doc2vec.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "doc2vec.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAgFQKDAwK31"
      },
      "source": [
        "## Importing necessary modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHkOpTyiH5zL"
      },
      "source": [
        "# gensim modules\n",
        "from gensim import utils\n",
        "from gensim.models.doc2vec import LabeledSentence\n",
        "from gensim.models import Doc2Vec\n",
        "\n",
        "# numpy\n",
        "import numpy\n",
        "\n",
        "# classifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# random\n",
        "import random\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8EimrYRcGbU"
      },
      "source": [
        "## Importing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSVTfZnuIZyG",
        "outputId": "0532aa00-b61d-49c0-8074-c3366ec80ee2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_Fu7ws-IYkX"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def load_data():\n",
        "\n",
        "    data_df = pd.read_excel('/content/drive/My Drive/dataset/dataset.xlsx', engine='openpyxl')\n",
        "\n",
        "    data_df.to_csv('output_reviews_top.csv')\n",
        "\n",
        "load_data()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxt49cHQImGV",
        "outputId": "35f49bf6-b628-4839-b891-a5988dc058e0"
      },
      "source": [
        "top_data_df = pd.read_csv('output_reviews_top.csv')\n",
        "print(\"Columns in the original dataset:\\n\")\n",
        "print(top_data_df.columns)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns in the original dataset:\n",
            "\n",
            "Index(['Unnamed: 0', 'URL', 'Text', 'Sentiment'], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqWnVTfCcRe2"
      },
      "source": [
        "## Data cleaning and analysing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "7ve99MyjJwdA",
        "outputId": "64a53fa4-7a62-4c05-a339-2b434405f2c0"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "\n",
        "print(\"Number of rows per star rating:\")\n",
        "print(top_data_df['Sentiment'].value_counts())\n",
        "\n",
        "# Function to map stars to sentiment\n",
        "def map_sentiment(sentiment):\n",
        "    if sentiment == 'NEG':\n",
        "        return -1\n",
        "    else:\n",
        "        return 1\n",
        "# Mapping stars to sentiment into three categories\n",
        "top_data_df['Sentiment_value'] = [map_sentiment(x) for x in top_data_df['Sentiment']]\n",
        "# Plotting the sentiment distribution\n",
        "plt.figure()\n",
        "pd.value_counts(top_data_df['Sentiment_value']).plot.bar(title=\"Sentiment distribution in df\")\n",
        "plt.xlabel(\"Sentiment\")\n",
        "plt.ylabel(\"No. of rows in df\")\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows per star rating:\n",
            "NEG    1000\n",
            "POS     999\n",
            "Name: Sentiment, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "s0Ml3VWrJnrM",
        "outputId": "fd38a6b2-abfe-4e51-ba2a-e5362fe7554c"
      },
      "source": [
        "# Function to retrieve top few number of each category\n",
        "def get_top_data(top_n):\n",
        "    top_data_df_positive = top_data_df[top_data_df['Sentiment_value'] == 1].head(top_n)\n",
        "    top_data_df_negative = top_data_df[top_data_df['Sentiment_value'] == -1].head(top_n)\n",
        "    top_data_df_small = pd.concat([top_data_df_positive, top_data_df_negative])\n",
        "    return top_data_df_small, top_data_df_positive, top_data_df_negative\n",
        "\n",
        "# Function call to get the top 10000 from each sentiment\n",
        "top_data_df_small, pos_review, neg_review = get_top_data(999)\n",
        "\n",
        "# After selecting top few samples of each sentiment\n",
        "print(\"After segregating and taking equal number of rows for each sentiment:\")\n",
        "print(top_data_df_small['Sentiment_value'].value_counts())\n",
        "top_data_df_small.head(10)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After segregating and taking equal number of rows for each sentiment:\n",
            " 1    999\n",
            "-1    999\n",
            "Name: Sentiment_value, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>URL</th>\n",
              "      <th>Text</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>http://www.imdb.com/title/tt0210075/usercomments</td>\n",
              "      <td>Girlfight follows a project dwelling New York ...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>http://www.imdb.com/title/tt0337640/usercomments</td>\n",
              "      <td>Hollywood North is an euphemism from the movie...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>http://www.imdb.com/title/tt0303549/usercomments</td>\n",
              "      <td>That '70s Show is definitely the funniest show...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>http://www.imdb.com/title/tt0716825/usercomments</td>\n",
              "      <td>9/10- 30 minutes of pure holiday terror. Okay,...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>http://www.imdb.com/title/tt0182225/usercomments</td>\n",
              "      <td>A series of random, seemingly insignificant th...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>http://www.imdb.com/title/tt0347779/usercomments</td>\n",
              "      <td>A very good adaptation of the novel by amrita ...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>http://www.imdb.com/title/tt0095655/usercomments</td>\n",
              "      <td>Ah, Moonwalker, I'm a huge Michael Jackson fan...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>http://www.imdb.com/title/tt0298131/usercomments</td>\n",
              "      <td>Although the beginning of the movie in New Yor...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>http://www.imdb.com/title/tt0088915/usercomments</td>\n",
              "      <td>As many reviewers here have noted, the film ve...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>http://www.imdb.com/title/tt0828154/usercomments</td>\n",
              "      <td>Bear in mind, any film (let alone documentary)...</td>\n",
              "      <td>POS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ... Sentiment_value\n",
              "0           0  ...               1\n",
              "1           1  ...               1\n",
              "2           2  ...               1\n",
              "3           3  ...               1\n",
              "4           4  ...               1\n",
              "5           5  ...               1\n",
              "6           6  ...               1\n",
              "7           7  ...               1\n",
              "8           8  ...               1\n",
              "9           9  ...               1\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_l1F_EFocW5t"
      },
      "source": [
        "## Dataset Splitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmBWUaXlNcdj"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train_pos, X_test_pos, Y_train_pos, Y_test_pos = train_test_split(pos_review['Text'], \n",
        "                                                        pos_review['Sentiment_value'], \n",
        "                                                        random_state=15)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItTGMOPdOjIg"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train_neg, X_test_neg, Y_train_neg, Y_test_neg = train_test_split(neg_review['Text'], \n",
        "                                                        neg_review['Sentiment_value'], \n",
        "                                                        random_state=15)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LInY7Qe9O1F7",
        "outputId": "2059439c-9207-4fa7-f05c-124746705c20"
      },
      "source": [
        "X_train_pos.shape, X_train_neg.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((749,), (749,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T353ZickO7GM",
        "outputId": "d9fdcae5-d9fe-4a6b-cfde-d289f8c639b7"
      },
      "source": [
        "X_test_pos.shape, X_test_neg.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((250,), (250,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30wTs2DtPDUz"
      },
      "source": [
        "X_train_pos.to_csv(r'train-pos.txt', header=None, index=None, sep=' ', mode='a')\n",
        "X_train_neg.to_csv(r'train-neg.txt', header=None, index=None, sep=' ', mode='a')\n",
        "X_test_pos.to_csv(r'test-pos.txt', header=None, index=None, sep=' ', mode='a')\n",
        "X_test_neg.to_csv(r'test-neg.txt', header=None, index=None, sep=' ', mode='a')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4c34jdUTwx9"
      },
      "source": [
        "class LabeledLineSentence(object):\n",
        "    def __init__(self, sources):\n",
        "        self.sources = sources\n",
        "        \n",
        "        flipped = {}\n",
        "        \n",
        "        # make sure that keys are unique\n",
        "        for key, value in sources.items():\n",
        "            if value not in flipped:\n",
        "                flipped[value] = [key]\n",
        "            else:\n",
        "                raise Exception('Non-unique prefix encountered')\n",
        "    \n",
        "    def __iter__(self):\n",
        "        for source, prefix in self.sources.items():\n",
        "            with utils.smart_open(source) as fin:\n",
        "                for item_no, line in enumerate(fin):\n",
        "                    yield LabeledSentence(utils.to_unicode(line).split(), [prefix + '_%s' % item_no])\n",
        "    \n",
        "    def to_array(self):\n",
        "        self.sentences = []\n",
        "        for source, prefix in self.sources.items():\n",
        "            with utils.smart_open(source) as fin:\n",
        "                for item_no, line in enumerate(fin):\n",
        "                    self.sentences.append(LabeledSentence(utils.to_unicode(line).split(), [prefix + '_%s' % item_no]))\n",
        "        return self.sentences\n",
        "    \n",
        "    def sentences_perm(self):\n",
        "        shuffled = list(self.sentences)\n",
        "        random.shuffle(shuffled)\n",
        "        return shuffled"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqXA0akgTz4H"
      },
      "source": [
        "sources = {'test-neg.txt':'TEST_NEG', 'test-pos.txt':'TEST_POS', 'train-neg.txt':'TRAIN_NEG', 'train-pos.txt':'TRAIN_POS'}\n",
        "\n",
        "sentences = LabeledLineSentence(sources)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wl9mZey0cn23"
      },
      "source": [
        "## Doc to vector: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDjFNy2MT2Qw",
        "outputId": "afbf3138-707b-4dc3-ee4b-25a8c2b85508"
      },
      "source": [
        "import gensim\n",
        "model = gensim.models.Doc2Vec(vector_size=300, window=10, min_count=5, workers=11,alpha=0.025, min_alpha=0.025, epochs=20)\n",
        "model.build_vocab(sentences.to_array())\n",
        "model.train(sentences.to_array(), epochs=model.iter, total_examples=model.corpus_count)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: DeprecationWarning: Call to deprecated `LabeledSentence` (Class will be removed in 4.0.0, use TaggedDocument instead).\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Call to deprecated `iter` (Attribute will be removed in 4.0.0, use self.epochs instead).\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PG_Cr1XiVEcw",
        "outputId": "1eacddc1-da1c-462e-9555-4e7ae4922e8c"
      },
      "source": [
        "model.most_similar('good')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('mediocre', 0.3071358799934387),\n",
              " ('nice', 0.2950599193572998),\n",
              " ('excellent', 0.26882946491241455),\n",
              " ('interesting', 0.2678501009941101),\n",
              " ('good.', 0.2642803192138672),\n",
              " ('effective.', 0.2637708783149719),\n",
              " ('superb', 0.24829038977622986),\n",
              " ('solid', 0.2452278733253479),\n",
              " ('fine', 0.23733077943325043),\n",
              " ('good,', 0.23542582988739014)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxOQZzV3VMhs",
        "outputId": "acc10c3a-c728-4310-f127-666d306c2773"
      },
      "source": [
        "model['TRAIN_NEG_0']"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.095671  ,  0.00717629,  0.28079307, -0.16983812,  0.2820339 ,\n",
              "        0.05735643,  0.5925636 ,  0.4694259 ,  0.10915232, -0.4761697 ,\n",
              "        0.13854563, -0.27969617,  0.6951446 ,  0.9157932 ,  0.35301104,\n",
              "       -0.4949389 , -0.86905664,  0.48165712,  0.1746926 ,  0.4392855 ,\n",
              "        0.43516627,  0.47208145, -0.5679434 ,  0.13417351, -0.7287388 ,\n",
              "        0.84886926, -0.79006207,  0.38959417, -0.82529485,  0.03120219,\n",
              "       -0.00282664, -0.6090412 , -0.44942608, -0.6743061 , -1.5048629 ,\n",
              "        0.87639344,  0.31255218,  0.30007532, -0.70307267, -1.3192974 ,\n",
              "        0.5565921 ,  0.5361204 , -0.9617022 , -0.8928383 ,  0.18892965,\n",
              "        0.61704874,  0.10736828,  0.9771666 ,  1.3513553 ,  0.56517655,\n",
              "       -0.7213032 ,  0.32775798, -0.5786961 ,  0.9006983 ,  0.5096848 ,\n",
              "       -0.5074681 ,  0.01321952, -0.39962286,  0.5078391 ,  0.59525543,\n",
              "        1.0509002 , -1.4565307 , -1.1114244 ,  1.547754  ,  0.46670696,\n",
              "        0.7027268 , -0.37143674, -0.1852383 ,  0.69961864,  0.30202502,\n",
              "       -1.3857659 , -0.16157135,  0.14452694,  0.7224824 , -0.7317256 ,\n",
              "        0.44003704,  0.46571752,  0.22706193,  0.99166065, -0.1555482 ,\n",
              "        0.7512446 , -0.11720637,  0.01308181, -0.5144048 , -0.3777424 ,\n",
              "       -0.31197613,  1.0777974 ,  0.96206355, -0.52739733,  0.8581198 ,\n",
              "        0.8363366 ,  0.38202056, -0.99830854,  0.30123824, -0.49755496,\n",
              "       -1.4738594 ,  0.10883867,  0.23869297,  1.3543719 , -0.4559895 ,\n",
              "       -0.19030823, -0.16654101, -0.826688  , -2.3983831 , -0.3779751 ,\n",
              "        0.42240012,  0.81233454, -0.05377793, -0.97037125, -0.36619747,\n",
              "       -1.2729524 ,  0.05370024, -0.58938146, -0.33374676,  1.823059  ,\n",
              "        1.5216863 ,  0.913081  ,  1.1281394 ,  0.1215561 , -0.17904872,\n",
              "        0.66244566, -0.22580986, -0.01828748, -0.6918041 , -0.49794596,\n",
              "       -2.0008955 , -0.15223184,  0.08964163, -0.16534528, -0.5175271 ,\n",
              "       -0.9988545 , -0.8978922 ,  0.4094048 , -0.69778603,  0.1729227 ,\n",
              "       -0.33837876,  0.30154246,  0.0860479 ,  0.6522953 ,  0.03101487,\n",
              "        0.3597365 ,  0.58924794, -0.00525738,  0.08780643, -0.3216599 ,\n",
              "       -0.12207897, -0.59226674,  0.24093363,  0.81383955,  0.42396256,\n",
              "        0.9488231 , -0.31599084,  0.7991316 ,  0.68924123, -0.09336779,\n",
              "       -0.88048357,  0.42036936,  0.19170666,  1.1642516 ,  0.8019947 ,\n",
              "        0.7798894 , -0.81755036,  0.97418356, -0.22776777, -0.4918153 ,\n",
              "       -0.01461239,  0.67271346,  0.0112146 ,  0.391694  , -0.5359574 ,\n",
              "       -0.5162328 , -0.03572346,  0.04873334, -0.036009  , -0.4883727 ,\n",
              "        1.3536534 , -0.15969197, -1.1901091 , -0.23519365,  0.01096063,\n",
              "        1.1185167 , -1.6288017 ,  0.04852893, -0.17332637, -0.11137196,\n",
              "       -0.13514762,  1.4292688 , -1.1001633 , -0.56992894,  0.570086  ,\n",
              "       -0.14457959, -0.14494336, -0.04146319, -1.0736569 , -1.3499733 ,\n",
              "        0.48414963,  0.49151433, -1.3371466 ,  0.6455884 , -1.5892774 ,\n",
              "        0.1587172 , -0.55069965,  0.34040228, -0.96134543,  0.8699802 ,\n",
              "        2.1725693 ,  1.1529039 ,  0.33623958, -0.9450953 , -1.1895998 ,\n",
              "       -0.78343225, -0.8878023 , -0.89967674, -0.11160866, -1.6500425 ,\n",
              "        1.8637851 , -0.21029401, -0.4366294 ,  0.9627581 ,  0.12713955,\n",
              "       -0.46224016, -0.22089596, -0.62743336,  1.2739702 ,  0.48181936,\n",
              "        0.82747966,  0.12366843,  1.4557513 ,  0.6961141 , -0.1855751 ,\n",
              "       -1.1099627 ,  0.13547   , -0.8828943 ,  0.83662766,  0.00998088,\n",
              "        0.0340547 , -0.37289223,  0.38435364,  0.46329576,  0.02506808,\n",
              "       -0.79665554, -0.38374692, -0.18336932,  1.3957174 , -1.1660578 ,\n",
              "       -1.04835   , -0.11486593, -0.05882766,  0.38777277, -0.77999455,\n",
              "        1.1150193 , -0.2990431 , -0.81120694, -0.44157103, -0.56295407,\n",
              "        0.8608844 ,  0.18943685,  0.05132207,  0.0925122 , -0.31590766,\n",
              "        0.31724963, -0.32205787,  1.2348418 ,  0.68786836, -0.21869735,\n",
              "       -0.3494995 , -0.1333029 ,  0.6538612 , -0.06956584, -0.46456012,\n",
              "       -0.11774707, -0.6249447 , -1.152466  , -0.12366404,  0.1058988 ,\n",
              "        0.3316054 ,  0.8688342 ,  1.1399575 ,  0.4831692 , -0.3561345 ,\n",
              "       -0.7268126 , -0.5318827 , -0.03740281,  0.3024329 , -0.16884121,\n",
              "       -0.34212586, -0.43700385, -0.24634376, -0.48561755, -0.08569048,\n",
              "       -1.23284   , -0.37864992,  1.2794517 ,  1.016878  , -0.13049951,\n",
              "        0.5082445 , -0.1637827 ,  0.64764637, -0.29670885,  0.23794135],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afjSqdV4c-nK"
      },
      "source": [
        "## Saving and loading the model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aa6oXX2KVQz4"
      },
      "source": [
        "model.save('./custom_model.d2v')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwQKA7JjVTPM"
      },
      "source": [
        "model = Doc2Vec.load('./custom_model.d2v')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Sgn9Y1lWPCG"
      },
      "source": [
        "train_arrays = numpy.zeros((1498, 300))\n",
        "train_labels = numpy.zeros(1498)\n",
        "\n",
        "for i in range(749):\n",
        "    prefix_train_pos = 'TRAIN_POS_' + str(i)\n",
        "    prefix_train_neg = 'TRAIN_NEG_' + str(i)\n",
        "    train_arrays[i] = model[prefix_train_pos]\n",
        "    train_arrays[749 + i] = model[prefix_train_neg]\n",
        "    train_labels[i] = 1\n",
        "    train_labels[749 + i] = 0"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ky7cW2aXgFX",
        "outputId": "98dae4f4-a545-477b-fe81-26d892d57bc7"
      },
      "source": [
        "print(train_arrays)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.10852978 -0.24347666  0.21917456 ...  0.25693107  0.15984973\n",
            "   0.18443446]\n",
            " [ 0.70967108 -0.31234238  1.42165732 ...  0.69416535  0.19784489\n",
            "   0.60700399]\n",
            " [ 0.50039095  0.14879006  0.37273309 ...  0.0552935  -0.09104238\n",
            "  -0.03669013]\n",
            " ...\n",
            " [ 0.85858911  0.25386077  0.51318568 ...  0.08112904  0.06827359\n",
            "  -0.163085  ]\n",
            " [ 0.27882776  0.27370474  0.45493779 ...  0.22501501  0.10853791\n",
            "  -0.05984343]\n",
            " [ 1.35302901  0.16273081  0.44986591 ...  0.63841552  0.51069659\n",
            "   0.06818765]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNgMq-M6XmZm",
        "outputId": "ab3adb19-85b3-4382-ce27-c93c60422bea"
      },
      "source": [
        "print(train_labels)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 1. 1. ... 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdqTJ0CPXq-_"
      },
      "source": [
        "test_arrays = numpy.zeros((500, 300))\n",
        "test_labels = numpy.zeros(500)\n",
        "\n",
        "for i in range(250):\n",
        "    prefix_test_pos = 'TEST_POS_' + str(i)\n",
        "    prefix_test_neg = 'TEST_NEG_' + str(i)\n",
        "    test_arrays[i] = model[prefix_test_pos]\n",
        "    test_arrays[250 + i] = model[prefix_test_neg]\n",
        "    test_labels[i] = 1\n",
        "    test_labels[250 + i] = 0"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gr7v6ZmbhNbw"
      },
      "source": [
        "import pickle\n",
        "from sklearn.metrics import accuracy_score, precision_score \n",
        "from sklearn.metrics import recall_score, confusion_matrix, classification_report\n",
        "def generate_report(pred_list, custom_model, y_test, model_name):\n",
        "  print(f'Confusion Matrix:\\n{confusion_matrix(y_test,pred_list)}')\n",
        "  print(f'\\nClassification Report:\\n{classification_report(y_test,pred_list)}')\n",
        "  acc = accuracy_score(y_test, pred_list) * 100\n",
        "  print(f'Accuracy: {acc}%')\n",
        "  prec = precision_score(y_test, pred_list, average='macro') * 100\n",
        "  print(f'Precision: {prec}%')\n",
        "  rec = recall_score(y_test, pred_list, average='macro') * 100\n",
        "  print(f'Recall: {rec}%')\n",
        "  pickle.dump(custom_model, open(model_name + '.sav', 'wb'))\n",
        "  print(model_name + ' trained Model Saved')\n",
        "  return acc, prec, rec"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xGvBD4JdFcL"
      },
      "source": [
        "## Logistic Regression:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vpf5WWhjYCO5",
        "outputId": "ae3253f2-e718-421d-eb9c-e29eec9acb3a"
      },
      "source": [
        "classifier = LogisticRegression(max_iter=1000)\n",
        "classifier.fit(train_arrays, train_labels)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnXpaeyQYSzu",
        "outputId": "26fad8bb-7dcb-4af0-a7ce-344d75051c5e"
      },
      "source": [
        "classifier.score(test_arrays, test_labels)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.774"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "as7B_X0SjVCz"
      },
      "source": [
        "LRpred = classifier.predict(test_arrays)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fq2P5oMGjMv3",
        "outputId": "8de23cf7-bf2c-49bf-a2b7-e712f7496f73"
      },
      "source": [
        "lr_metrics = [None, None, None]\n",
        "lr_metrics[0], lr_metrics[1], lr_metrics[2] = generate_report(LRpred, classifier, test_labels, 'LinearRegression_model')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[189  61]\n",
            " [ 52 198]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.78      0.76      0.77       250\n",
            "         1.0       0.76      0.79      0.78       250\n",
            "\n",
            "    accuracy                           0.77       500\n",
            "   macro avg       0.77      0.77      0.77       500\n",
            "weighted avg       0.77      0.77      0.77       500\n",
            "\n",
            "Accuracy: 77.4%\n",
            "Precision: 77.43555648119964%\n",
            "Recall: 77.4%\n",
            "LinearRegression_model trained Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfpc4XizdtZO"
      },
      "source": [
        "## Bernoulli Naive Bayes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HmNUqy6hluA"
      },
      "source": [
        "import pickle\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_score, recall_score\n",
        "\n",
        "classifier = BernoulliNB()\n",
        "\n",
        "classifier.fit(train_arrays, train_labels)\n",
        "BNBpred=classifier.predict(test_arrays)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Er4LqsDSjqL6",
        "outputId": "0e6652e3-f5fd-4543-dd1b-cdba90953e8d"
      },
      "source": [
        "bnb_metrics = [None, None, None]\n",
        "bnb_metrics[0], bnb_metrics[1], bnb_metrics[2] = generate_report(BNBpred, classifier, test_labels, 'BernoulliNB_model')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[175  75]\n",
            " [ 59 191]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.75      0.70      0.72       250\n",
            "         1.0       0.72      0.76      0.74       250\n",
            "\n",
            "    accuracy                           0.73       500\n",
            "   macro avg       0.73      0.73      0.73       500\n",
            "weighted avg       0.73      0.73      0.73       500\n",
            "\n",
            "Accuracy: 73.2%\n",
            "Precision: 73.29541803226014%\n",
            "Recall: 73.2%\n",
            "BernoulliNB_model trained Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfgLii8RCnil"
      },
      "source": [
        "###Multinomial Naive Bayes(MNB)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rubber-galaxy"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bBzqnGwMeJh"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "min_max_scaler = MinMaxScaler()\n",
        "train_arrays_norm = min_max_scaler.fit_transform(train_arrays)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcGl7yu3TquB"
      },
      "source": [
        "classifier = MultinomialNB()\n",
        "classifier.fit(train_arrays_norm, train_labels)\n",
        "# MNBmodel.fit(X_train,y_train)\n",
        "MNBpred=classifier.predict(test_arrays)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9vVl9IQi6FN",
        "outputId": "e2f95607-6259-4fdb-944b-88e7c29a627b"
      },
      "source": [
        "mnb_metrics = [None, None, None]\n",
        "mnb_metrics[0], mnb_metrics[1], mnb_metrics[2] = generate_report(MNBpred, classifier, test_labels, 'MultinomialNB_model')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[175  75]\n",
            " [ 42 208]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.81      0.70      0.75       250\n",
            "         1.0       0.73      0.83      0.78       250\n",
            "\n",
            "    accuracy                           0.77       500\n",
            "   macro avg       0.77      0.77      0.76       500\n",
            "weighted avg       0.77      0.77      0.76       500\n",
            "\n",
            "Accuracy: 76.6%\n",
            "Precision: 77.07169725293514%\n",
            "Recall: 76.6%\n",
            "MultinomialNB_model trained Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l21DrSbOdx7n"
      },
      "source": [
        "## Decision Tree classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGbTaS-0kieW"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IspVz0dikIC8"
      },
      "source": [
        "dt_clf = DecisionTreeClassifier()\n",
        "# dt_model = Pipeline([(\"cleaner\", predictors()), ('vectorizer', vectorizer), ('classifier', dt_clf)])\n",
        "\n",
        "# Train the Model\n",
        "dt_clf.fit(train_arrays, train_labels)   \n",
        "dt_pred = dt_clf.predict(test_arrays)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vUEdxjgj1d2",
        "outputId": "07db8468-a2ce-400f-b0be-ab75e21a2032"
      },
      "source": [
        "dt_metrics = [None, None, None]\n",
        "dt_metrics[0], dt_metrics[1], dt_metrics[2] = generate_report(dt_pred, dt_clf, test_labels, 'DecisionTree_model')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[143 107]\n",
            " [106 144]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.57      0.57      0.57       250\n",
            "         1.0       0.57      0.58      0.57       250\n",
            "\n",
            "    accuracy                           0.57       500\n",
            "   macro avg       0.57      0.57      0.57       500\n",
            "weighted avg       0.57      0.57      0.57       500\n",
            "\n",
            "Accuracy: 57.4%\n",
            "Precision: 57.40011840189443%\n",
            "Recall: 57.4%\n",
            "DecisionTree_model trained Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY_irSbQk0VZ",
        "outputId": "60559118-7c57-4e13-f854-73c36da788cd"
      },
      "source": [
        "train_arrays[:5]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.10852978, -0.24347666,  0.21917456, ...,  0.25693107,\n",
              "         0.15984973,  0.18443446],\n",
              "       [ 0.70967108, -0.31234238,  1.42165732, ...,  0.69416535,\n",
              "         0.19784489,  0.60700399],\n",
              "       [ 0.50039095,  0.14879006,  0.37273309, ...,  0.0552935 ,\n",
              "        -0.09104238, -0.03669013],\n",
              "       [ 0.95182222, -0.26135224,  0.71815544, ...,  1.02142286,\n",
              "         1.35163629,  0.68188328],\n",
              "       [ 0.82125646,  0.11760506,  0.22401454, ..., -0.22962822,\n",
              "         0.01296779,  0.47788537]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kt6N8-8d2mZ"
      },
      "source": [
        "## SVM:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlQlggiWmbne"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import classification_report\n",
        "# pipeline and standard scalar are already imported"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yemBlDSmdx0",
        "outputId": "9539808d-3571-44d3-8ab7-dc7904f792e1"
      },
      "source": [
        "%%time\n",
        "#Train the model\n",
        "# Perform classification with SVM, kernel=linear\n",
        "SVMclassifier = svm.SVC(kernel='linear')\n",
        "# SVMmodel = Pipeline([(\"cleaner\", predictors()), ('vectorizer', vectorizer), ('classifier', SVMclassifier)])\n",
        "SVMclassifier.fit(train_arrays, train_labels)\n",
        "SVMpred = SVMclassifier.predict(test_arrays)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 1.87 s, sys: 831 µs, total: 1.87 s\n",
            "Wall time: 1.88 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wuwx3UJbj9Io",
        "outputId": "3f2f981c-c65d-4a66-cf99-ebfc0b197bc2"
      },
      "source": [
        "svm_metrics = [None, None, None]\n",
        "svm_metrics[0], svm_metrics[1], svm_metrics[2] = generate_report(SVMpred, SVMclassifier, test_labels, 'SVM_model')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[179  71]\n",
            " [ 54 196]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.77      0.72      0.74       250\n",
            "         1.0       0.73      0.78      0.76       250\n",
            "\n",
            "    accuracy                           0.75       500\n",
            "   macro avg       0.75      0.75      0.75       500\n",
            "weighted avg       0.75      0.75      0.75       500\n",
            "\n",
            "Accuracy: 75.0%\n",
            "Precision: 75.11613701756924%\n",
            "Recall: 75.0%\n",
            "SVM_model trained Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOaiaMQIvdkW"
      },
      "source": [
        "## Results comparision plots and tables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyo7GEJTv5-4",
        "outputId": "a1815061-7328-40d0-e17e-3dc9ac102b86"
      },
      "source": [
        "!pip3 install tabulate\n",
        "from tabulate import tabulate\n",
        "table = [['Model Name', 'Accuracy', 'Precision', 'Recall'],\n",
        "         ['Bernoulli Naïve Bayes', bnb_metrics[0], bnb_metrics[1], bnb_metrics[2]],\n",
        "         ['Multinomial Naïve Bayes', mnb_metrics[0], mnb_metrics[1], mnb_metrics[2]],\n",
        "         ['Support Vector Machine', svm_metrics[0], svm_metrics[1], svm_metrics[2]],\n",
        "         ['Maximum Entropy', lr_metrics[0], lr_metrics[1], lr_metrics[2]],\n",
        "         ['Decision Tree', dt_metrics[0], dt_metrics[1], dt_metrics[2]]]\n",
        "\n",
        "print(tabulate(table, headers='firstrow', tablefmt='fancy_grid'))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (0.8.9)\n",
            "╒═════════════════════════╤════════════╤═════════════╤══════════╕\n",
            "│ Model Name              │   Accuracy │   Precision │   Recall │\n",
            "╞═════════════════════════╪════════════╪═════════════╪══════════╡\n",
            "│ Bernoulli Naïve Bayes   │       73.2 │     73.2954 │     73.2 │\n",
            "├─────────────────────────┼────────────┼─────────────┼──────────┤\n",
            "│ Multinomial Naïve Bayes │       76.6 │     77.0717 │     76.6 │\n",
            "├─────────────────────────┼────────────┼─────────────┼──────────┤\n",
            "│ Support Vector Machine  │       75   │     75.1161 │     75   │\n",
            "├─────────────────────────┼────────────┼─────────────┼──────────┤\n",
            "│ Maximum Entropy         │       77.4 │     77.4356 │     77.4 │\n",
            "├─────────────────────────┼────────────┼─────────────┼──────────┤\n",
            "│ Decision Tree           │       57.4 │     57.4001 │     57.4 │\n",
            "╘═════════════════════════╧════════════╧═════════════╧══════════╛\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "zI2RYG5Uv-5U",
        "outputId": "fd160302-fd20-49e8-86d4-7f9f79eec676"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "N = 5\n",
        "ind = np.arange(N) \n",
        "width = 0.25\n",
        "  \n",
        "acc = [mnb_metrics[0], bnb_metrics[0], svm_metrics[0], lr_metrics[0], dt_metrics[0]]\n",
        "bar1 = plt.bar(ind, acc, width)\n",
        "  \n",
        "prec = [mnb_metrics[1], bnb_metrics[1], svm_metrics[1], lr_metrics[1], dt_metrics[1]]\n",
        "bar2 = plt.bar(ind+width, prec, width)\n",
        "  \n",
        "recall = [mnb_metrics[2], bnb_metrics[2], svm_metrics[2], lr_metrics[2], dt_metrics[2]]\n",
        "bar3 = plt.bar(ind+width*2, recall, width)\n",
        "  \n",
        "# plt.xlabel(\"Dates\")\n",
        "plt.ylabel(\"Percentage\")\n",
        "plt.title(\"Comparison\")\n",
        "  \n",
        "plt.xticks(ind+width,['Multinomial NB','Bernoulli NB','SVM','Logistic Regression', 'Decision Tree'])\n",
        "plt.xticks(rotation = 45)\n",
        "plt.legend( (bar1, bar2, bar3), ('Accuracy', 'Precision', 'Recall'), bbox_to_anchor = (1.05, 0.6))\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "k_xnFLctwABD",
        "outputId": "aca9740a-c62e-4bc4-dc72-beebaae37b58"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "models = ['Multinomial NB','Bernoulli NB','SVM','Logistic Regression', 'Decision Tree']\n",
        "recall = [mnb_metrics[2], bnb_metrics[2], svm_metrics[2], lr_metrics[2], dt_metrics[2]]\n",
        "precision = [mnb_metrics[1], bnb_metrics[1], svm_metrics[1], lr_metrics[1], dt_metrics[1]]\n",
        "line_chart1 = plt.plot(models, recall, 'o-g')\n",
        "line_chart2 = plt.plot(models, precision, 'o-y')\n",
        "plt.title('Recall vs Precision')\n",
        "plt.ylabel('Score')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.legend(['Recall', 'Precision'], bbox_to_anchor = (1.3, 0.6))\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}