AMAZON_DECISIONTREES.ipynb

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  "metadata": {
    "colab": {
      "name": "AMAZON_DECISIONTREES",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bl8bYUHaiDI"
      },
      "source": [
        "  !pip install -U -q Pydrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plot\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "from sklearn import linear_model, datasets\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import precision_recall_fscore_support, classification_report\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrCPuHkqnH1Y",
        "outputId": "beb84ca0-e76e-4534-f74c-1f7a19eb60b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        }
      },
      "source": [
        "!pip install gensim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gensim\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/86/f3/37504f07651330ddfdefa631ca5246974a60d0908216539efda842fd080f/gensim-3.5.0-cp36-cp36m-manylinux1_x86_64.whl (23.5MB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/b7/31/05c8d001f7f87f0f07289a5fc0fc3832e9a57f2dbd4d3b0fee70e0d51365/jmespath-0.9.3-py2.py3-none-any.whl\n",
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            "\u001b[?25hBuilding wheels for collected packages: smart-open, bz2file\n",
            "  Running setup.py bdist_wheel for smart-open ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/73/f1/9b/ccf93d4ba073b6f79b1ed9df68ab5ce048d8136d0efcf90b30\n",
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            "Installing collected packages: boto, bz2file, docutils, jmespath, botocore, s3transfer, boto3, smart-open, gensim\n",
            "Successfully installed boto-2.49.0 boto3-1.9.4 botocore-1.12.4 bz2file-0.98 docutils-0.14 gensim-3.5.0 jmespath-0.9.3 s3transfer-0.1.13 smart-open-1.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fg_lv1hBnTAQ"
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth= GoogleAuth()\n",
        "gauth.credentials= GoogleCredentials.get_application_default()\n",
        "drive=GoogleDrive(gauth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXVRx3xvomgj"
      },
      "source": [
        "train_downloaded = drive.CreateFile({'id': '1LgNo7LS202N7BuUaQQt4tVwchHRJm8S9'})\n",
        "train_downloaded.GetContentFile('final_clean.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmzDD3Z8nm5c",
        "outputId": "8a7d6062-dc40-4e3c-b3fd-a62b4d947be9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "\n",
        "data = pd.read_csv('final_clean.csv')\n",
        "data.head(2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "      <th>cleanedText</th>\n",
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              "      <td>150524</td>\n",
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              "      <td>shari zychinski</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>939340800</td>\n",
              "      <td>EVERY book is educational</td>\n",
              "      <td>this witty little book makes my son laugh at l...</td>\n",
              "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
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              "      <td>1</td>\n",
              "      <td>1194739200</td>\n",
              "      <td>Love the book, miss the hard cover version</td>\n",
              "      <td>I grew up reading these Sendak books, and watc...</td>\n",
              "      <td>b'grew read sendak book watch realli rosi movi...</td>\n",
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            "text/plain": [
              "       Id   ProductId          UserId      ProfileName  HelpfulnessNumerator  \\\n",
              "0  150524  0006641040   ACITT7DI6IDDL  shari zychinski                     0   \n",
              "1  150506  0006641040  A2IW4PEEKO2R0U            Tracy                     1   \n",
              "\n",
              "   HelpfulnessDenominator  Score        Time  \\\n",
              "0                       0      1   939340800   \n",
              "1                       1      1  1194739200   \n",
              "\n",
              "                                      Summary  \\\n",
              "0                   EVERY book is educational   \n",
              "1  Love the book, miss the hard cover version   \n",
              "\n",
              "                                                Text  \\\n",
              "0  this witty little book makes my son laugh at l...   \n",
              "1  I grew up reading these Sendak books, and watc...   \n",
              "\n",
              "                                         cleanedText  \n",
              "0  b'witti littl book make son laugh loud recit c...  \n",
              "1  b'grew read sendak book watch realli rosi movi...  "
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          "metadata": {
            "tags": []
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      "metadata": {
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        "outputId": "6ff68335-fb28-46d9-831e-e4ab9becd7d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "df1=data.sort_values('Time', axis=0, ascending=True,\n",
        "                   inplace=False, kind='quicksort', na_position='last')\n",
        "df1.head(2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "      <th>cleanedText</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>150524</td>\n",
              "      <td>0006641040</td>\n",
              "      <td>ACITT7DI6IDDL</td>\n",
              "      <td>shari zychinski</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>939340800</td>\n",
              "      <td>EVERY book is educational</td>\n",
              "      <td>this witty little book makes my son laugh at l...</td>\n",
              "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
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              "    <tr>\n",
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              "      <td>150501</td>\n",
              "      <td>0006641040</td>\n",
              "      <td>AJ46FKXOVC7NR</td>\n",
              "      <td>Nicholas A Mesiano</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>This whole series is great way to spend time w...</td>\n",
              "      <td>I can remember seeing the show when it aired o...</td>\n",
              "      <td>b'rememb see show air televis year ago child s...</td>\n",
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              "        Id   ProductId         UserId         ProfileName  \\\n",
              "0   150524  0006641040  ACITT7DI6IDDL     shari zychinski   \n",
              "30  150501  0006641040  AJ46FKXOVC7NR  Nicholas A Mesiano   \n",
              "\n",
              "    HelpfulnessNumerator  HelpfulnessDenominator  Score       Time  \\\n",
              "0                      0                       0      1  939340800   \n",
              "30                     2                       2      1  940809600   \n",
              "\n",
              "                                              Summary  \\\n",
              "0                           EVERY book is educational   \n",
              "30  This whole series is great way to spend time w...   \n",
              "\n",
              "                                                 Text  \\\n",
              "0   this witty little book makes my son laugh at l...   \n",
              "30  I can remember seeing the show when it aired o...   \n",
              "\n",
              "                                          cleanedText  \n",
              "0   b'witti littl book make son laugh loud recit c...  \n",
              "30  b'rememb see show air televis year ago child s...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYK6hQABHZNP",
        "outputId": "9f31fe9f-fb19-4a1a-9b32-c7d4a7ee6adb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "final=df1.sample(n=200000 ,replace=True,random_state=None, axis=0)\n",
        "final.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ix0ANDLcnvUG"
      },
      "source": [
        "X = final['cleanedText']\n",
        "Y = final['Score']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwRZ6an0n19i",
        "outputId": "a4edc5ac-0de8-480c-8a0b-4ff3130e2fbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from sklearn import cross_validation\n",
        "X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.3, random_state=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
            "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lv52iQWwoDZ5"
      },
      "source": [
        "TFIDF\n",
        "----"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tb2RIQ5In6Iy"
      },
      "source": [
        "tf_idf_vect = TfidfVectorizer()\n",
        "train_tfidf = tf_idf_vect.fit_transform(X_train)\n",
        "test_tfidf = tf_idf_vect.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umwlQMmOCtob"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "def gridcv(train_feat,test_feat,y_train,y_test):\n",
        "    parameters = {'max_depth': [1,2,3,4,5,6,7,8,9,10]}\n",
        "    model= GridSearchCV(DecisionTreeClassifier(), param_grid=parameters, n_jobs=-1,scoring='f1',cv=5)\n",
        "    model.fit(train_feat, y_train)\n",
        "    print('Best Estimator:      ',model.best_estimator_)\n",
        "    print('Optimal parameters:  ',model.best_params_)\n",
        "    print('optimal score:       ',model.best_score_*(100))\n",
        "    print('----------------------------------------------------------------')\n",
        "\n",
        "    model.best_estimator_.fit(train_feat, y_train)\n",
        "    y_true, y_pred = y_test,model.best_estimator_.predict(test_feat)\n",
        "    confusion = confusion_matrix(y_true,y_pred)\n",
        "    print(\"Classification_Report\")\n",
        "  \n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print(precision_recall_fscore_support(y_true, y_pred, average='micro'))\n",
        "    print('<==========confusion matrix for test data=========>')\n",
        "    p= sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Spectral\",square=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1uO8ukhoZX6"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "def gridcv(train_feat,test_feat,y_train,y_test):\n",
        "    parameters = {'max_depth': [1,2,3,4,5,6,7,8,9,10]}\n",
        "    model= GridSearchCV(DecisionTreeClassifier(), param_grid=parameters, n_jobs=-1,scoring='accuracy',cv=5)\n",
        "    model.fit(train_feat, y_train)\n",
        "    print('Best Estimator:      ',model.best_estimator_)\n",
        "    print('Optimal parameters:  ',model.best_params_)\n",
        "    print('optimal score:       ',model.best_score_*(100))\n",
        "    print('-------------------------------------------------')\n",
        "\n",
        "    model.best_estimator_.fit(train_feat, y_train)\n",
        "    y_true, y_pred = y_test,model.best_estimator_.predict(test_feat)\n",
        "    confusion = confusion_matrix(y_true,y_pred)\n",
        "    print(\"Classification_Report\")\n",
        "  \n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print(precision_recall_fscore_support(y_true, y_pred, average='micro'))\n",
        "    print('<==========confusion matrix for test data=========>')\n",
        "    p= sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Spectral\",square=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RvvcBoP-OTY",
        "outputId": "d9211fd8-abd9-463c-e844-1632bca9fa38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "gridcv(train_tfidf,test_tfidf,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "Optimal parameters:   {'max_depth': 10}\n",
            "optimal score:        92.5917755682379\n",
            "----------------------------------------------------------------\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.73      0.22      0.34      9590\n",
            "          1       0.87      0.98      0.92     50410\n",
            "\n",
            "avg / total       0.85      0.86      0.83     60000\n",
            "\n",
            "(0.8630666666666666, 0.8630666666666666, 0.8630666666666666, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DBmjw4MHauHGjJGnz5s2Kj4+Xy+VSz549lZub2+AcjWFqCwAsdC5N86YqKCjQ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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa876641908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BSK2FHEotra"
      },
      "source": [
        "def plot_depth(train_feat,test_feat,y_train,y_test):\n",
        "    cv_score =[]\n",
        "    depth = [1,2,3,4,5,6,7,8,9,10]\n",
        "    for d in depth:\n",
        "        dtc = DecisionTreeClassifier(max_depth=d)\n",
        "        dtc.fit(train_feat,y_train)\n",
        "        pred = dtc.predict(test_feat)\n",
        "        acc = accuracy_score(y_test,pred,normalize=True)\n",
        "        cv_score.append(1-acc)\n",
        "    plt.plot(depth,cv_score)\n",
        "    plt.xlabel('Depth ------------->')\n",
        "    plt.ylabel('Cross Validation error --------------->')\n",
        "    plt.title(\"Depth    (VS)    CV-error\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfTyzCxArjP6",
        "outputId": "5de05594-6cd3-4079-eba8-9e5bfda1d78b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "plot_depth(train_tfidf,test_tfidf,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s3r0bS5cuFSMbGakezpZ45uFAaDQafLLtLHKLb0odiYjIqOkcxk9PT8f69eu1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\nBVJcIfyX3qwJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa876641f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzZerVTSwUGa"
      },
      "source": [
        "BAG OF WORDS\n",
        "------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWGpJ_JJwSiv"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count_vect = CountVectorizer() \n",
        "BOW_TRAIN = count_vect.fit_transform(X_train)\n",
        "BOW_TEST = count_vect.transform(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WjpEv8vpHDd",
        "outputId": "9a88e765-d548-485a-f3fe-4efd37275d29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "gridcv(BOW_TRAIN,BOW_TEST,Y_train,Y_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "Optimal parameters:   {'max_depth': 10}\n",
            "optimal score:        92.58625964044072\n",
            "----------------------------------------------------------------\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.74      0.22      0.33      9590\n",
            "          1       0.87      0.99      0.92     50410\n",
            "\n",
            "avg / total       0.85      0.86      0.83     60000\n",
            "\n",
            "(0.8627833333333333, 0.8627833333333333, 0.8627833333333333, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zqd/+9rc6dOiQKisrNXHiRHXt2lVTpkzRiRMn1K5dO82ZM0dRUVFau3atnn/+eblcLt15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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa8716a46d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tiidz0qCylcM",
        "outputId": "f1b6fca2-0def-4cf8-8e72-e8099d393d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "plot_depth(BOW_TRAIN,BOW_TEST,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8vLzg6emJ/fv3Y+nSpWJkIwM3PMgds0J8UFJRi3e/PIWCkiqpIxERGSStm/Ev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\nCSSDUU455RxYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa874adcdd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCwjnBHZzY5c"
      },
      "source": [
        " TRAINING OUR OWN WORD 2 VEC\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBMSFnd5zddA"
      },
      "source": [
        "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, ' ', sentence)\n",
        "    return cleantext\n",
        "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
        "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
        "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
        "    return  cleaned\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5GAw4zHqG0B"
      },
      "source": [
        "import re\n",
        "i=0\n",
        "list_of_train=[]\n",
        "for sent in X_train:\n",
        "    filtered_sentence=[]\n",
        "    sent=cleanhtml(sent)\n",
        "    for w in sent.split():\n",
        "        for cleaned_words in cleanpunc(w).split():\n",
        "            if(cleaned_words.isalpha()):    \n",
        "                filtered_sentence.append(cleaned_words.lower())\n",
        "            else:\n",
        "                continue \n",
        "    list_of_train.append(filtered_sentence)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfeGPR3W0KHv"
      },
      "source": [
        "i =0\n",
        "list_of_test =[]\n",
        "for sent in X_test:\n",
        "    final_test = []\n",
        "    sent = cleanhtml(sent)\n",
        "    for w in sent.split():\n",
        "        for cleaned_words in cleanpunc(w).split():\n",
        "            if(cleaned_words.isalpha()):\n",
        "                final_test.append(cleaned_words.lower())\n",
        "            else:\n",
        "                continue\n",
        "    list_of_test.append(final_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avpsWXuT0UHq"
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "w2v_train_model=Word2Vec(list_of_train,min_count=3,size=100,workers=5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bxV3sxP0lwt"
      },
      "source": [
        "w2v_test_model=Word2Vec(list_of_test,min_count=3,size=100,workers=5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rduIjLw50vWR"
      },
      "source": [
        "AVERAGE W2V\n",
        "-----"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArDcMENNZ6tX",
        "outputId": "00a12fdc-4bb5-468d-9c02-db36ab59b6d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "w2v_words=list(w2v_train_model.wv.vocab)\n",
        "sent_train=[]# the avg-w2v for each sentence/review is stored in this list\n",
        "for sentence in list_of_train:# for each review/sentence\n",
        "  sent_vect=np.zeros(100)# as word vectors are of zero length\n",
        "  cnt_words=0# num of words with a valid vector in the sentence/review\n",
        "  for words in sent:# for each word in a review/sentence\n",
        "    if words in w2v_words:\n",
        "      vect= w2v_train_model.wv[words]\n",
        "      sent_vect+=vect\n",
        "      cnt_words += 1\n",
        "  if cnt_words !=0:\n",
        "     sent_vect /=cnt_words\n",
        "  sent_train.append(sent_vect)\n",
        "print(len(sent_train))\n",
        "print(len(sent_train[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "140000\n",
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e6NwFj10tL3",
        "outputId": "86337f79-711f-4c43-dfbe-5ede7df2aae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "w2v_word=list(w2v_test_model.wv.vocab)\n",
        "\n",
        "sent_test = []; # the avg-w2v for each sentence/review is stored in this list\n",
        "for sent in list_of_test: # for each review/sentence\n",
        "    sent_vectors = np.zeros(100) # as word vectors are of zero length\n",
        "    ct_words =0; # num of words with a valid vector in the sentence/review\n",
        "    for word in sent: # for each word in a review/sentence\n",
        "        if word in w2v_word:\n",
        "            vectors = w2v_test_model.wv[word]\n",
        "            sent_vectors += vectors\n",
        "            ct_words += 1\n",
        "    if ct_words !=0: \n",
        "        sent_vectors /= ct_words\n",
        "        sent_test.append(sent_vectors)\n",
        "print(len(sent_test))\n",
        "print(len(sent_test[0]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPiOnvtO0tJz",
        "outputId": "2ed1fbb4-81f2-44e8-b259-69a17ef6e71f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "source": [
        "gridcv(sent_train,sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "Optimal parameters:   {'max_depth': 1}\n",
            "optimal score:        84.42785714285715\n",
            "-------------------------------------------------\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.00      0.00      0.00      9367\n",
            "          1       0.84      1.00      0.92     50633\n",
            "\n",
            "avg / total       0.71      0.84      0.77     60000\n",
            "\n",
            "(0.8438833333333333, 0.8438833333333333, 0.8438833333333333, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xq6GhQevXr5fP59OaNWuUm5ur5cuXS5IKCwuVn5+vtWvXqqmpSVu3bu38Gc/EXxQA4OzL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\ntpBIAAC2kEgAALaQSAAAtvwP4+xpXS/vGlIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f81c6fade10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDn5_yy-1pyg",
        "outputId": "2062f426-2f7f-4eb0-dc55-2e59bc7b1c45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "plot_depth(sent_train,sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iInLjcRn2Z86c4Xe/+x179uzBZrPRpUsXEhMTadKkiRn1iYiIyHVyec1+6tSp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\nsbj/B2/AeU6MTiiPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f81c7aaa5f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dZY9RZk4UCu"
      },
      "source": [
        "TFIDF WORD 2 VEC\n",
        "--------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKBtEiIQ3iP-"
      },
      "source": [
        "tf_idf_vect = TfidfVectorizer()\n",
        "w2v_train = tf_idf_vect.fit_transform(X_train)\n",
        "tf_idf_vectt = TfidfVectorizer()\n",
        "w2v_test = tf_idf_vectt.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUbYhZeb4dHM",
        "outputId": "f9d323e0-2e5b-41b4-8bae-6a4cf2403b8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "w2v_words=list(w2v_train_model.wv.vocab)\n",
        "\n",
        "tfidf_feat = tf_idf_vect.get_feature_names() # tfidf words/col-names\n",
        "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
        "\n",
        "tfidf_sent_train = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
        "row=0;\n",
        "for sent in list_of_train: # for each review/sentence\n",
        "    sent_vec = np.zeros(100) # as word vectors are of zero length\n",
        "    weight_sum =0;\n",
        "    # num of words with a valid vector in the sentence/review\n",
        "    for word in sent:\n",
        "       if word in w2v_words:\n",
        "          vec = w2v_train_model.wv[words]\n",
        "          # obtain the tf_idfidf of a word in a sentence/review\n",
        "          tfidf = w2v_train[row, tfidf_feat.index(words)]\n",
        "          sent_vec += (vec * tfidf)\n",
        "          weight_sum += tfidf\n",
        "    if weight_sum!=0:\n",
        "      sent_vec /= weight_sum\n",
        "    tfidf_sent_train.append(sent_vec)\n",
        "    row+=1\n",
        "print(len(tfidf_sent_train))\n",
        "print(len(tfidf_sent_train[1]))\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "140000\n",
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5LLfXgJ5s71",
        "outputId": "81d96cff-f38d-4ff5-9e1a-7ca8393aca97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "tfidf_featt = tf_idf_vectt.get_feature_names() # tfidf words/col-names\n",
        "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
        "w2v_words=list(w2v_test_model.wv.vocab)\n",
        "\n",
        "tfidf_sent_test = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
        "row=0;\n",
        "for sent in list_of_test: # for each review/sentence\n",
        "    sent_vect = np.zeros(100) # as word vectors are of zero length\n",
        "    weight_summ =0; # num of words with a valid vector in the sentence/review\n",
        "    for word in sent: # for each word in a review/sentence\n",
        "        if word in w2v_words:\n",
        "            vect = w2v_test_model.wv[words]\n",
        "            # obtain the tf_idfidf of a word in a sentence/review\n",
        "            tfidf_tst = w2v_test[row, tfidf_featt.index(words)]\n",
        "            sent_vect += (vect * tfidf_tst)\n",
        "            weight_summ += tfidf_tst\n",
        "    if weight_summ!=0:\n",
        "       sent_vect /= weight_summ\n",
        "    tfidf_sent_test.append(sent_vect)\n",
        "    row += 1\n",
        "print(len(tfidf_sent_test))\n",
        "print(len(tfidf_sent_test[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUULuwBz5uHV",
        "outputId": "c495310e-10b2-4b73-8734-30bd41f2a1bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "source": [
        "gridcv(tfidf_sent_train,tfidf_sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "Optimal parameters:   {'max_depth': 5}\n",
            "optimal score:        84.42857142857143\n",
            "-------------------------------------------------\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.00      0.00      0.00      9367\n",
            "          1       0.84      1.00      0.92     50633\n",
            "\n",
            "avg / total       0.71      0.84      0.77     60000\n",
            "\n",
            "(0.8438833333333333, 0.8438833333333333, 0.8438833333333333, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xq6GhQevXr5fP59OaNWuUm5ur5cuXS5IKCwuVn5+vtWvXqqmpSVu3bu38Gc/EXxQA4OzL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\ntpBIAAC2kEgAALaQSAAAtvwP4+xpXS/vGlIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f81c7021d68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fuIVkKO5-pY",
        "outputId": "d056d340-3052-4c95-e5d1-51c32248e3fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "plot_depth(tfidf_sent_train,tfidf_sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iInLjcRn2Z86c4Xe/+x179uzBZrPRpUsXEhMTadKkiRn1iYiIyHVyec1+6tSp\nVFZWEhcXR2xsLCUlJUyZMsWM2kRERMQNXJ7Zl5aW8vLLLzun+/fvT0JCgqFFiYiIiPu4PLM/d+4c\n586dc06fPXuWqqoqQ4sSERER93F5Zj98+HCio6Pp1KkTDoeDvXv3MnbsWDNqExERETdwGfaPP/44\n4eHh5OXlYbPZmDZtGiEhIWbUJiIiIm5QZ9hv27aNfv36sXbt2lrzv3373OOPP25sZSIiIuIWdYb9\n/v376devH1988cUVlyvsRUREbgx1hv0zzzwDQJ8+fXjooYdqLXvvvfeMrUpERETcps6w//rrr/nq\nq6948803a92Nf+nSJZYsWcKIESNMKVBERESuT51h7+Pjw8mTJzl9+nStoXybzcaECRNMKU5ERESu\nX51hHxoaSmhoKD179uT++++vtSw7O9vwwkRERMQ9XP70zm63k56eTllZGQAXLlwgNzeXqKgow4sT\nERGR6+fyCXoTJkygefPm7Nq1i06dOlFWVkZ6eroZtYmIiIgbuAx7T09PnnnmGYKCgnjyySd57bXX\nePfdd82oTURERNzAZdhXVVVRVFSEzWbj8OHDeHl5cfToUTNqExERETdwec1+1KhRfPbZZzz99NMM\nGzYMT09PhgwZYkZtIiIi4gYuwz4iIsL55x07dnDmzBmaNWtmaFEiIiLiPnWG/eTJk6+64Zw5c9xe\njIiIiLhfndfsu3btSteuXfHw8KCiooJ77rmH9u3bc/LkSW699VYzaxQREZHrUOeZ/RNPPAHAn/70\nJ5YvX+6c/9RTTzFmzBjjKxMRERG3cHk3/vHjxzl16pRz+syZMxw+fLheO09LS2P48OHExcXx5Zdf\n1lpWVVXFxIkTiYmJcc7Lzc2lZ8+eJCQkkJCQwKxZs2pt8+mnn9KhQ4d6HVtEREQuc3mDXlxcHJGR\nkfzHf/wHNpuNI0eOMHr0aJc73rFjB4WFhWRkZHDw4EFSUlLIyMhwLk9PTycsLIz8/Pxa2/Xo0YOF\nCxd+b39VVVUsX76c4ODg+nwuERER+f9chv2TTz7JsGHDKCwsxOFw0KpVK2677TaXO87JyXHeyR8a\nGkpFRQWVlZX4+fkBMG7cOMrLy8nKyqpXoUuXLiU+Pp558+bVa30RERG5rM6w//DDD3nsscd45ZVX\nrrh87NixV91xaWkpHTt2dE4HBgZSUlLiDHs/Pz/Ky8u/t11BQQGjR4+moqKCpKQkwsPDOXToEPv2\n7WPs2LH1CvuAAF+8vDxdrnczCA72b+wSbgrqsznUZ3Ooz+Yxq9d1hr2Hx+XL+Z6e7glNh8Phcp02\nbdqQlJREdHQ0hw8fJjExkY3HNhHuAAAVhElEQVQbNzJnzhymTJlS72OVlZ29nlItIzjYn5KS041d\nhuWpz+ZQn82hPpvH3b2+2heHOsN+2LBh1NTU8Nxzz13TQe12O6Wlpc7pEydOuLzeHhISwuDBgwFo\n1aoVQUFB7Nmzh3/+85+MHz/euZ+RI0eyatWqa6pLRETkZlNn2P/4xz/GZrN9b77D4cBms/H1119f\ndcfh4eEsWrSIuLg48vLysNvtziH8umRlZVFSUsLTTz9NSUkJJ0+e5N5772XTpk3OdQYMGKCgFxER\naYA6w37fvn11bvSvf/3L5Y67du1Kx44diYuLw2azMX36dDIzM/H39ycyMpLk5GSKioo4dOgQCQkJ\nxMbGMmDAAMaPH8/mzZu5ePEiM2bMwMfH55o+mIiIiFxmc7i4mF5dXc1f//pXysrKALhw4QJLly7l\nz3/+sykFXgtdb7pM197MoT6bQ302h/psnh/ENftv/epXv6KiooL9+/fTtWtXdu/ezS9+8Qu3FSci\nIiLGcvkEvaKiIlasWEHbtm1ZuHAhq1evZs+ePWbUJiIiIm7gMuy/denSJaqqqrjzzjspKCgwsiYR\nERFxI5fD+D179uT1118nIiKCmJgY7rzzTmpqasyoTURERNzAZdgnJydTXV2Np6cnXbp04eTJk4SH\nh5tRm4iIiLhBnWE/aNAgnnjiCR555BFatGgBXP45nYiIiNxY6rxmn5aWRmFhIUOGDGHMmDFs2bJF\nw/ciIiI3IJe/s79w4QKbN29m3bp1HDhwgKFDh/LYY4/RunVrs2psMP1G9DL9XtYc6rM51GdzqM/m\nMfN39i7vxvfx8SE6Opply5axZs0ajh49yqBBg9xWnIiIiBjL5Q16cPm1s7///e/ZsGEDYWFhvPrq\nq0bXJSIiIm5SZ9iXl5fzhz/8gczMTM6fP89jjz3GmjVrCAoKMrM+ERERuU51hv2DDz5IREQEv/71\nr+nWrZuZNYmIiIgb1Rn2W7ZsoWnTpmbWIiIiIgao8wY9Bb2IiIg11PvZ+CIiInJjqlfYV1ZWAlBa\nWsrOnTv1cB0REZEbiMuwnzVrFhs2bKC8vJy4uDhWrlzJjBkzTChNRERE3MFl2O/du5cnnniCDRs2\n8Oijj/LKK69QWFhoRm0iIiLiBi7D/tun6W7dupUBAwYAlx+hKyIiIjcGl2Hftm1bBg8ezJkzZwgL\nC2PdunU0a9bMjNpERETEDVw+Lnf27NkcOHCA0NBQANq1a+c8wxcREZEfPpdn9l9//TVFRUX4+Pjw\n29/+lvT0dA4cOGBGbSIiIuIGLsN+9uzZtG3blp07d7Jnzx6mTp3KwoULzahNRERE3MBl2N9yyy20\nadOGzZs3Exsby913342Hh57FIyIicqNwmdrnzp1jw4YNbNq0iT59+lBeXs6pU6fMqE1ERETcwOUN\nei+88ALvvPMOL7zwAn5+fixatIinnnqqXjtPS0tj9+7d2Gw2UlJS6Ny5s3NZVVUV06ZNIz8/n8zM\nTAByc3MZO3Ys7dq1A6B9+/ZMnTqV48ePM3nyZC5duoSXlxfz5s0jODj4Gj6uiIjIzcdl2Pfs2ZPO\nnTtz6NAh9u7dy6hRo7j11ltd7njHjh0UFhaSkZHBwYMHSUlJISMjw7k8PT2dsLAw8vPza23Xo0eP\n790TsGDBAmJjYxk8eDDvvvsub731FhMmTKjvZxQREbmpuQz7TZs2MWPGDFq2bElNTQ2lpaXMmjWL\nfv36XXW7nJwcIiIiAAgNDaWiooLKykr8/PwAGDduHOXl5WRlZbkscvr06dxyyy0ABAQEkJeX53Ib\nERERucxl2L/xxhtkZWURGBgIQHFxMWPHjnUZ9qWlpXTs2NE5HRgYSElJiTPs/fz8KC8v/952BQUF\njB49moqKCpKSkggPD8fX1xeA6upqVq9ezZgxY+r/CUVERG5yLsPe29vbGfQAISEheHt7N/hA3z52\n92ratGlDUlIS0dHRHD58mMTERDZu3IiPjw/V1dVMmDCBnj170qtXr6vuJyDAFy8vzwbXaEXBwf6N\nXcJNQX02h/psDvXZPGb12mXYN23alDfffJPevXsD8Ne//pWmTZu63LHdbqe0tNQ5feLECZc31YWE\nhDB48GAAWrVqRVBQEMXFxdx1111MnjyZ1q1bk5SU5PLYZWVnXa5zMwgO9qek5HRjl2F56rM51Gdz\nqM/mcXevr/bFweVP71JTU/nXv/7FpEmTmDx5MkePHiUtLc3lQcPDw8nOzgYgLy8Pu93uHMKvS1ZW\nFitWrACgpKSEkydPEhISQlZWFt7e3iQnJ7s8roiIiNRmc7gYX9+2bZvL6/N1mT9/Pjt37sRmszF9\n+nT27t2Lv78/kZGRJCcnU1RURH5+Pp06dSI2Npb+/fszfvx4Tp06xcWLF0lKSqJfv37ExcVRVVXl\n/LIQGhrKjBkz6jyuvpVepm/o5lCfzaE+m0N9No+ZZ/Yuw/7nP/85r7/+Ol5eLkf8fzD0F/Uy/Utr\nDvXZHOqzOdRn85gZ9i4T3N/fn4ceeogf//jHtW7MS09Pd091IiIiYiiXYd+/f3/69+9vRi0iIiJi\ngKuG/eHDh3n00Ued0+fOnaO4uJg2bdoYXZeIiIi4SZ134+fk5DBixAhOn/7f6wmHDx9m1KhRfPXV\nV6YUJyIiItevzrBfvHgxb775Jv7+/3vBv3379rz22mssWLDAlOJERETk+tUZ9g6Hg/bt239vfrt2\n7aiqqjK0KBEREXGfOsP+7Nm6n0J3pWfai4iIyA9TnWHfrl073nvvve/Nf/3117nvvvsMLUpERETc\np8678SdMmMCYMWP46KOP6NSpEzU1Nfz973/Hz8+PZcuWmVmjiIiIXIc6wz44OJg1a9aQk5NDfn4+\nnp6eREdH0717dzPrExERkevk8qE6vXr1cvlKWREREfnhcvnWOxEREbmxKexFREQsTmEvIiJicQp7\nERERi1PYi4iIWJzCXkRExOIU9iIiIhansBcREbE4hb2IiIjFKexFREQsTmEvIiJicQp7ERERi1PY\ni4iIWJzCXkRExOIU9iIiIhbn8n321yMtLY3du3djs9lISUmhc+fOzmVVVVVMmzaN/Px8MjMzAcjN\nzWXs2LG0a9cOgPbt2zN16lSOHz/OhAkTqK6uJjg4mHnz5uHj42Nk6SIiIpZhWNjv2LGDwsJCMjIy\nOHjwICkpKWRkZDiXp6enExYWRn5+fq3tevTowcKFC2vNW7hwIfHx8URHR/Pyyy+zdu1a4uPjjSpd\nRETEUgwbxs/JySEiIgKA0NBQKioqqKysdC4fN26cc7krubm5DBw4EID+/fuTk5Pj/oJFREQsyrCw\nLy0tJSAgwDkdGBhISUmJc9rPz++K2xUUFDB69GhGjBjB3/72NwDOnTvnHLZv0aJFrf2IiIjI1Rl6\nzf67HA6Hy3XatGlDUlIS0dHRHD58mMTERDZu3Njg/QQE+OLl5XnNtVpJcLB/Y5dwU1CfzaE+m0N9\nNo9ZvTYs7O12O6Wlpc7pEydOEBwcfNVtQkJCGDx4MACtWrUiKCiI4uJifH19OX/+PE2aNKG4uBi7\n3X7V/ZSVnb3+D2ABwcH+lJScbuwyLE99Nof6bA712Tzu7vXVvjgYNowfHh5OdnY2AHl5edjt9jqH\n7r+VlZXFihUrACgpKeHkyZOEhITQu3dv5742btzIAw88YFTZIiIilmNz1Gdc/BrNnz+fnTt3YrPZ\nmD59Onv37sXf35/IyEiSk5MpKioiPz+fTp06ERsbS//+/Rk/fjynTp3i4sWLJCUl0a9fP06cOMHE\niROpqqrijjvuYM6cOXh7e9d5XH0rvUzf0M2hPptDfTaH+mweM8/sDQ37xqK/qJfpX1pzqM/mUJ/N\noT6bxxLD+CIiIvLDoLAXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFicwl5ERMTi\nFPYiIiIWp7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFicwl5ERMTiFPYiIiIW\np7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFicwl5ERMTiFPYiIiIWp7AXERGx\nOEPDPi0tjeHDhxMXF8eXX35Za1lVVRUTJ04kJibme9udP3+eiIgIMjMzAfj8888ZMWIECQkJPPvs\ns1RUVBhZtoiIiKUYFvY7duygsLCQjIwMUlNTSU1NrbU8PT2dsLCwK2772muv0axZM+f0nDlzSE1N\nZeXKlXTp0oWMjAyjyhYREbEcw8I+JyeHiIgIAEJDQ6moqKCystK5fNy4cc7l33Xw4EEKCgr46U9/\n6pwXEBBAeXk5ABUVFQQEBBhVtoiIiOUYFvalpaW1QjkwMJCSkhLntJ+f3xW3mzt3LpMmTao1LyUl\nhTFjxhAVFcUXX3zBo48+akzRIiIiFuRl1oEcDofLddatW8f999/PXXfdVWv+rFmzWLx4Md26dWPu\n3LmsXr2axMTEOvcTEOCLl5fndddsBcHB/o1dwk1BfTaH+mwO9dk8ZvXasLC32+2UlpY6p0+cOEFw\ncPBVt9m6dSuHDx9m69atFBUV4ePjQ8uWLdm/fz/dunUDoHfv3qxfv/6q+ykrO3v9H8ACgoP9KSk5\n3dhlWJ76bA712Rzqs3nc3eurfXEwLOzDw8NZtGgRcXFx5OXlYbfb6xy6/9aCBQucf160aBF33nkn\nvXv3JigoiIKCAu6++2727NlD69atjSpbRETEcgwL+65du9KxY0fi4uKw2WxMnz6dzMxM/P39iYyM\nJDk5maKiIg4dOkRCQgKxsbEMHTr0ivuaOXMmU6ZMwdvbm2bNmpGWlmZU2SIiIpZjc9TnYvoNRkNQ\nl2k4zhzqsznUZ3Ooz+YxcxhfT9ATERGxOIW9iIiIxSnsRURELM6S1+xFRETkf+nMXkRExOIU9iIi\nIhansBcREbE4hb2IiIjFKexFREQsTmEvIiJicQp7i0pPT2f48OE89thjbNy4sbHLsazz588TERFB\nZmZmY5diaVlZWTz88MPExMSwdevWxi7Hks6cOUNSUhIJCQnExcXx6aefNnZJlnPgwAEiIiJYtWoV\nAMePHychIYH4+HjGjh3LhQsXDDu2wt6Ctm/fTn5+PhkZGbzxxht6cZCBXnvtNZo1a9bYZVhaWVkZ\nS5YsYfXq1SxdupTNmzc3dkmW9Pvf/562bduycuVKXnnlFVJTUxu7JEs5e/Yss2bNolevXs55Cxcu\nJD4+ntWrV9O6dWvWrl1r2PEV9hbUvXt3XnnlFQBuu+02zp07R3V1dSNXZT0HDx6koKCAn/70p41d\niqXl5OTQq1cv/Pz8sNvtzJo1q7FLsqSAgADKy8sBOHXqFAEBAY1ckbX4+Pjw+uuvY7fbnfNyc3MZ\nOHAgAP379ycnJ8ew4yvsLcjT0xNfX18A1q5dS9++ffH09Gzkqqxn7ty5TJo0qbHLsLwjR45w/vx5\nRo8eTXx8vKH/QbyZPfTQQxw7dozIyEhGjhzJxIkTG7skS/Hy8qJJkya15p07dw4fHx8AWrRoQUlJ\niXHHN2zP0ug2bdrE2rVrefPNNxu7FMtZt24d999/P3fddVdjl3JTKC8vZ/HixRw7dozExES2bNmC\nzWZr7LIs5aOPPuKOO+5gxYoV7Nu3j5SUFN2LYiKjn1yvsLeoTz/9lKVLl/LGG2/g71/3O47l2mzd\nupXDhw+zdetWioqK8PHxoWXLlvTu3buxS7OcFi1a0KVLF7y8vGjVqhVNmzblm2++oUWLFo1dmqX8\n/e9/p0+fPgDcc889nDhxgurqao0KGsjX15fz58/TpEkTiouLaw3xu5uG8S3o9OnTpKens2zZMpo3\nb97Y5VjSggUL+PDDD1mzZg1PPPEEzz33nILeIH369GH79u3U1NRQVlbG2bNndT3ZAK1bt2b37t0A\nHD16lKZNmyroDda7d2+ys7MB2LhxIw888IBhx9KZvQV9/PHHlJWV8fzzzzvnzZ07lzvuuKMRqxK5\nNiEhIURFRREbGwvAlClT8PDQeYq7DR8+nJSUFEaOHMmlS5eYMWNGY5dkKV999RVz587l6NGjeHl5\nkZ2dzfz585k0aRIZGRnccccdPPLII4YdX6+4FRERsTh9PRYREbE4hb2IiIjFKexFREQsTmEvIiJi\ncQp7ERERi9NP70RuAEeOHGHQoEF06dIFgIsXL/Kf//mfjBkzhltvvfWa9rl+/XoeeughPDw86NCh\nA3l5eXh56T8JIlakM3uRG0RgYCArV65k5cqVvP3225w5c4Zf/vKX17y/RYsWUVNT48YKf7i2bdtm\n+ONIRX7IFPYiN6BbbrmFlJQU9u3bR0FBAQAvv/wyI0eO5PHHH2fu3Lk4HA5yc3MZMWIEv/zlL4mL\ni+O///u/qaysZOHChRQWFvLUU08533S2cuVKEhISGDx4MPv27WvMj+d2f/vb33j88cd5++23OX36\ndGOXI2I6hb3IDcrb25tOnTpx4MABNmzYQHFxMatWrWLt2rX8+9//ZsuWLQDk5eUxYcIE3n//fZo3\nb05mZibJyckA/O53v3M+Ujk0NJSVK1cyZMgQPvjgg0b7XEZISUnhnXfewcvLi5///OfMmDGDoqKi\nxi5LxDQKe5Eb2OnTp/Hw8CA3N5ddu3aRkJBAQkICR48e5ciRIwDcfffdhISEANC1a1fnSMD/9ZOf\n/ASAli1bcurUKXM+gImaNm3Kk08+yezZs/niiy/Izc1t7JJETKO7cURuUOfOnePrr7+mY8eO/P3v\nfyc2Npann3661jq5ubm1rlU7HI46Xw373Zee/N/r28XFxYwfPx6A6OhovL29ycrKAiAtLY158+ZR\nVlZG27ZtiY2NZe7cuQAkJiZSUFDAZ599BsCyZct49tlnAejevTudO3dmxYoVALzwwgv84Q9/4MCB\nA/j5+TFjxgy3HPP111/H29ubP//5z7z77rv4+fmRkpJCr169GtRvkRuZno0vcgM4cuQI8fHx/OUv\nfwEu340/Y8YMzp8/z0svvUR2djZvvfUWq1atwsvLi8WLFzNkyBCKi4t55pln+NOf/oTdbueXv/wl\n3bp1Iz4+nnvuuYddu3bRpEmTWnfjZ2Zm8tlnnzF//vxG/tTu8/jjj9O7d29GjBjB7bff3tjliJhO\nZ/YiN4hvvvmGhIQEqqurOXXqFOHh4UybNg2ABx98kF27dhEXF4enpyc//vGPueuuuyguLubuu+/m\n5ZdfprCwkGbNmjnfrPXAAw/w2GOP8dprrzXmxzLF6tWr8fHxaewyRBqNzuxFLCw3N5cFCxbw3nvv\nNXYpItKIdIOeiIiIxenMXkRExOJ0Zi8iImJxCnsRERGLU9iLiIhYnMJeRETE4hT2IiIiFqewFxER\nsbj/B2/AeU6MTiiPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f81c79550f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgaoOPcH6Kyb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}