv_random_pred, eps=1e-15),4))\n",
    "# https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html\n",
    "y_cv_random_pred = np.argmax(y_cv_random_pred, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(y_true_cv, y_cv_random_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jEOyWp6Wd0Q3"
   },
   "source": [
    "**For Testing data**\n",
    "* Worst log-loss value for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "uBgiLlBaL-Jr",
    "outputId": "9a4eb5fe-425c-40c8-f0b6-aa03558fc5a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data using Random Model 0.8841\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data using Random Model\",round(log_loss(y_true_test, y_test_random_pred, eps=1e-15), 4))\n",
    "y_test_random_pred = np.argmax(y_test_random_pred, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(y_true_test, y_test_random_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J6QY9A_EeltS"
   },
   "source": [
    "__NOTE:__ So any reasonalbe model  must have log-loss value in range of [0, 0.886)  for test data and for train data in range of [0, 0.8842)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AiLIBXw5fXZZ"
   },
   "source": [
    "## Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cmG8ojyomMrO",
    "outputId": "81c36d91-1efa-47ca-fcb6-a72d12e2a414"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.37, 1: 0.63}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = Y_train.value_counts()\n",
    "cl_weight = {0: round(a[1]/(a[0]+a[1]),2), 1: round(a[0]/(a[0]+a[1]), 2)}\n",
    "cl_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2u17ualk8Lr"
   },
   "outputs": [],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.calibration import calibration_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "moXitWuIq-R5"
   },
   "outputs": [],
   "source": [
    "def plot_log_loss(param, train_auc, cv_auc, hyp_name):\n",
    "    plt.figure(figsize = (10,6))\n",
    "    plt.plot(param, train_auc, 'bo:', label = 'Train')\n",
    "    plt.plot(param, cv_auc, 'ro:', label = 'CV')\n",
    "    plt.title(\"log-loss vs hyperparameter '{}' for train and CV\".format(hyp_name), fontsize = 15)\n",
    "    plt.xlabel('hyperparameter '+str(hyp_name), fontsize = 12)\n",
    "    plt.ylabel('log-loss', fontsize = 12)\n",
    "    plt.legend()\n",
    "    plt.grid(1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "73AxtfJcfoIk"
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pWuUgcHljXh4"
   },
   "source": [
    "#### TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "OP1ra9Tuozly",
    "outputId": "9d7b4c70-d8eb-4e6e-9bfc-4186ea2e2b7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for C = 0.0001 log-loss for train is 0.39765905545733765 and for CV is 0.41974957079363856\n",
      "for C = 0.001 log-loss for train is 0.37878313209022374 and for CV is 0.4111872262593457\n",
      "for C = 0.01 log-loss for train is 0.3756560517693538 and for CV is 0.4121976782144737\n",
      "for C = 0.1 log-loss for train is 0.37613976117284625 and for CV is 0.41334444516010654\n",
      "for C = 1.0 log-loss for train is 0.37634981795908995 and for CV is 0.4136488360143397\n",
      "for C = 10.0 log-loss for train is 0.376403041116564 and for CV is 0.41362647111411166\n",
      "for C = 100.0 log-loss for train is 0.37641779875914066 and for CV is 0.41369398987272005\n",
      "for C = 1000.0 log-loss for train is 0.3763942193349363 and for CV is 0.4136825217479535\n",
      "for C = 10000.0 log-loss for train is 0.37645437070839866 and for CV is 0.413646092784445\n"
     ]
    }
   ],
   "source": [
    "log_loss_train = []\n",
    "log_loss_cv=[]\n",
    "c = np.array([10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3, 10**4])\n",
    "for j,i in enumerate(c):\n",
    "    clf = LogisticRegression(penalty = 'l2', class_weight= cl_weight, C = i, random_state = 42)\n",
    "    clf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    \n",
    "    calib_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    calib_clf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    \n",
    "    Y_pred_train = calib_clf.predict_proba(tfidf_x_train)\n",
    "    Y_pred_CV = calib_clf.predict_proba(tfidf_x_cv) \n",
    "    \n",
    "    log_loss_train.append(log_loss(tfidf_y_train, Y_pred_train, labels= calib_clf.classes_, eps=1e-15))\n",
    "    log_loss_cv.append(log_loss(tfidf_y_cv, Y_pred_CV, labels= calib_clf.classes_, eps=1e-15)) \n",
    "    \n",
    "    print('for C = {} log-loss for train is {} and for CV is {}'.format(i,log_loss_train[j], log_loss_cv[j]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "MPUpAdSDYGKu",
    "outputId": "a44d2980-594f-4076-bb63-42f9fce7ace3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_loss(np.log10(c), log_loss_train, log_loss_cv, hyp_name = 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R8_QPI8rUvds"
   },
   "outputs": [],
   "source": [
    "LR = LogisticRegression(penalty = 'l2', class_weight= cl_weight, random_state = 42, C = 0.001) #L2 norm\n",
    "LR.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "calib_LR = CalibratedClassifierCV(LR, method=\"sigmoid\")\n",
    "calib_LR.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "y_prob_train = calib_LR.predict_proba(tfidf_x_train)\n",
    "y_prob_test = calib_LR.predict_proba(tfidf_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "QfLXMam5UxfI",
    "outputId": "a343dd12-9758-467e-aacc-9a7aec68a091"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.3788\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.4114\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1pP3W1AOjQ4r"
   },
   "source": [
    "#### TF-IDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "RdaERSBopKuS",
    "outputId": "3d3eac83-78e1-48b6-f3ee-dc51acb3e555"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for C = 0.0001 log-loss for train is 0.5071876339575201 and for CV is 0.5060628156186036\n",
      "for C = 0.001 log-loss for train is 0.48656719225320205 and for CV is 0.48522592215519145\n",
      "for C = 0.01 log-loss for train is 0.4814374420883692 and for CV is 0.47997694216673537\n",
      "for C = 0.1 log-loss for train is 0.48073789963915464 and for CV is 0.4792445884988061\n",
      "for C = 1.0 log-loss for train is 0.48071268884278867 and for CV is 0.47922061927523923\n",
      "for C = 10.0 log-loss for train is 0.48071102338508503 and for CV is 0.4792235737768213\n",
      "for C = 100.0 log-loss for train is 0.4807096557957907 and for CV is 0.4792169706366858\n",
      "for C = 1000.0 log-loss for train is 0.48071004272630713 and for CV is 0.479218543743171\n",
      "for C = 10000.0 log-loss for train is 0.4807090703385007 and for CV is 0.4792191606185114\n"
     ]
    }
   ],
   "source": [
    "log_loss_train = []\n",
    "log_loss_cv=[]\n",
    "c = np.array([10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3, 10**4])\n",
    "for j,i in enumerate(c):\n",
    "    clf = LogisticRegression(penalty = 'l2', class_weight= cl_weight, C = i, random_state = 42)\n",
    "    clf.fit(X_train, Y_train.values)\n",
    "    \n",
    "    calib_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    calib_clf.fit(X_train, Y_train.values)\n",
    "    \n",
    "    Y_pred_train = calib_clf.predict_proba(X_train)\n",
    "    Y_pred_CV = calib_clf.predict_proba(X_cv) \n",
    "    \n",
    "    log_loss_train.append(log_loss(Y_train, Y_pred_train, labels= calib_clf.classes_, eps=1e-15))\n",
    "    log_loss_cv.append(log_loss(Y_cv, Y_pred_CV, labels= calib_clf.classes_, eps=1e-15)) \n",
    "    \n",
    "    print('for C = {} log-loss for train is {} and for CV is {}'.format(i,log_loss_train[j], log_loss_cv[j]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "gBboYus3txBF",
    "outputId": "5d1445d0-9ab7-49d7-ef34-cff0b09f71fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_loss(np.log10(c), log_loss_train, log_loss_cv, hyp_name = 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cDUOaazCWOXf"
   },
   "outputs": [],
   "source": [
    "LR = LogisticRegression(penalty = 'l2', class_weight= cl_weight, random_state = 42, C = 10**0) #L2 norm\n",
    "LR.fit(X_train, Y_train)\n",
    "calib_LR = CalibratedClassifierCV(LR, method=\"sigmoid\")\n",
    "calib_LR.fit(X_train, Y_train)\n",
    "y_prob_train = calib_LR.predict_proba(X_train)\n",
    "y_prob_test = calib_LR.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "GC1A-1DVYfVv",
    "outputId": "1cd112e2-c6d7-4497-8b15-d3b22810a70e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.4807\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.4812\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(Y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KZTQesqSfrRF"
   },
   "source": [
    "### Linear SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XeYwNWUGjfAb"
   },
   "source": [
    "#### TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "jToa_OStjfAh",
    "outputId": "3748ea5c-6206-4d84-8b97-775b2006e363"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for C = 0.0001 log-loss for train is 0.5844609085723569 and for CV is 0.5911596289268622\n",
      "for C = 0.001 log-loss for train is 0.6500506821404113 and for CV is 0.6506259814554174\n",
      "for C = 0.01 log-loss for train is 0.6577583865100847 and for CV is 0.6578513484333947\n",
      "for C = 0.1 log-loss for train is 0.6581889549310322 and for CV is 0.6581696680825685\n",
      "for C = 1.0 log-loss for train is 0.6583561815551606 and for CV is 0.6583266625820053\n",
      "for C = 10.0 log-loss for train is 0.6585131537851824 and for CV is 0.6585002061171275\n",
      "for C = 100.0 log-loss for train is 0.6585288179345656 and for CV is 0.6585311838712791\n",
      "for C = 1000.0 log-loss for train is 0.6585288179345656 and for CV is 0.6585311838712791\n",
      "for C = 10000.0 log-loss for train is 0.6585288179345656 and for CV is 0.6585311838712791\n"
     ]
    }
   ],
   "source": [
    "log_loss_train = []\n",
    "log_loss_cv=[]\n",
    "c = np.array([10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3, 10**4])\n",
    "for j,i in enumerate(c):\n",
    "    clf = SGDClassifier(alpha = i, penalty='l1', loss='hinge', \n",
    "                        class_weight= cl_weight, n_jobs=-1, random_state=42)\n",
    "    clf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    \n",
    "    calib_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    calib_clf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    \n",
    "    Y_pred_train = calib_clf.predict_proba(tfidf_x_train)\n",
    "    Y_pred_CV = calib_clf.predict_proba(tfidf_x_cv) \n",
    "    \n",
    "    log_loss_train.append(log_loss(tfidf_y_train, Y_pred_train, labels= calib_clf.classes_, eps=1e-15))\n",
    "    log_loss_cv.append(log_loss(tfidf_y_cv, Y_pred_CV, labels= calib_clf.classes_, eps=1e-15)) \n",
    "    \n",
    "    print('for C = {} log-loss for train is {} and for CV is {}'.format(i,log_loss_train[j], log_loss_cv[j]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "XixLEwhunuvA",
    "outputId": "7f14634d-c479-4a31-9398-972f78f926dc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_loss(np.log10(c), log_loss_train, log_loss_cv, hyp_name = 'alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w0TE75HsnuvO"
   },
   "outputs": [],
   "source": [
    "SVM = SGDClassifier(alpha = 10**-4, penalty='l1', loss='hinge', \n",
    "                    class_weight= cl_weight, n_jobs=-1, random_state=42) #L2 norm\n",
    "SVM.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "calib_LR = CalibratedClassifierCV(SVM, method=\"sigmoid\")\n",
    "calib_LR.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "y_prob_train = calib_LR.predict_proba(tfidf_x_train)\n",
    "y_prob_test = calib_LR.predict_proba(tfidf_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "nCNuQ3JtnuvW",
    "outputId": "638d45fb-c9ef-4291-9df8-0b8dd2a2a595"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.5845\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.591\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kgoH0n3xjfAn"
   },
   "source": [
    "#### TF-IDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "D9-6BddCjfAp",
    "outputId": "ccae7a27-ea9f-45b7-88b7-e1d9a74e8bb2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 0.0001: log-loss of train is 0.483104082569391 and of CV is 0.48188969844121404\n",
      "for alpha = 0.001: log-loss of train is 0.4857015404690512 and of CV is 0.48470117656904926\n",
      "for alpha = 0.01: log-loss of train is 0.5107242079058342 and of CV is 0.5092548064372067\n",
      "for alpha = 0.1: log-loss of train is 0.5403912886112396 and of CV is 0.5389912950310661\n",
      "for alpha = 1.0: log-loss of train is 0.6585288179345656 and of CV is 0.6585311838712787\n",
      "for alpha = 10.0: log-loss of train is 0.6585288179345655 and of CV is 0.6585311838712787\n",
      "for alpha = 100.0: log-loss of train is 0.6585288179345656 and of CV is 0.6585311838712788\n",
      "for alpha = 1000.0: log-loss of train is 0.6585288179345654 and of CV is 0.6585311838712787\n",
      "for alpha = 10000.0: log-loss of train is 0.6585288179345656 and of CV is 0.6585311838712791\n"
     ]
    }
   ],
   "source": [
    "log_loss_train = []\n",
    "log_loss_cv=[]\n",
    "c = np.array([10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3, 10**4])\n",
    "for j,i in enumerate(c):\n",
    "    clf = SGDClassifier(alpha = i, penalty='l1', loss='hinge', \n",
    "                        class_weight= cl_weight, n_jobs=-1, random_state=42)\n",
    "    clf.fit(X_train, Y_train.values)\n",
    "    \n",
    "    calib_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    calib_clf.fit(X_train, Y_train.values)\n",
    "    \n",
    "    Y_pred_train = calib_clf.predict_proba(X_train)\n",
    "    Y_pred_CV = calib_clf.predict_proba(X_cv) \n",
    "    \n",
    "    log_loss_train.append(log_loss(Y_train, Y_pred_train, labels= calib_clf.classes_, eps=1e-15))\n",
    "    log_loss_cv.append(log_loss(Y_cv, Y_pred_CV, labels= calib_clf.classes_, eps=1e-15)) \n",
    "    \n",
    "    print('for alpha = {}: log-loss of train is {} and of CV is {}'.format(i,log_loss_train[j], log_loss_cv[j]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "JAyco6vUYoYU",
    "outputId": "045438c0-7092-4645-af59-e188d0883d43"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_log_loss(np.log10(c), log_loss_train, log_loss_cv, hyp_name = 'alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3atZxC92TycY"
   },
   "outputs": [],
   "source": [
    "SVM = SGDClassifier(alpha = 10**-4, penalty='l1', loss='hinge', \n",
    "                    class_weight= cl_weight, n_jobs=-1, random_state=42) \n",
    "SVM.fit(X_train, Y_train.values)\n",
    "calib_LR = CalibratedClassifierCV(SVM, method=\"sigmoid\")\n",
    "calib_LR.fit(X_train, Y_train.values)\n",
    "y_prob_train = calib_LR.predict_proba(X_train)\n",
    "y_prob_test = calib_LR.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "y7_pgKaTNvX1",
    "outputId": "c57a1392-dbf4-4f4a-b362-08c735f2cd6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.4831\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.4838\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(Y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WeCY0Srwfwsb"
   },
   "source": [
    "### GBDT (XGBoost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9PS7YSFCjgAM"
   },
   "source": [
    "#### TF-IDF weighted W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0Y5RCoAlthf3"
   },
   "source": [
    "**I'll use LighGBM to train GBDT model**\n",
    "* Ref.: https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5kUhat9SNJJG"
   },
   "source": [
    "#### With TF-IDF (only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "X3AXtm5XXFli",
    "outputId": "526c2577-f80f-4f5f-ecfb-b9a6cf27142f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 pairs are left to be check to tune the hyperparamter:\n"
     ]
    }
   ],
   "source": [
    "estimators = np.array([10, 50, 100, 200, 400, 500]) #estimators\n",
    "depths = np.array( [2, 4, 6, 8, 10])#\n",
    "\n",
    "try:\n",
    "    train_log_loss1 = pd.read_csv('./Data/xgb_light_train.csv')\n",
    "    #to make dtype of index and column as intiger, otherwise they'll be string: https://stackoverflow.com/a/45452124\n",
    "    train_log_loss1.index, train_log_loss1.columns  = estimators, depths \n",
    "    cv_log_loss1 = pd.read_csv('./Data/xgb_light_cv.csv')\n",
    "    cv_log_loss1.index, cv_log_loss1.columns = estimators, depths\n",
    "except:\n",
    "    train_log_loss1 = pd.DataFrame(data = np.zeros((len(estimators), len(depths))), index = estimators, columns = depths)\n",
    "    cv_log_loss1    = pd.DataFrame(data = np.zeros((len(estimators), len(depths))), index = estimators, columns = depths)\n",
    "    \n",
    "print('{} pairs are left to be check to tune the hyperparamter:'.format((cv_log_loss1 == 0).values.sum()))\n",
    "\n",
    "for est in estimators:\n",
    "    for depth in depths:\n",
    "        if train_log_loss1.loc[est,depth] == 0 and cv_log_loss1.loc[est,depth] == 0: \n",
    "            print('Training and calibrating for: n_estimators = {}, max_depth = {}'.format(est, depth))\n",
    "           \n",
    "            # scikit-learn API: https://lightgbm.readthedocs.io/en/latest/Python-API.html#lightgbm.LGBMClassifier\n",
    "            XGB = LGBMClassifier(boosting_type = 'gbdt', max_depth = depth, n_estimators= est, class_weight = cl_weight)\n",
    "            XGB.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "\n",
    "            calib_clf = CalibratedClassifierCV(XGB, method=\"sigmoid\")\n",
    "            calib_clf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "\n",
    "            Y_pred_train = calib_clf.predict_proba(tfidf_x_train)\n",
    "            Y_pred_CV = calib_clf.predict_proba(tfidf_x_cv)\n",
    "            \n",
    "            # finding the log-loss value\n",
    "            train_log_loss1.loc[est,depth] = np.round(log_loss(tfidf_y_train, Y_pred_train, labels = XGB.classes_, eps=1e-15),4)\n",
    "            cv_log_loss1.loc[est,depth] = np.round(log_loss(tfidf_y_cv, Y_pred_CV, labels= XGB.classes_, eps=1e-15),4)\n",
    "            \n",
    "            # save the value in each iteration\n",
    "            train_log_loss1.to_csv('./Data/xgb_light_train.csv', index=False)\n",
    "            cv_log_loss1.to_csv('./Data/xgb_light_cv.csv', index=False)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "colab_type": "code",
    "id": "ag0lWO5GXFlr",
    "outputId": "7306a75c-90bd-4999-e016-0db2d605db33",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1 = sns.heatmap(train_log_loss1, annot = True, fmt = '0.3f', annot_kws={\"size\":15})\n",
    "plt.ylabel('estimators', fontsize = 12)\n",
    "plt.xlabel('depth', fontsize = 12)\n",
    "plt.title('For Train Data\\nlog-loss values for (estimator, depth) pair', fontsize = 15)  \n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2 = sns.heatmap(cv_log_loss1, annot = True, fmt = '0.3f', annot_kws={\"size\":15})\n",
    "plt.ylabel('estimators', fontsize = 12)\n",
    "plt.xlabel('depth', fontsize = 12)\n",
    "plt.title('For CV Data\\nlog-loss values for (estimator, depth) pair', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LEkhuNVcUeJU"
   },
   "source": [
    "* From above heatmap of log-loss values of different pair of estimators, I'll pick __n_estimators = 500__ and __max_depth = 10__ as best hyper-paramter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "EThkl1i2Nlg4",
    "outputId": "072e2fd1-a4e4-4460-d9f2-b7d64d51a0b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.isfile(d+'tfidf_GBDT_model.pkl'):\n",
    "    with open(d+'tfidf_GBDT_model.pkl', 'rb') as f:\n",
    "        calib_gbdt_tfidf = pickle.load(f)\n",
    "else:\n",
    "    XGB = LGBMClassifier(boosting_type = 'gbdt', max_depth = 10, n_estimators= 500, \n",
    "                         class_weight = cl_weight, random_state=42) \n",
    "    XGB.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    calib_gbdt_tfidf = CalibratedClassifierCV(XGB, method=\"sigmoid\")\n",
    "    calib_gbdt_tfidf.fit(tfidf_x_train, tfidf_y_train.values)\n",
    "    \n",
    "    #save the model\n",
    "    with open(d+'tfidf_GBDT_model.pkl', 'wb') as f:\n",
    "        pickle.dump(calib_gbdt_tfidf, f)\n",
    "\n",
    "#let's whether it needs calibration or not\n",
    "true_uncalib, pred_uncalib = calibration_curve(tfidf_y_test, XGB.predict_proba(tfidf_x_test)[:,1])\n",
    "true_calib, pred_calib = calibration_curve(tfidf_y_test, calib_gbdt_tfidf.predict_proba(tfidf_x_test)[:,1])\n",
    "\n",
    "#let's plot them:\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.plot([0,1], [0,1], 'r-', label = 'Ideal')\n",
    "plt.plot(true_uncalib, pred_uncalib, 'bo--', label = 'Uncalibrated')\n",
    "plt.plot(true_calib, pred_calib, 'go--', label = 'Calibrated')\n",
    "plt.title('Calibrarion Plot (Reliability Plot)', fontsize = 15)\n",
    "plt.xlabel('Predicted probability', fontsize = 12)\n",
    "plt.ylabel('True probability', fontsize = 12)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MtmTWmjRU_Jh"
   },
   "source": [
    "**Note:** From above reliability plot we can see that our model with calibration will yield better probability preidiction. So I'll calibrate next model also which GBDT (using LighGBM) of TF-IDF weighted W2V."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "jQ1WUsb8NlhC",
    "outputId": "efc1d9a5-59fc-492f-e517-9eff9d217b4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.2965\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.3423\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_prob_train = calib_gbdt_tfidf.predict_proba(tfidf_x_train)\n",
    "y_prob_test = calib_gbdt_tfidf.predict_proba(tfidf_x_test)\n",
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(tfidf_y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nncU-KIwNCrI"
   },
   "source": [
    "#### With TF-IDF weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zMMsRnW4FiNf",
    "outputId": "c9fdd1f5-6b56-4917-da18-57b118a5b73a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 pairs are left to be check to tune the hyperparamter:\n"
     ]
    }
   ],
   "source": [
    "estimators = np.array([10, 50, 100, 200, 400, 500]) #estimators\n",
    "depths = np.array( [2, 4, 6, 8, 10])#\n",
    "\n",
    "try:\n",
    "    train_log_loss = pd.read_csv('./Data/xgb_light_train5.csv')\n",
    "    #to make dtype of index and column as intiger, otherwise they'll be string: https://stackoverflow.com/a/45452124\n",
    "    train_log_loss.index, train_log_loss.columns  = estimators, depths \n",
    "    cv_log_loss = pd.read_csv('./Data/xgb_light_cv5.csv')\n",
    "    cv_log_loss.index, cv_log_loss.columns = estimators, depths\n",
    "except:\n",
    "    train_log_loss = pd.DataFrame(data = np.zeros((len(estimators), len(depths))), index = estimators, columns = depths)\n",
    "    cv_log_loss    = pd.DataFrame(data = np.zeros((len(estimators), len(depths))), index = estimators, columns = depths)\n",
    "    \n",
    "print('{} pairs are left to be check to tune the hyperparamter:'.format((cv_log_loss == 0).values.sum()))\n",
    "\n",
    "for est in estimators:\n",
    "    for depth in depths:\n",
    "        if train_log_loss.loc[est,depth] == 0 and cv_log_loss.loc[est,depth] == 0: \n",
    "            print('Training and calibrating for: n_estimators = {}, max_depth = {}'.format(est, depth))\n",
    "           \n",
    "            # scikit-learn API: https://lightgbm.readthedocs.io/en/latest/Python-API.html#lightgbm.LGBMClassifier\n",
    "            XGB = LGBMClassifier(boosting_type = 'gbdt', max_depth = depth, n_estimators= est, class_weight = cl_weight)\n",
    "            XGB.fit(X_train, Y_train.values)\n",
    "\n",
    "            calib_clf = CalibratedClassifierCV(XGB, method=\"sigmoid\")\n",
    "            calib_clf.fit(X_train, Y_train.values)\n",
    "\n",
    "            Y_pred_train = calib_clf.predict_proba(X_train)\n",
    "            Y_pred_CV = calib_clf.predict_proba(X_cv)\n",
    "            \n",
    "            # finding the log-loss value\n",
    "            train_log_loss.loc[est,depth] = np.round(log_loss(Y_train, Y_pred_train, labels = XGB.classes_, eps=1e-15),4)\n",
    "            cv_log_loss.loc[est,depth] = np.round(log_loss(Y_cv, Y_pred_CV, labels= XGB.classes_, eps=1e-15),4)\n",
    "            \n",
    "            # save the value in each iteration\n",
    "            train_log_loss.to_csv('./Data/xgb_light_train5.csv', index=False)\n",
    "            cv_log_loss.to_csv('./Data/xgb_light_cv5.csv', index=False)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "colab_type": "code",
    "id": "ULrqBkGIWG_x",
    "outputId": "b2031e43-0d7c-4b37-b8fd-058b13ba3228"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1 = sns.heatmap(train_log_loss, annot = True, fmt = '0.3f', annot_kws={\"size\":15})\n",
    "plt.ylabel('estimators', fontsize = 12)\n",
    "plt.xlabel('depth', fontsize = 12)\n",
    "plt.title('For Train Data\\nlog-loss values for (estimator, depth) pair', fontsize = 15)  \n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2 = sns.heatmap(cv_log_loss, annot = True, fmt = '0.3f', annot_kws={\"size\":15})\n",
    "plt.ylabel('estimators', fontsize = 12)\n",
    "plt.xlabel('depth', fontsize = 12)\n",
    "plt.title('For CV Data\\nlog-loss values for (estimator, depth) pair', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DqirEAIbcq-4"
   },
   "source": [
    "* From above heatmap of log-loss values of different pair of estimators, I'll pick __n_estimators = 500__ and __max_depth = 6__ as best hyper-paramter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zdo-_EPhNvGO"
   },
   "outputs": [],
   "source": [
    "if os.path.isfile(d+'tfidf_w2v_GBDT_model.pkl'):\n",
    "    with open(d+'tfidf_w2v_GBDT_model.pkl', 'rb') as f:\n",
    "        calib_gbdt_tfidf_w2v = pickle.load(f)\n",
    "else:\n",
    "    XGB = LGBMClassifier(boosting_type = 'gbdt', max_depth = 6, n_estimators= 500, \n",
    "                         class_weight = cl_weight, random_state=42) \n",
    "    XGB.fit(X_train, Y_train.values)\n",
    "    calib_gbdt_tfidf_w2v = CalibratedClassifierCV(XGB, method=\"sigmoid\")\n",
    "    calib_gbdt_tfidf_w2v.fit(X_train, Y_train.values)\n",
    "    \n",
    "    #save the model\n",
    "    with open(d+'tfidf_w2v_GBDT_model.pkl', 'wb') as f:\n",
    "        pickle.dump(calib_gbdt_tfidf_w2v, f)\n",
    "        \n",
    "y_prob_train = calib_gbdt_tfidf_w2v.predict_proba(X_train)\n",
    "y_prob_test = calib_gbdt_tfidf_w2v.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "SSQyVcWQNvGV",
    "outputId": "078a3013-2a91-48c1-9b6f-59dc49460610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on train Data 0.2959\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on test Data 0.3732\n"
     ]
    },
    {
     "data": {
      "image/png": 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xZvYBwd/Aqe6+PM56xelBcNK7t7uvseB2w98TzKzepwz7EZEKwN3dzO4iGGV7qLt/TDCi4WJgdHgLxukEl0R0IJgM/WF3n2pmgwjuvrM5MIaggHuKu3cjyKrZ4fJbCO7icxuFbh+ZpPewnqBAXJKRwONmdhPBl3pHU+jLQXdfa2YzCG7nPongdso/lLE7dxBM6Huou68KM3aMmZ3n7s+VcV9SgWkEQ5Zw96sJrtVtC7xCEBZXE1RaY4dxnRAu60dwnakRzAI+LUlduS3c7x0EhYGJBLdWjPUVwa0mhxLcvqcJwTCx2cQRXp91GEEgP0NQ/fwD6Jx3QpwBZxL8UXkB6E9QHX6h0DqfERRuehIEdlmLIQ8SFH9i709/DcGJc3GjK0Skgggn49qXIPseJpjE9j6gAeGJXfit/okE3wJdQXAN7x0EBdh45hFcK3tTuO4TwBRiRmC5+1MEuXUiG25zdrW735PUN5ggdx9OUAQ5meCSkYMILlMr7FqCYvX/gHHAXokeI7xV873AbeHIOtz9t/C4N5nZnhvzHkQksl4DfiW4nJewyHowwbn0bQS53J/g/Dv2i7JLwuVnE0y22I/gDjV557UnEUzuOJTgMri7CSaKzISBBOecPQluzd6C+JfjXkxwzv4RQcZunegBwi9AryS409xcAHf/gqCQ3i+8RE8EACs4YlFEREREREREpOw0gkFERERERERENpoKDCIiIiIiIiKy0VRgEBEREREREZGNpgKDiIiIiIiIiGy0SNymct2C6ZqJsgLacYfCt2mXiuLXvydYebYry7/16k1al+sYUn7K4ornlD0vz3QXJEXe+eO9cmeksjh7KYcrplpbd8p0FyRF1q+dU+nOiTWCQUREREREREQ2WiRGMIhIJZGbk+keiIiIslhEJLMinMMqMIhI9shZn+keiIiIslhEJLMinMMqMIhI1nDPzXQXREQqPWWxiEhmRTmHVWAQkeyRG90wFRGpMJTFIiKZFeEcVoFBRLJHhKu1IiIVhrJYRCSzIpzDKjCISPaI8IQ2IiIVhrJYRCSzIpzDuk2liGQPz038kQAza2hmQ83sZzObYmb7mVljMxtpZr+GPxuF65qZPWJm08zsBzPbM2Y/54br/2pm58a072VmP4bbPGJmWXUfYhGRcklyFouISBlFOIdVYBCRrOE56xN+JKg/8IG7bw/sBkwBbgA+dve2wMfha4CjgLbhozswAMDMGgO3AvsAHYBb84oS4ToXxWzXZaN+ASIiWSAFWSwiImUQ5RxWgUFEskdubuKPUphZA+BA4BkAd1/r7kuArsDz4WrPAyeEz7sCL3hgLNDQzLYCjgRGuvsid18MjAS6hMvqu/tYd3fghZh9iYhEVxKzWEREyiHCOaw5GEQke5RhmJeZdScYaZBnkLsPinndCvgbeNbMdgMmAD2BLdx9brjOPGCL8HlTYFbM9rPDtpLaZ8dpFxGJtiwccisiUqlEOIdVYBCR7FGGCW3CYsKgElapBuwJXObuX5tZfzZcDpG3DzczL09XRUQqrAhPLiYiUiFEOId1iYSIZI/kTmgzG5jt7l+Hr4cSFBz+Ci9vIPw5P1w+B2ges32zsK2k9mZx2kVEoi3Ck4uJiFQIEc5hFRhEJHvkrE/8UQp3nwfMMrN2YdOhwE/AMCDvThDnAu+Ez4cB54R3k9gXWBpeSjECOMLMGoWTOx4BjAiXLTOzfcO7R5wTsy8RkehKYhaLiEg5RDiHdYmEiGSP5E9UcxnwspltAkwH/kVQWH3dzC4AZgKnhesOB44GpgGrwnVx90VmdjswLlyvr7svCp9fAjwH1ALeDx8iItGWhZOGiYhUKhHOYRUYRCRruCf3ejN3nwi0j7Po0DjrOtCjmP0MBgbHaR8P7LyR3RQRySrJzmIRESmbKOewCgwikj2y8DoyEZFKR1ksIpJZEc5hFRhEJHtEeDiYiEiFoSwWEcmsCOewCgwikj0iXK0VEakwlMUiIpkV4RxWgUFEskfOukz3QERElMUiIpkV4RxWgUFEskeEh4OJiFQYymIRkcxKcg6bWRegP1AVeNrd7ym0/GHg4PBlbWBzd28YLssBfgyX/eHux5d0LBUYRCR7RHg4mIhIhaEsFhHJrCTmsJlVBR4HDgdmA+PMbJi7/5R/OPcrY9a/DNgjZher3X33RI+nAoOIZA99ayYiknnKYhGRzEpuDncAprn7dAAzGwJ0BX4qZv0zgFvLe7Aq5d1QRCTpcnMTf4iISGooi0VEMqsMOWxm3c1sfMyje6G9NQVmxbyeHbYVYWYtgFbAqJjmmuF+x5rZCaV1XSMYRCRreIQntBERqSiUxSIimVWWHHb3QcCgJB26GzDU3XNi2lq4+xwzaw2MMrMf3f234nagAoOIZA9d9ysiknnKYhGRzEpuDs8Bmse8bha2xdMN6FGgK+5zwp/TzWw0wfwMKjCISARouK2ISOYpi0VEMiu5OTwOaGtmrQgKC92AMwuvZGbbA42Ar2LaGgGr3H2NmTUBDgDuK+lgKjCISPbQt2YiIpmnLBYRyawk5rC7rzezS4ERBLepHOzuk82sLzDe3YeFq3YDhri7x2y+AzDQzHIJ5m+8J/buE/GowCAi2UPfmomIZJ6yWEQks5Kcw+4+HBheqK13odd94mz3JbBLWY6lAoOIZA99ayYiknnKYhGRzIpwDqvAICLZY/36TPdARESUxSIimRXhHFaBQUSyR4SrtSIiFYayWEQksyKcwyowiEj20HW/IiKZpywWEcmsCOewCgwikj0iXK0VEakwkpjFZtYF6E8wc/nT7n5PoeUPAweHL2sDm7t7w3BZDvBjuOwPdz8+aR0TEclmET4nVoFBRLJHhKu1IiIVRpKy2MyqAo8DhwOzgXFmNiz2FmfufmXM+pcBe8TsYrW7756UzoiIREmEz4lVYBCR7BHhaq2ISIWRvCzuAExz9+kAZjYE6AoUdw/1M4Bbk3VwEZHIivA5sQoMIpI9IjxjrohIhVGGLDaz7kD3mKZB7j4ofN4UmBWzbDawTzH7aQG0AkbFNNc0s/HAeuAed3874Y6JiERZhM+JVWAQkezhnukeiIhIGbI4LCYMKnXF0nUDhrp7TkxbC3efY2atgVFm9qO7/5aEY4mIZLcInxOrwCAi2SPC15uJiFQYycviOUDzmNfNwrZ4ugE9YhvcfU74c7qZjSaYn0EFBhGp+CJ8TqwCg4hkjwiHqYhIhZG8LB4HtDWzVgSFhW7AmYVXMrPtgUbAVzFtjYBV7r7GzJoABwD3JatjIiJZLcLnxCowiEj2iPCENiIiFUaSstjd15vZpcAIgttUDnb3yWbWFxjv7sPCVbsBQ9wLjAneARhoZrlAFYI5GIqbHFJEpGKJ8DmxCgwikj1yckpfR0REUiuJWezuw4Hhhdp6F3rdJ852XwK7JK0jIiJREuFzYhUYRCR7RHg4mIhIhaEsFhHJrAjnsAoMIpI9IhymIiIVhrJYRCSzIpzDKjCISPaI8PVmIiIVhrJYRCSzIpzDKjCISNbw3Oje81dEpKJQFouIZFaUc1gFBhHJHhEeDiYiUmEoi0VEMivCOawCQzmtX5/Dc6++wZvvjWDuX/Np3LABRxzciet7/huAvxcs4vkhb/LluG+ZNWcu9evVZZ89d+OKi//F5pttWmR//31nOC//dxh/zPmTunXqsPceu/Lg7b0AmDP3L4485by4/WjZvCnvDXkagEWLl/Dkc6/yw+Sf+fnX6WzepDEfvvF8an4BFdQ2rZpxUY9z2L39rrTdvjXjx37H2Sf8O3959erVeGDAHeyy2w5stkUTVq1czY/f/8TDdz3B5B9+LrCvY044gosuO5eW227D8mUr+GrMNzxw+6PM/2tBmfb10tsD2eeA9nH7e+pR5zFx/I8p+E1kSJJnzDWz34HlQA6w3t3bm1lj4DWgJfA7cJq7LzYzA/oDRwOrgPPc/dtwP+cCN4e7vcPdnw/b9wKeA2oRzJLes9Bt1iRNfpsxk7seHsD3k36mXt06nHzckfzn/LOoWrVqidtNmvIL/Qc+x+SffwVgx3ZtuLz7uey60/b56+x8wFFxt61evRrfjX43//W06TO595GBfPfDT9SsWYMjDu7INT0upHbtWkl4h5VX87bNuajvxWy/ZztWLlvJyFc/ZEi/V8kt4eSrza5tOfqco9mxw0403qIxC/5cwKfvfMqbA4aybs26/PUGffEMWzTfIu4+zmv/fyyev5hDTjmUng9dGXedD15+nwG9Ht+4N5iNIjx7uWRWKrP4y2++5a3/fcj3k6bw57z5/Of8s+hxwdlF9qUsTr4ddmhL/4fvYN9992LJkqUMfvZV+t7+UIk5XL16de7oez377LMne+21K7Vq1aLaJk3jrnf9dT04+6xTaNp0S+bMmcerQ97i7nseZe3atWXaV4US4RxWgaGcbrrzQb6Z8D3/Of8sWrVoxrz5C/htxh/5yydP/ZWPx3zJycd1Ydcd27Fg8WIGPPMyZ198FW+/+GSBkHtk0PO8+sa7dD+3GzvvsB0LFy1h/MQNHxo327QRLw98qMDx16xZS/crb6Ljvhs+eM7/eyEffDyGXXdsx/ZtW7No8ZIU/gYqprbttuWgww5g4oRJVK9e9J9HlapVcXee7P8cf/w+m7r16vCvi8/kxbcG0vWQM5k1cw4Ahxx5IP2eupsXn3mNe/v0Y7MtmnBlr0t46tX+nHDo2bh7wvvqc9091K1Xp0A/el7/H3bcpR0/flfBbgmemmrtwe6+IOb1DcDH7n6Pmd0Qvr4eOApoGz72AQYA+4QFiVuB9oADE8xsmLsvDte5CPiaoMDQBXg/FW9Cird02XIu7Hkj27bahkfu6c2sOXN54LGnyHXn8u7nFrvd3L/+5qIrbmSH7dpwd+9rAXj2laFcdMWNvPXiALbeMvjgWTh/AS69vg977LJT/uvlK1Zy/uU30LJ5Ux7oewNLli7noSeeYcHCxTxyT+8i20ti6jSoQ99X7mDWr7O488I72KrFVvzr5guwKsbLD7xU7HYdj+vEli224o0BbzB3xp+03KElZ159Ni23b8m9F9+dv97d3e+k+ibVC2zb497LyM3JZfH8xQCMHzWOa7teXWCd7fZox0V9uvPtJxOS+G6zSIS/OZPMSXUWf/H1BH6ZNoN92u/OBx99GndfyuLka9iwASPeH8KUKb9y0sn/onXrltx/X2+qVKlC71vvK3a72rVrcf75ZzBu3ES++moChxzSMe56d995I927/x+9b72PiRMnscceu9D3tmtp0KA+V119a5n2VaFEOIdVYCiHz8eOZ8THY3jj+cfZtlWLuOvsuetOvPvKU1SrtqFiu+N2bTj2jIsYOfpzuh59OBBUWZ9+8XWefPB29u+wZ/66XQ49MP/5Jptswm4771Bg/yNGfcb6nByOPrxzftt2bVox5r1XAbj/sacY+cnnG/1eK5tRI8bw8QfBH61HB99Lo8YNCyxf888arrioV4G2L8d8zbipozjsqM48++TLABx3chcmfT+FvjdsCN4Vy1cy8KWHad2mBb/9+nvC+5r2y4wC61SvXo2dd9+B4W+PJCfC1c240hOmXYHO4fPngdEEBYauwAvhCISxZtbQzLYK1x3p7osAzGwk0MXMRgP13X1s2P4CcAIqMKTd628PZ83atfS762bq1gmKcStXreKJZ17m/LNOyW8rbMyX37By1Wr6330L9eoG6+y+8w50OqYbY74aR7cTjwUokr8/TpnK4iXLOOqwg/Lbhrz5HmvWrOGx+/pQv15dABo2qMel19/GpCm/sPMO2yX9fVcGXc4+mk1q1uDu7neyesVqvv9sIrXq1uaMK8/gzSffYPWK1XG3e+OJoSxfvCz/9aSxP7J2zVp63HMZmzXdjL/n/A3AjMnTC2zXcLOGNG/TnJfufyG/bdmiZSxbtKzAegefdDArlq5gwujxyXqr2SXCJ7aSOanO4qt7XMC1l10EwCefjY27L2Vx8v27+/9Rq1ZNTjntQpYvXwEff0b9+nXpfcvV3P/AE0FbHEuXLmOzLYJC/CX/Oa/YokC3bicwcOAL9Os/CIDRn35J06235IwzTswvMCS6rwolwjlcJdMdiKK3/vchHfbardjiAkD9enVN85MfAAAgAElEQVQLFBcAWm7TjFo1azB/waL8tnfe/4htmm5VoLiQiOEjR9Ns6y0LDB2rUkX/OTdWeUa3r1q5mjVr1hb4Fqx69WosX1YwcJcvWw5AMBI/8X0V1umQ/WnYqAHvvfVBmfua9dwTfphZdzMbH/PoHm+PwIdmNiFm+RbuPjd8Pg/IGx/dFJgVs+3ssK2k9tlx2iXNPh87nv077Fng5PWoQw/inzVrGP9d8ZcQrV+/nqpVq1CrZs38ttq1a1G1apXg/5xivD/yU2rVqknnjvvkt/3863R22n67/BNagP323hMzY8xX48r5zmSvznvx3affFigkfDZsDDVq1WTnfXcpdrvY4kKe6ZOCYkLjLYpeppin47GdsCrGZ8PGFLtOlSpV2P+Yjoz94CvWr12fyNuInjJksUieVGdxIue5yuLk63LkwXw48tMChYTXXn+H2rVrcdCB+230/qtXr87SZQUze8nSpSWeL1cKEc5hfSIthx8mT6Vl86bc+eAT7HP4SbQ/5AR69rqd+X8vLHG7qdNmsPqfNbRsvuEzyI8//Uyb1i15/JmX6Hj06ezR+Tgu7NmL337/o9j9rFi5ks/Hji/w7ZmkX9WqVWmy+aZcf2tPcnJyeO/NDR/4h74yjPb77sEJpx1D3bp1aNl6G67sdQlfjvmmyIiE0vZV2LEnHsncOfMY99V3KXlfGZWbm/DD3Qe5e/uYx6A4e+zo7nsSXP7Qw8wOjF0YjlbIvmSWMpkxcxatWjQv0LbVlptTq2YNps+cXcxWcHjnjtSqWZP7H3uKhYuXsHDxEu7rP4j69epxxMHxvx1xd0aMGsMhHfctcDK8du3aIpdVVa1alSpVjOkl5LmUrOm2zZj9W8H/hgv+/Jt/Vv1Ds22blWlf2++1PTk5OcybObfYdToddyBTv/05f4RDPLsesBsNmzRkzLD4Q7QrhDJksUiedGZxcZTFydeuXRumTp1WoG3WrD9ZuXIV7dptu9H7H/zsK1x04dnsv1976tSpTccDOvDv7ufwxIBnN3rfkRbhHE7LJRJmtj3B8OO8T9ZzgGHuPiUdx0+2BYsW8fbwj2jXthX333YDK1et5qEnBtPzxtt5ZdDDcStuubm53NPvSVo0b0rnTvtu2NfCxfw09Temz/yDPtdfTrWqVXnkqRe4+KpbeO/Vp6hRY5Mi+/p4zFesWbtWBYYM6n75eVx7y2UALPx7ERedcTl/zp6Xv3z0yM+54bI+3NXvFu5/vC8AE76ZyKVnX1vmfcWqWasmh3Q5kCHPv5nst5QdknxLHnefE/6cb2ZvAR2Av8xsK3efG14CMT9cfQ4Qe2bULGybw4ZLKvLaR4ftzeKsn5UqWg7HWrZ8BfXrFh16W79eXZYVM3QTYPPNNmXwI/fQ47pbefm/7wCw2aaNGfjQHTRu1DDuNhO+n8Rffy8skr/bNNua/438hHXr11O9WvCn9aepv5KTk8vScPSSlF3dBnVZuazof8MVS1dQp0HdOFvE13Czhpx62emMfvMTli5cGnedzZpuxnZ7tuPpPvFqlRt0Ov5Alvy9mB+/+CHh40dOhG+Plu2UxUWVJ4uLoyxOvkaNGrBkSdFRYYsXL6VRGf/7xNPrxruoWbMmYz59J7/tiQHPcced/TZ635EW4RxO+QgGM7seGAIY8E34MODVcIK1yHEHx3n0nls5cP8OHHXYQdzd+xp+/GkqX0+YGHebfk8+y/eTp3D3LdfkBx4EX52u/ucfHr7zZg476AA6d9yX/nfdwvwFC3jvw0/i7uv9jz6lTasWbLdtq1S8PUnAm0Pe5cTDzubfZ13BpB+mMPDl/rTZbsN/j30OaM9tD/Ti+UGvclbX7vS86AYaNmzAE88/WGSIX2n7inXIkZ2oU6d2iSMcIi0nJ/FHKcysjpnVy3sOHAFMAoYBebNNnQvk/UUbBpxjgX2BpeGlFCOAI8yskZk1CvczIly2zMz2De9AcU7MvrJKRczhZPh7wSKuuuUudmzXlicfvJ0nH7ydHbdvQ49rezN33vy42wwfOZr69epywD57FWg/+fguLF6ylLseGsCChYuYNn0mdzz4OFWrVtHlaxlWrXo1rnviBv5Z9Q/P9H2q2PU6HX8gnut8/l7x8xdVq16Nfbvsxxf/+6LE2dMjL4lZLBsoi+MrTxYXR1kcPddc/R/OOvMkLu95EwcfchI9r7iZM884kT63XpPprmVWhHM4HSMYLgB2cvd1sY1m9hAwGbgn3kbh9dLdAZ548A4uPOeMVPczYfXr1aX51lvSsEH9/LY9d92J6tWr8duMP9i3/R4F1h/y5ns8+8ob3Nfn+gJzJuTta9PGDdm25Tb5bc2bbsXWW24RdyjXkqXLGDvuO/5zwVlJfldSFgvmL2TB/OCSmE8//pLhn79O98vP47pLg8loevW9klEfjOH+2x/N32bKj7/w4dg3Oeyog/jwf58kvK9Yx5xwJL9P/4NJ30f+i464PLkn7FsAb4UjiqoBr7j7B2Y2DnjdzC4AZgKnhesPJ7hF5TSC21T+C8DdF5nZ7UDexZt98yZ8BC5hw20q3yd7J3gsVw6H62RtFuepX68uy1euKtK+bPmKAtfhFvbsK0NZv349D915U37hd5+9duPo0y/k2Vff4MYr/1Ng/fXrc/ho9Bcc3rkj1asXnCeldYvm3Hrd5dz3yCD++85wqlSpwinHdwGMJo0bbfybrKRWLF1B7XpFvxGt26AuK5cW/41orCsevorm223DDSddx8qlK4tdr9NxB/LjVz+wdEHxd2Das/Ne1G1Qt2JfHkHSs1g2qHDnxLHSlcUlURYn3+LFS2nQoF6R9kaNGrB4I+9Yt+mmjeh723VcdvlNPDP4FQA++/xr1q5dxyP97+DxJ57l71IuQa+oopzD6Sgw5AJbE5zIx9oqXBZXeD31IIB1C6Zn1RiR1i2bs3bN2iLt7kUnoBn5yefc9fAArrrk/LiXNLRu0Zw/41RnPZjErkj7h598zvqcHI46VJdHZIucnBx+mTKN5i03jJZv3aZlkVEGM36byepV/7BNy+KvG463rzx169XloEP356nHXoizZQWRxOFg7j4d2C1O+0Lg0DjtDvQoZl+DgcFx2scDO290Z1OvXDkM2Z3FeVq1aM6MmbMKtM39629W/7OG1i2K//c2Y+Ystm3VosCosurVq9Om1TbMmlP0Ov2vJ0xk0ZKlHH14/Pw96dgjOebwg5k5ew6NGzWkUYP6dDz6dE4+7shyvjOZ89vsInMtNNmqCTVr1ywyN0M8F/bpTocj9uHWs25hTgnrN23dlNY7b8sj1/QvcX+djj+Q+bPn8/P4ilnkzRfhoblZrsKdE8dKVxaXRlmcXFOnTqNduzYF2po125o6dWozdepvG7Xv1q1asMkmm/D995MLtE+cOInq1avTYptmlbbAEOUcTsdYoSuAj83sfTMbFD4+AD4Geqbh+El30P4d+HX67yxesuE6zvETJ7F+/XratdkwtP2bb3/g+r73cebJx/GvM0+Jv68DOrBw0WJ+m7Hhb80fs/9k7l/zade2dZH1h380ml12bMc2zbZO4juSjbFJjU3YaZftmT1zw+X3f86ey067Fhytsm3bltSqXZPZs4r/YxlvX3mOOOZgatSsUXEvjwDw3MQfUhYVLodjddy3PV98PYGVMd+cffDxp9SsUYP2exR/p4GtttycadNnsm7dhi8T165dy68zZtJ0qy2KrD985Gg227Qxe++xa7H7rFFjE7bbthVNGjfivRGjyM3NLXDbYSmbCaMnsMdBe1KrTq38to7HdWLN6n+YNLb4WekBTu5xKkefewwP93yQKeN+KnHdTl0PYt2adYz94Mti16lRqwYdDt+Hz94t/g4TFYayOFWUxXGUNYsToSxOng9GfMIRhx9E3Zj5NU479ThWrVrNp2O+2qh9z/wjKPzuUej/jz33DP7O/l6oYFWpRDiHUz6CIRySvB3B5GqxE9qMc/fsu2gkAad2PYqXhw7j0uv6cNE5pweTPA4YzL7t92DP3YIvM3/7/Q969upLq22a0+Wwg/h+0oZvOxo1bJBfIDj0wP3ZsV0brrjxDi7rfg5VqlTh8adfokXzpkWCcP7fC/n2+8lce+mFxfbtw08+A2DmH3P45581+a/b775LmSfKqYxq1qpJ58MOAGCLLTenbr06dDku+LJ79EdfcFiXgzjw0P35bNRX/DXvbzbfoglnnn8qm23RhGeffDl/P68+/wY33n4Vf81bwJiPv6DJZpvS45oLmTVzDp9+FFzfe+yJRya0rzzHnHgEUyZN5bdff0/9LyJTIlytzWYVMYdjnXbC0bw89B163ngHF5x9KrP/nMsTg1/mnG4nFrxd2mnn036PXbi915UAnHxcF958dwSX97qdbicdi7sz5M33WLBgEaccf1SBY6xdu5ZRn31F16MOi3sd74qVKxn0/BD22n0XqlWtyjfffs/zr75Jn+t70qB+0aGlkpgPXhrOsf86jhsG3cibA4ayxTZb0u3KM3nnqbcL3LryyTGDmDR2Eo9d9wgAB3Y9iHOuP5ePXx/JwnkL2W6Pdvnrzps5l2WLCk5Y1vHYTkwYPZ6Vy4q/hKLDYftQs3ZNPnunYl8eASiLU0RZHChvFv857y8mTfkFgHXr1jH99z/48JPPqFWzJp322xtQFqfCwEEvcmmP8xn6+tPc/8ATtGq1Db1vuZp+/QcVuHXlzz99zpjPxtL93xvmTuhy5MHUrlOb3XbbCYCTTjoGgPHjJ/LHH3OYP38Bb7/zPnffdSM1a9bgxx+nsNtuO9H7lqv479B3WbBgUcL7qnAinMNpuYuEu+cCY9NxrHSoW6cOgx+5h7sfHsC1t95D9erVObjjvlx3eff8dX6cPJXlK1Yyddp0zv73VQW273rUYdx589VAcOucJx7oy739B9L77n7k5jr77r07vXpeXGCoGMCIUcG3JkceUnwF9qqb74r7evCj99JBBYZSbdqkEY8Ovq9AW97rznsey/Rpv9P11KPpdfuVNGhQn/l/LeD7bydx4jV3MW3q9Pxtnh/0KuvWruOM807hjHNPZtmy5Uz4eiIP3vEYq1f9A5DwvgAaNW7Ifp060O+eASn+DWTY+sifX2WtipbDsRrUr8cz/e/mzocGcOl1fahXrw7nnHYilxSaqyYnJ4fcnA2V/p22b8uTD93OgMGv0Kvv/QBst21LBvW7i+0LjSD7bOx4lq9YWezde6pUqcqUX35j6LAPWLNmLW1at+DBO27k0AP3T/K7rVxWLl1J7zNuonvfi7lpcG9WLlvJsKffYcjDrxRYr0rVqlSpuqHws/uBwVxIh552OIeedniBdftf9TCjhn6c/7rVjq1o3rY5r/V7tcS+dDy+E7N+ncWMn4rearjCURanjLK4/Fn8zYQfuPmuh/Jfjxj1GSNGfcbWW27Oh288DyiLU2HJkqUc0eV0Hul3J2+/9SxLliyj/yNPcVvfBwusV61aNapWrVqg7bFH76Zlyw036Hp9SHCXnvMvuJIXXnwdgH+dfwW33HQll/Y4n6233oI5c+bx1NMvFbmLRCL7qlAinMMWXHac3bL5ejMpvx13ODXTXZAU+fXvCUUnEEnAyltOS/jfep3bXy/XMaT8lMUVzyl7Xp7pLkiKvPPHe+XOSGVx9lIOV0y1tu6U6S5IiqxfO6fSnROnZQSDiEhCIjwcTESkwlAWi4hkVoRzWAUGEckaUb4lj4hIRaEsFhHJrCjnsAoMIpI9IlytFRGpMJTFIiKZFeEcVoFBRLJHhMNURKTCUBaLiGRWhHNYBQYRyR450Z0xV0SkwlAWi4hkVoRzWAUGEckaHuFqrYhIRaEsFhHJrCjnsAoMIpI9IhymIiIVhrJYRCSzIpzDKjCISPaI8Iy5IiIVhrJYRCSzIpzDCRUYzGwzYLW7rzCzqsA5QC7wortH992LSHaJcLU2HZTFIpIWyuJiKYdFJC0inMOJjmB4D7gY+A64EzgOWAfsDlyZmq6JSKUT4TBNE2WxiKSesrgkymERSb0I53CiBYbtgInh87OB/YEVwGQUpiKSJJ6jL39KoSwWkZRTFpdIOSwiKRflHK6S4Ho5wCZmtguw1N3/AJYAdVPWMxGpfHI98UflpCwWkdRTFpdEOSwiqZfkHDazLmY21cymmdkNxaxzmpn9ZGaTzeyVmPZzzezX8HFuacdKdATD+8DrwKbAkLBtR2BOgtuLiJQqyrfkSRNlsYiknLK4RMphEUm5ZOZwOF/M48DhwGxgnJkNc/efYtZpC/QCDnD3xWa2edjeGLgVaA84MCHcdnFxx0u0wHAhcC7BNWYvhm1NgD5leG8iIiXTSW1plMUiknrK4pIoh0Uk9ZKbwx2Aae4+HcDMhgBdgZ9i1rkIeDyvcODu88P2I4GR7r4o3HYk0AV4tbiDJVRgcPc1wKC812ZWC/gqbBcRSY7oXm6WFspiEUkLZXGxlMMikhZlyGEz6w50j2ka5O6DYl43BWbFvJ4N7FNoN9uF+/oCqAr0cfcPitm2aUn9SWgOBjN7wMw6hM+PARYBi83suES2FxFJhK/PTfhRGSmLRSQdlMXFUw6LSDqUJYfdfZC7t495DCr9CEVUA9oCnYEzgKfMrGF5+p7oJI9nAZPC570JZs09HrirPAcVEYkrtwyPyklZLCKpl8QsTufEYmmiHBaR1EvuOfEcoHnM62YUnTdmNjDM3de5+wzgF4KCQyLbFpDoHAy13X2VmW0KtHb3NwDMrEWC24uIlEoTi5VKWSwiKZesLE73xGJpohwWkZRL8jnxOKCtmbUiKA50A84stM7bBCMXnjWzJgSXTEwHfgPuMrNG4XpHEGR2sRItMPxiZmcBbYCRAOGBVye4vYhI6SrvyIREKYtFJPWSl8VpnVgsTZTDIpJ6STwndvf1ZnYpMIJgfoXB7j7ZzPoC4919WLjsCDP7ieB2vNe6+0IAM7udoEgB0Dcvl4uTaIHhEqA/sBa4IGw7Evgw8bcmIlIyjWAolbJYRFKuLFlcyuRiaZ1YLE2UwyKScsk+J3b34cDwQm29Y547cFX4KLztYGBwosdK9C4S44D9C7W9DLyc6IFEREqlEQwlUhaLSFqUIYvDYkJ5JhTLEzuxWDNgjJntshH7SynlsIikRYTPiRMdwYCZbQK0I7jXr+W1u/uoFPRLRCohX5/pHmQ/ZbGIpFoSszjRicW+dvd1wAwzi51YrHOhbUcnrWcbQTksIqkW5XPihAoMZtYR+C9QA6gPLAPqEQxda52y3olIpeIRrtamg7JYRNIhiVmc1onF0kE5LCLpEOVz4kRHMDwM3OfuD5vZYndvbGa9gVUp7JuIVDYRDtM0URaLSOolKYvTPbFYmiiHRST1InxOnGiBYTuCCW1i3QPMAB5Iao9EpNKKcrU2TZTFIpJyyczidE4slibKYRFJuSifEydaYFhKMAxsCTDXzHYEFgJ1U9UxEal8ohymaaIsFpGUUxaXSDksIikX5RxOtMDwJnA08ApBJfkTYB0wNEX9EpFKyHOs9JUqN2WxiKScsrhEymERSbko53Cit6m8Iub5A2b2NUGldkSqOiYilU8qqrVmVhUYD8xx92PDycaGAJsCE4D/c/e1ZlYDeAHYi+DbqNPd/fdwH70I7neeA1zu7iPC9i4EQ2WrAk+7+z3JfwcbKItFJB2i/M1ZqimHRSQdopzDCd+mMpa7f5bsjoiIeG5KqrU9gSkEQ1oB7gUedvchZvYkQeFgQPhzsbu3MbNu4Xqnh8NfuwE7AVsDH5nZduG+HgcOJ7jN2jgzG+buP6XiTcSjLBaRVEhRFldIymERSYUo53CxBQYz+wzw0nbg7gcmtUciUmklu1prZs2AY4A7gavMzIBD2HCbtOeBPgQFhq7hcwiGuj4Wrt8VGOLuawju0T4N6BCuN83dp4fHGhKum9QCg7JYRNItyt+cpYJyWETSLco5XNIIhqfT1gsREcA96dXafsB1BPcoh+CyiCXuvj58PRtoGj5vSnAf87xbqy0N128KjI3ZZ+w2swq175PsN4CyWETSLAVZHHXKYRFJqyjncLEFBnd/Pp0dEREpS7XWzLoD3WOaBrn7oJjlxwLz3X2CmXVOVh/TTVksIukW5W/OUkE5LCLpFuUcrlLSQjM71swGFrPsSTM7KjXdEpHKKDfHEn64+yB3bx/zGFRodwcAx5vZ7wSTOh5CMCFjQzPLK642A+aEz+cAzQHC5Q0IJnvMby+0TXHtSacsFpF0KksWVxbKYRFJpyjncIkFBuBq4KVilr0EXJvc7ohIZea5lvCj1H2593L3Zu7ekmCSxlHufhbBLcVOCVc7F3gnfD4sfE24fJS7e9jezcxqhHegaAt8A4wD2ppZKzPbJDzGsGT8HuJQFotI2iQziysQ5bCIpE2Uc7i0u0jsWMLsuF8QzKouIpIUaQrJ64EhZnYH8B3wTNj+DPBiOInjIoKCAe4+2cxeJ5i8cT3Qw91zAMzsUoJbk1UFBrv75BT1WVksImmTjSesWUA5LCJpE+UcLq3AUMvM6rn78jjL6gK1UtAnEamkvNQ5usu7Xx8NjA6fT2fDXSBi1/kHOLWY7e8kuBNF4fbhwPAkdrU4ymIRSZtUZXHEKYdFJG2inMOlXSLxHRuGEhd2EjAxud0RkcosysPBUkxZLCJpoyyOSzksImkT5RwubQTDXcDrZtYIeAOYC2wFnAzcApye2u6JSGUS5VvypJiyWETSRlkcl3JYRNImyjlcYoHB3UeY2QXAg8D9MYtmARe6+4ep7JyIVC45WTgTbjZQFotIOimLi1IOi0g6RTmHSxvBgLsPBYaaWTtgU2Chu09Nec9EpNKJcrU21ZTFIpIuyuL4lMMiki5RzuFSCwx5FKAikmrZeB1ZtlEWi0iqKYtLphwWkVSLcg4nXGAQEUm1KM+YKyJSUSiLRUQyK8o5rAKDiGSNKFdrRUQqCmWxiEhmRTmHVWAQkayRk1vanXNFRCTVlMUiIpkV5RwutsBgZq0T2YG7T09ed0SkMovycLBUURaLSLopiwtSDotIukU5h0sawTANcKCk8RkOVE1qj0Sk0sqN8Iy5KaQsFpG0UhYXoRwWkbSKcg4XW2Bw9+iOyxCRSIryLXlSRVksIummLC5IOSwi6RblHNYcDCKSNaI8HExEpKJQFouIZFaUczihAoOZVQMuAQ4CmhAzRMzdD0xN1zaotXWnVB9CMmDclu0z3QXJMlEeDpYOymJJtuVPnJ7pLkgWUhYXL9M5XK9Z51QfQjJg1e8fZroLkmWinMOJDvl6GPg3MAbYC3gD2BwYlaJ+iUgllJNbJeFHJaUsFpGUUxaXSDksIikX5RxOtEcnAUe5e39gffjzBODglPVMRCodL8OjklIWi0jKKYtLpBwWkZSLcg4nOgdDbWBW+Hy1mdV295/NbI8U9UtEKqEoDwdLE2WxiKScsrhEymERSbko53CiBYYpwN7AN8B4oI+ZLQPmpKpjIlL5RHnG3DRRFotIyimLS6QcFpGUi3IOJ1pg6AnkhM+vAgYA9YDuqeiUiFROuZnuQPZTFotIyimLS6QcFpGUi3IOJ1RgcPdxMc9/BQ5LWY9EpNJyolutTQdlsYikg7K4eMphEUmHKOdwoiMYMLN2wG5A3dh2dx+c7E6JSOW0PsLDwdJFWSwiqaYsLplyWERSLco5nFCBwcxuBHoD3wOrYhY5oDAVkaSIcrU2HZTFIpIOyuLiKYdFJB2inMOJjmC4Aujg7j+ksjMiUrlF+XqzNFEWi0jKKYtLpBwWkZSLcg4nWmBYDfycyo6IiES5WpsmymIRSTllcYmUwyKSclHO4SoJrncL8KiZbWVmVWIfqeyciFQuuWV4VFLKYhFJuWRmsZl1MbOpZjbNzG4oYb2TzczNrH34uqWZrTazieHjyY18W8miHBaRlIvyOXGiIxieC39eGNNmBNebVU1mh0Sk8sqJcLU2TZ4LfyqLRSRlkpXFZlYVeBw4HJgNjDOzYe7+U6H16hHc/vHrQrv4zd13T0pnkue58KdyWERSJsrnxIkWGFqltBciIkBudLM0XZTFIpJyScziDsA0d58OYGZDgK7AT4XWux24F7g2aUdOHeWwiKRclM+JEyowuPvMVHdERCQ3wtXadFAWi0g6lCWLzaw70D2maZC7DwqfNwVmxSybDexTaPs9gebu/j8zK1xgaGVm3wHLgJvd/bOEO5YiymERSYdknxObWRegP8FIq6fd/Z5i1jsZGArs7e7jzawlMAWYGq4y1t0vLulYxRYYzGyQu3cPn79IMPSrCHc/p8R3IyKSoLghU8kpi0Uk3cqSxWExYVCpK8YRzlvwEHBenMVzgW3cfaGZ7QW8bWY7ufuy8hxrYyiHRSTdknlOnO7L1UoawTAj5vm0RHcoIlJe2ThRTRZQFotIWiUxi+cAzWNeNwvb8tQDdgZGmxnAlsAwMzve3ccDawDcfYKZ/QZsB4xPXvcSphwWkbRK8jlxWi9XK7bA4O53xzy/bWMOIiKSiFzTJRKFKYtFJN2SmMXjgLZm1oqgsNANODNvobsvBZrkvTaz0cA14bDczYBF7p5jZq2BtsD0ZHWsLJTDIpJuZcnhUi5VgzRfrpbQHAxmdkgxi9YAs3U9mogkQ06mO5DllMUikg7JymJ3X29mlwIjCK77Hezuk82sLzDe3YeVsPmBQF8zW0fwZd7F7r4oSV0rN+WwiKRDWXJ4Yy5Vg+RfrpboXSSeAbYOny8ENg2fzwe2NLMfgG7u/muC+xMRKSLKM+amibJYRFIumVns7sOB4YXaehezbueY528AbySvJ0mjHBaRlEvyOXFaL1erkmCnngEeARq6+9ZAQ6Af8GT4fBzwRIL7EhGJKxdL+FEaM6tpZt+Y2fdmNtnMbgvbW5nZ12Y2zcxeM7NNwvYa4etp4fKWMfvqFbZPNbMjY9q7hG3TzOyGpP9CilIWi0jKJTOLKyDlsJYMBkkAACAASURBVIikXJJzOP9ytfC8txuQP4LM3Ze6exN3b+nuLYGxwPF5l6uFk0SS6OVqiY5g6Als5e7rw06sNrObgT/d/U4zu5rgWg4RkXJL8l0k1gCHuPsKM6sOfG5m7wNXAQ+7+xAzexK4ABgQ/lzs7m3MrBvBJDenm9mOBEG8E8G3Vh+Z2XbhMUqdkTfJlMUiknK6o0+JlMMiknLJzOF0X66WaIFhJbA38FVM217AqvC5Jn8XkY2W5GG5DqwIX1YPHw4cwoZJxp4H+hAUGLqGzyG4/+9jFowT6woMcfc1wAwzm0YwGy8kNiNvMimLRSTldLlaiZTDIpJyyc7hdF6ulmiBoTfwoZkNI5iBshlwHHBZuPxQghNyEZFyK8tZWQIz5ubd93cC0IZgtMFvwJK8b54IvmVqGj7Pn2E3rPQuJbi2tinBUDHibFPijLwpoCwWkZTTJ+QSKYdFJOWinMMJFRjc/QUzGw+cTDBE+Bdgv7yhwO7+HvBeynopIpVCThmqtYnMmOvuOcDuZtYQeAvYfmP6l2nKYhFJh7JkcWWjHBaRdIhyDic6goEwOFM59FdEKrlUVWvdfYmZfQLsBzQ0s2rhKIbYWXTzZtidbWbVgAYEM4SXNPNuSTPypoSyWERSLcrfnKWDclhEUi3KOVxsgcHMBrl79/D5ixQz14S7n5OivolIJZPMMDWzzYB1YXGhFsFkjPcCnwCnAEOAc4F3wk2Gha+/CpePcncPh8G+YmYPEXxb1Rb4BjDCGXkJCgvd2DC3QzLfh7JYRNIqyie2qaAcFpF0i3IOlzSCYUbM82mp7oiIiCd3ONhWwPPhPAxVgNfd/T0z+wkYYmZ3AN8R3HKM8OeL4SSOiwgKBoSz7L5O8G3VeqBHeOkF8WbkTeo7CCiLRSStkpzFFYFyWETSKso5XGyBwd3vhvxJ0mYBr7j7P+nqmIhUPsms1rr7D8Aecdqns+EuELHt/wCnFrOvO4E747QXmZE32ZTFIpJuUf7mLBWUwyKSblHO4SqlrRB+U/eQglREUi2nDI/KRlksIumiLI5POSwi6RLlHC61wBB618yOS2lPRKTSy7XEH5WUslhEUk5ZXCLlsIikXJRzONG7SNQEhprZVwRDw/Int9GENiKSLFEeDpYmymIRSTllcYmUwyKSclHO4UQLDJPCh4hIykQ5TNNEWSwiKacsLpFyWERSLso5nFCBwd1vS3VHRETi3vdL8imLRSQdlMXFUw6LSDpEOYdLLTCYWTXgbIJ7yDcBFgAfAS+5+7rUdk9EKpNsvI4sWyiLRSRdlMXxKYdFJF2inMMlTvJoZg2AL4H7gHXw/+3dd3xUVf7G8c83ISGBEIoiHQQMXakCq2IviCuoq2JHVFDBuoqCYsPOgm1FFBUR1wL6cxXXgogiKKIUEUVa6E06CS1AkvP7Yy5hAklIyJCZO3nevOZFcu69595JyJPhO+eewyzv72eAqd52EZGQ8POMuUeSslhESpKy+GDKYREpSX7O4UONYHga2ACc4Zzbsa/RzJKAMd72Pkfu8kSkNMn29YCwI0pZLCIlRlmcJ+WwiJQYP+fwoZapvAi4NThIAZxz24G+wMVH6sJEpPTJLsKjlFEWi0iJURbnSTksIiXGzzl8qBEMFYHV+WxbBSSH9nJEpDTzb632iFMWi0iJURbnSTksIiXGzzl8qBEMi4Ez89l2FrAktJcjIqWZn6u1R5iyWERKjLI4T8phESkxfs7hQxUYngNGm9k/zCwGwMxizOxSYJS3XUQkJDLNFfpRyiiLRaTEKIvzpBwWkRLj5xwu8BYJ59woMzuKQHC+b2YbCSzLsxsY5Jx768hfooiUFpEXkZFBWSwiJUlZfDDlsIiUJD/n8KHmYMA5N9TMRgAnsX/N35+cc+lH+uJEpHSJxGFekUJZLCIlRVmcN+WwiJQUP+fwIQsMAM65bcD4I3wtIlLK+XlJnpKgLBaRkqAszp9yWERKgp9zuFAFBhGRkuDfKBURiR7KYhGR8PJzDqvAICIRw8/DwUREooWyWEQkvPycwyowiEjEyPJ1vVZEJDooi0VEwsvPOawCg4hEDD9Xa0VEooWyWEQkvPycwyowhNDll3fl3nv60CilAWlp6Xz73Y888OBTrF27Ltd+LVo04cnHB3DKKe2JiYlh/vxF9L1tALN+/T1nn6ZNU3jx+Sfo2LEtW7emMfKt9xn0+HNkZwf+ucXFxTH67X/Tts0J1KhxDNu372TmzN94+JHBufqRoqnU5SSq3tSNhIY1iUlMYM/qDWz++DvWv/pf3N7MnP0SGtejZv9rSTqxGcTEkJG6kpUPvsqu3xcDEF+vOtVuuZjybZqQ0KgO23/5k9TuA3OdK6ljC1LGPpnndaR//yuLr310f0NsDNVuvpgq3c8mvmZVMjensfXzqawe9GbIvwbh5HxcrZXwOlRm5iUuLo4nBt1Phw5taNv2BBITEykTXyvXPjExMdzzz1u4oMvZNG3aCIBZs+bw0MPPMmPmb7n2ffCBuzi1U0dOPLEVyckVaJjSgeXLV4X+yZYyizdu49mJc5mzZgsVysZx8Ql1uPmkRsTGWL7HDP9xIa9NXZTntts7NebGjseRle0YPX0JkxevY8mm7QA0rVaR2zo1pkWNSjn7b9yewRMT/mD+ujQ279xDckIcrWpV5vZTm1CvcvnQPtkIoSyWw9WkSQrPPz+IDh3asHVrOqNGvc8TT7xwyCx+7LF+dOjQhjZtTiAxMYGEhLp57nv//bdx001XU7XqUcybt4iHHnqWb76ZnLO9bdsTuPnm6zj55PbUqFGNVavWMGbMpwwZMpzdu3eH/PmWFouXreTpl97gtz8XUCGpPJd0OZtbe1xObGxsgcfNXZDKi2+8y9wFgdfHTVMacMeNV3FCs0a59vv2h1/498j3WLFqLbVrVuPW6y6n85mnHFZf0cLPOawCQ4j8/e/n8N5/hjPslbfo3/8Jqtc4hkGP3se4T0fTvkNnnAv8I2nZsjmTvv2YcZ+N56qrbwWgXbtWJCYm5PRVqVJFxn/5AfPmLeKSf/SkQYNj+dfgh4mJieHhRwYDEBsbi3OOZwe/zJIly6mQnMRdd/Riwtdjadf+PJYuXVHyX4QoEFu5AtunzmH9a/8lK30H5VqlUOPuK4irWplVD48AILFZfVI+epq0r39mad9/AVCuZQoxCfE5/SQ2qkvyGe3Y8esCrEze4bvzj8Us6NYvV1t8rarUf+U+0ifNzNVeb+idVDj5BNa+8AG7U1cRV/NoElLqhPKpRwQ/V2slfAqTmXkpVy6RG264kunTZ/PTTzM584AXMwCJiQnc168vb789lmcHv4xzjj639uT7Sf+l06ndchV0e910DYsXL2XS91PpeuF5R+S5ljbpGXu5ZezPNDgqiecvbseqrTsZOmke2Q5u69Q43+MuOaEOJ9evmqvtu0XreOuXxTntuzOzGPlzKt1a1OGGjsdhwAe/LqPn+z/x9lUn0ax6RQAyMrOpUDaOPqc0pkZyIpt27ObNaan0HjOND68/leSEuCP2/MNFWSyHo1KlinzxxXvMn7+Iyy67ifr16/HsswOJiYnh0UeH5HtcuXKJ9Ox5JTNmzGbatJmcccbJee7Xr19fHnjgTgYNeo7ffpvLVVddwscfj+SMMy5h5sw5AFx66YU0aFCPoUOHk5q6lBYtmvLII/fQokUTrrzyliPyvKNd2rbt9Lr3URrUq8OLT/Rn1Zp1DBk+imznuOPGq/I97q/1G+l1z6M0bdSApwfcAcBbYz6ld7/H+PjN56lZ/RgAZv0+j38+Mpju3Toz4PYbmfLzLO574nmSKyRx0omtitRXNPFzDqvAECJXXnExM2fN4c679r9LnZ6+jU8+HkXjxg2ZPz8VgFdefob/fT6BHtffkbPf+K8n5err5t7XkpiYwKWX38S2bdth4hSSk5N4+KF7+NeQV9i2bTsZGRk5BYp9Jk6cwvq//qBb18688OKII/dko9imd3OvPLX9p9+JTSrH0dedn1NgqPPUraR98wvL73o+Z79t3/+a67i0b6aTNuEXAI599X7KVK5w0Lmyt+9i568Lc7UltW+Oy8piy/9+zGmrcFprKl94CvM730XGopXFe4IRzs9L8kj4FCYz85KWlk7Vas0B6HPr9XkWGHbtyiCl8Uls3ZqW0zbx2x+YN3cKffr05KZe/8xpr9/wRJxzXNDlbBUYQuTD2cvJyMxi6EVtSSob+I/89t2ZvDZ1Ide3b5DTdqBqFRKpViExV9uInxZRv0p5mlQLFA7Klonl895n5ioQdKh3NF3fmMQHvy5j0PktAahdqRyPd2mZq6+m1SvS7Y1JTF+xkbMa1QjZ840UymI5HL16XUNiYgLdu/f2cjeQxQMH3s3Qoa8WmMU1ahwPwC239MizwBAXF0e/fn147rlXGTp0OADffDOZJk1SePDBu7nkkp4ADBnyCps2bck5bvLkaezevZthw56hbt1arFixOsTPOvp9OG48Gbv38MKg+0gqXw6A7Tt2MvztMdxwxUU5bQeaPG0mO3Zl8MKg+6mQFBjt1bJFE0696Hqm/DyL7t06A/Da6A9p27IZA+64CYD2rY8nddlKXh09NqfAUNi+oomfczgm3BcQLeLiypCeti1XW9rWdADMAsM4mzZNoUOHNgwb9laBfXU+7wy+nvB9riAeM/ZTypVL5LRT/5bvcTt27CQjYzfx8dH3bko4ZW5JJ8b7miak1KF8m8ZsGPV5wQe5wwuFyl07sX3aXDLXbc5pO6r72Wyb+nvUFxcgsCRPYR8i+xxuZhZGdnZ2ruICwN69e/nzz4XUrFEtV7s7zJ97yd+PSzdwUv2quQoJnZvWJCMzm5krNxdwZG5bd+1h2rKNdG66/xaY2Bg7aPRBXGwMDY9OYsP2jAL7q+QdtzcrOr/nymI5HOeddzoTDsjiDz8cR7lyiXTq1LFYfTdoUI/k5ApMnDglV/vEiZM566xTiIsL/EwGFxf2mT37DwBqHJDZUjhTfvmVk09slauQcP6Zp5Cxew8zfpub73F7MzOJjY3JNUq7XGICsbExOb8v9+zZyy+z/+C803MXlc4/42R++3Mh27bvKHRf0cbPOawCQ4iMGjWGU05pzzXXXEqFCkmkpDRg0GP38e23PzBvXuA+0PYntgagcuWKzJwxgYydy1kw70d6Xn9Frr4aNz6OBQtSc7WtXLmGHTt20rhxw4POHRsbS7VqVXn26YFkZWXxwZhPjtCzLEViYrCEeMqf2JSqPf/Oxne+BKBcq8B9XmUqJtHkqxdoteRjmk15lSrdzy72KcvWr0m54xuyZdzkXO3lWzVi95LV1B7UmxPmvk/LBWOp/1p/ylSrUuxzRppMXKEfIvsUNTOLKz4+ntatW7Bw0ZKQ9y25Ld28nWOrJOVqq5GcSEJcLEs35/1uaF6+WfgXmdmOzk1rFrjfnsws5q9Lz3NuhWzn2JuVzZq0nQz+9k9qJCfSqWH0DcsFZbEcnkaNGrJw4eJcbaHK4oSEskDgP6TB9uzZS9myZalfP+85GwA6dGhLVlYWS5YsL9Y1lFbLVqyift3c8xPVqFaVxISyLC1gRMg5p/6NhLJlGfLKKDZt2cqmLVsZPOwtkpOSOPf0kwBYueYvMjMzD+q/Qb3aZGdns3zVmkL3FW38nMO6RSJEvvhyIjfcdDevvzaEUSNfBGDq1Olc5A3ZAqju3R/01sgXGTL0FWbM+I1LLrmA10cM5a+/1vPlV98CgQLEVm/0Q7AtW9KoXLlSrrb7+vXlqScfAGD9+o1c2PU6Df8KgZbzx+TMqbDpo29Z/eQoAOKOCXz96z1/F+te/Zidv6VSqctJ1PvX7WSu30L6dzPz6fHQKnftRPaevWz94qdc7WWqVqbKZWexa95Slt02hJikRGoN6EGDEQNYeMAcDn7n5wltJHyKkpmh8MCAO6hSpRKvDB8V8r4lt20Ze6mQx20QyWXjSM/Ym8cReRs/fw1NqyUfclLGN6alkpaxlyvaHHvQtqcm/MFHvwXmN6pdsRyvXt6B8vHR+TJKWSyHo+AsrlisvpcuXUF2djZt27bkl1/235barl3g9qUqVfLO+mrVqtK//+28997HbNiwqVjXUFqlb9uRc1tCsApJ5UnP57YXgGOOrsLI5wfRd8CTvPtxYORv1aMq89rgh6hSqaLX9/acvoIlV0jKOXdh+4o2fs7hsI5gMLOeBWzrbWYzzGxGdvaOkrysw3L6aSfxysvP8O+X3+Sssy/lyqtvpXKVSvzfh28SExP4Mu+7VWLkW+8xZOhwJn0/lTvufJDvvvuR+++77bDO+/bosXToeD4XXXw9s2bN4dNP3qZp05SQPa/SauEl97PwH/1ZNehNKp7TntqP9/a2BL6Hmz6YwPpX/8v2n35n1UOvse3HOVTr+49inbNS105smzKbrLQDwtoCjyU3PkX6dzPZ+tkPLLvrecq3bkTSyScU65yRJrsIDwmdaMriI63L+WcxoP8dDHjgqYPeqZPItGF7BjNXbqJzk4JHL0xevI43pqVy56lNDho1AXBjx+P4zzUn86+ubahcLp5bP/yZTTuic1b6UGaxmXU2swVmlmpm/fPYfouZ/W5ms83sBzNrFrRtgHfcAjOL6olNCpvDWVmFH7kTTdLTtzF27Kfcf/9tnHba36hcuSK3Bs2dk9cqFXFxcbz77ivs2LGTfv0GlfQll3obNm3mnkeH0KxRQ4Y/O5Dhzw6kWaOG9BnwJGvXbQhbX37h59fE4b5F4rH8NjjnRjjn2jnn2sXERP4yUIMHP8xn//uaAQ88xfeTf+LDD8fxj0tv5PTTT6Jr18DvxC1bAvfxTpo0Ndex3036MWf5s337Vax48KSAlStXZMuWrbna1q3bwMxZc/jf5xPodvH1bNq8hfv6HV6xQvbb9ccSdkyfx4Y3xrH60Teoel0X4utVz/nP/7apuZcC3TZ1TrFWdUhseiyJKXXY8umUg7Zlpe0gY/5ysrbun+Njx/R5ZO/eG3UrSbgi/JGQ8nUWFyUzi6Nd25a89+5wXhvxDi/9+42Q9Sv5q5AQx/bdB49USN+9t9CrN3y9YC3OwXkFFBj+WLuV+z/7lUtb1uOadvXz3KdGciItalTinMY1GH5Ze7Zl7GXMr8sKdQ1+E6osNrNYYBhwPtAMuDK4gOB5zzl3vHOuFTAYeM47thlwBdAc6Ay84vUXrQqVw7GxBxe/IkXBWZyWxxFFc++9jzF//iLGjx/D2rW/889/3swzz/wbCLwePtCbbz5P06aN6Natx0Fz6UjhJVcoz/YdOw9q37Z9R85Ig7y89cGnZGZm8txj/TilfRtOad+G5x/rR2xsDKPGfOr1neT1lbv/fSMbkiuUL3Rf0cbPr4mP+Ng+M5uT3yYgamZbadL4OMYcMPfBwoWL2blzFw0b1ANg/vzAXAz7RjLsY2a5Kq8LFqTSuPFxufapXbsm5cuXY8GC/N8xy8rK4o8/5tOggPvQpOh2/h74mpetU42MVG9N+wOXXzfDZR/+D3ilrp3I3rWbtK9/PmhbRupKYsrGH3yQAcU4ZySKxCpstIjmLD7czCyKlJQGjPt0NN9+9wN33f1QSPqUQ6tfJYllm3OPnPkrfRcZe7Oon8cog7yMn7eG1rWrUD05Mc/tyzdv5/aPp9Oh3tHcf1bzQvWZVDaOOpXKs2rrwS+6o0EIs7g9kOqcWwJgZh8A3YA/9+3gnAseU1+e/XOWdQM+cM7tBpaaWarXX+77CH0kmnMYAq97D5xroXbtGiHL4o0bN9O585XUqlWd5ORkFi5czO2338jatetZvnxVrn2HDHmUCy88lwsuuFqjzYrp2Lq1D5pr4a/1G9mVsfuguROCLV25mobH1iGuzP7/bsbFxdHw2LqsXPMXAHVqVqdMmTIsXbGKE1vtz9+lK1YTExNDvdo1C91XtPHza+KSGMFQDbgOuDCPR9TcDLV8xSpatz4+V1uTJsdRrlwiy7zQm/rTDDZv3nLQ8jtnnnEyc+bk/K7lq/Hfce45p5EUdD/S5ZddyM6du/h+cv6/V8uWLUvrVi1Yuiz6VxsoSeVPbArA7pXr2DFzPplbt1HhgFsTKpx8Ahnzlh32OSpf2Im0b6aTvfPgmcvTJ84goUk9YoOWukzq0JyY+Dh2zVt62OeMRFnOFfohRRa1WXy4mVlY1asfwxf/e5clS5Zz9TV98hyKK0fGyfWrMnXZBnbsycxpGz9/DQllYmhb59AT3a5O28mctVvzvT1iw/YMbv3oF+pULMfTf29NbMyB1eO8bdm5h2Wbt1OrUt7Ls/ldUbI4ePi+9+gd1FUtIPhFySqvLRcz62tmiwmMYLijKMf6TNTmMMD48ZM4++zcWXzppYEsnjJlWsjOs3r1X8ybt5AyZcrQo0d3Ro8ek2t7v359ufXWHvTseSdTp04P2XlLq07tW/Pj9Nns2Lkrp+2r734goWw87VrmX5StWa0qqctWsHfv/lFoe/bsJXXpCmp689LFx8fRvlULvv4+9+/qr777kZbNGuXMzVCYvqKNn18Tl8TsRP8Dkpxzsw/cYGaTSuD8JWLEiHcYOuRR1q5Zx1fjv+OYakcz8MG7Wbp0BV9+OREILG32xJMv8MzTD7J1azozZszm4osvoFOnjpx51v77918b8Q639b2Bj8a+wb+GvEL9+nV5+KF7eOHFETlL/3Tv3o3O553J+K+/Y+2adVSvcQy33tyDGjWO4YUXXwvL1yAaNBz9CNt++I2MhStwWdmUP7Epx/TqxpZxU9izPFAh/evFMdQc0IOstB3snLOISuefRFKH5iy6/MGcfiwhnopntgMgvloVYiqUo1KXwCy3ad/OwGXsydm3XOtGlK1bjdWD3szzmja+N56qPf9Ow5ED+WvYR8SWT6TmgB6kT5nNjunzjtSXIiz8vOavD0RtFhcmMwHm//kDk6dMo/fN9+a0dT7vDMqVL0dL70XSJZdcAMCMGbNZsWI1CQkJ/O+z/1C5ckXuvGsgJxy/f3T37j27mT17/xJdp3bqyNFVj6Jtm+O9vs9kw8ZNzJu3MGc1ISmay1rV4/1Zy/jnJzPp2b4hq9J28urURVzTrkGupSsvfP072tapwqOdW+Y6fvz8NZSJMc5pXOOgvjP2ZtH3o+mkZ+yl/1nNWbRh/xvp8bExNKkWmDhs9PQlrE7bSZvaVahSriyr03by7oylxJeJ4dKW0TlisChZ7JwbAYwozvmcc8OAYWZ2FTAQ6FGc/iJY1OYwwOuv/4c+fXoyZswIhg4dTv36dRk48G5eeun1XFk8d+5kpkyZxi233JfTdu65p1O+fDlatgxk7MUXdwFg5szfciYvv+qqS4iLK8PSpSuoU6cWt99+I1lZWQwePCynn+7du/H44/czevRY1qz5i/btW+dsW7JkORs3Fn55Wwm4rOt5vPvx59z18LPccOXFrFqzjldGjeXay7rmWrqyy9V9aNeyOYPu6wvAJV3O5uPPv+HOh57lim6dccD7n3zJxk1buOzv5+Qcd/N1l3HDXQ/x7MtvcuYpHZgybRZTfp7Fq8/uHy1Y2L6iiZ9fE5sf1g4tE18r8i8SuLn3ddx887U0bHAsW7em8ePU6Tw48GmWLl2Ra7+77uxN3z49qVWrOgsWLuaxQUP55JMvc+3TtGkKL73wJB07tmHr1nRGvvU+jw0amvPOWatWzRn06P20aXM8lStXZO3a9fwy/VeeePJ5/vxzYYk95+KYXr1duC/hIDXuuYqKnTsSX/sYXGYWe1asY9OHE9n4n68gMytnv6o3daXq9X8nrnoVdi9Zzdrn3iftq/3V+fjax9B86ut5nmPuSb3Ys2p9zue1HrmRKpeeyR9te+CC3qULFl+vOrUf60VSxxa4vZmkff0zqwe9SVZaZE6613rFp4V7G/AAV9a7qNA/6+8v/+SwziGHL5Kz+FCZCZC6cBrfT/6JG2+6O1fbsccePJfJDTfezeh3xlKvXm0WLzr41iWAZctWclyj/Wu7T5zwIaeddvByWYMeH8qgx58rztM7Yra90j3cl3BIizdu45mJc5mzZgsVysZx8Ql1uOWkRrlGG5z/2re0q3MUj3fJXWC4fNQUji5fllcua39Qv6vTdnLBiO/yPGeN5ES+vPlMAKYt28jIn1NZtGEbO/ZkUq1CAu3qHEXvk1Kokc9tF5Eg8abnDjsjQ5XFZvY34FHn3Hne5wMAnHNP57N/DLDFOVfxwH3NbLzXl29vkQiFhIS6EZvDAE2apPDCC4Po0KEtW7emM2rU+zz++PMH3Ar8I5MnT6NXr3tytdWrd3AW9+r1T9555yMArrnmUu67ry9169YiLW0bn332NQ8//CybN++fa+f114dy7bWX5XltwX1FmvQlXx56pzBavGwlT730Or/NXUiFpHJccsHZ9OnRndjY/dOinHfFzbRr1YIn+9+e0zZt5hyGjx5Lqvd/oZT6denb8wpObNUiV/8Tf/iZl998j+Wr11KrejX6XN+d870JPIvaV6SJr9m81L0mVoFBwiYSCwwSGodbYOhehDAdc4gwNbM6wGgCQ1IdMMI596KZVQHGAMcCy4DLnXNbLDA5yotAF2AncL1zbpbXVw8C76oBPOGce9trbwuMAhKBL4A7nR9C9TApi6OPHwoMcniKU2AIVRabWRlgIXAWsBqYDlzlnJsbtE+Kc26R9/GFwCPOuXZm1hx4j8C8CzWBiUCKcy6LUizSCwxyeCK9wCCH73ALDKF8TVzSwr2KhIhIjmxcoR+FkAnc45xrBnQE+nqzkvcHJjrnUgi8YN23bNr5QIr36A0MB/AKEo8AHQi80H3EzCp7xwwHegUd17nYXwQRkTALVRY75zKB24DxwDxgrHNurpkNMrOu3m63mdlcM5sN/BPv9givCDGWwISQXwF9S3txQURKjxC/Ji5RJTEHg4hIoYRyqR3n3FpgrffxNjObR2CCsG7A6d5ubwOTkOq2YAAAEBZJREFUgPu99tHeCIRpZlbJzGp4+05wzm0GMLMJQGfvftlk59w0r300cBGgtyFExNdCnMVfEBjhFdz2cNDHdxZw7JPAkyG7GBERn4jE5ScLSwUGEYkYRZkJ15upPHi28hHeZGN57Xss0Br4GajmFR8A/mL/0mD5zVheUPuqPNpFRHwtEmclFxEpTfycwyowiEjEOBIzl5tZEvB/wF3OufTAVAs5fTgz82+Ci4gcAZE45FZEpDTxcw5rDgYRiRjZRXgUhpnFESguvOuc+9hrXufd+oD3974lPVYDwVNY1/baCmqvnUe7iIivhTqLRUSkaI7Aa+LOZrbAzFLNrH8e228xs9/NbLaZ/eDNW7Zv2wDvuAVmdt6hzqUCg4hEDFeEP4firQrxJjDPORe8RuA49q+x3gP4NKj9OgvoCKR5t1KMB841s8re5I7nAuO9belm1tE713VBfYmI+FYos1hERIouxK+JY4FhBCY0bwZcGVxA8LznnDveOdcKGAw85x3bDLgCaE5gMvNXvP7ypVskRCRihHg42MnAtcDv3uzkAA8AzwBjzexGYDlwubftCwJLVKYSWKayJ4BzbrOZPU5geTWAQfsmfAT6sH+Zyi/RBI8iEgX8PDRXRCQahDiH2wOpzrklAGb2AYHJzf/ct4NzLj1o//KQcwHdgA+cc7uBpWaW6vX3U34nU4FBRCKGC+GENs65H4D81gU+K4/9HdA3n75GAiPzaJ8BtCjGZYqIRJxQZrGIiBRdUXK4EBOf5zVheYc8+ulLYLngeODMoGOnHXBsgZOaq8AgIhEjS++aiYiEnbJYRCS8ipLDhZ34vBD9DAOGmdlVwED231JcJCowiEjE0LBcEZHwUxaLiIRXiHM4vwnL8/MBMPwwj9UkjyISOZxzhX6IiMiRoSwWEQmvEOfwdCDFzOqbWTyBSRvHBe9gZilBn14ALPI+HgdcYWZlzaw+kAL8UtDJNIJBRCKG3jUTEQk/ZbGISHiFMoedc5lmdhuBldFigZHOublmNgiY4ZwbB9xmZmcDe4EteLdHePuNJTAhZCbQ1zmXVdD5VGAQkYihJc9ERMJPWSwiEl6hzmHn3BcEVkwLbns46OM7Czj2SeDJwp5LBQYRiRhZGm4rIhJ2ymIRkfDycw6rwCAiEUPDckVEwk9ZLCISXn7OYRUYRCRi+DlMRUSihbJYRCS8/JzDKjCISMTQjOQiIuGnLBYRCS8/57AKDCISMfxcrRURiRbKYhGR8PJzDqvAICIRQzOXi4iEn7JYRCS8/JzDKjCISMTIctnhvgQRkVJPWSwiEl5+zmEVGEQkYvj5fjMRkWihLBYRCS8/57AKDCISMfx8v5mISLRQFouIhJefc1gFBhGJGH6+30xEJFooi0VEwsvPOawCg4hEjGwfDwcTEYkWymIRkfDycw6rwCAiEcPP1VoRkWihLBYRCS8/57AKDCISMfw8Y66ISLRQFouIhJefc1gFBhGJGH4eDiYiEi2UxSIi4eXnHFaBQUQihp+Hg4mIRAtlsYhIePk5h1VgEJGI4edqrYhItFAWi4iEl59zWAUGEYkYfq7WiohEC2WxiEh4+TmHVWAQkYiR5bLCfQkiIqWeslhEJLz8nMMqMIhIxHA+Hg4mIhItlMUiIuHl5xxWgUFEIka2j4eDiYhEC2WxiEh4+TmHVWAQkYjh52qtiEi0UBaLiISXn3NYBQYRiRh+njFXRCRaKItFRMLLzzmsAoOIRAw/z5grIhItlMUiIuHl5xyOCfcFiIjsk+WyC/0QEZEjI5RZbGadzWyBmaWaWf88tp9qZrPMLNPMLj1gW5aZzfYe40L4FEVEIpqfXxNrBIOIRAw/328mIhItQpXFZhYLDAPOAVYB081snHPuz6DdVgDXA/fm0cUu51yrkFyMiIiP+Pk1sQoMIhIx/Hy/mYhItAhhFrcHUp1zSwDM7AOgG5BTYHDOLfO2Rd7bcCIiYeLn18S6RUJEIoZzrtCPQzGzkWa23sz+CGqrYmYTzGyR93dlr93M7CVvCO8cM2sTdEwPb/9FZtYjqL2tmf3uHfOSmVmIvxwiImFRlCw2s95mNiPo0Tuoq1rAyqDPV3lthZXg9TnNzC4KyZMTEfGBUL4mLmkqMIhIxMjGFfpRCKOAzge09QcmOudSgIne5wDnAyneozcwHAIFCeARoAOBd+Ie2VeU8PbpFXTcgecSEfGlomSxc26Ec65d0GNECC+lnnOuHXAV8IKZNQxh3yIiESvEr4lLlAoMIhIxQlmtdc5NBjYf0NwNeNv7+G3goqD20S5gGlDJzGoA5wETnHObnXNbgAlAZ29bsnNumgtczOigvkREfC2EWbwaqBP0eW2vrbDXsdr7ewkwCWhdtGciIuJPfh7BoDkYRCRiFGUmXG8YbvBQ3BGFeOesmnNurffxX0A17+P8hvEW1L4qj3YREd8L4azk04EUM6tPoLBwBYHRCIfkjRbb6ZzbbWZHAycDg0N1YSIikSwSV4coLBUYRCRiFGVCG6+YcNhDcZ1zzswir+wrIhJmoZpczDmXaWa3AeOBWGCkc26umQ0CZjjnxpnZicB/gcrAhWb2mHOuOdAUeM2b/DEGeOaA1SdERKKWnyd5VIFBRCJGCQzzWmdmNZxza73bHNZ77fkN410NnH5A+ySvvXYe+4uI+F4os9g59wXwxQFtDwd9PJ3cebqvfSpwfMguRETERyLx1ofC0hwMIhIxXBH+HKZxwL6VIHoAnwa1X+etJtERSPNupRgPnGtmlb3huucC471t6WbW0Vs94rqgvkREfK0EslhERArg5xzWCAYRiRihrNaa2fsERh8cbWarCKwG8Qww1sxuBJYDl3u7fwF0AVKBnUBP73o2m9njBO4jBhjknNs3cWQfAitVJAJfeg8REd/z8ztnIiLRwM85rAKDiESMUN5v5py7Mp9NZ+WxrwP65tPPSGBkHu0zgBbFuUYRkUjk53t/RUSigZ9z2PxcHYlGZtY7xGtISwTQ91XEX/QzG530fRXxD/28Rid9X6Of5mCIPL0PvYv4kL6vIv6in9nopO+riH/o5zU66fsa5VRgEBEREREREZFiU4FBRERERERERIpNBYbIo3uSopO+ryL+op/Z6KTvq4h/6Oc1Oun7GuU0yaOIiIiIiIiIFJtGMIiIiIiIiIhIsanAICIiIiIiIiLFpgJDBDGzzma2wMxSzax/uK9His/MRprZejP7I9zXIiKHphyOTspiEX9RFkcf5XDpoQJDhDCzWGAYcD7QDLjSzJqF96okBEYBncN9ESJyaMrhqDYKZbGILyiLo9YolMOlggoMkaM9kOqcW+Kc2wN8AHQL8zVJMTnnJgObw30dIlIoyuEopSwW8RVlcRRSDpceKjBEjlrAyqDPV3ltIiJSMpTDIiLhpywW8TEVGERERERERESk2FRgiByrgTpBn9f22kREpGQoh0VEwk9ZLOJjKjBEjulAipnVN7N44ApgXJivSUSkNFEOi4iEn7JYxMdUYIgQzrlM4DZgPDAPGOucmxveq5LiMrP3gZ+Axma2ysxuDPc1iUjelMPRS1ks4h/K4uikHC49zDkX7msQEREREREREZ/TCAYRERERERERKTYVGERERERERESk2FRgEBEREREREZFiU4FBRERERERERIpNBQYRERERERERKTYVGOSQzOxYM3NmVsb7/Esz61EC533UzP5TjOOXmdnZobwmEZFwURaLiISXcljk0FRgiBJecOwys+1mts7MRplZ0pE4l3PufOfc24W8piMWZmaWbGYvmNkK73kv9j4/+kidU0SkIMpiZbGIhJdyWDks4aUCQ3S50DmXBLQB2gEDD9zBAnz/fTezeGAi0BzoDCQDfwM2Ae3DeGkiIspiZbGIhJdyWDksYeL7Hyo5mHNuNfAl0ALAzCaZ2ZNm9iOwE2hgZhXN7E0zW2tmq83sCTOL9faPNbMhZrbRzJYAFwT37/V3U9DnvcxsnpltM7M/zayNmb0D1AU+8yqp93n7djSzqWa21cx+M7PTg/qpb2bfe/1MAAqqul7n9X+xc+5P51y2c269c+5x59wXB+5sZu3N7CfvvGvN7GUvkPf9gnnezNabWbqZ/W5m+752XbzntM37Ot1b1O+HiJROymJlsYiEl3JYOSwlTwWGKGRmdYAuwK9BzdcCvYEKwHJgFJAJHAe0Bs4F9gVkL+DvXns74NICznUZ8CiBcEsGugKbnHPXAivwKsjOucFmVgv4HHgCqALcC/yfmVX1unsPmEkgRB8HCrqn7WzgK+fc9oK/GjmygLu9vv8GnAX08badC5wKNAIqApcTqPoCvAnc7JyrQOCX07eFPJ+IlHLK4jwpi0WkxCiH86QcliNKBYbo8omZbQV+AL4HngraNso5N9c5l0kgyLoAdznndjjn1gPPA1d4+14OvOCcW+mc2ww8XcA5bwIGO+emu4BU59zyfPa9BvjCOfeFV12dAMwAuphZXeBE4CHn3G7n3GTgswLOexSwtoDtuTjnZjrnpjnnMp1zy4DXgNO8zXsJ/JJpAphzbp5zbm3QtmZmluyc2+Kcm1XYc4pIqaUszoeyWERKiHI4H8phOdJUYIguFznnKjnn6jnn+jjndgVtWxn0cT0gDljrDY/aSiBcjvG21zxg//zCEaAOsLiQ11cPuGzfOb3zngLU8M65xTm3o5Dn3eQdVyhm1sjM/mdmf5lZOoFfNEcDOOe+BV4GhgHrzWyEmSV7h/6DwC+e5d5Qtb8V9pwiUmopi/OhLBaREqIczodyWI40FRhKDxf08UpgN3C0F76VnHPJzrnm3va1BEJyn7oF9LsSaFiIc+7b952gc1ZyzpV3zj3jnbOymZUv5Hm/Ac47YP+CDAfmAynOuWTgAcByLtS5l5xzbYFmBIaF9fPapzvnuhH4RfMJMLaQ5xMRyYuyWFksIuGlHFYOyxGkAkMp5A11+hoYaoFlbWLMrKGZ7RseNRa4w8xqm1lloH8B3b0B3Gtmbb2JYY4zs3retnVAg6B9/wNcaGbnWWDSnAQzO93MantDyGYAj5lZvJmdAlxYwHnfIRDO/2dmTbzncJSZPWBmXfLYvwKQDmw3sybArfs2mNmJZtbBzOKAHUAGkO1dx9VmVtE5t9c7PruAaxIRKTRlsbJYRMJLOawcltBTgaH0ug6IB/4EtgAfsX941evAeOA3YBbwcX6dOOc+BJ4kMBnNNgIVzSre5qeBgd7Qr3udcyuBbgQqpRsIhGE/9v87vAroAGwGHgFGF3De3QQmtZkPTCAQdL8QGOL1cx6H3Ov1v817fmOCtiV7bVsIDEHbBPzL23YtsMwbQnYLcHV+1yQichiUxfspi0UkHJTD+ymHpdjMuQNH7IiIiIiIiIiIFI1GMIiIiIiIiIhIsanAICIiIiIiIiLFpgKDiIiIiIiIiBSbCgwiIiIiIiIiUmwqMIiIiIiIiIhIsanAICIiIiIiIiLFpgKDiIiIiIiIiBSbCgwiIiIiIiIiUmz/Dxp2+hnPc7roAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Log loss on train Data\",round(log_loss(Y_train, y_prob_train, eps=1e-15),4))\n",
    "y_train_pred = np.argmax(y_prob_train, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(Y_train, y_train_pred)\n",
    "\n",
    "print(\"Log loss on test Data\",round(log_loss(y_test, y_prob_test, eps=1e-15),4))\n",
    "y_test_pred = np.argmax(y_prob_test, axis = 1) # row wise getting the index whichever is bigger, returns 0 or 1\n",
    "plot_confusion_matrices(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uu45fMzBg5ZU"
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T6br92MEhNwP"
   },
   "source": [
    "**Summary**\n",
    "* Read the data from test.csv file and dropped the columns where rows where NaN was present. There was 3 such rows.\n",
    "* EDA was done and some simple features was created using text data (i.e. questions). 9 simple feature were created. In advance feature extraction 15 more features were created.\n",
    "* Data was not splitted before basic and advance featurization because any feature which has been created in basic/advance featurization is independent of each rows.\n",
    "* Then TF-IDF W2V and TFIDF vectorization of questions was done.Both 'question1' and 'question2' were used to make the dictionary of TF-IDF vectorizer (from training data only). max_feature was used during TF-IDF vectorization  to avoid very large number of features (aroound 2*74K). max_feature was not used in TFIDF-W2V.\n",
    "* A random model or worst model  log-loss of 0.88, so any sensible model must have less than this test log-loss.\n",
    "* Logistric regression, Linear SVM and GBDT were trained using best hyper-parameter which came from hyper-paramter tunning, on both TF-IDF and TFIDF-W2V vectorized data.\n",
    "* My __best model__ is __GBDT__ with __TFIDF__ vectors and it has  test __log-loss__ of __0.3422\n",
    "3__.\n",
    "* My Second best model will be __GBDT__ with __TFIDF-W2V__ vectors and it has a __log loss__ of __0.3753__.\n",
    "* __Logistic regression__ with __TFIDF__ vector is also doing very good job and we can also consider this as our model when we've low latency problem. It has __log-loss = 0.3732__ fot test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "ieufOE117vKy",
    "outputId": "32a48011-9547-4be5-df7a-f71450cb836b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------------------+------------------------------+----------------+---------------+\n",
      "|              Model              |        hyperparameter        | Train Log-Loss | Test Log-Loss |\n",
      "+---------------------------------+------------------------------+----------------+---------------+\n",
      "|           Random Model          |             None             |     0.8778     |     0.884     |\n",
      "|   Logistic Regression (TFIDF)   |          C = 0.001           |     0.3788     |     0.4114    |\n",
      "| Logistic Regression (TFIDF-W2V) |            C = 1             |     0.4808     |     0.4812    |\n",
      "|        Linear SVM (TFIDF)       |        alpha = 10**-4        |     0.5845     |     0.591     |\n",
      "|      Linear SVM (TFIDF-W2V)     |        alpha = 10**-4        |     0.4831     |     0.4838    |\n",
      "|           GBDT (TFIDF)          | depth = 10, estimators = 500 |     0.2965     |     0.3423    |\n",
      "|         GBDT (TFIDF-W2V)        | depth = 8, estimators = 500  |     0.2959     |     0.3732    |\n",
      "+---------------------------------+------------------------------+----------------+---------------+\n"
     ]
    }
   ],
   "source": [
    "# Please compare all your models using Prettytable library\n",
    "from prettytable import PrettyTable\n",
    "#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Model\", \"hyperparameter\", \"Train Log-Loss\", \"Test Log-Loss\"]\n",
    "x.add_row(['Random Model', 'None', 0.8778 , 0.884])\n",
    "x.add_row(['Logistic Regression (TFIDF)', 'C = 0.001', 0.3788, 0.4114])\n",
    "x.add_row(['Logistic Regression (TFIDF-W2V)', 'C = 1', 0.4808, 0.4812])\n",
    "x.add_row(['Linear SVM (TFIDF)', 'alpha = 10**-4',0.5845, 0.591])\n",
    "x.add_row(['Linear SVM (TFIDF-W2V)', 'alpha = 10**-4', 0.4831, 0.4838])\n",
    "x.add_row(['GBDT (TFIDF)', 'depth = 10, estimators = 500', 0.2965, 0.3423])\n",
    "x.add_row(['GBDT (TFIDF-W2V)', 'depth = 8, estimators = 500', 0.2959, 0.3732])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z9oNaE6bhE6d"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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