MovieLens_Tfidf_Based_Predictions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MovieLens Tfidf Based Predictions",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzGJOp1a71Kx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2efc84ff-201c-475e-c86f-fbab8fc4c334"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/My Drive/University/ΕΞΑΜΗΝΟ 7/ΑΝΑΚΤΗΣΗ ΠΛΗΡΟΦΟΡΙΑΣ/Project/data\""
      ],
      "execution_count": 1,
      "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": "DuX1fBkRjEgC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext google.colab.data_table"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lby0NMBVlt47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9b1430f4-ee7c-44ab-d9a0-4ac6c66b4796"
      },
      "source": [
        "!pip install elasticsearch"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: elasticsearch in /usr/local/lib/python3.6/dist-packages (7.9.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (2020.6.20)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (1.24.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVrVyJsU6Rzn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "from elasticsearch import Elasticsearch\n",
        "\n",
        "\n",
        "pd.options.mode.chained_assignment = None\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics \n",
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZEDoyb9UQ4Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies = pd.read_csv(\"{}/movies.csv\".format(path))\n",
        "reviews = pd.read_csv(\"{}/ratings.csv\".format(path))\n",
        "tf_idf_pre = pd.read_csv(\"./temp.csv\".format(path))\n",
        "\n",
        "movies[\"title_m\"] = movies[\"title\"].apply(lambda x: x[:-6])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_UaPhP4lwVF",
        "colab_type": "text"
      },
      "source": [
        "# **TFidf**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PonRRFJIr-rp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "es = Elasticsearch([{'host': 'snf-874957.vm.okeanos.grnet.gr', 'port': 9200}])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBL7hIKQI0Nz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def TfIdf(corpus, N):\n",
        "\n",
        "    data = {(i,j):corpus[i][j] for i in corpus.keys() for j in corpus[i].keys()}\n",
        "\n",
        "    df = pd.DataFrame.from_dict(data, orient='index').sort_index()\n",
        "    \n",
        "    df[\"tf-idf\"] = np.multiply(df[\"term_freq\"], np.log( N / df[\"doc_freq\"]))\n",
        "\n",
        "    df.index.names = [\"movieId\", \"word\"]\n",
        "    \n",
        "    tfidf = df.reset_index()\n",
        "    tfidf.drop([\"term_freq\",\"doc_freq\"],axis=1, inplace=True)\n",
        "    tfidf.movieId = tfidf.movieId.astype(\"int64\")\n",
        "    tfidf[\"tf-idf\"] = tfidf[\"tf-idf\"].astype(\"float32\")\n",
        "\n",
        "    return tfidf"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0itW1nvBIBie",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res = es.search(index=\"movies_test\", body={\"query\": {\"match_all\":{} }}, size = 10000)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FtgYCvzI9D3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ids = dict()\n",
        "corpus = dict()\n",
        "word_params_dict = dict()\n",
        "total_hits = res['hits']['total']\n",
        "\n",
        "for hit in range(total_hits):\n",
        "    movieId = res['hits']['hits'][hit]['_source']['movieId']\n",
        "    es_id = res['hits']['hits'][hit]['_id']\n",
        "    ids[es_id] = movieId"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqBBmlmOgnCW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for movie in es.mtermvectors(index=\"movies_test\",doc_type='_doc',body=dict(ids=list(ids.keys()),parameters=dict(term_statistics=True,field_statistics=True,fields=[\"title\"])))['docs']:                                                                                                                                                            \n",
        "    \n",
        "    title = movie['term_vectors'] \n",
        "    movieId = ids.get( movie['_id'], None ) \n",
        "    words = title['title']['terms'].keys()\n",
        "\n",
        "    for word in words:\n",
        "        term_freq = title['title']['terms'][word]['term_freq']\n",
        "        doc_freq = title['title']['terms'][word]['doc_freq']\n",
        "        word_params_dict[word] = {'term_freq': term_freq, 'doc_freq' : doc_freq}\n",
        "\n",
        "    corpus[movieId] = word_params_dict\n",
        "    word_params_dict = dict()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J13Q56DQj97f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_idf_pre = TfIdf(corpus, len(corpus))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zheu1uRfj4J3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "f011bdf0-6c57-4b6d-bcc7-211b46ef6646"
      },
      "source": [
        "tf_idf_pre"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>word</th>\n",
              "      <th>tf-idf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1995</td>\n",
              "      <td>5.007899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>story</td>\n",
              "      <td>6.553824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>toy</td>\n",
              "      <td>8.425626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>1995</td>\n",
              "      <td>5.007899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>9.118773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38177</th>\n",
              "      <td>164977</td>\n",
              "      <td>the</td>\n",
              "      <td>2.677827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38178</th>\n",
              "      <td>164979</td>\n",
              "      <td>69</td>\n",
              "      <td>9.118773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38179</th>\n",
              "      <td>164979</td>\n",
              "      <td>of</td>\n",
              "      <td>3.877026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38180</th>\n",
              "      <td>164979</td>\n",
              "      <td>unboxed</td>\n",
              "      <td>9.118773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38181</th>\n",
              "      <td>164979</td>\n",
              "      <td>women</td>\n",
              "      <td>7.172863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>38182 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       movieId     word    tf-idf\n",
              "0            1     1995  5.007899\n",
              "1            1    story  6.553824\n",
              "2            1      toy  8.425626\n",
              "3            2     1995  5.007899\n",
              "4            2  jumanji  9.118773\n",
              "...        ...      ...       ...\n",
              "38177   164977      the  2.677827\n",
              "38178   164979       69  9.118773\n",
              "38179   164979       of  3.877026\n",
              "38180   164979  unboxed  9.118773\n",
              "38181   164979    women  7.172863\n",
              "\n",
              "[38182 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "203VOK2Aj3HW",
        "colab_type": "text"
      },
      "source": [
        "# **Data Proccess**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQkGSTqULlP-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = movies[['movieId',\"title\",\"genres\"]].set_index('movieId').genres.str.split('|', expand=True).stack()\n",
        "genres = pd.get_dummies(temp).groupby(level=0).sum().reset_index()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MN6PKbVJW_e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reviews['class'] = reviews['rating'].apply(lambda x: int(x*2))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G23lAku6O7WQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_idf_pre = tf_idf_pre[[\"movieId\",'word',\"tf-idf\"]]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcn3i8vtvcKt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_idf_pre[\"tf-idf\"] = tf_idf_pre[\"tf-idf\"].astype('float32')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TdNK9m9PucE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_idf = tf_idf_pre.pivot(index='movieId', columns='word', values='tf-idf').reset_index().fillna(0)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iehvNb9oxKu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library = genres.merge(tf_idf, how='left', on='movieId')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7_jt5WIovHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "extra_data = reviews[['class',\"userId\",\"movieId\"]].merge(genres, on='movieId', how='left')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnOK0DANwVro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "extra_data[\"class\"] = extra_data[\"class\"].astype('category')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dACq2ISQqtOv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final = extra_data.merge(tf_idf, how='left', on='movieId')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0NhnbvEn9aC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "7326031b-4864-4812-b3f1-570448b216e2"
      },
      "source": [
        "final.info()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 100004 entries, 0 to 100003\n",
            "Columns: 9184 entries, class_x to über\n",
            "dtypes: category(1), float32(9161), int64(2), uint8(20)\n",
            "memory usage: 3.4 GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XURXxM7fnmF6",
        "colab_type": "text"
      },
      "source": [
        "# Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apS2QpUlsZce",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e380d9d9-9564-457e-b26c-418cd91f6a23"
      },
      "source": [
        "accs = []\n",
        "\n",
        "for i in final.userId.unique():\n",
        "\n",
        "    temp = final[final.userId == i]\n",
        "    y = temp['class_x']\n",
        "    temp.drop(columns=[\"class_x\",\"movieId\",\"userId\"], inplace=True)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(temp, y, test_size=0.2, random_state=20)\n",
        "\n",
        "    clf = SGDClassifier(max_iter=300, tol=1e-3)\n",
        "    #Train the model using the training sets y_pred=clf.predict(X_test)\n",
        "    clf.fit(X_train,y_train)\n",
        "\n",
        "    y_pred = clf.predict(X_test)\n",
        "    print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "    accs.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.0\n",
            "Accuracy: 0.3125\n",
            "Accuracy: 0.2727272727272727\n",
            "Accuracy: 0.4634146341463415\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.3333333333333333\n",
            "Accuracy: 0.4444444444444444\n",
            "Accuracy: 0.20833333333333334\n",
            "Accuracy: 0.3333333333333333\n",
            "Accuracy: 0.5\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.23076923076923078\n",
            "Accuracy: 0.36363636363636365\n",
            "Accuracy: 0.5\n",
            "Accuracy: 0.17058823529411765\n",
            "Accuracy: 0.16666666666666666\n",
            "Accuracy: 0.1232876712328767\n",
            "Accuracy: 0.09090909090909091\n",
            "Accuracy: 0.4823529411764706\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.45454545454545453\n",
            "Accuracy: 0.11363636363636363\n",
            "Accuracy: 0.0821917808219178\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.16666666666666666\n",
            "Accuracy: 0.08571428571428572\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.7\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.3497536945812808\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.47368421052631576\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.42857142857142855\n",
            "Accuracy: 0.42857142857142855\n",
            "Accuracy: 0.43478260869565216\n",
            "Accuracy: 0.23076923076923078\n",
            "Accuracy: 0.4444444444444444\n",
            "Accuracy: 0.4\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.36363636363636365\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.875\n",
            "Accuracy: 0.125\n",
            "Accuracy: 0.08737864077669903\n",
            "Accuracy: 0.3\n",
            "Accuracy: 0.6\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.3\n",
            "Accuracy: 0.1111111111111111\n",
            "Accuracy: 0.5\n",
            "Accuracy: 0.5523809523809524\n",
            "Accuracy: 0.30952380952380953\n",
            "Accuracy: 0.26666666666666666\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.2727272727272727\n",
            "Accuracy: 0.2647058823529412\n",
            "Accuracy: 0.18181818181818182\n",
            "Accuracy: 0.05\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.16666666666666666\n",
            "Accuracy: 0.4\n",
            "Accuracy: 0.3333333333333333\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.058823529411764705\n",
            "Accuracy: 0.5294117647058824\n",
            "Accuracy: 0.4\n",
            "Accuracy: 0.3076923076923077\n",
            "Accuracy: 0.2608695652173913\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.1724137931034483\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.19047619047619047\n",
            "Accuracy: 0.2641509433962264\n",
            "Accuracy: 0.45454545454545453\n",
            "Accuracy: 0.125\n",
            "Accuracy: 0.15625\n",
            "Accuracy: 0.5\n",
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            "Accuracy: 0.2909090909090909\n",
            "Accuracy: 0.375\n",
            "Accuracy: 0.3392857142857143\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.8\n",
            "Accuracy: 0.15675675675675677\n",
            "Accuracy: 0.4\n",
            "Accuracy: 0.17857142857142858\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.20512820512820512\n",
            "Accuracy: 0.43333333333333335\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.3069306930693069\n",
            "Accuracy: 0.45454545454545453\n",
            "Accuracy: 0.5555555555555556\n",
            "Accuracy: 0.5555555555555556\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.1111111111111111\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.13333333333333333\n",
            "Accuracy: 0.21212121212121213\n",
            "Accuracy: 0.1326530612244898\n",
            "Accuracy: 0.4146341463414634\n",
            "Accuracy: 0.36507936507936506\n",
            "Accuracy: 0.2564102564102564\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.1\n",
            "Accuracy: 0.19230769230769232\n",
            "Accuracy: 0.34782608695652173\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.36363636363636365\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.11627906976744186\n",
            "Accuracy: 0.48333333333333334\n",
            "Accuracy: 0.8214285714285714\n",
            "Accuracy: 0.058823529411764705\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.21428571428571427\n",
            "Accuracy: 0.18181818181818182\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.125\n",
            "Accuracy: 0.26666666666666666\n",
            "Accuracy: 0.42857142857142855\n",
            "Accuracy: 0.2222222222222222\n",
            "Accuracy: 0.05714285714285714\n",
            "Accuracy: 0.15789473684210525\n",
            "Accuracy: 0.7142857142857143\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.26512968299711814\n",
            "Accuracy: 0.07142857142857142\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.15384615384615385\n",
            "Accuracy: 0.2777777777777778\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.625\n",
            "Accuracy: 0.7142857142857143\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.2\n",
            "Accuracy: 0.6666666666666666\n",
            "Accuracy: 0.6\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.45454545454545453\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.4642857142857143\n",
            "Accuracy: 0.375\n",
            "Accuracy: 0.6\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.3333333333333333\n",
            "Accuracy: 0.4411764705882353\n",
            "Accuracy: 0.36666666666666664\n",
            "Accuracy: 0.34615384615384615\n",
            "Accuracy: 0.2222222222222222\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.4074074074074074\n",
            "Accuracy: 0.36363636363636365\n",
            "Accuracy: 0.2222222222222222\n",
            "Accuracy: 0.42857142857142855\n",
            "Accuracy: 0.5769230769230769\n",
            "Accuracy: 0.25\n",
            "Accuracy: 0.3333333333333333\n",
            "Accuracy: 0.27586206896551724\n",
            "Accuracy: 0.3684210526315789\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.5\n",
            "Accuracy: 0.5\n",
            "Accuracy: 0.16346153846153846\n",
            "Accuracy: 0.3793103448275862\n",
            "Accuracy: 0.375\n",
            "Accuracy: 0.2857142857142857\n",
            "Accuracy: 0.0\n",
            "Accuracy: 0.375\n",
            "Accuracy: 0.14285714285714285\n",
            "Accuracy: 0.43478260869565216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_Lr2YqHtf2d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "4136f973-144a-4ad8-dc52-2259aba9460b"
      },
      "source": [
        "pd.DataFrame(accs).describe()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"count\",\n{\n            'v': 671.0,\n            'f': \"671.0\",\n        }],\n [\"mean\",\n{\n            'v': 0.2841880331088681,\n            'f': \"0.2841880331088681\",\n        }],\n [\"std\",\n{\n            'v': 0.1792877007457331,\n            'f': \"0.1792877007457331\",\n        }],\n [\"min\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }],\n [\"25%\",\n{\n            'v': 0.16666666666666666,\n            'f': \"0.16666666666666666\",\n        }],\n [\"50%\",\n{\n            'v': 0.25,\n            'f': \"0.25\",\n        }],\n [\"75%\",\n{\n            'v': 0.4,\n            'f': \"0.4\",\n        }],\n [\"max\",\n{\n            'v': 0.875,\n            'f': \"0.875\",\n        }]],\n        columns: [[\"string\", \"index\"], [\"number\", \"0\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>671.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.284188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.179288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.875000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                0\n",
              "count  671.000000\n",
              "mean     0.284188\n",
              "std      0.179288\n",
              "min      0.000000\n",
              "25%      0.166667\n",
              "50%      0.250000\n",
              "75%      0.400000\n",
              "max      0.875000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9ks0xn9oGzU",
        "colab_type": "text"
      },
      "source": [
        "The accuracy is very low but on average is threefold the one of a dummy classifier ( random ). The data even when reduced are very big to train as one in our machine"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Qe03p8anfkK",
        "colab_type": "text"
      },
      "source": [
        "# **Lets Predict**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DjHQ6KOuuz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = []\n",
        "for i in final.userId.unique():\n",
        "\n",
        "    user_data = final[final.userId == i]\n",
        "    y = user_data['class_x']\n",
        "    user_data.drop(columns=[\"class_x\",\"movieId\",\"userId\"], inplace=True)\n",
        "\n",
        "    clf = SGDClassifier(max_iter=300, tol=1e-3)\n",
        "    clf.fit(user_data,y)\n",
        "\n",
        "    user_i = library.drop(columns=[\"movieId\"], inplace=False)\n",
        "    user_predictions = clf.predict(user_i)\n",
        "    predictions.append(user_predictions.tolist())\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imay9f6nI89J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q4 = pd.DataFrame(predictions, columns=library['movieId'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EThKQVkJSTN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q4[\"userId\"] = final.userId.unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y57gW4E5JyzQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q4.to_csv(\"predictions.csv\", index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}