Netflix.ipynb

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  "metadata": {
    "colab": {
      "name": "Netflix.ipynb",
      "provenance": [],
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      "machine_shape": "hm",
      "mount_file_id": "1z-uGZzpr0BY6vONtOLMvZHFAHzFRIYQT",
      "authorship_tag": "ABX9TyMTTrE+Ru5mSEYoVOTgFskC"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJWH1Veo4UqZ",
        "colab_type": "text"
      },
      "source": [
        "# Netflix Movie Recommendation System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dmfAlpeW2DN",
        "colab_type": "text"
      },
      "source": [
        "## Objective and Constraints\n",
        "\n",
        "1. Minimize RMSE.\n",
        "2. Try to provide some interpretability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6FqqxdSW8sQ",
        "colab_type": "code",
        "outputId": "95a4da29-2722-4905-e382-90282ccf6d5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "# this is just to know how much time will it take to run this entire ipython notebook \n",
        "from datetime import datetime\n",
        "# globalstart = datetime.now()\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "matplotlib.use('nbagg')\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'figure.max_open_warning': 0})\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_style('whitegrid')\n",
        "import os\n",
        "from scipy import sparse\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import random"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KS4CMH8bXB42",
        "colab_type": "text"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blRAqAvDXRWP",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivo8YVxYXUCx",
        "colab_type": "text"
      },
      "source": [
        "### Converting / Merging whole data to required format: u_i, m_j, r_ij\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pU2lNHIaXA4x",
        "colab_type": "code",
        "outputId": "1984c229-75c5-4efa-93f8-a77ffd7dd176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "start = datetime.now()\n",
        "\n",
        "if not os.path.isfile(\"/content/drive/My Drive/AI projects/Netflix /data.csv\"):\n",
        "    data = open('/content/drive/My Drive/AI projects/Netflix /data.csv', mode='w')\n",
        "\n",
        "    row = []\n",
        "    files = [\"/content/drive/My Drive/AI projects/Netflix /combined_data_1.txt\",\n",
        "             \"/content/drive/My Drive/AI projects/Netflix /combined_data_2.txt\",\n",
        "             \"/content/drive/My Drive/AI projects/Netflix /combined_data_3.txt\",\n",
        "             \"/content/drive/My Drive/AI projects/Netflix /combined_data_4.txt\"]\n",
        "\n",
        "    for file in files:\n",
        "        print(\"Reading ratings from {}...\".format(file))\n",
        "        with open(file) as f:\n",
        "            for line in f:\n",
        "                line = line.strip()\n",
        "                if line.endswith(':'):\n",
        "                    movie_id = line.replace(\":\", \"\")\n",
        "                else:\n",
        "                    row = [x for x in line.split(\",\")]\n",
        "                    row.insert(0, movie_id)\n",
        "                    data.write(','.join(row))\n",
        "                    data.write('\\n')\n",
        "            print(\"Done..\\n\")\n",
        "    data.close()\n",
        "\n",
        "print(\"Time taken : \", datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading ratings from /content/drive/My Drive/AI projects/Netflix /combined_data_1.txt...\n",
            "Done..\n",
            "\n",
            "Reading ratings from /content/drive/My Drive/AI projects/Netflix /combined_data_2.txt...\n",
            "Done..\n",
            "\n",
            "Reading ratings from /content/drive/My Drive/AI projects/Netflix /combined_data_3.txt...\n",
            "Done..\n",
            "\n",
            "Reading ratings from /content/drive/My Drive/AI projects/Netflix /combined_data_4.txt...\n",
            "Done..\n",
            "\n",
            "Time taken :  0:02:49.154104\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh7sMZ6Oay1z",
        "colab_type": "code",
        "outputId": "38fe1ab7-d0a9-4a32-e14f-6b2fe5b4edf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "print(\"creating the dataframe from data.csv file..\")\n",
        "df = pd.read_csv('/content/drive/My Drive/AI projects/Netflix /data.csv', sep=',', names=['movie', 'user','rating','date'])\n",
        "df.date = pd.to_datetime(df.date)\n",
        "print('Done.\\n')\n",
        "\n",
        "# we are arranging the ratings according to time.\n",
        "print('Sorting the dataframe by date..')\n",
        "df.sort_values(by='date', inplace=True)\n",
        "print('Done..')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "creating the dataframe from data.csv file..\n",
            "Done.\n",
            "\n",
            "Sorting the dataframe by date..\n",
            "Done..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgVt5VG2mS7K",
        "colab_type": "code",
        "outputId": "98475a08-12db-4d17-ee80-a3b1b6fc4853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>movie</th>\n",
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              "          movie    user  rating       date\n",
              "56431994  10341  510180       4 1999-11-11\n",
              "9056171    1798  510180       5 1999-11-11\n",
              "58698779  10774  510180       3 1999-11-11\n",
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              "81893208  14660  510180       2 1999-11-11"
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      "cell_type": "code",
      "metadata": {
        "id": "LlvWPjmppJjV",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "df.describe()['rating']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.004805e+08\n",
              "mean     3.604290e+00\n",
              "std      1.085219e+00\n",
              "min      1.000000e+00\n",
              "25%      3.000000e+00\n",
              "50%      4.000000e+00\n",
              "75%      4.000000e+00\n",
              "max      5.000000e+00\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_HLs6ZJEKmF",
        "colab_type": "code",
        "outputId": "75f94d9d-325c-4525-f6bc-3f4f93130936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie     0\n",
              "user      0\n",
              "rating    0\n",
              "date      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQ_4vHYjEgRb",
        "colab_type": "code",
        "outputId": "8cfbe4fc-a036-481b-bc86-2f789bd6eb32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.duplicated().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXqsT8g9EoXs",
        "colab_type": "code",
        "outputId": "eeddde6a-612b-4cfa-cf21-1c1aa3006676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print(\"Total data \")\n",
        "print(\"-\"*50)\n",
        "print(\"\\nTotal no of ratings :\",df.shape[0])\n",
        "print(\"Total No of Users   :\", len(np.unique(df.user)))\n",
        "print(\"Total No of movies  :\", len(np.unique(df.movie)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total data \n",
            "--------------------------------------------------\n",
            "\n",
            "Total no of ratings : 100480507\n",
            "Total No of Users   : 480189\n",
            "Total No of movies  : 17770\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LScAaSt3GSSU",
        "colab_type": "text"
      },
      "source": [
        "### Spliting data into Train and Test(80:20)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y55z6JP_E4wd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.isfile('/content/drive/My Drive/AI projects/Netflix /train.csv'):\n",
        "    df.iloc[:int(df.shape[0] * 0.80)].to_csv('/content/drive/My Drive/AI projects/Netflix /train.csv', index= False)\n",
        "\n",
        "if not os.path.isfile('/content/drive/My Drive/AI projects/Netflix /test.csv'):\n",
        "    df.iloc[int(df.shape[0] * 0.80):].to_csv('/content/drive/My Drive/AI projects/Netflix /test.csv', index= False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQzBhzDqHvs3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv(\"/content/drive/My Drive/AI projects/Netflix /train.csv\", parse_dates= ['date'])\n",
        "test_df = pd.read_csv(\"/content/drive/My Drive/AI projects/Netflix /test.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IIy21NUKQj0",
        "colab_type": "code",
        "outputId": "fb4e16b0-0653-4152-e0a9-f65d4a8a0ea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   movie    user  rating       date\n",
              "0  10341  510180       4 1999-11-11\n",
              "1   1798  510180       5 1999-11-11\n",
              "2  10774  510180       3 1999-11-11\n",
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            ]
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          "metadata": {
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        }
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   movie     user  rating        date\n",
              "0   5926  2294429       2  2005-08-08\n",
              "1  10158  1743373       4  2005-08-08\n",
              "2  17064   381625       5  2005-08-08\n",
              "3   1443  1252933       5  2005-08-08\n",
              "4   1201  1434500       4  2005-08-08"
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          "metadata": {
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTGKpknQMOzy",
        "colab_type": "code",
        "outputId": "2f1c8138-ed59-4f8f-e5db-ff37021fbcf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print(\"Training data \")\n",
        "print(\"-\"*50)\n",
        "print(\"\\nTotal no of ratings :\",train_df.shape[0])\n",
        "print(\"Total No of Users   :\", len(np.unique(train_df.user)))\n",
        "print(\"Total No of movies  :\", len(np.unique(train_df.movie)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data \n",
            "--------------------------------------------------\n",
            "\n",
            "Total no of ratings : 80384405\n",
            "Total No of Users   : 405041\n",
            "Total No of movies  : 17424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFSQb7epMXTF",
        "colab_type": "code",
        "outputId": "fbf6de49-6355-4f8d-a73c-d2d13bbc957e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print(\"Test data \")\n",
        "print(\"-\"*50)\n",
        "print(\"\\nTotal no of ratings :\",test_df.shape[0])\n",
        "print(\"Total No of Users   :\", len(np.unique(test_df.user)))\n",
        "print(\"Total No of movies  :\", len(np.unique(test_df.movie)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test data \n",
            "--------------------------------------------------\n",
            "\n",
            "Total no of ratings : 20096102\n",
            "Total No of Users   : 349312\n",
            "Total No of movies  : 17757\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMneAi-aMcHY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# method to make y-axis more readable\n",
        "\n",
        "def fig_metric(num, units = 'M'):\n",
        "    units = units.lower()\n",
        "    num = float(num)\n",
        "    if units == 'k':\n",
        "        return str(num / 10**3) + \" K\"\n",
        "    elif units == 'm':\n",
        "        return str(num / 10**6) + \" M\"\n",
        "    elif unit == 'b':\n",
        "        return str(num / 10**9) + \" B\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5_-qrQeNnVm",
        "colab_type": "text"
      },
      "source": [
        "Distribution of ratings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Txkw9dOFNlSE",
        "colab_type": "code",
        "outputId": "d163d5e8-180d-4b12-c899-b493b70abf02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "plt.title('Distribution of ratings over Training dataset', fontsize=15)\n",
        "sns.countplot(train_df.rating)\n",
        "ax.set_yticklabels([fig_metric(item, 'M') for item in ax.get_yticks()])\n",
        "ax.set_ylabel('No. of Ratings(Millions)')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "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": "markdown",
      "metadata": {
        "id": "xKBD5HKvTf8D",
        "colab_type": "text"
      },
      "source": [
        "Add new column (week day) to the data set for analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ufx06DEOZJz",
        "colab_type": "code",
        "outputId": "36112d54-d784-4548-f74a-76cdc990361e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# It is used to skip the warning ''SettingWithCopyWarning''.. \n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "\n",
        "train_df['day_of_week'] = train_df.date.dt.day_name()\n",
        "\n",
        "train_df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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>movie</th>\n",
              "      <th>user</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>80384400</th>\n",
              "      <td>12074</td>\n",
              "      <td>2033618</td>\n",
              "      <td>4</td>\n",
              "      <td>2005-08-08</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80384401</th>\n",
              "      <td>862</td>\n",
              "      <td>1797061</td>\n",
              "      <td>3</td>\n",
              "      <td>2005-08-08</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80384402</th>\n",
              "      <td>10986</td>\n",
              "      <td>1498715</td>\n",
              "      <td>5</td>\n",
              "      <td>2005-08-08</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80384403</th>\n",
              "      <td>14861</td>\n",
              "      <td>500016</td>\n",
              "      <td>4</td>\n",
              "      <td>2005-08-08</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80384404</th>\n",
              "      <td>5926</td>\n",
              "      <td>1044015</td>\n",
              "      <td>5</td>\n",
              "      <td>2005-08-08</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          movie     user  rating       date day_of_week\n",
              "80384400  12074  2033618       4 2005-08-08      Monday\n",
              "80384401    862  1797061       3 2005-08-08      Monday\n",
              "80384402  10986  1498715       5 2005-08-08      Monday\n",
              "80384403  14861   500016       4 2005-08-08      Monday\n",
              "80384404   5926  1044015       5 2005-08-08      Monday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZ1BY3iNTpmr",
        "colab_type": "text"
      },
      "source": [
        "Number of Ratings per a month"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YXRLhg-TmTu",
        "colab_type": "code",
        "outputId": "17ae8e11-3d20-410e-bb9c-43c080d6affb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "ax = train_df.resample('m', on='date')['rating'].count().plot()\n",
        "ax.set_title('No of ratings per month (Training data)')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('No of ratings(per month)')\n",
        "ax.set_yticklabels([fig_metric(item, 'M') for item in ax.get_yticks()])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "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": "markdown",
      "metadata": {
        "id": "Z3ZNx5AmCbjt",
        "colab_type": "text"
      },
      "source": [
        "Analysis on the Ratings given by user"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtOYuHOQg7zs",
        "colab_type": "code",
        "outputId": "6d478a8d-fbb7-46c8-d336-4442481d3a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "no_of_rated_movies_per_user = train_df.groupby(by='user')['rating'].count().sort_values(ascending=False)\n",
        "\n",
        "no_of_rated_movies_per_user.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user\n",
              "305344     17112\n",
              "2439493    15896\n",
              "387418     15402\n",
              "1639792     9767\n",
              "1461435     9447\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiD3OQfoCepC",
        "colab_type": "code",
        "outputId": "b06a6cdf-e3c9-4b6a-f7c5-f47e1692371b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "fig = plt.figure(figsize=plt.figaspect(.5))\n",
        "\n",
        "ax1 = plt.subplot(121)\n",
        "sns.kdeplot(no_of_rated_movies_per_user, shade=True, ax=ax1)\n",
        "plt.xlabel('No of ratings by user')\n",
        "plt.title(\"PDF\")\n",
        "\n",
        "ax2 = plt.subplot(122)\n",
        "sns.kdeplot(no_of_rated_movies_per_user, shade=True, cumulative=True,ax=ax2)\n",
        "plt.xlabel('No of ratings by user')\n",
        "plt.title('CDF')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YM6kHWqpCifV",
        "colab_type": "code",
        "outputId": "59dc385a-6784-4582-a2a0-1f206514fbe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "no_of_rated_movies_per_user.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    405041.000000\n",
              "mean        198.459921\n",
              "std         290.793238\n",
              "min           1.000000\n",
              "25%          34.000000\n",
              "50%          89.000000\n",
              "75%         245.000000\n",
              "max       17112.000000\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADhfF-ahDEr7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "quantiles = no_of_rated_movies_per_user.quantile(np.arange(0,1.01,0.01), interpolation='higher')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4F_H9S2DKYW",
        "colab_type": "code",
        "outputId": "5e183e11-8730-42af-9ab3-901e14029cd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.title(\"Quantiles and their Values\")\n",
        "quantiles.plot()\n",
        "# quantiles with 0.05 difference\n",
        "plt.scatter(x=quantiles.index[::5], y=quantiles.values[::5], c='orange', label=\"quantiles with 0.05 intervals\")\n",
        "# quantiles with 0.25 difference\n",
        "plt.scatter(x=quantiles.index[::25], y=quantiles.values[::25], c='m', label = \"quantiles with 0.25 intervals\")\n",
        "plt.ylabel('No of ratings by user')\n",
        "plt.xlabel('Value at the quantile')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "# annotate the 25th, 50th, 75th and 100th percentile values....\n",
        "for x,y in zip(quantiles.index[::25], quantiles[::25]):\n",
        "    plt.annotate(s=\"({} , {})\".format(x,y), xy=(x,y), xytext=(x-0.05, y+500)\n",
        "                ,fontweight='bold')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "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": {
        "id": "-uPvgSkRDNk6",
        "colab_type": "code",
        "outputId": "58832988-9f0d-4c90-c672-d5b9ccad3815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "quantiles[::5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00        1\n",
              "0.05        7\n",
              "0.10       15\n",
              "0.15       21\n",
              "0.20       27\n",
              "0.25       34\n",
              "0.30       41\n",
              "0.35       50\n",
              "0.40       60\n",
              "0.45       73\n",
              "0.50       89\n",
              "0.55      109\n",
              "0.60      133\n",
              "0.65      163\n",
              "0.70      199\n",
              "0.75      245\n",
              "0.80      307\n",
              "0.85      392\n",
              "0.90      520\n",
              "0.95      749\n",
              "1.00    17112\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mp4NVgyZDZ2h",
        "colab_type": "code",
        "outputId": "3b2ecd25-e51b-4cbe-ed94-dc81b51b57d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "print('\\n No of ratings at last 5 percentile : {}\\n'.format(sum(no_of_rated_movies_per_user>= 749)) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " No of ratings at last 5 percentile : 20305\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdryuTZUDiBu",
        "colab_type": "text"
      },
      "source": [
        "Analysis of ratings of a movie given by a user"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKJCFq8VDeIi",
        "colab_type": "code",
        "outputId": "cea925c1-d087-43a2-d97f-5bea35cfb7ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "no_of_ratings_per_movie = train_df.groupby(by='movie')['rating'].count().sort_values(ascending=False)\n",
        "\n",
        "fig = plt.figure(figsize=plt.figaspect(.5))\n",
        "ax = plt.gca()\n",
        "plt.plot(no_of_ratings_per_movie.values)\n",
        "plt.title('# RATINGS per Movie')\n",
        "plt.xlabel('Movie')\n",
        "plt.ylabel('No of Users who rated a movie')\n",
        "ax.set_xticklabels([])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uup-CGpyD0l5",
        "colab_type": "text"
      },
      "source": [
        "- It is very skewed.. just like nunmber of ratings given per user\n",
        "- There are some movies (which are very popular) which are rated by huge number of users.\n",
        " \n",
        "- But most of the movies(like 90%) got some hundereds of ratings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPNEx7MTD7Vj",
        "colab_type": "text"
      },
      "source": [
        "Number of ratings on each day of the week"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXWLMXqYDuUu",
        "colab_type": "code",
        "outputId": "5f6e40b5-f41c-434c-83ce-391f596ab1d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "sns.countplot(x='day_of_week', data=train_df, ax=ax)\n",
        "plt.title('No of ratings on each day...')\n",
        "plt.ylabel('Total no of ratings')\n",
        "plt.xlabel('')\n",
        "ax.set_yticklabels([fig_metric(item, 'M') for item in ax.get_yticks()])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id='2c45a552-5a08-4a32-b0a5-72da9e8d1cdf'></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-111b2df2ac49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'day_of_week'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No of ratings on each day...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Total no of ratings'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8BtykoeD9qA",
        "colab_type": "code",
        "outputId": "ecde4320-cade-4315-8d12-36834ceaad84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "fig = plt.figure(figsize=plt.figaspect(.45))\n",
        "sns.boxplot(y='rating', x='day_of_week', data=train_df)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIFO0AkCEMzo",
        "colab_type": "code",
        "outputId": "9d33e6ea-f2cd-42f5-f836-9be082ff9cbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "avg_week_df = train_df.groupby(by=['day_of_week'])['rating'].mean()\n",
        "print(\" Average ratings\")\n",
        "print(\"-\"*30)\n",
        "print(avg_week_df)\n",
        "print(\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Average ratings\n",
            "------------------------------\n",
            "day_of_week\n",
            "Friday       3.585274\n",
            "Monday       3.577250\n",
            "Saturday     3.591791\n",
            "Sunday       3.594144\n",
            "Thursday     3.582463\n",
            "Tuesday      3.574438\n",
            "Wednesday    3.583751\n",
            "Name: rating, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teUuHczBEbjg",
        "colab_type": "text"
      },
      "source": [
        "- As avg ratings given is same for all days, we can observe that day is not useful feature here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaBjQjV5ErBd",
        "colab_type": "text"
      },
      "source": [
        "###  Creating sparse matrix from train data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEEkciFLEYcU",
        "colab_type": "code",
        "outputId": "50c88c7a-25f0-4d07-c8a2-3416b62d95c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "start = datetime.now()\n",
        "if os.path.isfile('/content/drive/My Drive/AI projects/Netflix /train_sparse_matrix.npz'):\n",
        "    print(\"It is present in your pwd, getting it from disk....\")\n",
        "    # just get it from the disk instead of computing it\n",
        "    train_sparse_matrix = sparse.load_npz('/content/drive/My Drive/AI projects/Netflix /train_sparse_matrix.npz')\n",
        "    print(\"DONE..\")\n",
        "else: \n",
        "    print(\"We are creating sparse_matrix from the dataframe..\")\n",
        "    # create sparse_matrix and store it for after usage.\n",
        "    # csr_matrix(data_values, (row_index, col_index), shape_of_matrix)\n",
        "    # It should be in such a way that, MATRIX[row, col] = data\n",
        "    train_sparse_matrix = sparse.csr_matrix((train_df.rating.values, (train_df.user.values, train_df.movie.values)),)\n",
        "    \n",
        "    print('Done. It\\'s shape is : (user, movie) : ',train_sparse_matrix.shape)\n",
        "    print('Saving it into disk for furthur usage..')\n",
        "    # save it into disk\n",
        "    sparse.save_npz(\"/content/drive/My Drive/AI projects/Netflix /train_sparse_matrix.npz\", train_sparse_matrix)\n",
        "    print('Done..\\n')\n",
        "\n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We are creating sparse_matrix from the dataframe..\n",
            "Done. It's shape is : (user, movie) :  (2649430, 17771)\n",
            "Saving it into disk for furthur usage..\n",
            "Done..\n",
            "\n",
            "0:01:07.362917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swRFVSAMF0I3",
        "colab_type": "text"
      },
      "source": [
        "The Sparsity of Train Sparse Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g8c2ZpkFYj0",
        "colab_type": "code",
        "outputId": "eb4b81c9-5541-46d7-f303-87cc6659a3f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "us,mv = train_sparse_matrix.shape\n",
        "elem = train_sparse_matrix.count_nonzero()\n",
        "\n",
        "print(\"Sparsity Of Train matrix : {} % \".format(  (1-(elem/(us*mv))) * 100) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Of Train matrix : 99.8292709259195 % \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAYEtZn_F-K-",
        "colab_type": "text"
      },
      "source": [
        "Creating sparse matrix from test data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N54z4H0vF4D4",
        "colab_type": "code",
        "outputId": "7ce45a1f-8084-4038-cb94-114dfbc050bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "start = datetime.now()\n",
        "if os.path.isfile('/content/drive/My Drive/AI projects/Netflix /test_sparse_matrix.npz'):\n",
        "    print(\"It is present in your pwd, getting it from disk....\")\n",
        "    # just get it from the disk instead of computing it\n",
        "    test_sparse_matrix = sparse.load_npz('/content/drive/My Drive/AI projects/Netflix /test_sparse_matrix.npz')\n",
        "    print(\"DONE..\")\n",
        "else: \n",
        "    print(\"We are creating sparse_matrix from the dataframe..\")\n",
        "    # create sparse_matrix and store it for after usage.\n",
        "    # csr_matrix(data_values, (row_index, col_index), shape_of_matrix)\n",
        "    # It should be in such a way that, MATRIX[row, col] = data\n",
        "    test_sparse_matrix = sparse.csr_matrix((test_df.rating.values, (test_df.user.values,\n",
        "                                               test_df.movie.values)))\n",
        "    \n",
        "    print('Done. It\\'s shape is : (user, movie) : ',test_sparse_matrix.shape)\n",
        "    print('Saving it into disk for furthur usage..')\n",
        "    # save it into disk\n",
        "    sparse.save_npz(\"/content/drive/My Drive/AI projects/Netflix /test_sparse_matrix.npz\", test_sparse_matrix)\n",
        "    print('Done..\\n')\n",
        "    \n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We are creating sparse_matrix from the dataframe..\n",
            "Done. It's shape is : (user, movie) :  (2649430, 17771)\n",
            "Saving it into disk for furthur usage..\n",
            "Done..\n",
            "\n",
            "0:00:18.265233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nV-BZVW3U0i-",
        "colab_type": "code",
        "outputId": "1b83880d-0c5d-4749-ea68-68835184bdc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "us,mv = test_sparse_matrix.shape\n",
        "elem = test_sparse_matrix.count_nonzero()\n",
        "\n",
        "print(\"Sparsity Of Test matrix : {} % \".format(  (1-(elem/(us*mv))) * 100) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Of Test matrix : 99.95731772988694 % \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2FwToBejH37-",
        "colab_type": "text"
      },
      "source": [
        "### Finding Global average of all movie ratings, Average rating per user, and Average rating per movie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJehxyKexrn7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get the user averages in dictionary (key: user_id/movie_id, value: avg rating)\n",
        "\n",
        "def get_average_ratings(sparse_matrix, of_users):\n",
        "    \n",
        "    # average ratings of user/axes\n",
        "    ax = 1 if of_users else 0 # 1 - User axes,0 - Movie axes\n",
        "\n",
        "    # \".A1\" is for converting Column_Matrix to 1-D numpy array \n",
        "    sum_of_ratings = sparse_matrix.sum(axis=ax).A1\n",
        "    # Boolean matrix of ratings ( whether a user rated that movie or not)\n",
        "    is_rated = sparse_matrix!=0\n",
        "    # no of ratings that each user OR movie..\n",
        "    no_of_ratings = is_rated.sum(axis=ax).A1\n",
        "    \n",
        "    # max_user  and max_movie ids in sparse matrix \n",
        "    u,m = sparse_matrix.shape\n",
        "    # creae a dictonary of users and their average ratigns..\n",
        "    average_ratings = { i : sum_of_ratings[i]/no_of_ratings[i]\n",
        "                                 for i in range(u if of_users else m) \n",
        "                                    if no_of_ratings[i] !=0}\n",
        "\n",
        "    # return that dictionary of average ratings\n",
        "    return average_ratings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZJOWz0OICav",
        "colab_type": "code",
        "outputId": "e05f8ed9-0fd2-4492-e530-b2d2ae1ad935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train_averages = dict()\n",
        "# get the global average of ratings in our train set.\n",
        "train_global_average = train_sparse_matrix.sum()/train_sparse_matrix.count_nonzero()\n",
        "train_averages['global'] = train_global_average\n",
        "train_averages"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'global': 3.582890686321557}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDIjYtTBIEvo",
        "colab_type": "code",
        "outputId": "15ada166-da0c-4a22-a94b-44f3016d8fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "train_averages['user'] = get_average_ratings(train_sparse_matrix, of_users=True)\n",
        "print('\\nAverage rating of user 10 :',train_averages['user'][10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Average rating of user 10 : 3.3781094527363185\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfP6_1DeIHpX",
        "colab_type": "code",
        "outputId": "24c916bf-89dc-4953-8c24-492a003842a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "train_averages['movie'] =  get_average_ratings(train_sparse_matrix, of_users=False)\n",
        "print('\\n Average rating of movie 15 :',train_averages['movie'][15])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Average rating of movie 15 : 3.3038461538461537\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oj0dHhUhIQIb",
        "colab_type": "text"
      },
      "source": [
        "PDF's & CDF's of Avg.Ratings of Users & Movies (In Train Data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lFRr8EEIMQz",
        "colab_type": "code",
        "outputId": "267764ab-c212-45e8-d029-145fbc524f37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "# draw pdfs for average rating per user and average\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=plt.figaspect(.5))\n",
        "fig.suptitle('Avg Ratings per User and per Movie', fontsize=15)\n",
        "\n",
        "ax1.set_title('Users-Avg-Ratings')\n",
        "# get the list of average user ratings from the averages dictionary..\n",
        "user_averages = [rat for rat in train_averages['user'].values()]\n",
        "sns.distplot(user_averages, ax=ax1, hist=False, \n",
        "             kde_kws=dict(cumulative=True), label='Cdf')\n",
        "sns.distplot(user_averages, ax=ax1, hist=False,label='Pdf')\n",
        "\n",
        "ax2.set_title('Movies-Avg-Rating')\n",
        "# get the list of movie_average_ratings from the dictionary..\n",
        "movie_averages = [rat for rat in train_averages['movie'].values()]\n",
        "sns.distplot(movie_averages, ax=ax2, hist=False, \n",
        "             kde_kws=dict(cumulative=True), label='Cdf')\n",
        "sns.distplot(movie_averages, ax=ax2, hist=False, label='Pdf')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JdXwzZmnGI0DiQA8D1mf6CyZYaIbV3zTgUUxyETcmQOjftNaN1rGTMJHbTkyqxGcwN8VErXWPNSf7FmZp1gJMkNXHPKMXZYoSPIh50v4lZgnVp7XWbyillgV4vYThzV8w0xaPWK8/iVmS9B9weq30I5gEO22Y3/VDmIIZJ4CLtFU1yVrTfRSTAW8O8KTWOs96rz+9D/Q3O6x1vD9ZYxWJpg4fX8OsJRyHcfveB7itJ8wXMZmJcjGp+b5sLeXxsAYT2TsaU6zhYczypVGYKjd9LplQSqViCj381fq7VSmVpE1O6q2YDD4ePoZZb9yNeeq9GuOaOx//kkF4+ozHuIK7MZGsYDJt/RfGBTgbk/nnfgCt9f/D3LCu11qP1Fr/pI+mP2a1m43Jee2p2DQHc+P6OOYmk4G5lh78vl6CgFmWNUqZdJ7xmHzS3rnJH8H8xqZiItlvx+S17sSsPPBe93sLsFlrfVYWMD/0XnQ8MB0fcsjIOHx0Y35Ek6155bfg9BPwOH0mqf5hpdSjmBuAp2jDVq21J91du1KqG5iulMqynhr7K6LwEUzihXWY7zeRM1Vp/ob58T9qzTndivmxg7mJPOxZzqOU+hHmhtEfK5RSjZik9D3AJzw3Iesze+bTa5VS/83Zye794XGtdYklzzOcSUJyE2aJx9vWe9/HZNDyEMj1EgQ4MzrejFla51kf7THOi6ysV81KqZ8D/w8zn/o3zJKw71rHf8x63Zul9K/3ouOB6fiQQ4xx8Dg5t65rIkapwARC3Y+pxALwe2v94GRgovUD9xDP2et+e9dLvQvj5ilWSpUBP9Rav2QlLvAEiz2ktX4I44J6xnI79Sil/mntew5Tcu0RZarTzMS4rTz9TuzV7+n/rahQT5agI1rrudb/26z5pJGYG9PFnMl0lYN52r8YSMd4YfqqMtMX1V7/e4pKnCOr1rrNSpzgwef1CrBvYXjxF8wa4wLOzpqVhdHrI177jnBmlLYRSFVKLce4rBdhdK03A+m96HhgOj7kEGMcPEcxCS2KvPYVACUA1lP014CvKVPKb4NSaifmB1amtZ7RT9tnLf7WpsLNbZar6yOYcohjtdafxyRWAEAplYeZf1mmlPK4qlKBFM8TtzLlFT+KcSs9ZSV/AJPlyFM1B7wSyltRoX1GQWqtW5QpCnFYKfVHrfVuzJyaG5ivtW5QSt3A2QXiQ1ngfhyvKFVlUlyO9ZKnr+vVGkKfwhBGa33EMoLXYAyjhzrMA/ZkzqRGnYQ1ctZaO60R3W0YY/yS9p03ul+9Fx0/h351fCgixjh4nga+p0zt0iqMglyPVbZQmTyyxZiAilOYkbQLk2KxWSn1LUxWqC6M0ozoKyJSKfUJ4DWtda3Xk7WvFJX/D/MwcFmv/Vs4Ey36N0yh9MmWzB6eAb6klHoZsxzoW/5dBoOljI9hKh99GPOkfAo4pZTKxaQz9OYEZg4uGJ4FtimT+3gXxgNxujB9ANdLELy5CxijtW5VJjkMGL19BvhPK6AoE5NS9Wde5/0NU0WpnjPu6t70q/ei4+fQr44PRSSAK3gewCjA2xjXzE+Aj3uiKjERh29goia3Av+jtd6ozWL36zDurDLMk/djmACFvrgKOKCUasG4hW7VvhPAf9Lqp9r7DxMB6ck29YIlW7XWeo/XuY9i5qD2YhJ/rMXMEQWyOP+XwDXKVD76ISZI5BTwMibQxZv/wjzMNCqlvh5AH2itD2DSdT6FeYJuweQe9hQp8Pd6CcJptNaH9JnKY97cgzFehzH6/jdMGlHPedut9ydydtEH77YH0nvRcS/80PEhh6TDFHyilLoa+J3WenKkZRkIaz6rEZOEpSzS8ghCLCA6Hl2Im1oATs/JXIZ5cs7BREX6CkSJCpRS12OKBzgwLsN9QHkkZRKEaEZ0PLoRN7XgwYFxO53EuLCKMHND0coazFx9FcYld6tXoIogCOciOh7FiJtaEARBECKMjIwFQRAEIcJEbM74vffecycnJw98YC86OzsJ5rxIIfKGj1iSFYKXt62trW7x4sXjwiCSbQSrz76Ihe9VZLSH4SZjf7ocMWOcnJzM7NmzAz6vqKgoqPMihcgbPmJJVghe3sLCwiMDHxVZgtVnX8TC9yoy2sNwk7E/XRY3tSAIgiBEGDHGgiAIghBhxBgLgiAIQoSJqqQf3d3dVFZW0tHR0e8xRUVFfb4fTaSkpCBLx4Thij/63Nd50abjKSkp5OXlkZjYu1CbINhDVBnjyspK0tPTmTJlCg6H75zg7e3tjBgxYpAlCxy32019fT0tLS2RFkUQIoI/+uyLaNNxjy5XVlZSUFAQaXGEIcqAxlgp9UdMgvMarfU8H+87MInNr8HUo7xDa/1uMMJ0dHQErLjRisPhYOzYsVRVVUVaFEE4jehz4Hh0uba2NtKiCEMYf+aMn8BUFOmLqzGpymYAnwV+G4pAsa643gylzyIMGZ5A9DlghsrnEKKXAY2x1vpNoKGfQ9YAf9Zau7XW24DRSqkJdgkoCEMRp8tNV8/gl1gWfRYEe3G7jS47XaHFB9kxZ5wLVHi9rrT2Hbeh7YhQW1vLQw89xL59+xg1ahRjx47lvvvuO2u+6Nvf/jarVq3iqquuYteuXfzgBz8gISGBp59+mpSUlAhKL0QjjW1dvFB0ih++tZWSEy00tHbhcMBvP34+V82LKls3pPRZdHl44XK5OdXeTX1rFw2tXTS0dtLU3kN7t9P8dTnp8P6/x0V3j4sel4tup5tup4sep5tul7V1umht7yDupWp6nG66nC56vI7pdrpPG+G5E0fx8r0XBy17xAK4Ojs7z4mY7O7upr29/xrwbrd7wGNCwe1284UvfIHrr7+ehx56CACtNVVVVYwfP/70cT09PXR1ddHe3s5zzz3HnXfeybXXXnuOfG63O+oiQ/ujo6MjZuSNZlndbjc1rT2U1HWyo7KNt8pb6XS6mTI6kRW5KWSOSCM5wUFGVz1FRY2RFjdkgtVnX9il43brsneUdzT/9jwMZRm99aukvpPKU90ca+qmurmH7gFGqMnxDpITzF9SfByJcRAf5yAhzkF8HCTGOYiPczAizsHIRMhMjCc5MY4EB8THJZxzXIJ1fsGYpJCutx3G+BiQ7/U6z9rXL77S5xUVFQ0YRRnuSMutW7eSlJTE7bfffnrfokWLcLvdPPjgg7zzzjtMmDCBxMREkpKSeOmll3j99dfZunUrW7du5ec///lZ7TkcjqhP9+ZNLKSn8xAtsjpdboqON3Gg6hRFx5t5/3gTRcebaO7oAWBUSgIfPj+Pi8e7uHblooDbLywstFvk/hhUffaFXTputy4nJiae/ozR8tvrj6EmY0e3k3dK61hfXMPG4hqOnzJL5pLi4yjISmNOXjpXj00jZ1QKWSOTyEwzfxkjEklNSmBEYjzJCXHExQU2/29zOsw+37PDGL8AfFEp9RSwHDiltQ7ZpfXPwkqe2VVxzn6Xy0VcXHC5Sm5Zks+Ni/P6PebgwYPMnTv3nP2vv/46ZWVlrF27lrq6Oq699lpuvPFGbr75ZgoLC0+7uYThwbHGdl7dX82W0jp2lDecNrypSfHMGp/OhxZOZNaEUcyZMIoFeRkkxsdF/SjFYlD12Rf+6vhA+iy6PDQ4Wt/Gk9uP8PTOCk61d5OWFM8lM8fxhcuyWJiXgRqfTnJCfKTFDBl/ljb9HVgFZCmlKoEfAIkAWuvfAWsxyyBKMUsh7gyXsJFk586dXHvttcTHx5OTk8OKFSsiLZIwyJxq6+bZdyt5YU8VeyqMa3lqVhrXLZjIiqmZLMwbzaTM1ICfvAcT0WfR5VihvK6Vn67TrN13nDiHg6vmjuejS/NZPjVzSBjf3gxojLXWtw3wvhv4N9sksrhxcZ7Pp95wu6lnzJjBa6+9Frb2hdijuLqJx98u5197jtHR7WJ+bgbfumoWV88bz5SstEiLFxDRps++sEvHRZdjk26ni//dfIhfrS8lId7B3ZdO4/YLpjA+Y2gH00lu6l6sWLGCrq4unn766dP7iouLycjI4JVXXsHpdFJTU8P27dsjKKUwGJTXtXLv33dz9cNv8cKeKj58Xh5r772YF++5iLtXTYs5QzzcEF2OPWqbO/nYo9v42boSPjg3h01fX8U3r5o15A0xRFk6zGjA4XDw61//moceeohHH32U5ORkcnNzue+++2hoaOCaa65h4sSJLFoUeCCOEBt0dDv56WuaJ7aUkxQfx92XTuNzl0wjI1XyEscSosuxRVldKx9/dBsNbV08fOsi1izKjbRIg4oYYx/k5OTw8MMPn7P/+9//vs/jf/SjH4VbJGGQKDnRzN1PFnKotpXblk3iK1fMIDt96D+VD1VEl2ODw7Ut3Pr7bfS43Dz7+QuZl5sRaZEGHTHGgmDx6v7jfPWZPaQmJfDkXcu5aEZWpEUShCHPydYu7nxiJz0uN099dgUzc9IjLVJEEGMsDHvcbjePvnWY/3qlmEX5o/ndJxaTM0pGw4IQbpwuN1/467scP9XB3z+zfNgaYhBjLAxzepwufvDCAf66/SjXzp/Az29ZSEri0Fs2IQjRyDP7Gtl6+CQ/vWkBiydnRlqciCLGWBi2dDtd3Pv33byyv5rPXzqNb16ponqNsCAMJd6raOTJPSdZs2giN/m57G0oI8ZYGJY4XW6+/NR7vLK/mu9dO5tPXzw10iIJwrCh2+ni2//cy5gR8Tx4wzwpUYmsMxaGIW63mwdfep+X9x3nu9eIIRaEweaPb5dRXN3M3cuyGJUiSwZBjPE5zJ49mzVr1nDddddx7733+qwe88gjj/CHP/wBgEOHDrFmzRpuuOEGjh49OtjiCkHw5LYjPLGlnLsuKuAzl4ghHsqIPkcfNU0dPLz+IKtn57BysiTO8SDGuBcpKSn861//4qWXXiIxMZGnnnqq3+PXr1/PlVdeyfPPP8+kSZMGSUohWN49epIHXnqfy2dl891roruijRA6os/Rx8/WabqdLr53reifNzJn3A9LlixBaw3Ab3/7W55//nkyMzOZMGECc+fOZfPmzfzpT38iLi6OrVu38pe//CXCEgv9caqtmy/+9V3GZ6Twi1sWSbDWMEP0OfIUHW/iH4WVfPqiAqZkpVFUG2mJoofoNcbv/R12P3nO7iSXE+KCXHpy3idgUb958k/T09PDm2++ycUXX8z+/ftZu3Ytzz//PE6nkw9/+MPMnTuXSy+9lFtvvZXU1FTuuuuu4GQSBo1//9d+TjR38n93XyipLQebPvTZF37ruOhzzPHwGwcZmZTAFy+bEWlRoo7oNcYRoqOjgzVr1gDmSfqmm27ib3/7G6tXrz5dSebyyy+PpIhCELyy7zgv7Knia1fMZGH+6EiLIwwSos/RQ9HxJl49UM29H5ghD8M+iF5jvOg2n0+9XWEuoeiZYxLCTHcHbPxPyJppRjhhXNrQ0tnDD198nzkTRnH3qmlh60fohz702Rd26rjoc/TwyIaDpCcncNfKgkiLEpVIAJcfLF26lDfeeIOOjg5aWlrYuHFjpEWKbdoa4IlrYcuv4IUvwgv3gMsVtu5+tf4g1U0dPHjDPBLi5Sc/3BF9HnyKq5tYu6+aO1ZOkVFxH0TvyDiKmDt3Ltdccw1r1qwhMzOT+fPnR1qk2GbtN6B6L9z8J6jaDe/8EiatMCNkm9HVzfzh7TJuXZrP4sljbG9fiD1EnwefR9aXMjI5gbsuklFxX4gx7sXu3bt97r/77ru5++67z9l/zz33hFukoUXJa7D/WbjsuzD3Bpj9ITi6Fdb9O6hrINW+/LRut5t/f34/o1IS+NZVs2xrV4gdRJ8jz6HaFtbuP86/rZrO6NSkSIsTtYjPTgg/bjeUvwPP3A5/vw3GzYaVXzbvxcXBdb+AjlOw+ce2dvvK/mp2lDfwratmMSZNbgKCEAn+tKWcxLg47lg5JdKiRDVijIXw89p98MQ1UPYmXHgP3P4vSPAyjjlzYeGtUPgENJ+wpUuny80vXi9hevZIbl6Sb0ubgiAERlNHN88WVnL9wolkjUyOtDhRTdQZY7fbHWkRbGMofZagcTnhvb8ZF/RX3ocrfgjpOeced/HXwNkFW39tS7cv7qniYE0LX1k9k3hJ7hExhooODJXPMdg8u6uStpScNYYAACAASURBVC4nd1w4JdKiRD1RZYxTUlKor68fEj98t9tNfX29VCOpeg86GmHejZCU2vdxY6fB3I/Arj9C97n5gwOhx+nil2+UMHvCKK6eNz6ktoTgGSr67NHllJSUSIsSU7hcbv60tZzFk8cwPy8j0uJEPVEVwJWXl0dlZSW1tX3nSOvu7iYxMTZC41NSUoiPH+aF6g9tABww9bKBjz3vEya4q3Q9zL4u6C7/791jlNe38ejtSyTlZQTxR599EY06npKSQl6e1NwNhE0lNRypb+NrH1SRFiUmiCpjnJiYSEFB/6HvRUVFzJ4dOwnGi4qKIi1CZDm0HiYshLSxAx875SIYkQnv/ytoY9zV4+Lh9QdZmJfB6tnZQbUh2IM/+uyLWNNxwTePv1NOzqhk8U75SVS5qYUhRkcTVOyAaX6mG4xPhFnXgn4FejqD6nLtvuMca2zny6tnyhSBIESII/WtvHWwjo8vn0yiJNrxC7lKQvio3gtuJ0xe6f85c2+ArmbLvR04j28pZ+q4NC6dOS6o8wVBCJ1nCyuJc8DNS8S17y9ijIXwUWO56HPm+H/OlEsgaSQcXBdwd+8ePcmeikbuuHCKzBULQoRwutz8s7CSi2aMY0JG+OoIDDXEGAvho+Z9SMmA9An+n5OQBAWXQOkbJllIADz+TjnpKQnceL48jQtCpNh6qJ6qUx3ctFj0MBDEGAvho6YIsucEXpFp+geg8SjUH/L7lOpTHbyy7zgfXZJPWnJUxSUKwrDiH4UVjEpJ4INzfOQTEPpEjLEQHtxuY4zHBZETetoHzPbQer9P+cu2cpxuN5+U5AKCEDFOtXfz6v5qPrRoIimJw3xZZ4CIMRbCQ3O1SfaRHcB8sYfMAsicalzVftDZ4+TvOypYPTuH/Mx+EosIghBWXt57nM4eFzcvlhS0gSLGWAgPNe+bbXaQ60WnXgZHtph0mgOwvqiGhtYuPrFicnB9CYJgC/8orGBmzkgWSMatgBFjLIQHTyR1sMZ40groajlj1PvhH7sqmJCRwkXTs4LrSxCEkDlS38ruo43ceH6erPEPAr8iXZRSVwEPA/HAY1rrH/V6fxLwJ2C0dcy3tdZrbZZViCVqiiBtHKQFaSDzl5ltxXYY33fx95qmDjaX1HL3qmlSEMIPRJeFcPHS3uMAXLdwYoQliU0GHBkrpeKB3wBXA3OA25RSvScCvwc8o7U+D7gV+B+7BRVijOo9/RrRARk9GdKyTQavfvjXe1W43MhyJj8QXRbCyYt7qlgyeQy5o2VtcTD446ZeBpRqrQ9rrbuAp4A1vY5xA6Os/zOAKvtEFGKOnk4zMp6wMPg2HA4zOh7AGL+87zjzczOYOm5k8H0NH0SXhbBQcqKZ4upmrpdRcdD446bOBSq8XlcCy3sdcz+wTil1D5AGrB6o0c7OzqCKKHR0dMRU8YXhKG9KQzEFrh4qnWNpDqGtzOQCck6+RMnut3GmnFtoorKhlfcqGrnz/MyYuMZR8FsIiy5D8Prsiyi4TgMiMp7Nn3c3EOeA6SnNAfUp1/EMdmVHuA14Qmv9c6XUBcBflFLztNauvk5ITk4OqjJLrFV0GZbyFprRbN6Sa0yd4mBJux72PMLMEadg1kXnvP38P7cBcPvlCyjISgu+n0Ei2GtbWFgYBmn6JGBdhuD12RexoDMi4xncbjfbXt7MBdPGsvL8wKamhtt17E+X/XFTHwO8F43lWfu8uQt4BkBrvRVIASS0dbhSvReS0mFM4OXzzmL8fHDEwfG9Pt9++0grs8anx4QhjhJElwXbOVDVRFldK9cvEBd1KPhjjHcCM5RSBUqpJExQxwu9jjkKfABAKTUbo8CBVRQXhg7H98CEBRAX4sq5pFQYO90Y917Ut3Tyfk0HV86VWqkBILos2M6Le6tIiHNwldQtDokB75Za6x7gi8BrQBEm0vKAUuoBpdSHrMO+BnxGKbUH+Dtwh9Y6sCz/wtDA5YQTB2D8AnvaG7/A58h4c0ktbuADs7Pt6WcYILos2I3b7ealPce5eEYWo1OTIi1OTOPXnLG1znBtr33f9/r/fSCAorXCkKW+FLrbQouk9mbCAtj/LLQ1QGrm6d3ri2sYMyKeeRMl008giC4LdnKgqoljje18afWMSIsS80gGLsFeju8x2wk2jozhLFd1t9PFmyW1LM1NlbrFghBBXt1fTXycg9WzpUJTqIgxFuzl+B6IT4asmfa05zHGXq7qXeUnae7oYVmeFIUQhEjy6oFqlhdkkpkmLupQEWMs2MvxPZAzF+IT7WkvbSyMyj1rZLxR15AY7+C8iZLpRxAiRWlNM6U1LRK4ZRNijAX7cLuN0bTLRe1hwkKo2n365YbiGpYXjCU1UX6+ghApXjtwAoAPzhFjbAdyNxPso/EIdJyyL3jLQ94SExjW1kBFQxulNS1cNkuiqAUhkrx2oJpF+aMZn5ESaVGGBGKMBfvwzOuOt9sYWxWcjhWyobgGgMvUOHv7EATBb441trO38pS4qG1EjLFgH9V7TcasnN6FgEJk4nmm3YodbCiuYcrYVCkMIQgR5LX91QCSdMdGxBgL9lFTZDJmJdocWJU8ErLn4qzYwdbD9eKiFoQI89qBalSOpKK1EzHGgn3UvA/ZYUr6nrcEd+Uuunt6uFyMsSBEjFNt3ew6cpLVc0QP7USMsWAP3e3QUAbZNruoPeQvI6G7hSVJR1lWkGkitwVBGHTePFiL0+WWh2KbEWMs2EOtBtwwblZYmnfPuJIm0ngg7VmSCx+Dn80kvrMxLH0JgtA3G4trGJOayKL8MZEWZUghxliwhxqr+HaYRsa6OZGfdd/E7PZ34ZVvQmsNqTXvhqUvQRhS2OhFcrrcbCqp5dKZ44iXVLS2IsZYsIea9yE+CTKnhqX5jcW1/NW5mq4JS2HGByEhhRF1vuscC4IAuHrgubvhp9PhnYehpyvkJvdUNtLQ2iVBlGHAr6pNgjAgNUWQpSA+PD+pjcU1qAljSPrMOlMn+Y9XkyrGWBB843YzcfsDcHSdScLz+vdNTMf1vwyp2Y3FNcQ54NKZss7fbmRkLNhDbXHYIqlPtXVTePSkCRiJs36yk5aTclJDV1tY+hSEmKZ6HxlH18El34DPvQkX3gOFj4N+NaRmNxTXsHjyGKldHAbEGAuh09kMpypgnE2Vmnqx2YrePMs1lr8Ch9sJVTJvLAjnsPdp3HEJsOIL5vXl/w458+CFL0JLbVBNnmjq4EBVk7iow4QYYyF06kvNNkuFpflNxTVkpiWxKH/0mZ35VorMo9vC0qcgxCzOHtj7DM0TVkJqptmXkAwfeRQ6muCFe84N6upqhW2/g8dWw+8ugmc/BZWFZx2y0UpFK0uawoMYYyF06jzGeIbtTbvdbt48WMdF07POjt5MzaR7xDhoOGx7n4IQ0xzeBK01NE25+uz9OXNg9Q+g5BVY9z1wuYxRPvgG/M8KePVb0NMJ6RPh0AZ47HITANbZDBgX9cSMFFRO+uB/pmGABHAJoVNXYnJHhyGSWp9opq6lk4tmZJ3znjN5DIlt9bb3KQgxTenrkDCClgkXnvve8rvhZDls/TWUbQZHPBx/DzKnwR0vw5SLzHGdzfD2L8xf5U46b32at0vr+PB5uTgcsqQpHMjIWAid+oMwZopxhdnM2wfrALho+rnGuCd5NIgxFoSzqdhu0sfG+wiyiouDq38CV/0YUkaDwwHX/Ay+sPWMIQZITocPfB9ufwFaa3H84Qryu8vERR1GZGQshE7dQRhrv4sa4O3SOqaOS2Pi6HOLTziTR0PzwbD0KwgxSVerKWV60Zf7PsbhgBWfN38DUXAx3PU6Hb+/mseTfsqYnA/ZJ6twFjIyFkLD5TIBXGGYL+7qcbH9cIPPUTGAMzkD2hps71cQYpaq3eB2Qv5y+9ocN5Ovxt9HZlwrI/7vDnA57WtbOI0YYyE0TlVAT0dYjPHuoydp73aysk9jPAY6T4Gz2/a+BSEm8awuyFtqW5OHa1t4o3E8u+Z9Dyp3wLt/sq1t4QxijIXQqLPcxFn2rzF+u7SOOAdcMG2sz/d7kjPMPzJvLAiGih1miaFnSZMNbLCWNE1e9SmYcjG8cT+01tnWvmAQYyyERr1ljMMwZ/x2aR0L80czKiXR5/vOZKtqjBhjQTDLlI7tgnz7RsUAG3UNM7JHkj82zQR7dTTBtt/a2ocgxlgIlboSE5WZ5tuVHCxNHd3sqWjsc74YrDljEGMsCAAtNUYXcubb12RnDzvKGs5EUWfPglnXws7HoLPFtn4EMcZCqNQdNC5qm9cebj1Uj8vte0mTB2eSlZFLXGaCALWeMqb21RR/+2At3c5eqWhXfgk6GmH3X2zrRxBjLIRK3cGwBG+9U1rHiMR4zpvUdwHznhTLGMvIWBDCUlN8Q3EN6SkJLJ7spYf5y0y09o7f21orebgjxlgIno4maKkOizHedriepQWZJCX0/RN1JombWhBOU1MEIzIhzZ7yhi6Xm426lktmjiMxvpcenv9Jk4q2YrstfQlijIVQCFPwVl1LJyUnWlgxdYCI0LgESMkQYywIYIxx9hzbpowOVDVR29zJ5cpH1q05ayAxDXY/aUtfghhjIRTCtKxpR5lJ5LFiqu8lTWeRmiXGWBDcbqumuH3zxRuKa3A4YJXyMdJOHglzb4ADz5msX0LIiDEWgqeuxIxOMwtsbXbb4XpSk+KZn5sx8MGpYyWASxCajkFnE2TPtq3JDbqGhXmjGTuyj5zzC2+DrhbQr9jW53DGr9zUSqmrgIeBeOAxrfWPfBxzC3A/4Ab2aK0/ZqOcQjRSZxWIiPe9DjhYth9uYPHkMefOU/kiLQsaK2ztfygjujxEqSk223H2GOO6lk72VjbyldX9eL0mXwgjx5vR8fybbOl3ODPg3U4pFQ/8BrgamAPcppSa0+uYGcB3gJVa67lAP1nKhSGDZ1mTjdS3dKJPNPvnogaTaUjc1H4hujyEqXnfbG0aGW/Stbjd9F+lKS7ezB0ffN0Ecwoh4Y+behlQqrU+rLXuAp4C1vQ65jPAb7TWJwG01jX2iilEHS4nNByCsdNtbTag+WIwbuq2Olli4R+iy0OV2mIYmWNbGsyNxTVkpyczd+Ko/g+c9xFwdoqr2gb8Mca5gLcfsNLa581MYKZS6h2l1DbLFSYMZRqPgLPL9pHx9rIGRiTGsyDPj/liMMs4nF1mvkwYCNHloUrN+7aNirudLt4sqeUylY1joMjsvGUwKhfef96WvoczdtUzTgBmAKuAPOBNpdR8rXVjXyd0dnZSVFQUcEcdHR1BnRcphqq8I6veIR8ob02i3cbPt6moillZSZSW6AGP7ejo4FiTk1zg0J4tdI2aYpsc4SBGfgsB6zIEr8++iIXrFFUyul2oE0U0Tl3DCS+ZgpVxb3U7zZ09zBjZ5df5OdkXMLr0RUr27cadkBJQX1F1HftgsGT0xxgfA/K9XudZ+7ypBLZrrbuBMqVUCUahd/bVaHJyMrNnB/4kV1RUFNR5kWLIyntyPQBTFl9hm2usobWL8pOHuflKxezZA7u/i4qKyFWLYRtMG5cKU6P7Ogf7WygsLLRLhLDoMgSvz76IBZ2JKhkbysDZQeaslWR6yRSsjM+VFZEY7+DWyxYxMtkPE5H8/6D0WWYlHIPZ1wXUV1Rdxz6wU8b+dNkfN/VOYIZSqkAplQTcCrzQ65jnMU/SKKWyMK6uw8EIK8QIdSVmvtbGUm07ykwg1oDJPrxJn2C2LSdsk2MII7o8FKm1IqltclNvKK5hecFY/wwxwOSVJvmOXmtL/8OVAY2x1roH+CLwGlAEPKO1PqCUekAp9SHrsNeAeqXU+8BG4BtaawlxHcrUl9qeeWvb4QZSEuOYnzva/5PSc8y2+bitsgxFRJeHKJ6c1ONCT/hR0dBGaU3L2YUhBiI+EWZcaYK4nD0hyzBc8evRR2u9Fljba9/3vf53A1+1/oThQF0JzLQ3tmdneQPn5Y/pNx/1OSSnQ9JIaK62VZahiujyEKSmCEblQcoAkc9+sKHYBM/3u6TJF+pq2PeMqac8aUXIcgxHJAOXEDjtJ6G11tZI6uaOboqON7G0IAi398gcMcbC8KW2yLY0mBuKayjISqMgKy2wE6ddBo44KH3DFjmGI2KMhcCpKzVbG6s1FR45icsNy6YEYYzTJ4gxFoYnLifUltgyX9zW1cPWw/Vc5qswxECMGGOWOR18PWQ5hitijIXAqSsxWxtHxrvKTxIf5+C8SQHMF3tIzzGlHAVhuNFQZpJu2JAGc0tpPV09rsBd1B5mrIbj70GL5IkJBjHGQuDUH4S4RBg92bYmd5Q3MG/iKNL8jeD0xjMylixcwnDDxjSYG3QNaUnxLAtmqghg+mqzPbQhZFmGI2KMhcCpOwiZUyHenpwxnT1O3qtoZGkwLmqA9PHQ3QadzbbIIwgxg2dZ0zgVUjNut5uNxTVcNCMrsABKb8YvNCVNxRgHhRhjIXDqSmydL95XeYquHhdLgjXGI8ebrcwbC8ONmvdN5bSkAAOuelFc3czxUx3Bu6gB4uKg4GIoe0u8VEEgxlgIDGe3maey0RjvKDfFIZZOGRNcA+mWMZZ5Y2G4UVNsy3yxZ0lTUMFb3hRcAs1V0CB5YgJFjLEQGCePgKvb1uCtnWUNTBuX1ncR84FIl5GxMAzp6TLxGzbMF28srmFe7iiyRwWWW/ocplxitmWbQ5ZpuCHGWAiM+oNma1P2LZfLza4jJ4MPGoEzxripyhaZBCEmaDgErp6QjfHJ1i7ePXqSy0MdFQOMnQbpE42rWggIMcZCYJxe1mRPHWN9opnmjp7gg7fAZOFKG3fmQUEQhgMnDphtiMb4zYO1uNwElgKzLxwOM29cLvPGgSLGWAiMuoOQlm0W+dvAztPzxSEWnMiebebPBGG4UL3PLDHMCi2SekNxDWPTkliYF8Qaf19MvtBk6JN544AQYywERt1Be4O3yhoYPyqFvDEjQmsoe45Z5uFy2SOYIEQ7J/ab4hAJSUE34XS52VxSy6VqHHFxDnvkyl9uthXb7WlvmCDGWAgMG5c1ud1udpY3sLQgE4cjxBtB9mzoaoFTFbbIJghRT/U+GD8/pCZ2Hz1JY1t3aEuaepOlIDkDKnbY1+YwQIyx4D+t9dDeYFvwVuXJdk40dbIs2CVN3niWd3jKyQnCUKalxtTwHj8vpGY2FNcQH+fg4hnjbBIMs944bwlU7rSvzWGAGGPBfzwBUjYta9pRZs0XhxJJ7cFTtaZWjLEwDKjeZ7Y5oRvjJZPHkDEi0QahvMhfZgLMOprsbXcII8ZY8J86jzG2Z2S8s7yBUSkJzMxOD72xlAxT01VGxsJw4MR+sw3BTV3V2E5xdbO9LmoPeUsBNxwrtL/tIYoYY8F/6kogPhlGT7KluR3lDSyZkmlf4Ej2rDOJ8wVhKFO9H0blQmrwXqWN2mTdCo8xXgI4ZN44AMQYC/5Td9As6o+LD72plk4O17aGvqTJm+zZprary2lfm4IQjVTthvELQmpiY3ENeWNGMD17pE1CeZGSYfSxUoyxv4gxFvyn/iCMtSfZxy5rffGyAnvWKwNmeZOz0+TOFoShSnuj0cW8xUE30dHt5J3Sei6flR36Soa+yF8GFTtluaGfiDEW/KOnyyoQYVfw1kmSE+KYn2tTogE4k4lIXNXCUOb4e2abG7wx3na4nvZupz1Zt/oibxl0njqTtU/oFzHGgn+cLAe30zZjvLO8gfMmjQ6+dqovshTgkCAuYWjjCYqaeF7QTWwsriElMY4Lpo61SSgf5C8zW0n+4RdijAX/sDEndXNHNweqTrHMzvligKRUU9tVljcJQ5lj70LmtJBS0m4qqeXCaVmkJIYe/9EnY6cbGWXe2C/EGAv+4THGNiT8ePdoIy63TeuLe5M9R0bGwtDmWGFILuqyulaO1LexStmY6MMXDodxVUtEtV+IMRb8o74URo6HlFEhN7WzrIH4OAfnT7IxeMtD9iwja0+n/W0LQqRpqoLm4yEZ403WkqZVM8M4X+whf6l5kG9vDH9fMY4YY8E/bMxJvaO8gbkTR5GWnGBLe2eRPcfUeK0vtb9tQYg0nvnXvCVBN7G5pJapWWlMGptqk1D9kLfUbCX5x4CIMRYGxu22qjWFHrzV2ePkvYpGe9cXe5MtOaqFIczRbZAwAiYsDOr0jm4nWw/Vc8nMMLuoPUw8H3BA5a7B6S+GEWMsDExrHXQ02jIy3ld5iq4eF8vCMV8MZk47LuFM4XVBGEoc3WZGxfHB5ZLedriezh5X+OeLPaSMMmUej4kxHggxxsLAnI6kDt0Y77CSfYRtZJyQZJY4iTEWhhqdzVC9FyatCLqJTbqW5IQ4VoRzSVNv8habCk5u9+D1GYOIMRYGxlOtyYZI6p1lDUzPHklmWvAF0QckZ64YY2HoUbkL3K6QjPGbJbVcMG1seJc09SZvKbSfhIbDg9dnDCLGWBiYuoOQkAIZ+SE143S52VV+MnyjYg85c6Gp0twABGGocHQbOOLMcqFgTq9v43BdK5cO1nyxB08Ql8wb94sYY2Fg6krMAv640H4uxdVNNHf22JuP2heeGq8nJC2mMIQ48o550AxyeeGmEmtJkxqEJU3ejJsFSSONq1roEzHGwsDUFBuFCpGdZWGeL/aQM9dsPTVfBSHW6e4wyTOmXBJ0E5t1LZPHplKQlWajYH4QF29Sd4ox7hcxxkL/dLbAqaMmmUaI7Cw/Se7oEeSNCfP6xvTxMCJTjLEwdKjcaSqSFVwc1Okd3U62HKoffBe1h7ylRh+72yPTfwzgV9YFpdRVwMNAPPCY1vpHfRx3I/AssFRrLRMEQ4FabbYhjozdbjc7yhtYOW0QojgdDjM6rhZj3BvR5Ril/C0zXzz5wqBO31neQHu3c/CWNPUmb4lJxnN8T0gBaEOZAUfGSql44DfA1cAc4Dal1Bwfx6UDXwKkRMdQorbYbMfNDqmZI/Vt1DZ3hicftS8mLDQR1ZIW8zSiyzFM2VvmN52SEdTpm3QtSQlxXDA1y2bB/CTXyhgmruo+8cdNvQwo1Vof1lp3AU8Ba3wc9yDwY6DDRvmESFNbBPHJkFkQUjOe9cW2V2rqi7ylxq0no2NvRJdjka42Y8SmBOeiBpMCc3lBJiOSBnFJkzfpOTB6kkRU94M/bupcoMLrdSWw3PsApdT5QL7W+mWl1Df86bizs5OiosBTFnZ0dAR1XqSIdXnzy3aRkD6JMh1agfDX36thVHIc3fUVFDU4QhUT6P/aJnSMZgZQXfgSJ5sHIQevH0TBbyEsugzB67MvouA6DchgyphavYPJrm6OJkyhNYA+PTKeaOmmtKaFyyclRfS6TkyfSWr5Vkq9ZJDv+gwhZ+pXSsUB/w3cEch5ycnJzJ4duOuzqKgoqPMiRczL+2ol5C8P+TOUvFTN8mnjmDPnHK9o0PR7bd2zYON4xvdUMj5Krn+wv4XCwsFJsh+sLkPw+uyLWNCZQZWx6h/giGfSylsgOd3v0zwyFm47AlRwyyXzmZ49MnxyDkTjB+C1N5idOxpGTThLxmjGThn702V/3NTHAO9sD3nWPg/pwDxgk1KqHFgBvKCUCr6siBAddDbDqYqQI6lrmjo4Ut82eC5qsGqpLpE5qrMRXY5Fyt6C3PMDMsTebC6pJW/MCKaNG+QlTb3xVJqSPNU+8WdkvBOYoZQqwCjurcDHPG9qrU8Bp6MClFKbgK9LBOYQwDPfmj03pGZOzxcPVvCWh7wlUPwStNZD2iDm4o1eRJdjjc4WqHoXLrw3qNO7elxsKa3jhvNycTjsmR4KmvELIC7RPCDPvj6yskQhA46MtdY9wBeB14Ai4Bmt9QGl1ANKqQ+FW0AhglS9a7a554fUzM6yBlKT4pk7MbjMQUFzOg2fjI5BdDkmObrNLAkKcn3xrvIGWrucg591yxeJKTBhAVRKbWNf+DVnrLVeC6ztte/7fRy7KnSxhKigajekTzRJNEJgR/lJzp80hoT4Qc4xM/F88yR+dCuoqwa37yhFdDnGKNtsfsP5wa3N3VRSS2K8gwsHY32/P+Qugd1/AWcPxIccsjSkkAxcQt8cezfkUfGp9m6Kq5vCnwLTF0mpMHGRMcaCEIsc2mCSZCQFtyJgs65l6ZRM0pKjxPDlLYXuNqiRvPG9EWMs+Ka9ERoOmZyyIfDukZO43bA03MUh+mLyheahQtLwCbFG03GTQnL66qBOr23tQZ9ojlzWLV9IEFefiDEWfFO122xDHBlvL2sgMd7BefkRMsaTLgRXtyQbEGKPQxvMdvoHgjp917E2IAJVmvpjzBRIHSv66AMxxoJvPMFbIY6Md5Y3MD83I3KZfyYtBxziqhZij9I3YGTOmZKgAVJ4rI2JGSnMiOTa4t44HMZVLUGV5yDGWDgXlwv2/gPGz4cRwY9oO7qd7K1sHLx81L4YMcYUjTiyJXIyCEKguJxmZDztA8aABUi308Xu4+1cqsZFfklTb/KWmBrp7Y2RliSqEGMsnMvB10xO6iDXNnp49+hJup1ulkfSGAPkL4NjheYGJwixQNVu6GgM3kVdfpK2bnd0uag9eIpGHJMlTt6IMRbO4HIxonYPbPovk9R97kdCam5LaT3xcY7IRFJ7k78cOpvOVKAShGin9A3AAdMuD+r0jbqGhDi4aHqEqjT1R+75gEPmjXshxlg4w5ZfMWXD5+D4XrjseyGvA9xyqI4FeRmkpyTaJGCQ5C8z24odkZVDEPyldL0xWqnBPchuLK5hfs6I6FnS5E1KBoxTElHdCzHGwhkOb6IzfTJ8oxQWfjSkppo7utlTeYqV06LgyXxMAaRmiTEWYoP2k8ZQBbmkqaKhjYM1LSzNi45qZT6ZsBCq90VaiqhCjLFgcDmhchdt2edDWugGdEdZA06XOzoy/zgcxlVdsT3SkgjCwBzeBG6XCd4Kgk26BoCluSNsFMpmxs+H5uPEd5yMtCRRNn6X7AAAIABJREFUgxhjwVDzPnQ105a1wJbmthyqJykhjvMnR2h9cW/yl5okJq31kZZEEPqneC2MyITcxUGdvqG4hiljU8nLSLJZMBsZPx+A5MaDERYkehBjLBisUWN71kJbmnuntI4lk8eQkhih9cW9yV9utpXiqhaimJ4uKHkN1DVBxWx0dDvZcqg+OqOovckxxjhFjPFpxBgLhqPbYeR4utMmhNxUfUsnxdXNrIymSM6J50FcgswbC9FN+ZvQeSroEoNbD9XT2ePi8llRbozTxsKoXFIaSyItSdQgxlgwVGw3Ucc2JAjYeti4gi+IhvliD4kjTD1VMcZCNFP0IiSNhKmrgjp9o65hRGL84NcOD4bx80k+KSNjD2KMBejphMYjQafd6807pfWMTE5gQW6GLe3ZRv4yk+bT2R1pSQThXJw9UPwyzLjC1P4NELfbzYbiGlZOHxs900P9MX4+yc1HpIiLhRhjAZqOme3ofFua23qojuUFmYNfv3gg8peZ8m0n9kdaEkE4l8OboLUW5t0Y1OkHqpqoPNnOFXNy7JUrXOTMw+F2SjIeiyi7WwoR4VSl2WbkhdxURUMb5fVtXBhN88Ue8jzJPyRJvRCF7HvGJMSY8cGgTn91fzXxcQ6umDPeZsHCRM5csz0htY1BjLEAZ4zxqNyQm9pUUgvApTOjqIaqh4w8SJ8oFZyE6KOrFYpegjlrICE5qCZe2X+c5QWZZKZF8ZImbzKn4opLMssqBTHGArYa4826hvzMEUwblxZyW7bjcMDkC00FJ7c70tIIwhmKXoLuVph/S1CnHzzRzKHaVq6eFyOjYoC4eLpGTRFjbCHGWIBTFZCWHVTQiDedPU7eKa1n1czs6Cvb5mHKRdBSDfWHIi2JIJxh1x8hcypMXhnU6a/sr8bhgCvnxpAxBjpGTxc3tYUYY8GMjG2YL95R1kB7t5PLZkWhi9rDlIvNtvytyMohCB5OHICKbbDkUxAX3C35lf3VnD9pDNmjQnugHmw6M6aZh+O2hkiLEnHEGAu2GeNNupakhDgumBqFwVsexk6DkTlw5J1ISyIIhl1/hPhkWPTxoE4/Ut9K0fGm2HJRW3RmTDP/iKtajPGwx+2GU8cgI/RlTZt0DcsLMhmRFMVrHB0O46ouf1vmjYXI09kCe56GeR8JulziK/urgdhzUQN0Zkw1/4irWozxsKf9pAkcCXFkXNHQxqHaVi6L9py4YOblmo9Dw+FISyIMd/Y9A13NxkUdJK/sO8783AzyM6O4ZGIf9IwYBymjoeZApEWJOGKMhzs2rTH2lG1bpaJ4vtjDpBVmWynrjYUI4nbDzj+aogl5S4NqorSmmT2Vp1izaKLNwg0SDodZb1xTFGlJIo4Y4+GObca4lkmZqRRkReGSpt6MmwVJ6ZKnWogsFdvhxD5Y+qmgc8I/W3iM+DgHaxaFviwxYmTPNsZ4mE8biTEe7jQeMdvRk4Ju4kzZtnHRu6TJm7h4yFss5RSFyLLlERgxBhZ8NKjTnS43z+2u5DI1jnHpwSUKiQqy50Bnk1liOYwRYzzcqTto5mxSg6+wtLmklvZuZ2wFkOQvN0tKOpsjLYkwHKkrNUUhln4akoLzJr11sJYTTZ3ctDj0lRARxZMWc5i7qsUYD3fqSiBrRkilE1/dX83o1MTYKNvmIW8ZuF1w7N1ISyIMR7Y+AvFJsOyzQTfxbGElo1MTuSzaaxcPxLhZZntieAdxiTEe7tSXQtbMoE/v6nHxRtEJrpidQ2K0VWnqj7wlZivzxsJg01ID7/0dFt0GI4MzpKfau1n3/gnWLJxIckIULyX0hxGjYVTesF9rHEN3T8F2OprMEp+x04NuYsuhOpo7erh6fgy5qMHcALLnwpG3Iy2JMNzY8XtwdsEF9wTdxEt7q+jqcXHTYnvKnkacnDnDfq2xGOPhTP1Bsw1hZPzq/mpGJiewMhpLJg5EwSVwdDv0dEZaEmG40NkCOx6FWddCVnAPwW63m6d2VKBy0pmXO8pmASNE9hwzZebsjrQkESPBn4OUUlcBDwPxwGNa6x/1ev+rwKeBHqAW+JTW+ojNsgp2U1dqtlkzgjrd6XKz7v0TXD4rOzZdZQUXw/bfQuUumBJcgv5YQ3Q5wux+EjoaYeWXgm6i8MhJ9h07xYM3zIuN1Qv+kD0HXN1m2ix7dqSliQgDjoyVUvHAb4CrgTnAbUqpOb0O2w0s0VovAJ4FfmK3oEIYqCsBRzyMKQjq9B1lDTS0dsVkTlzAZOJyxA2bohGiyxHG2QPbfgP5KyB/WdDNPP5OOaNSErjx/BheW9ybHOtnOIyDuPxxUy8DSrXWh7XWXcBTwBrvA7TWG7XWbdbLbUCMx9oPE+pKYMwUSAiuGPmr+4+TkhjHpbGQdcsXI0bD+AVQ9makJRksRJcjyfvPQ+NRWHlv0E1UNbbz6oFqbl02idQkvxybsUHWTDMwGMbLm/z5NnMB79XYlcDyfo6/C3hloEY7OzspKgr8wnd0dAR1XqSIZnmnHttL18hcKr3k81dep8vNi+9Vcv6EFI4cOhhOMfvEjmubnTGXzJKnKdmzHVdSeOffouC3EBZdhuD12RdRcJ0GJGAZ3W6mbPgJcemTOeyaAkF+vscLG3C73Vw4rnvA/mPtOk5Nz6fr0HYqJ0aXzIN1HW19tFJKfQJYAlw60LHJycnMnh343EBRUVFQ50WKqJW3qQqaykledsdZ8vkr7yZdQ0N7GbdfMovZsyeEU9I+seXajv4cFP8V1bEHFga/5tMfgpW3sLAwDNL0TyC6DMHrsy+iVme8CFjGw5vhpIbrH2b2nLlB9dne5WTdP9ZzxZwcVi1dYL+MEeAsGfefR/Kxd6NOZjuvY3+67I+b+hjgHT+fZ+07C6XUauC7wIe01hKeGu2UvmG2Mz4Y1OnPFlYyJjWRy2fl2ChUBJiw0Py9++fhkBtXdDlSbPkVpGXDgluDbuL5947R2NbNnSuDi/GIerLnmvS8nS2RliQi+GOMdwIzlFIFSqkk/n97dx5XZZk2cPzHetg3FRBBEcXb3HDLPcc0zbKsnMpqxmwqrfeTNe2900yb71Qz2VQzbWp7U2lmWVqmmWtNKkluCN4pIAoosggo+1neP25stEI5R+A5zzn39/M5HxCe5+FSuc71LPd93XAdsPzUDYQQg4AFqOQ92vphaq1u31cQnqBGMTqpsqap4cDALgT6e8DsuME3qob9RduNjqSt6Vw2wuGd6uR3+GwICHLpEFabnQUbc+ibEMFwM3W6c8bJUdQle42NwyBnfSeVUlqBOcBqIBtYIqXcI4SYK4SY2rTZPCAM+EgIsUMIsbyZw2nuwNYIORsgdaJLbTBX/NRwwEPG9vS7GiwRsPJ+sDYYHU2b0blskE3zwBJ5Tq0vl+8s4kBZDXeOT/Wc6Uw/5+Ujqlv0zFhKuRJY+bOvPXrK5xe1clxaW8r/Ti1onjrRpd0/yiigd3w4fRM8pOFAcBRc8RIsuRHWPAKX/N3oiNqMzuV2VrwHslfAbx6CoEiXDmGzO3hp3X56x4czqY/JHwudSVQyBIbBkV1GR2IID7jHqDlt5yJ1JdhjvNO77is+zs5DFVw9JNGzztD7XAHnz4KtC7y+LZ/WijbNU2tnD7/d5UOs2FlEbmk1f5yQiq+vB+Xcz/n6QpfBqgmPF9LF2NvUVsCeT6H/1S4t3bb0hwLzL2benAsfBks4rH/S6Eg0T1AiVa4NmwUhrj3ntdkd/GvdPkRcuLmWKHVV4vlQnAkNNWff1sPoYuxtMpeCtVYNWnJSvdXGxxkFXChizb2YeXNCYmDUnbD3c28YzKW1tU3PQkAIjJzj8iE+31VEbkk1d3n6VfFJieeD3QqHdxgdSbvTxdjb/PAuxPWHzgOd3vWLXYcpPdHAjSO7tUFgbmL4beDrD1l63JJ2Dor3qBPf82+B0A4uHaKu0cYzqyS948PN23LWWV2aljYt+N7YOAygi7E3KdqhplkMmenSKOp3vjtASqdQxphxhaaWCopUZ+e5G4yORDOzNY+pRx5j7nH5EG98m0dhRS2PXt7HO66KAcI6qV75uhhrHu2Hd8A/GPpf4/SuGfnH2FlQyU2jkj3/jSFlnLpNXXvM6Eg0M8rdAPvXwAX3u/ys+GhVHS+v38+kPnGM6uHBJ7+/JvF8OPS9NzThOY0uxt6ioRp2fQR9r1RTeZw0f2MOUSEB/Hawh8wtPpOUcYAD8rxjNSetFVkbYOWDENXtnOYVz1stabTZ+fMU92oN2S66jYQTR6DUmJ73RtHF2FvsWabmFg+e6fSuPxYfZ01WMTNHJhNq8aCVYprTZYia76hvVWvO2voqlEq4dJ7L3bZ2F1Sy9IcCbh7dnW4dnJ/xYHo9JqiPOWuNjaOd6WLsLTLeUcuUdR3h9K6vbsghOMCPmaOSWz8ud+QXAMljVAtDL7tVpp2DshzY8DcQl0Kvi106RKPNzkMf76JjmIU7xvds5QBNIrobdOgJ+3Ux1jzN0WwoSFfTmZwcuLWv+Dif7ihkxshuxIS6tu6xKYlLVdP64kyjI9HMwG6DT/9HnchN+YfLh3l1Qw5Zh6t48sp+RAQFtGKAJtNjPBz4FhrrjI6k3ehi7A0y3gbfAEi73uldn1vzI6GB/tz+mx6tH5c7E5eCj69qZahpZ7P5ZTi0FS6ZBxEJLh1i75EqXly3j6lpCUzyhgYfZ9JjguqHcHCz0ZG0G12MPV11mZpb3G8ahDo3KvOHg8f4MvMIt4zp7l1XxaCmWHQdpYuxdnZH98K6v0Lvy2DAtS4dwmqz88BHu4gICuDxqa6td+xRul8A/kGqAY+X0MXY0215BRprYcy9Tu1mtzt4YkUWseEWZo9NaaPg3Nx5l8PRLCj50ehINHfVWAuf3AqWMLjseZfm7wP8a+0+dhdWMveKft534vtrAkNBXKIGntoajY6mXehi7MnqKiH9NVVUYns7tesn2wvZeaiChyb39o4R1L+m71Xq7PybZ42ORHNHDgesuBuOZMKVr0JYrEuH2fRjCS+u38/VQxKZMqBzKwdpYv2vhZoyyFlndCTtQhdjT5b+GtRXwtj7ndrtWHUDT63MZlDXKK4a5IELQrRUeJxabWfXEvWGq2mnSl8IuxbDuD+5PHq6uKqOez7cQWpsGP93Rb9WDtDkel4EwdGw+yOjI2kXuhh7qoZqNagkdRJ0TnNq1ydXZlNV28jT0/p7fretsxlzNwRFwMoHwGY1OhrNTQQf3Q6rH1YD/cY+4NIxrDY7dy7aTm2jjVd+N5jgQL9WjtLk/AOh7zQ1bqO61Oho2pwuxp7q+9ehtly15HPCjsO1LM0oYPbYFHrHR7RRcCYSHK1GyB78Dr5+zPXjlEg6b52rnjFq5lZZSOJ3D0N0Mlw1X63D6ySHw8HjK/aQnlfOk1f1o2dseOvH6QmGzQZrHWx70+hI2pwuxp7oWL5qPpA6CboOb/FudY02XtxcQrcOIdw1IbUNAzSZtOkw7DbY/JIame4suw2W3U7UgZVQ+EPrx6e1H2s9LLkRH1s9TH9fLSzigje+zeO9LQe57TcpXDXIC1rMuiq2N/ScqB4JePicY12MPY3dDp/frebITnnOqV1fWrefouNWnrqqP0EB+pbZaS5+Us19XHE37PvauX3TF0JRUxEula0fm9Z+Vj4Ahds4POwvTg+KPGn1niM8uTKbS/rF89DFrh3Dq4y8A6pLYMf7RkfSpnQx9jQbnlKjDyfOhaikFu8mjxxn/sYcLuoRxmhPXiLRVX4BcO070Km36rTkzIpOWxdA8gXY/YL0NCkz2/aWWvlszL0cTxrv0iHS88r54+LtpCVG8fz0gXpMRkukjIOkEbDxGWioMTqaNqOLsSfZvRQ2zYNBM2DozS3e7WQ/3IjgAG4d6tpC6F7BEg5XvqKmW3z1SMv2qS6DY3nQ8yLqI7rpK2OzytsEK+9XI3zH/8WlQ3x/oJyb3kqnS1Qwr88cqu8+tZSPD0x8Qq3ktPVVo6NpM7oYe4qCDPjsDtU1aspzTjUfeGbVXnYcquCJqX2JDNJvEGeUMBBGzYHt/4a9K8++/cnb04lDaYjorq+Mzah0P3w4Qy1ecPWb4Ot8jmTkl3PTm+nERwSxaNYIOoZZ2iBQD9Z1hBq5/s1zUFlodDRtQhdjT1BVBItvUE0Hpv9bTQlooWXbC3jtmzxuHNmNy9Nc66nrdcY9DPED1O3qikNn3rYwQz2/7zyQ+ohkqCqA+hPtEqbWCmqPwaLp6v/w+sUuDdja+GMJM9/8ntiIIBbNHkFshGtLK3q9yU+rwZBfPmh0JG1CF2Oza6iGRddDwwm4/kOn+k9/vquI+5bsZGRKB+9cxNxVAUFwzdvqjeG9aXDiaPPbFmxTz5ktYTREJKuvleqrY1Ooq4L3r1GzE6a/BzHdnT7E+1vzufnt70mKCWHRrBHE6ULsuuhkGPeQ6led+bHR0bQ6XYzNyOGAwztBfgkLL1Sf//Z1iOvT4kO8/Z887ly0naHdYnjjpqFY/PXtaad06AE3fAiVBfD2FCjP/eU2Doe6Mu4yBEBdGYMuxmZQUw7v/RaKtsM1b0HyaKd2b7TZ+evnWfx5WSZjUzvy0e0jiY/UhficjZwDiefDinvUSZIH0cXYbIqz4M3JsGAsLLpO9Z+e8Ylqqt4CNQ1WHvhoJ4+vyOKi8+J45+ZhhAR6ae/pc5U8Gn63VF0ZL7wQctaf/v1jearxSlMxbghLBF9/KNGDuNxaeS68fpE6yb36LdXb3QmHymu4dsFmXv82j5kju/HajUMJ89b+7q3NL0BdeOCAJTeqO4MeQv+GmMmJEnj3CnDY4dJnIbYPxPdX7RpbYO+RKuZ8sJ2ckhPcOb4nd1/UCz89teLcJI+G2eth0Q3qlvXEuers3ccHtsxXzxq7j1Xb+vpDx15QvMfYmLXmHUpXJ7kOB8xcrgYOtZDD4eCzHUU88qnqY/7i9YP0OIy2EJ0M016DxdfDJ7Ph2nddGlTnbnQxNguHA5bPUVfCs9dDXMvXPK2ut7JgYw7zN+USGRzA+7cMZ5SeS9x6YlLg1jWw7Hb46i+Q/x30v0a1JB3yB3VL+6T4/pD3jXGxas3L+ky9uUckqDsep/6/nUVeaTWPfpbJN/tKGdQ1in9dN4ikmJA2DNbLiclw8dOw6iFYdhtcOR/8zF3OzB29t3A4YPWf4cdVMPnvLS7EjTY7y7YX8uxqydHj9UxNS+DRy/voaRVtwRKuBvlsnQ9rHgW5EiyRcOHDp28XPwB2faga3zsx2E5rQ3Y7bHpGtZBNGgbXLYLQls23r6xt5LVNuSz8JheLny+PX96HGSOT9R2n9jDidmishrVz1QyFaQtbfJfQHeli7O5KJGx6FnYvUcv5Db/trLuUVzewKP0g724+QHFVPWlJUbz6+yEM6Rbd9vF6Mx8fGPE/MGA65P8HwhN+WXDj+6uPh3dCzwntH6N2uppy+GQW7P8a0q6Hy56HgOCz7lbbaOfl9ftZsDGHqjorU9MS+MuU8/S0pfZ2wX1giYAvH4LXJ8B1H0BHc/bV18W4rez4ANIXEhsmIHGuWhu3pcpz1SolWZ9BxUG1wP0F98H4R5pt5nGi3srXWcWs2FnEpn0lNNocXJDakaen9Wdcr1jddq89hcQ0P+jnZDE+slsXY6Pt+1r1cT9RDJe9AENuOmuznPyyat7dnM/irQepbrQzoXcs907qRd8E1xaM0FrBsFkQex4smQmvjYfLX1BLLzrR+Mgd6GLcFspy4Iv7ICiK6COZsHgf/GHVmZtx2G2w7yv1nHH/12qwT8+JajBQ32kQ1um0zWsbbGzLL2dLbhmbc8rYVVCJ1e4gITKIm0Ylc+3QJFLj9LJsbickBiKT4MguoyPxTg4HHNwC3z4P+1arrlo3r/ppxPuvqahpYFXmEVbsKuK7nDL8fHwY3TWEe6YMZGBSVDsGrzUreQzM3gAfzYSlN8Puj9Vgyo49jY6sxVpUjIUQk4F/An7A61LKv/3s+xbgXWAIUAZMl1IeaNVIj2bD8ruIDUmBLo+pQRbuyNqgBvL4BcCstRRtWabWPv3iHtWm0v+U57U2q1onN3uFeh0/DOGdYdyfYPBMiOiM3e6g+Hgdh/LKOVBWze6CSnYWVJB9uIpGmwN/Xx8GJEYye2wK43vHMrhrtL4KdnfxA9SVsQHcIpfbW2MtHNyspp7t/QLKcyA4BiY8qk52/U8fQ1FvtZFZWMV3+0v5dn8pGfnHsNodJHcI4c7xqfxueFfKC/M4Txdi9xKVBDd/pZY63fgMvDJc3aEa+Hs166EFjx+MdNZiLITwA14GJgIFwPdCiOVSyqxTNrsFOCal7CmEuA74OzC91aI88K2aOuIDMfUZ8OpqmPGp6hPsThwOWHEXFKSrHrYRCWp1lwvug2/+gS1/K/Vxg7DXVeJ/LJfAqnx87Q1Y/YI43HE0e5LuJt0ykqNFNkp/PEBxlaTwWC0NNvtPPyLM4s+AxEhuvSCF4d1jOD85hlA9h9FcOg9QA7xK97Xr8y23yOWfs9vg0FbIWq5WG6ssINXHHzb3hdSJ6s3U2X+jhmo1RengZjWy/VA62OrBNwC6jYIx9+DoexVVdgsl5XXkl1WQV1pNTkk1mYWV7D2iTnR9fKBvQgSzxqYwpX9n+iZE4NN067PcM9sjm5+fP4y5GwbeAN++ADvegz3LwM+ibmXH9VPNkWL7qN+r8M6nT4uy29WCFOV5qk9AXRXRJWXgez50GaxaDreRlryLDwP2SylzAYQQi4ErgFMT+Arg8abPlwIvCSF8pJSOc4qurhKyPsPxxX04opOpvuZDZHY2aRkP4vv25RQNvo+KpPFY/UKw+liw4YPDbsNus4K9EbvdBlYrdnsjPrYGsDVidziwOwBbA6HH8wk9kUtITRFWXws1lk4cD0qgwTcEh8OOn60OX1sdvtY6fOyN0LSvj6MRP1s9fvb6nz762+pIqM6iQ/0h1sbfyvLMFMq2bKWorIoT1lGkWX2ZU/oxHcrWUeuwkOvoTJ5jEjvsPdloH0BtdRDkQ5jlCJ3CLXQMC6RP5wgm9Y0jKTqEpJgQuja99EhNkxs0Q61x/OEMmLUWAkPb6ycbl8sA1nqorVCNUEr24sjdCHu/wKf6KA4/C41dx9CQOJaK0iN0qDlA8NonYO0TVEf0oDjhIkqiBnAioCM2hx82AFsDAfXHCKwvJ6S2iKjqPCKr84ip3o+fw4YdXw4Hp7Ivciq7Agexw7cvJVX+lK6up+zjb047yQWIDA6gX5cIbhmTQlpiJMNTOhAT2vI+75obCYuFyU/BhEfUVMK8jVCcqR5N7Hjvv9v5+qu7rJYIdQelskCduJ0iHqBpvRdiUtRyjl1HqDa3YbEQFqeuus/xGXVLinEX4NRu+AXA8Oa2kVJahRCVQAeg1NXAsjevpNfqG/DDQbq9N7ML7qXy+eymH/YA/wx8maFbHiNpy2Ou/ggA7A4fSogkACsxPs418K93BFBHIPUEUE8gWXTha58pLC8eT3hVBTGhgXQK9WdofAwdwq4mPeQGwoP8CbH4ExroR1qgP6Msftwf6E+oxY/okEC9rJo3iOyiugj9e5pa9nLIzPb6yYbkMsCev42nb13GaV+rcVhYbx/IKtt01tsHUp19+m3EzpQx0W8bFx/bxvDK10jxOb14/lyBoyN77F3Y45hCuv08Mv0ENEQQ6uNPiN2PUIs/HcL86RUXTsfwQDqFWegYZiEpJoTuHUOJDgn46cpX8xABwdBrknqddKIEju5RA2UrDkHlIVWI/QKg9xSI7qYai8T0gOBofszOpFdHf3WH5dBWVdB3fvCzH+QDXUfCzV+6HKph9zdrampKMzIymm8uGhjHjsvXAirIN0/7Zjwwn4xf7nVO8s5x/xBgatPrv4IAa9OrqXXbyT/WQCNQ2fQqOsef31oyMlr7X7btmClWODXeSGj6/ebsf4dubRhSqzhrPk+c96v52hn4Q9Prl+KBvsBMdrYwjnBgRNOreackoB0ohbxS5/PfDL97OsbmhINPGkSnwa/N+KwEKsuBciCYjBIgaDSkjoYzPTU5h1xuSTEuBJJO+XNi09d+bZsCIYQ/EIka/NGsIUOGdDrT9zVNa3Vtksug81nTzlVLivH3QKoQojsqUa8DbvjZNsuBmcBm4GpgXas8Y9I0rTXpXNY0N3XWVZuklFZgDrAayAaWSCn3CCHmCiFO3pF9A+gghNgP3Av8b1sFrGmaa3Qua5r78nE49EmvpmmaphlJr2esaZqmaQbTxVjTNE3TDGaa1k1CiDeBy4CjUsp+RsdzJkKIJFRLwTjAASyUUv7T2KiaJ4QIAjYBFtTvxFIp5blN4G4HTR2ltgGFUsrLjI7nTIQQB4DjgA2wSimHGhqQG3L3HDdLXpsln82Qv+2Zt2a6Mn4bmGx0EC1kBe6TUvZBTXm8QwjRx+CYzqQeGC+lTAMGApOFEGeequke/ogaiGQWF0opB+pC3Ky3ce8cN0temyWfzZK/7ZK3pinGUspNqBnYbk9KeVhK+UPT58dRv3BdjI2qeVJKh5TyZPuxgKaXW4/sE0IkAlOA142ORWsd7p7jZslrM+Szzt9fMk0xNishRDIwCNhqcChnJITwE0LsAI4Ca6SUbh0v8ALwIKqHkhk4gK+EEBlCiNlGB6OdG3fPaxPks1nyt93yVhfjNiSECAM+Bu6WUlYZHc+ZSCltUsqBqK5Mw4QQbvfM7iQhxMnniu7f6++/xkgpBwOXoG5vjjU6IM01Zshrd85nk+Vvu+WtLsZtRAgRgErY96WUnxgdT0tJKSuA9bj3s7vRwNSmwRWLgfFCiPfOuIfBpJSFTR+PAstQKyhpJmO2vHbTfDZN/rZn3upi3AaEED6oTkbZUsrnjI7nbIQQnYQQUU2fB6PWu91rbFTNk1L+SUqZKKVMRrV0XCel/L3BYTVLCBEqhAg/+TkwCcg0NirNWWbJa3fPZ7Pkb3vnrZlTfrEoAAAAoElEQVSmNi0CxgEdhRAFwGNSyjeMjapZo4EZwO6m5zYAD0spVxoY05l0Bt5pmmrgi2qT+LnBMXmSOGCZEAJUzn0gpVxlbEjuxwQ5bpa81vncOto1b3U7TE3TNE0zmL5NrWmapmkG08VY0zRN0wymi7GmaZqmGUwXY03TNE0zmC7GmqZpmmYwXYw1TdM0zWC6GGuapmmawXQx1jRN0zSD/T+fmK8KDwUIUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8DjBWraO1oA",
        "colab_type": "text"
      },
      "source": [
        "### Cold Start problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqiAz7FEO4ky",
        "colab_type": "text"
      },
      "source": [
        "Cold Start problem with Users"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfTLK-ZXIU9B",
        "colab_type": "code",
        "outputId": "a8e89faa-e1d0-406e-c661-008fa1ddcda9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "total_users = len(np.unique(df.user))\n",
        "users_train = len(train_averages['user'])\n",
        "new_users = total_users - users_train\n",
        "\n",
        "print('\\nTotal number of Users  :', total_users)\n",
        "print('\\nNumber of Users in Train data :', users_train)\n",
        "print(\"\\nNo of Users that didn't appear in train data: {}({} %) \\n \".format(new_users,\n",
        "                                                                        np.round((new_users/total_users)*100, 2)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total number of Users  : 480189\n",
            "\n",
            "Number of Users in Train data : 405041\n",
            "\n",
            "No of Users that didn't appear in train data: 75148(15.65 %) \n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zi4txYnVCLU",
        "colab_type": "text"
      },
      "source": [
        "We might have to handle new users ( 75148 ) who didn't appear in train data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5W5sHhjVErd",
        "colab_type": "text"
      },
      "source": [
        "**Cold Start problem with Movies**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHWo9NHqPSH-",
        "colab_type": "code",
        "outputId": "35de1aeb-043f-4557-c085-7305812c7e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "total_movies = len(np.unique(df.movie))\n",
        "movies_train = len(train_averages['movie'])\n",
        "new_movies = total_movies - movies_train\n",
        "\n",
        "print('\\nTotal number of Movies  :', total_movies)\n",
        "print('\\nNumber of Users in Train data :', movies_train)\n",
        "print(\"\\nNo of Movies that didn't appear in train data: {}({} %) \\n \".format(new_movies,\n",
        "                                                                        np.round((new_movies/total_movies)*100, 2)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total number of Movies  : 17770\n",
            "\n",
            "Number of Users in Train data : 17424\n",
            "\n",
            "No of Movies that didn't appear in train data: 346(1.95 %) \n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veAyBMJSVOL7",
        "colab_type": "text"
      },
      "source": [
        "We might have to handle 346 movies (small comparatively) in test data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnzVUMXVVQr9",
        "colab_type": "text"
      },
      "source": [
        "# Computing Similarity matrices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H73ZenATVV__",
        "colab_type": "text"
      },
      "source": [
        "##  Computing User-User Similarity matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "CzaXbFY2xrol",
        "colab_type": "text"
      },
      "source": [
        "1. Calculating User User Similarity_Matrix is __not very easy__(_unless you have huge Computing Power and lots of time_) because of number of. usersbeing lare.\n",
        "\n",
        "    * You can try if you want to. Your system could crash or the program stops with **Memory Error**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dz91QIn9VkFB",
        "colab_type": "text"
      },
      "source": [
        " Trying with all dimensions (17k dimensions per user)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGjlYyN4VkpP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "\n",
        "def compute_user_similarity(sparse_matrix, compute_for_few=False, top = 100, verbose=False, verb_for_n_rows = 20,\n",
        "                            draw_time_taken=True):\n",
        "    no_of_users, _ = sparse_matrix.shape\n",
        "    # get the indices of  non zero rows(users) from our sparse matrix\n",
        "    row_ind, col_ind = sparse_matrix.nonzero()\n",
        "    row_ind = sorted(set(row_ind)) # we don't have to\n",
        "    time_taken = list() #  time taken for finding similar users for an user..\n",
        "    \n",
        "    # we create rows, cols, and data lists.., which can be used to create sparse matrices\n",
        "    rows, cols, data = list(), list(), list()\n",
        "    if verbose: print(\"Computing top\",top,\"similarities for each user..\")\n",
        "    \n",
        "    start = datetime.now()\n",
        "    temp = 0\n",
        "    \n",
        "    for row in row_ind[:top] if compute_for_few else row_ind:\n",
        "        temp = temp+1\n",
        "        prev = datetime.now()\n",
        "        \n",
        "        # get the similarity row for this user with all other users\n",
        "        sim = cosine_similarity(sparse_matrix.getrow(row), sparse_matrix).ravel()\n",
        "        # We will get only the top ''top'' most similar users and ignore rest of them..\n",
        "        top_sim_ind = sim.argsort()[-top:]\n",
        "        top_sim_val = sim[top_sim_ind]\n",
        "        \n",
        "        # add them to our rows, cols and data\n",
        "        rows.extend([row]*top)\n",
        "        cols.extend(top_sim_ind)\n",
        "        data.extend(top_sim_val)\n",
        "        time_taken.append(datetime.now().timestamp() - prev.timestamp())\n",
        "        if verbose:\n",
        "            if temp%verb_for_n_rows == 0:\n",
        "                print(\"computing done for {} users [  time elapsed : {}  ]\"\n",
        "                      .format(temp, datetime.now()-start))\n",
        "            \n",
        "        \n",
        "    # lets create sparse matrix out of these and return it\n",
        "    if verbose: print('Creating Sparse matrix from the computed similarities')\n",
        "    #return rows, cols, data\n",
        "    \n",
        "    if draw_time_taken:\n",
        "        plt.plot(time_taken, label = 'time taken for each user')\n",
        "        plt.plot(np.cumsum(time_taken), label='Total time')\n",
        "        plt.legend(loc='best')\n",
        "        plt.xlabel('User')\n",
        "        plt.ylabel('Time (seconds)')\n",
        "        plt.show()\n",
        "        \n",
        "    return sparse.csr_matrix((data, (rows, cols)), shape=(no_of_users, no_of_users)), time_taken      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5U4LqsTVnCk",
        "colab_type": "code",
        "outputId": "e0b39afb-3822-4384-c1dd-4ef55144f30f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "start = datetime.now()\n",
        "u_u_sim_sparse, _ = compute_user_similarity(train_sparse_matrix, compute_for_few=True, top = 100,\n",
        "                                                     verbose=True)\n",
        "print(\"-\"*100)\n",
        "print(\"Time taken :\",datetime.now()-start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing top 100 similarities for each user..\n",
            "computing done for 20 users [  time elapsed : 0:01:23.168578  ]\n",
            "computing done for 40 users [  time elapsed : 0:02:46.650198  ]\n",
            "computing done for 60 users [  time elapsed : 0:04:08.503092  ]\n",
            "computing done for 80 users [  time elapsed : 0:05:30.309038  ]\n",
            "computing done for 100 users [  time elapsed : 0:06:52.198891  ]\n",
            "Creating Sparse matrix from the computed similarities\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "Time taken : 0:07:02.735422\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nECE2jIpHIP_",
        "colab_type": "text"
      },
      "source": [
        "- This will take a lot of time for computing similarities even if we reduce the dimensions.\n",
        "-An alternative is to compute similar users for a particular user, whenenver required (ie., Run time) - We maintain a binary Vector for users, which tells us whether we already computed or not.. - If not : - Compute top (let's just say, 1000) most similar users for this given user, and add this to our datastructure, so that we can just access it(similar users) without recomputing it again. - - If It is already Computed: - Just get it directly from our datastructure, which has that information. - In production time, We might have to recompute similarities, if it is computed a long time ago. Because user preferences changes over time. If we could maintain some kind of Timer, which when expires, we have to update it ( recompute it ). - - Which datastructure to use: - It is purely implementation dependant. - One simple method is to maintain a Dictionary Of Dictionaries. - - key : userid - value: Again a dictionary - key : Similar User - value: Similarity Value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGmEQw0KHfr_",
        "colab_type": "text"
      },
      "source": [
        "### Computing Movie-Movie Similarity matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEAb2De6YGbC",
        "colab_type": "code",
        "outputId": "69084bf5-4e23-4a18-e3ef-ebd610ba7cf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "start = datetime.now()\n",
        "if not os.path.isfile('/content/drive/My Drive/AI projects/Netflix /m_m_sim_sparse.npz'):\n",
        "    print(\"It seems you don't have that file. Computing movie_movie similarity...\")\n",
        "    start = datetime.now()\n",
        "    m_m_sim_sparse = cosine_similarity(X=train_sparse_matrix.T, dense_output=False)\n",
        "    print(\"Done..\")\n",
        "    # store this sparse matrix in disk before using it. For future purposes.\n",
        "    print(\"Saving it to disk without the need of re-computing it again.. \")\n",
        "    sparse.save_npz(\"/content/drive/My Drive/AI projects/Netflix /m_m_sim_sparse.npz\", m_m_sim_sparse)\n",
        "    print(\"Done..\")\n",
        "else:\n",
        "    print(\"It is there, We will get it.\")\n",
        "    m_m_sim_sparse = sparse.load_npz(\"/content/drive/My Drive/AI projects/Netflix /m_m_sim_sparse.npz\")\n",
        "    print(\"Done ...\")\n",
        "\n",
        "print(\"It's a \",m_m_sim_sparse.shape,\" dimensional matrix\")\n",
        "\n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It seems you don't have that file. Computing movie_movie similarity...\n",
            "Done..\n",
            "Saving it to disk without the need of re-computing it again.. \n",
            "Done..\n",
            "It's a  (17771, 17771)  dimensional matrix\n",
            "0:09:07.828705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjGcfeyFxrpp",
        "colab_type": "text"
      },
      "source": [
        "- Even though we have similarity measure of each movie, with all other movies, We generally don't care much about least similar movies.\n",
        "\n",
        "\n",
        "- Most of the times, only top_xxx similar items matters. It may be 10 or 100.\n",
        "\n",
        "\n",
        "- We take only those top similar movie ratings and store them  in a saperate dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDD3TMw-IGWz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movie_ids = np.unique(m_m_sim_sparse.nonzero()[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BYdJKtYOh0w",
        "colab_type": "code",
        "outputId": "bd710c05-43be-42d0-c417-ad022f287c61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "start = datetime.now()\n",
        "similar_movies = dict()\n",
        "for movie in movie_ids:\n",
        "    # get the top similar movies and store them in the dictionary\n",
        "    sim_movies = m_m_sim_sparse[movie].toarray().ravel().argsort()[::-1][1:]\n",
        "    similar_movies[movie] = sim_movies[:100]\n",
        "print(datetime.now() - start)\n",
        "\n",
        "# just testing similar movies for movie_15\n",
        "similar_movies[15]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:00:30.122671\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8279,  8013, 16528,  5927, 13105, 12049,  4424, 10193, 17590,\n",
              "        4549,  3755,   590, 14059, 15144, 15054,  9584,  9071,  6349,\n",
              "       16402,  3973,  1720,  5370, 16309,  9376,  6116,  4706,  2818,\n",
              "         778, 15331,  1416, 12979, 17139, 17710,  5452,  2534,   164,\n",
              "       15188,  8323,  2450, 16331,  9566, 15301, 13213, 14308, 15984,\n",
              "       10597,  6426,  5500,  7068,  7328,  5720,  9802,   376, 13013,\n",
              "        8003, 10199,  3338, 15390,  9688, 16455, 11730,  4513,   598,\n",
              "       12762,  2187,   509,  5865,  9166, 17115, 16334,  1942,  7282,\n",
              "       17584,  4376,  8988,  8873,  5921,  2716, 14679, 11947, 11981,\n",
              "        4649,   565, 12954, 10788, 10220, 10963,  9427,  1690,  5107,\n",
              "        7859,  5969,  1510,  2429,   847,  7845,  6410, 13931,  9840,\n",
              "        3706])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rspKPDD8OwDt",
        "colab_type": "text"
      },
      "source": [
        "**Finding most similar movies using similarity matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyTtu34IOrEI",
        "colab_type": "code",
        "outputId": "28acc04c-1ae2-4217-e4ef-6a73162a6fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# First Let's load the movie details into soe dataframe..\n",
        "# movie details are in 'netflix/movie_titles.csv'\n",
        "\n",
        "movie_titles = pd.read_csv(\"/content/drive/My Drive/AI projects/Netflix /movie_titles.csv\", sep=',', header = None,\n",
        "                           names=['movie_id', 'year_of_release', 'title'], verbose=True,\n",
        "                      index_col = 'movie_id', encoding = \"ISO-8859-1\")\n",
        "\n",
        "movie_titles.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenization took: 3.72 ms\n",
            "Type conversion took: 9.35 ms\n",
            "Parser memory cleanup took: 0.01 ms\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>year_of_release</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movie_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>Dinosaur Planet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004.0</td>\n",
              "      <td>Isle of Man TT 2004 Review</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1997.0</td>\n",
              "      <td>Character</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1994.0</td>\n",
              "      <td>Paula Abdul's Get Up &amp; Dance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2004.0</td>\n",
              "      <td>The Rise and Fall of ECW</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          year_of_release                         title\n",
              "movie_id                                               \n",
              "1                  2003.0               Dinosaur Planet\n",
              "2                  2004.0    Isle of Man TT 2004 Review\n",
              "3                  1997.0                     Character\n",
              "4                  1994.0  Paula Abdul's Get Up & Dance\n",
              "5                  2004.0      The Rise and Fall of ECW"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XShF0sU9O_vQ",
        "colab_type": "code",
        "outputId": "83af9f68-6101-4e5f-989d-9bba6b7263f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "mv_id = 1\n",
        "\n",
        "print(\"\\nMovie ----->\",movie_titles.loc[mv_id].values[1])\n",
        "\n",
        "print(\"\\nIt has {} Ratings from users.\".format(train_sparse_matrix[:,mv_id].getnnz()))\n",
        "\n",
        "print(\"\\nWe have {} movies which are similarto this  and we will get only top most..\".format(m_m_sim_sparse[:,mv_id].getnnz()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Movie -----> Dinosaur Planet\n",
            "\n",
            "It has 395 Ratings from users.\n",
            "\n",
            "We have 17297 movies which are similarto this  and we will get only top most..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Spl3xwnBPI8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "similarities = m_m_sim_sparse[mv_id].toarray().ravel()\n",
        "\n",
        "similar_indices = similarities.argsort()[::-1][1:]\n",
        "\n",
        "similarities[similar_indices]\n",
        "\n",
        "sim_indices = similarities.argsort()[::-1][1:] # It will sort and reverse the array and ignore its similarity (ie.,1)\n",
        "                                               # and return its indices(movie_ids)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toLnl1zpPhn5",
        "colab_type": "code",
        "outputId": "34e9d6c5-a988-4770-847d-b70a81f1bca0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "plt.plot(similarities[sim_indices], label='All the ratings')\n",
        "plt.plot(similarities[sim_indices[:100]], label='top 100 similar movies')\n",
        "plt.title(\"Similar Movies of {}(movie_id)\".format(mv_id), fontsize=20)\n",
        "plt.xlabel(\"Movies (Not Movie_Ids)\", fontsize=15)\n",
        "plt.ylabel(\"Cosine Similarity\",fontsize=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "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": "markdown",
      "metadata": {
        "id": "M7Kuz9pUPsdm",
        "colab_type": "text"
      },
      "source": [
        "**Top 10 similar movies**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXcOLZjTPkeQ",
        "colab_type": "code",
        "outputId": "a8c44b4c-ed8e-4a47-c403-c19aa6467991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "movie_titles.loc[sim_indices[:10]]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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>year_of_release</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movie_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>694</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>When Dinosaurs Roamed America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5302</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>Chased by Dinosaurs: Three Walking with Dinosa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1084</th>\n",
              "      <td>2001.0</td>\n",
              "      <td>Walking with Prehistoric Beasts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13586</th>\n",
              "      <td>2001.0</td>\n",
              "      <td>Allosaurus: A Walking with Dinosaurs Special</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1173</th>\n",
              "      <td>1999.0</td>\n",
              "      <td>Walking with Dinosaurs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4181</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>Walking with Cavemen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8800</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>Prehistoric America: A Journey Through the Ice...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10656</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>Before We Ruled the Earth: Mastering the Beasts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15648</th>\n",
              "      <td>2002.0</td>\n",
              "      <td>National Geographic: Dinosaur Hunters: Secrets...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10257</th>\n",
              "      <td>2002.0</td>\n",
              "      <td>Prehistoric Planet: The Complete Dino Dynasty</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          year_of_release                                              title\n",
              "movie_id                                                                    \n",
              "694                2000.0                      When Dinosaurs Roamed America\n",
              "5302               2003.0  Chased by Dinosaurs: Three Walking with Dinosa...\n",
              "1084               2001.0                    Walking with Prehistoric Beasts\n",
              "13586              2001.0       Allosaurus: A Walking with Dinosaurs Special\n",
              "1173               1999.0                             Walking with Dinosaurs\n",
              "4181               2003.0                               Walking with Cavemen\n",
              "8800               2003.0  Prehistoric America: A Journey Through the Ice...\n",
              "10656              2003.0    Before We Ruled the Earth: Mastering the Beasts\n",
              "15648              2002.0  National Geographic: Dinosaur Hunters: Secrets...\n",
              "10257              2002.0      Prehistoric Planet: The Complete Dino Dynasty"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QdtPFenXytK",
        "colab_type": "text"
      },
      "source": [
        "# Machine Learning Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQmqa6gEPpkK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_sample_sparse_matrix(sparse_matrix, no_users, no_movies, path, verbose = True):\n",
        "    \"\"\"\n",
        "        It will get it from the ''path'' if it is present  or It will create \n",
        "        and store the sampled sparse matrix in the path specified.\n",
        "    \"\"\"\n",
        "\n",
        "    # get (row, col) and (rating) tuple from sparse_matrix...\n",
        "    row_ind, col_ind, ratings = sparse.find(sparse_matrix)\n",
        "    users = np.unique(row_ind)\n",
        "    movies = np.unique(col_ind)\n",
        "\n",
        "    print(\"Original Matrix : (users, movies) -- ({} {})\".format(len(users), len(movies)))\n",
        "    print(\"Original Matrix : Ratings -- {}\\n\".format(len(ratings)))\n",
        "\n",
        "    # It just to make sure to get same sample everytime we run this program..\n",
        "    # and pick without replacement....\n",
        "    np.random.seed(15)\n",
        "    sample_users = np.random.choice(users, no_users, replace=False)\n",
        "    sample_movies = np.random.choice(movies, no_movies, replace=False)\n",
        "    # get the boolean mask or these sampled_items in originl row/col_inds..\n",
        "    mask = np.logical_and( np.isin(row_ind, sample_users),\n",
        "                      np.isin(col_ind, sample_movies) )\n",
        "    \n",
        "    sample_sparse_matrix = sparse.csr_matrix((ratings[mask], (row_ind[mask], col_ind[mask])),\n",
        "                                             shape=(max(sample_users)+1, max(sample_movies)+1))\n",
        "\n",
        "    if verbose:\n",
        "        print(\"Sampled Matrix : (users, movies) -- ({} {})\".format(len(sample_users), len(sample_movies)))\n",
        "        print(\"Sampled Matrix : Ratings --\", format(ratings[mask].shape[0]))\n",
        "\n",
        "    print('Saving it into disk for furthur usage..')\n",
        "    # save it into disk\n",
        "    sparse.save_npz(path, sample_sparse_matrix)\n",
        "    if verbose:\n",
        "            print('Done..\\n')\n",
        "    \n",
        "    return sample_sparse_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaRbaq-EYNtA",
        "colab_type": "text"
      },
      "source": [
        "## Sampling Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jvDI29uYUOH",
        "colab_type": "text"
      },
      "source": [
        "### Build sample train data from the train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzTGD-u3X8KA",
        "colab_type": "code",
        "outputId": "563cf15f-4485-4297-bce4-36183be4d2c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "start = datetime.now()\n",
        "path = \"/content/drive/My Drive/AI projects/Netflix /sample_train_sparse_matrix.npz\"\n",
        "if os.path.isfile(path):\n",
        "    print(\"It is present in your pwd, getting it from disk....\")\n",
        "    # just get it from the disk instead of computing it\n",
        "    sample_train_sparse_matrix = sparse.load_npz(path)\n",
        "    print(\"DONE..\")\n",
        "else: \n",
        "    # get 10k users and 1k movies from available data \n",
        "    sample_train_sparse_matrix = get_sample_sparse_matrix(train_sparse_matrix, no_users=10000, no_movies=1000,\n",
        "                                             path = path)\n",
        "\n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It is present in your pwd, getting it from disk....\n",
            "DONE..\n",
            "0:00:00.987352\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USqi6aWBYqCY",
        "colab_type": "text"
      },
      "source": [
        "### Build sample test data from the test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-fMt-5LYjqs",
        "colab_type": "code",
        "outputId": "7c865c45-1c3e-4220-a154-0ff8d798e381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "start = datetime.now()\n",
        "\n",
        "path = \"/content/drive/My Drive/AI projects/Netflix /sample_test_sparse_matrix.npz\"\n",
        "if os.path.isfile(path):\n",
        "    print(\"It is present in your pwd, getting it from disk....\")\n",
        "    # just get it from the disk instead of computing it\n",
        "    sample_test_sparse_matrix = sparse.load_npz(path)\n",
        "    print(\"DONE..\")\n",
        "else:\n",
        "    # get 5k users and 500 movies from available data \n",
        "    sample_test_sparse_matrix = get_sample_sparse_matrix(test_sparse_matrix, no_users=5000, no_movies=500,\n",
        "                                                 path = path)\n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It is present in your pwd, getting it from disk....\n",
            "DONE..\n",
            "0:00:00.989463\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5c1rJYoPVjS",
        "colab_type": "text"
      },
      "source": [
        "### Finding Global Average of all movie ratings, Average rating per User, and Average rating per Movie (from sampled train)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TjvgRdZZS1i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_train_averages = dict()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrAZFN1RPZ8n",
        "colab_type": "text"
      },
      "source": [
        "Finding Global Average of all movie ratings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vG5GAv4bPZP8",
        "colab_type": "code",
        "outputId": "d61ba21b-97cb-4389-dac4-3f5fd2ddf4a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# get the global average of ratings in our train set.\n",
        "global_average = sample_train_sparse_matrix.sum()/sample_train_sparse_matrix.count_nonzero()\n",
        "sample_train_averages['global'] = global_average\n",
        "sample_train_averages"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'global': 3.581679377504138}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-HZEB8zPik3",
        "colab_type": "text"
      },
      "source": [
        "Finding Average rating per User"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lqb3N_BsPfYM",
        "colab_type": "code",
        "outputId": "bf2d5f06-4bcf-45bb-e773-bfa44fbd8159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "sample_train_averages['user'] = get_average_ratings(sample_train_sparse_matrix, of_users=True)\n",
        "print('\\nAverage rating of user 1515220 :',sample_train_averages['user'][1515220])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Average rating of user 1515220 : 3.9655172413793105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJAORfivP-jZ",
        "colab_type": "text"
      },
      "source": [
        "Finding Average rating per Movie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_jBnxrPPkov",
        "colab_type": "code",
        "outputId": "deb858f2-5c34-4431-dbf8-d7505c552567",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "sample_train_averages['movie'] =  get_average_ratings(sample_train_sparse_matrix, of_users=False)\n",
        "print('\\n AVerage rating of movie 15153 :',sample_train_averages['movie'][15153])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " AVerage rating of movie 15153 : 2.6458333333333335\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXYDTU4vQDP7",
        "colab_type": "text"
      },
      "source": [
        "##  Featurizing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWB56biKQA0c",
        "colab_type": "code",
        "outputId": "f2d7833d-c681-47dd-edf3-2f2f01b79b6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print('\\n No of ratings in Our Sampled train matrix is : {}\\n'.format(sample_train_sparse_matrix.count_nonzero()))\n",
        "print('\\n No of ratings in Our Sampled test  matrix is : {}\\n'.format(sample_test_sparse_matrix.count_nonzero()))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " No of ratings in Our Sampled train matrix is : 129286\n",
            "\n",
            "\n",
            " No of ratings in Our Sampled test  matrix is : 7333\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnG7RvJ8QJI9",
        "colab_type": "text"
      },
      "source": [
        "### Featurizing data for regression problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_V_BjSHbQMd9",
        "colab_type": "text"
      },
      "source": [
        " Featurizing train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da2qjgRKQyH7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get users, movies and ratings from our samples train sparse matrix\n",
        "sample_train_users, sample_train_movies, sample_train_ratings = sparse.find(sample_train_sparse_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "to2wjpG_QGY5",
        "colab_type": "code",
        "outputId": "1f073072-8f82-456d-ce71-985e53cb9205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "############################################################\n",
        "# It took me almost 10 hours to prepare this train dataset.#\n",
        "############################################################\n",
        "start = datetime.now()\n",
        "if os.path.isfile('/content/drive/My Drive/AI projects/Netflix /reg_train.csv'):\n",
        "    print(\"File already exists you don't have to prepare again...\" )\n",
        "else:\n",
        "    print('preparing {} tuples for the dataset..\\n'.format(len(sample_train_ratings)))\n",
        "    with open('/content/drive/My Drive/AI projects/Netflix /reg_train.csv', mode='w') as reg_data_file:\n",
        "        count = 0\n",
        "        for (user, movie, rating)  in zip(sample_train_users, sample_train_movies, sample_train_ratings):\n",
        "            st = datetime.now()\n",
        "        #     print(user, movie)    \n",
        "            #--------------------- Ratings of \"movie\" by similar users of \"user\" ---------------------\n",
        "            # compute the similar Users of the \"user\"        \n",
        "            user_sim = cosine_similarity(sample_train_sparse_matrix[user], sample_train_sparse_matrix).ravel()\n",
        "            top_sim_users = user_sim.argsort()[::-1][1:] # we are ignoring 'The User' from its similar users.\n",
        "            # get the ratings of most similar users for this movie\n",
        "            top_ratings = sample_train_sparse_matrix[top_sim_users, movie].toarray().ravel()\n",
        "            # we will make it's length \"5\" by adding movie averages to .\n",
        "            top_sim_users_ratings = list(top_ratings[top_ratings != 0][:5])\n",
        "            top_sim_users_ratings.extend([sample_train_averages['movie'][movie]]*(5 - len(top_sim_users_ratings)))\n",
        "        #     print(top_sim_users_ratings, end=\" \")    \n",
        "\n",
        "\n",
        "            #--------------------- Ratings by \"user\"  to similar movies of \"movie\" ---------------------\n",
        "            # compute the similar movies of the \"movie\"        \n",
        "            movie_sim = cosine_similarity(sample_train_sparse_matrix[:,movie].T, sample_train_sparse_matrix.T).ravel()\n",
        "            top_sim_movies = movie_sim.argsort()[::-1][1:] # we are ignoring 'The User' from its similar users.\n",
        "            # get the ratings of most similar movie rated by this user..\n",
        "            top_ratings = sample_train_sparse_matrix[user, top_sim_movies].toarray().ravel()\n",
        "            # we will make it's length \"5\" by adding user averages to.\n",
        "            top_sim_movies_ratings = list(top_ratings[top_ratings != 0][:5])\n",
        "            top_sim_movies_ratings.extend([sample_train_averages['user'][user]]*(5-len(top_sim_movies_ratings))) \n",
        "        #     print(top_sim_movies_ratings, end=\" : -- \")\n",
        "\n",
        "            #-----------------prepare the row to be stores in a file-----------------#\n",
        "            row = list()\n",
        "            row.append(user)\n",
        "            row.append(movie)\n",
        "            # Now add the other features to this data...\n",
        "            row.append(sample_train_averages['global']) # first feature\n",
        "            # next 5 features are similar_users \"movie\" ratings\n",
        "            row.extend(top_sim_users_ratings)\n",
        "            # next 5 features are \"user\" ratings for similar_movies\n",
        "            row.extend(top_sim_movies_ratings)\n",
        "            # Avg_user rating\n",
        "            row.append(sample_train_averages['user'][user])\n",
        "            # Avg_movie rating\n",
        "            row.append(sample_train_averages['movie'][movie])\n",
        "\n",
        "            # finalley, The actual Rating of this user-movie pair...\n",
        "            row.append(rating)\n",
        "            count = count + 1\n",
        "\n",
        "            # add rows to the file opened..\n",
        "            reg_data_file.write(','.join(map(str, row)))\n",
        "            reg_data_file.write('\\n')        \n",
        "            if (count)%10000 == 0:\n",
        "                # print(','.join(map(str, row)))\n",
        "                print(\"Done for {} rows----- {}\".format(count, datetime.now() - start))\n",
        "\n",
        "\n",
        "print(datetime.now() - start)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File already exists you don't have to prepare again...\n",
            "0:00:00.001601\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx6cohroQchJ",
        "colab_type": "code",
        "outputId": "fc921ad0-c516-4462-d608-2f86ed485fc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "reg_train = pd.read_csv('/content/drive/My Drive/AI projects/Netflix /reg_train.csv', names = ['user', 'movie', 'GAvg', 'sur1', 'sur2', 'sur3', 'sur4', 'sur5','smr1', 'smr2', 'smr3', 'smr4', 'smr5', 'UAvg', 'MAvg', 'rating'], header=None)\n",
        "reg_train.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>53406</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.370370</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99540</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>99865</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.714286</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101620</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.584416</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>112974</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user  movie      GAvg  sur1  sur2  ...  smr4  smr5      UAvg      MAvg  rating\n",
              "0   53406     33  3.581679   4.0   5.0  ...   3.0   1.0  3.370370  4.092437       4\n",
              "1   99540     33  3.581679   5.0   5.0  ...   3.0   5.0  3.555556  4.092437       3\n",
              "2   99865     33  3.581679   5.0   5.0  ...   5.0   4.0  3.714286  4.092437       5\n",
              "3  101620     33  3.581679   2.0   3.0  ...   4.0   5.0  3.584416  4.092437       5\n",
              "4  112974     33  3.581679   5.0   5.0  ...   5.0   3.0  3.750000  4.092437       5\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e90d1zMCxrql",
        "colab_type": "text"
      },
      "source": [
        "-----------------------\n",
        "\n",
        "- __GAvg__ : Average rating of all the ratings \n",
        "\n",
        "\n",
        "- __Similar users rating of this movie__:\n",
        "    - sur1, sur2, sur3, sur4, sur5 ( top 5 similar users who rated that movie.. )\n",
        "    \n",
        "\n",
        "\n",
        "- __Similar movies rated by this user__:\n",
        "    - smr1, smr2, smr3, smr4, smr5 ( top 5 similar movies rated by this movie.. )\n",
        "\n",
        "\n",
        "- __UAvg__ : User's Average rating\n",
        "\n",
        "\n",
        "- __MAvg__ : Average rating of this movie\n",
        "\n",
        "\n",
        "- __rating__ : Rating of this movie by this user.\n",
        "\n",
        "-----------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuEHPOC1r8dX",
        "colab_type": "text"
      },
      "source": [
        "Featurizing test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3QalNOdkX1U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get users, movies and ratings from the Sampled Test \n",
        "sample_test_users, sample_test_movies, sample_test_ratings = sparse.find(sample_test_sparse_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjEtrI2Gr-_u",
        "colab_type": "code",
        "outputId": "1c24018a-6e2d-4feb-9e7f-30933fad73ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "sample_train_averages['global']"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.581679377504138"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eybpZpi8sBZW",
        "colab_type": "code",
        "outputId": "8db7d503-08c1-4f86-89e9-5fb5630da0c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "start = datetime.now()\n",
        "\n",
        "if os.path.isfile('/content/drive/My Drive/AI projects/Netflix /reg_test.csv'):\n",
        "    print(\"It is already created...\")\n",
        "else:\n",
        "\n",
        "    print('preparing {} tuples for the dataset..\\n'.format(len(sample_test_ratings)))\n",
        "    with open('/content/drive/My Drive/AI projects/Netflix /reg_test.csv', mode='w') as reg_data_file:\n",
        "        count = 0 \n",
        "        for (user, movie, rating)  in zip(sample_test_users, sample_test_movies, sample_test_ratings):\n",
        "            st = datetime.now()\n",
        "\n",
        "        #--------------------- Ratings of \"movie\" by similar users of \"user\" ---------------------\n",
        "            #print(user, movie)\n",
        "            try:\n",
        "                # compute the similar Users of the \"user\"        \n",
        "                user_sim = cosine_similarity(sample_train_sparse_matrix[user], sample_train_sparse_matrix).ravel()\n",
        "                top_sim_users = user_sim.argsort()[::-1][1:] # we are ignoring 'The User' from its similar users.\n",
        "                # get the ratings of most similar users for this movie\n",
        "                top_ratings = sample_train_sparse_matrix[top_sim_users, movie].toarray().ravel()\n",
        "                # we will make it's length \"5\" by adding movie averages to .\n",
        "                top_sim_users_ratings = list(top_ratings[top_ratings != 0][:5])\n",
        "                top_sim_users_ratings.extend([sample_train_averages['movie'][movie]]*(5 - len(top_sim_users_ratings)))\n",
        "                # print(top_sim_users_ratings, end=\"--\")\n",
        "\n",
        "            except (IndexError, KeyError):\n",
        "                # It is a new User or new Movie or there are no ratings for given user for top similar movies...\n",
        "                ########## Cold STart Problem ##########\n",
        "                top_sim_users_ratings.extend([sample_train_averages['global']]*(5 - len(top_sim_users_ratings)))\n",
        "                #print(top_sim_users_ratings)\n",
        "            except:\n",
        "                print(user, movie)\n",
        "                # we just want KeyErrors to be resolved. Not every Exception...\n",
        "                raise\n",
        "\n",
        "\n",
        "\n",
        "            #--------------------- Ratings by \"user\"  to similar movies of \"movie\" ---------------------\n",
        "            try:\n",
        "                # compute the similar movies of the \"movie\"        \n",
        "                movie_sim = cosine_similarity(sample_train_sparse_matrix[:,movie].T, sample_train_sparse_matrix.T).ravel()\n",
        "                top_sim_movies = movie_sim.argsort()[::-1][1:] # we are ignoring 'The User' from its similar users.\n",
        "                # get the ratings of most similar movie rated by this user..\n",
        "                top_ratings = sample_train_sparse_matrix[user, top_sim_movies].toarray().ravel()\n",
        "                # we will make it's length \"5\" by adding user averages to.\n",
        "                top_sim_movies_ratings = list(top_ratings[top_ratings != 0][:5])\n",
        "                top_sim_movies_ratings.extend([sample_train_averages['user'][user]]*(5-len(top_sim_movies_ratings))) \n",
        "                #print(top_sim_movies_ratings)\n",
        "            except (IndexError, KeyError):\n",
        "                #print(top_sim_movies_ratings, end=\" : -- \")\n",
        "                top_sim_movies_ratings.extend([sample_train_averages['global']]*(5-len(top_sim_movies_ratings)))\n",
        "                #print(top_sim_movies_ratings)\n",
        "            except :\n",
        "                raise\n",
        "\n",
        "            #-----------------prepare the row to be stores in a file-----------------#\n",
        "            row = list()\n",
        "            # add usser and movie name first\n",
        "            row.append(user)\n",
        "            row.append(movie)\n",
        "            row.append(sample_train_averages['global']) # first feature\n",
        "            #print(row)\n",
        "            # next 5 features are similar_users \"movie\" ratings\n",
        "            row.extend(top_sim_users_ratings)\n",
        "            #print(row)\n",
        "            # next 5 features are \"user\" ratings for similar_movies\n",
        "            row.extend(top_sim_movies_ratings)\n",
        "            #print(row)\n",
        "            # Avg_user rating\n",
        "            try:\n",
        "                row.append(sample_train_averages['user'][user])\n",
        "            except KeyError:\n",
        "                row.append(sample_train_averages['global'])\n",
        "            except:\n",
        "                raise\n",
        "            #print(row)\n",
        "            # Avg_movie rating\n",
        "            try:\n",
        "                row.append(sample_train_averages['movie'][movie])\n",
        "            except KeyError:\n",
        "                row.append(sample_train_averages['global'])\n",
        "            except:\n",
        "                raise\n",
        "            #print(row)\n",
        "            # finalley, The actual Rating of this user-movie pair...\n",
        "            row.append(rating)\n",
        "            #print(row)\n",
        "            count = count + 1\n",
        "\n",
        "            # add rows to the file opened..\n",
        "            reg_data_file.write(','.join(map(str, row)))\n",
        "            #print(','.join(map(str, row)))\n",
        "            reg_data_file.write('\\n')        \n",
        "            if (count)%1000 == 0:\n",
        "                #print(','.join(map(str, row)))\n",
        "                print(\"Done for {} rows----- {}\".format(count, datetime.now() - start))\n",
        "    print(\"\",datetime.now() - start)  "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It is already created...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ruZfRXXsKF_",
        "colab_type": "code",
        "outputId": "f65889e0-d12c-44b3-ba4b-4cd410d96761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        }
      },
      "source": [
        "reg_test_df = pd.read_csv('/content/drive/My Drive/AI projects/Netflix /reg_test.csv', names = ['user', 'movie', 'GAvg', 'sur1', 'sur2', 'sur3', 'sur4', 'sur5',\n",
        "                                                          'smr1', 'smr2', 'smr3', 'smr4', 'smr5',\n",
        "                                                          'UAvg', 'MAvg', 'rating'], header=None)\n",
        "reg_test_df.head(4)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>808635</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>941866</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1737912</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1849204</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      user  movie      GAvg      sur1  ...      smr5      UAvg      MAvg  rating\n",
              "0   808635     71  3.581679  3.581679  ...  3.581679  3.581679  3.581679       5\n",
              "1   941866     71  3.581679  3.581679  ...  3.581679  3.581679  3.581679       4\n",
              "2  1737912     71  3.581679  3.581679  ...  3.581679  3.581679  3.581679       3\n",
              "3  1849204     71  3.581679  3.581679  ...  3.581679  3.581679  3.581679       4\n",
              "\n",
              "[4 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vWmzUruxrqx",
        "colab_type": "text"
      },
      "source": [
        "-----------------------\n",
        "\n",
        "- __GAvg__ : Average rating of all the ratings \n",
        "\n",
        "\n",
        "- __Similar users rating of this movie__:\n",
        "    - sur1, sur2, sur3, sur4, sur5 ( top 5 simiular users who rated that movie.. )\n",
        "    \n",
        "\n",
        "\n",
        "- __Similar movies rated by this user__:\n",
        "    - smr1, smr2, smr3, smr4, smr5 ( top 5 simiular movies rated by this movie.. )\n",
        "\n",
        "\n",
        "- __UAvg__ : User AVerage rating\n",
        "\n",
        "\n",
        "- __MAvg__ : Average rating of this movie\n",
        "\n",
        "\n",
        "- __rating__ : Rating of this movie by this user.\n",
        "\n",
        "-----------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrMljo5osbpi",
        "colab_type": "text"
      },
      "source": [
        "### Transforming data for Surprise models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPCWNA0EWNq_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "810ca511-19c4-4ba4-be4c-7c0e7270b5e6"
      },
      "source": [
        "!pip install scikit-surprise"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-surprise\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/da/b5700d96495fb4f092be497f02492768a3d96a3f4fa2ae7dea46d4081cfa/scikit-surprise-1.1.0.tar.gz (6.4MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5MB 6.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (0.14.1)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (1.12.0)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.0-cp36-cp36m-linux_x86_64.whl size=1673649 sha256=4ef5da6dc32280fbb4f06ae34b1ab67f863c3c0fb6278804cf73c2190fdfe5e6\n",
            "  Stored in directory: /root/.cache/pip/wheels/cc/fa/8c/16c93fccce688ae1bde7d979ff102f7bee980d9cfeb8641bcf\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise\n",
            "Successfully installed scikit-surprise-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXAxMKdOsTWD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from surprise import Reader, Dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_-uAUQ5vQYl",
        "colab_type": "text"
      },
      "source": [
        "### Transforming train data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0l0rgL1qxrq1",
        "colab_type": "text"
      },
      "source": [
        "- We can't give raw data (movie, user, rating) to train the model in Surprise library.\n",
        "\n",
        "\n",
        "- They have a saperate format for TRAIN and TEST data, which will be useful for training the models like SVD, KNNBaseLineOnly....etc..,in Surprise.\n",
        "\n",
        "\n",
        "- We can form the trainset from a file, or from a Pandas  DataFrame. \n",
        "http://surprise.readthedocs.io/en/stable/getting_started.html#load-dom-dataframe-py "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f99t0ET6se5e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# It is to specify how to read the dataframe.\n",
        "# for our dataframe, we don't have to specify anything extra..\n",
        "reader = Reader(rating_scale=(1,5))\n",
        "\n",
        "# create the traindata from the dataframe...\n",
        "train_data = Dataset.load_from_df(reg_train[['user', 'movie', 'rating']], reader)\n",
        "\n",
        "# build the trainset from traindata.., It is of dataset format from surprise library..\n",
        "trainset = train_data.build_full_trainset() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6DmLeAkvbSk",
        "colab_type": "text"
      },
      "source": [
        "### Transforming test data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwKbEi_uxrq2",
        "colab_type": "text"
      },
      "source": [
        "- Testset is just a list of (user, movie, rating) tuples. (Order in the tuple is impotant) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tpg5UsZtvXOp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3a9111ad-b1a1-4134-895e-ff8d0941e92a"
      },
      "source": [
        "testset = list(zip(reg_test_df.user.values, reg_test_df.movie.values, reg_test_df.rating.values))\n",
        "testset[:3]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(808635, 71, 5), (941866, 71, 4), (1737912, 71, 3)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSDSMO4avqCT",
        "colab_type": "text"
      },
      "source": [
        "# Applying Machine Learning models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRL_TP7axrq9",
        "colab_type": "text"
      },
      "source": [
        "-  Global dictionary that stores rmse and mape for all the models....\n",
        "\n",
        "    - It stores the metrics in a dictionary of dictionaries\n",
        "\n",
        "    > __keys__ : model names(string)\n",
        "\n",
        "    > __value__: dict(__key__ : metric, __value__ : value ) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBv9fy2ZvgZE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ac093fc1-3cad-42e0-923a-e0c31f55b8d3"
      },
      "source": [
        "models_evaluation_train = dict()\n",
        "models_evaluation_test = dict()\n",
        "\n",
        "models_evaluation_train, models_evaluation_test   "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({}, {})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cg4ii0eRxrrB",
        "colab_type": "text"
      },
      "source": [
        " > __Utility functions for running regression models__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxjaS50Vvxww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# to get rmse and mape given actual and predicted ratings..\n",
        "def get_error_metrics(y_true, y_pred):\n",
        "    rmse = np.sqrt(np.mean([ (y_true[i] - y_pred[i])**2 for i in range(len(y_pred)) ]))\n",
        "    mape = np.mean(np.abs( (y_true - y_pred)/y_true )) * 100\n",
        "    return rmse, mape\n",
        "\n",
        "###################################################################\n",
        "###################################################################\n",
        "def run_xgboost(algo,  x_train, y_train, x_test, y_test, verbose=True):\n",
        "    \"\"\"\n",
        "    It will return train_results and test_results\n",
        "    \"\"\"\n",
        "    \n",
        "    # dictionaries for storing train and test results\n",
        "    train_results = dict()\n",
        "    test_results = dict()\n",
        "    \n",
        "    \n",
        "    # fit the model\n",
        "    print('Training the model..')\n",
        "    start =datetime.now()\n",
        "    algo.fit(x_train, y_train, eval_metric = 'rmse')\n",
        "    print('Done. Time taken : {}\\n'.format(datetime.now()-start))\n",
        "    print('Done \\n')\n",
        "\n",
        "    # from the trained model, get the predictions....\n",
        "    print('Evaluating the model with TRAIN data...')\n",
        "    start =datetime.now()\n",
        "    y_train_pred = algo.predict(x_train)\n",
        "    # get the rmse and mape of train data...\n",
        "    rmse_train, mape_train = get_error_metrics(y_train.values, y_train_pred)\n",
        "    \n",
        "    # store the results in train_results dictionary..\n",
        "    train_results = {'rmse': rmse_train,\n",
        "                    'mape' : mape_train,\n",
        "                    'predictions' : y_train_pred}\n",
        "    \n",
        "    #######################################\n",
        "    # get the test data predictions and compute rmse and mape\n",
        "    print('Evaluating Test data')\n",
        "    y_test_pred = algo.predict(x_test) \n",
        "    rmse_test, mape_test = get_error_metrics(y_true=y_test.values, y_pred=y_test_pred)\n",
        "    # store them in our test results dictionary.\n",
        "    test_results = {'rmse': rmse_test,\n",
        "                    'mape' : mape_test,\n",
        "                    'predictions':y_test_pred}\n",
        "    if verbose:\n",
        "        print('\\nTEST DATA')\n",
        "        print('-'*30)\n",
        "        print('RMSE : ', rmse_test)\n",
        "        print('MAPE : ', mape_test)\n",
        "        \n",
        "    # return these train and test results...\n",
        "    return train_results, test_results\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoWkYNSBv6EM",
        "colab_type": "text"
      },
      "source": [
        "**Utility functions for Surprise modes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVgudAtjv24R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# it is just to makesure that all of our algorithms should produce same results\n",
        "# everytime they run...\n",
        "\n",
        "my_seed = 15\n",
        "random.seed(my_seed)\n",
        "np.random.seed(my_seed)\n",
        "\n",
        "##########################################################\n",
        "# get  (actual_list , predicted_list) ratings given list \n",
        "# of predictions (prediction is a class in Surprise).    \n",
        "##########################################################\n",
        "def get_ratings(predictions):\n",
        "    actual = np.array([pred.r_ui for pred in predictions])\n",
        "    pred = np.array([pred.est for pred in predictions])\n",
        "    \n",
        "    return actual, pred\n",
        "\n",
        "################################################################\n",
        "# get ''rmse'' and ''mape'' , given list of prediction objecs \n",
        "################################################################\n",
        "def get_errors(predictions, print_them=False):\n",
        "\n",
        "    actual, pred = get_ratings(predictions)\n",
        "    rmse = np.sqrt(np.mean((pred - actual)**2))\n",
        "    mape = np.mean(np.abs(pred - actual)/actual)\n",
        "\n",
        "    return rmse, mape*100\n",
        "\n",
        "##################################################################################\n",
        "# It will return predicted ratings, rmse and mape of both train and test data   #\n",
        "##################################################################################\n",
        "def run_surprise(algo, trainset, testset, verbose=True): \n",
        "    '''\n",
        "        return train_dict, test_dict\n",
        "    \n",
        "        It returns two dictionaries, one for train and the other is for test\n",
        "        Each of them have 3 key-value pairs, which specify ''rmse'', ''mape'', and ''predicted ratings''.\n",
        "    '''\n",
        "    start = datetime.now()\n",
        "    # dictionaries that stores metrics for train and test..\n",
        "    train = dict()\n",
        "    test = dict()\n",
        "    \n",
        "    # train the algorithm with the trainset\n",
        "    st = datetime.now()\n",
        "    print('Training the model...')\n",
        "    algo.fit(trainset)\n",
        "    print('Done. time taken : {} \\n'.format(datetime.now()-st))\n",
        "    \n",
        "    # ---------------- Evaluating train data--------------------#\n",
        "    st = datetime.now()\n",
        "    print('Evaluating the model with train data..')\n",
        "    # get the train predictions (list of prediction class inside Surprise)\n",
        "    train_preds = algo.test(trainset.build_testset())\n",
        "    # get predicted ratings from the train predictions..\n",
        "    train_actual_ratings, train_pred_ratings = get_ratings(train_preds)\n",
        "    # get ''rmse'' and ''mape'' from the train predictions.\n",
        "    train_rmse, train_mape = get_errors(train_preds)\n",
        "    print('time taken : {}'.format(datetime.now()-st))\n",
        "    \n",
        "    if verbose:\n",
        "        print('-'*15)\n",
        "        print('Train Data')\n",
        "        print('-'*15)\n",
        "        print(\"RMSE : {}\\n\\nMAPE : {}\\n\".format(train_rmse, train_mape))\n",
        "    \n",
        "    #store them in the train dictionary\n",
        "    if verbose:\n",
        "        print('adding train results in the dictionary..')\n",
        "    train['rmse'] = train_rmse\n",
        "    train['mape'] = train_mape\n",
        "    train['predictions'] = train_pred_ratings\n",
        "    \n",
        "    #------------ Evaluating Test data---------------#\n",
        "    st = datetime.now()\n",
        "    print('\\nEvaluating for test data...')\n",
        "    # get the predictions( list of prediction classes) of test data\n",
        "    test_preds = algo.test(testset)\n",
        "    # get the predicted ratings from the list of predictions\n",
        "    test_actual_ratings, test_pred_ratings = get_ratings(test_preds)\n",
        "    # get error metrics from the predicted and actual ratings\n",
        "    test_rmse, test_mape = get_errors(test_preds)\n",
        "    print('time taken : {}'.format(datetime.now()-st))\n",
        "    \n",
        "    if verbose:\n",
        "        print('-'*15)\n",
        "        print('Test Data')\n",
        "        print('-'*15)\n",
        "        print(\"RMSE : {}\\n\\nMAPE : {}\\n\".format(test_rmse, test_mape))\n",
        "    # store them in test dictionary\n",
        "    if verbose:\n",
        "        print('storing the test results in test dictionary...')\n",
        "    test['rmse'] = test_rmse\n",
        "    test['mape'] = test_mape\n",
        "    test['predictions'] = test_pred_ratings\n",
        "    \n",
        "    print('\\n'+'-'*45)\n",
        "    print('Total time taken to run this algorithm :', datetime.now() - start)\n",
        "    \n",
        "    # return two dictionaries train and test\n",
        "    return train, test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaGyqxYDwD0T",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost with initial 13 features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbX4URpgwATp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XB0sUAQwGpX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "393613aa-0e69-4fb2-efb4-0bfc1fd11b43"
      },
      "source": [
        "# prepare Train data\n",
        "x_train = reg_train.drop(['user','movie','rating'], axis=1)\n",
        "y_train = reg_train['rating']\n",
        "\n",
        "# Prepare Test data\n",
        "x_test = reg_test_df.drop(['user','movie','rating'], axis=1)\n",
        "y_test = reg_test_df['rating']\n",
        "\n",
        "# initialize Our first XGBoost model...\n",
        "first_xgb = xgb.XGBRegressor(silent=False, n_jobs=13, random_state=15, n_estimators=100)\n",
        "train_results, test_results = run_xgboost(first_xgb, x_train, y_train, x_test, y_test)\n",
        "\n",
        "# store the results in models_evaluations dictionaries\n",
        "models_evaluation_train['first_algo'] = train_results\n",
        "models_evaluation_test['first_algo'] = test_results\n",
        "\n",
        "xgb.plot_importance(first_xgb)\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model..\n",
            "[13:39:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done. Time taken : 0:00:03.855607\n",
            "\n",
            "Done \n",
            "\n",
            "Evaluating the model with TRAIN data...\n",
            "Evaluating Test data\n",
            "\n",
            "TEST DATA\n",
            "------------------------------\n",
            "RMSE :  1.076373581778953\n",
            "MAPE :  34.48223172520999\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": "markdown",
      "metadata": {
        "id": "ElS6zsIUwOio",
        "colab_type": "text"
      },
      "source": [
        "### Suprise BaselineModel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2aW4G-9wIUo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from surprise import BaselineOnly "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9FkmEuTxrrR",
        "colab_type": "text"
      },
      "source": [
        "**Predicted_rating : ( baseline prediction )**\n",
        "\n",
        "    -  http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly \n",
        " >$   \\large {\\hat{r}_{ui} = b_{ui} =\\mu + b_u + b_i} $\n",
        "\n",
        "\n",
        "- $\\pmb \\mu $ : Average of all trainings in training data.\n",
        "- $\\pmb b_u$ : User bias\n",
        "- $\\pmb b_i$ : Item bias (movie biases) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITW64wCDxrrR",
        "colab_type": "text"
      },
      "source": [
        "**Optimization function ( Least Squares Problem )**\n",
        "\n",
        "    - http://surprise.readthedocs.io/en/stable/prediction_algorithms.html#baselines-estimates-configuration \n",
        "\n",
        "> $ \\large \\sum_{r_{ui} \\in R_{train}} \\left(r_{ui} - (\\mu + b_u + b_i)\\right)^2 +\n",
        "\\lambda \\left(b_u^2 + b_i^2 \\right).\\text {        [mimimize } {b_u, b_i]}$ "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gD_jizZwwRRt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "509f6274-51db-4485-cfba-3850a63a2716"
      },
      "source": [
        "\n",
        "# options are to specify.., how to compute those user and item biases\n",
        "bsl_options = {'method': 'sgd',\n",
        "               'learning_rate': .001\n",
        "               }\n",
        "bsl_algo = BaselineOnly(bsl_options=bsl_options)\n",
        "# run this algorithm.., It will return the train and test results..\n",
        "bsl_train_results, bsl_test_results = run_surprise(bsl_algo, trainset, testset, verbose=True)\n",
        "\n",
        "\n",
        "# Just store these error metrics in our models_evaluation datastructure\n",
        "models_evaluation_train['bsl_algo'] = bsl_train_results \n",
        "models_evaluation_test['bsl_algo'] = bsl_test_results"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model...\n",
            "Estimating biases using sgd...\n",
            "Done. time taken : 0:00:00.903268 \n",
            "\n",
            "Evaluating the model with train data..\n",
            "time taken : 0:00:01.205679\n",
            "---------------\n",
            "Train Data\n",
            "---------------\n",
            "RMSE : 0.9347153928678286\n",
            "\n",
            "MAPE : 29.389572652358183\n",
            "\n",
            "adding train results in the dictionary..\n",
            "\n",
            "Evaluating for test data...\n",
            "time taken : 0:00:00.059761\n",
            "---------------\n",
            "Test Data\n",
            "---------------\n",
            "RMSE : 1.0730330260516174\n",
            "\n",
            "MAPE : 35.04995544572911\n",
            "\n",
            "storing the test results in test dictionary...\n",
            "\n",
            "---------------------------------------------\n",
            "Total time taken to run this algorithm : 0:00:02.170411\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Q0F7lzWw77O",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost with initial 13 features + Surprise Baseline predictor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntpJ2KGowfvZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "dcfe9890-870b-4af5-9cb2-992108eb5f99"
      },
      "source": [
        "# add our baseline_predicted value as our feature..\n",
        "reg_train['bslpr'] = models_evaluation_train['bsl_algo']['predictions']\n",
        "reg_train.head(2) "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>53406</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.370370</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>4</td>\n",
              "      <td>3.898982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99540</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>3</td>\n",
              "      <td>3.371403</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user  movie      GAvg  sur1  ...      UAvg      MAvg  rating     bslpr\n",
              "0  53406     33  3.581679   4.0  ...  3.370370  4.092437       4  3.898982\n",
              "1  99540     33  3.581679   5.0  ...  3.555556  4.092437       3  3.371403\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNf95oEow-hC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "017f3557-7906-44f7-8f53-03a235129302"
      },
      "source": [
        "# add that baseline predicted ratings with Surprise to the test data as well\n",
        "reg_test_df['bslpr']  = models_evaluation_test['bsl_algo']['predictions']\n",
        "\n",
        "reg_test_df.head(2)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>808635</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5</td>\n",
              "      <td>3.581679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>941866</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4</td>\n",
              "      <td>3.581679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user  movie      GAvg      sur1  ...      UAvg      MAvg  rating     bslpr\n",
              "0  808635     71  3.581679  3.581679  ...  3.581679  3.581679       5  3.581679\n",
              "1  941866     71  3.581679  3.581679  ...  3.581679  3.581679       4  3.581679\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtehoZT1xA7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "3dcaaa3c-9501-48d8-b504-e96a4a426969"
      },
      "source": [
        "# prepare train data\n",
        "x_train = reg_train.drop(['user', 'movie','rating'], axis=1)\n",
        "y_train = reg_train['rating']\n",
        "\n",
        "# Prepare Test data\n",
        "x_test = reg_test_df.drop(['user','movie','rating'], axis=1)\n",
        "y_test = reg_test_df['rating']\n",
        "\n",
        "# initialize Our first XGBoost model...\n",
        "xgb_bsl = xgb.XGBRegressor(silent=False, n_jobs=13, random_state=15, n_estimators=100)\n",
        "train_results, test_results = run_xgboost(xgb_bsl, x_train, y_train, x_test, y_test)\n",
        "\n",
        "# store the results in models_evaluations dictionaries\n",
        "models_evaluation_train['xgb_bsl'] = train_results\n",
        "models_evaluation_test['xgb_bsl'] = test_results\n",
        "\n",
        "xgb.plot_importance(xgb_bsl)\n",
        "plt.show()\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model..\n",
            "[13:43:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done. Time taken : 0:00:04.511990\n",
            "\n",
            "Done \n",
            "\n",
            "Evaluating the model with TRAIN data...\n",
            "Evaluating Test data\n",
            "\n",
            "TEST DATA\n",
            "------------------------------\n",
            "RMSE :  1.0765603714651855\n",
            "MAPE :  34.4648051883444\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": "markdown",
      "metadata": {
        "id": "vBv-aQE-xF6P",
        "colab_type": "text"
      },
      "source": [
        "### Surprise KNNBaseline predictor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuu4i_0WxC4s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from surprise import KNNBaseline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFn0o57Lxrrk",
        "colab_type": "text"
      },
      "source": [
        "- __predicted Rating__ : ( ___ based on User-User similarity ___ )\n",
        "\n",
        "\\begin{align} \\hat{r}_{ui} = b_{ui} + \\frac{ \\sum\\limits_{v \\in N^k_i(u)}\n",
        "\\text{sim}(u, v) \\cdot (r_{vi} - b_{vi})} {\\sum\\limits_{v \\in\n",
        "N^k_i(u)} \\text{sim}(u, v)} \\end{align}\n",
        "\n",
        "- $\\pmb{b_{ui}}$ -  _Baseline prediction_ of (user,movie) rating\n",
        "\n",
        "- $ \\pmb {N_i^k (u)}$ - Set of __K similar__ users (neighbours) of __user (u)__ who rated __movie(i)__  \n",
        "\n",
        "- _sim (u, v)_ - __Similarity__ between users __u and v__  \n",
        "    - Generally, it will be cosine similarity or Pearson correlation coefficient. \n",
        "    - But we use __shrunk Pearson-baseline correlation coefficient__, which is based on the pearsonBaseline similarity ( we take base line predictions instead of mean rating of user/item)\n",
        "       "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1QCM5rlkxrrl",
        "colab_type": "text"
      },
      "source": [
        "**Predicted rating** ( based on Item Item similarity ):\n",
        " \\begin{align} \\hat{r}_{ui} = b_{ui} + \\frac{ \\sum\\limits_{j \\in N^k_u(i)}\\text{sim}(i, j) \\cdot (r_{uj} - b_{uj})} {\\sum\\limits_{j \\in N^k_u(j)} \\text{sim}(i, j)} \\end{align}\n",
        "\n",
        "**Notations follows same as above (user user based predicted rating )**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLnoJndYxjr7",
        "colab_type": "text"
      },
      "source": [
        "### Surprise KNNBaseline with user user similarities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKaO_P2qxIoo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "b54f5488-c98d-48b8-a312-1ab09429ad9e"
      },
      "source": [
        "# we specify , how to compute similarities and what to consider with sim_options to our algorithm\n",
        "sim_options = {'user_based' : True,\n",
        "               'name': 'pearson_baseline',\n",
        "               'shrinkage': 100,\n",
        "               'min_support': 2\n",
        "              } \n",
        "# we keep other parameters like regularization parameter and learning_rate as default values.\n",
        "bsl_options = {'method': 'sgd'} \n",
        "\n",
        "knn_bsl_u = KNNBaseline(k=40, sim_options = sim_options, bsl_options = bsl_options)\n",
        "knn_bsl_u_train_results, knn_bsl_u_test_results = run_surprise(knn_bsl_u, trainset, testset, verbose=True)\n",
        "\n",
        "# Just store these error metrics in our models_evaluation datastructure\n",
        "models_evaluation_train['knn_bsl_u'] = knn_bsl_u_train_results \n",
        "models_evaluation_test['knn_bsl_u'] = knn_bsl_u_test_results\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model...\n",
            "Estimating biases using sgd...\n",
            "Computing the pearson_baseline similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Done. time taken : 0:00:34.191970 \n",
            "\n",
            "Evaluating the model with train data..\n",
            "time taken : 0:01:54.739581\n",
            "---------------\n",
            "Train Data\n",
            "---------------\n",
            "RMSE : 0.33642097416508826\n",
            "\n",
            "MAPE : 9.145093375416348\n",
            "\n",
            "adding train results in the dictionary..\n",
            "\n",
            "Evaluating for test data...\n",
            "time taken : 0:00:00.070678\n",
            "---------------\n",
            "Test Data\n",
            "---------------\n",
            "RMSE : 1.0726493739667242\n",
            "\n",
            "MAPE : 35.02094499698424\n",
            "\n",
            "storing the test results in test dictionary...\n",
            "\n",
            "---------------------------------------------\n",
            "Total time taken to run this algorithm : 0:02:29.003146\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJJIPnj8xwvj",
        "colab_type": "text"
      },
      "source": [
        "### Surprise KNNBaseline with movie movie similarities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_8RGtSSxoNg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "eed68696-1152-4ef4-808f-3b631efe9192"
      },
      "source": [
        "# we specify , how to compute similarities and what to consider with sim_options to our algorithm\n",
        "\n",
        "# 'user_based' : Fals => this considers the similarities of movies instead of users\n",
        "\n",
        "sim_options = {'user_based' : False,\n",
        "               'name': 'pearson_baseline',\n",
        "               'shrinkage': 100,\n",
        "               'min_support': 2\n",
        "              } \n",
        "# we keep other parameters like regularization parameter and learning_rate as default values.\n",
        "bsl_options = {'method': 'sgd'}\n",
        "\n",
        "\n",
        "knn_bsl_m = KNNBaseline(k=40, sim_options = sim_options, bsl_options = bsl_options)\n",
        "\n",
        "knn_bsl_m_train_results, knn_bsl_m_test_results = run_surprise(knn_bsl_m, trainset, testset, verbose=True)\n",
        "\n",
        "# Just store these error metrics in our models_evaluation datastructure\n",
        "models_evaluation_train['knn_bsl_m'] = knn_bsl_m_train_results \n",
        "models_evaluation_test['knn_bsl_m'] = knn_bsl_m_test_results\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model...\n",
            "Estimating biases using sgd...\n",
            "Computing the pearson_baseline similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Done. time taken : 0:00:01.170437 \n",
            "\n",
            "Evaluating the model with train data..\n",
            "time taken : 0:00:09.423642\n",
            "---------------\n",
            "Train Data\n",
            "---------------\n",
            "RMSE : 0.32584796251610554\n",
            "\n",
            "MAPE : 8.447062581998374\n",
            "\n",
            "adding train results in the dictionary..\n",
            "\n",
            "Evaluating for test data...\n",
            "time taken : 0:00:00.067880\n",
            "---------------\n",
            "Test Data\n",
            "---------------\n",
            "RMSE : 1.072758832653683\n",
            "\n",
            "MAPE : 35.02269653015042\n",
            "\n",
            "storing the test results in test dictionary...\n",
            "\n",
            "---------------------------------------------\n",
            "Total time taken to run this algorithm : 0:00:10.663206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5sEnlX6zf1l",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost with initial 13 features + Surprise Baseline predictor + KNNBaseline predictor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-S5MhTjxrrr",
        "colab_type": "text"
      },
      "source": [
        "- - - First we will run XGBoost with predictions from both KNN's ( that uses User\\_User and Item\\_Item similarities along with our previous features.\n",
        "\n",
        " \n",
        "- - - Then we will run XGBoost with just predictions form both knn models and preditions from our baseline model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUp8eigUycct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "221334e9-3c13-4230-9e5a-a015f68ff430"
      },
      "source": [
        "# add the predicted values from both knns to this dataframe\n",
        "reg_train['knn_bsl_u'] = models_evaluation_train['knn_bsl_u']['predictions']\n",
        "reg_train['knn_bsl_m'] = models_evaluation_train['knn_bsl_m']['predictions']\n",
        "\n",
        "reg_train.head(2)"
      ],
      "execution_count": 43,
      "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 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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "      <th>knn_bsl_u</th>\n",
              "      <th>knn_bsl_m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>4.092437</td>\n",
              "      <td>4</td>\n",
              "      <td>3.898982</td>\n",
              "      <td>3.93002</td>\n",
              "      <td>3.867958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99540</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>3</td>\n",
              "      <td>3.371403</td>\n",
              "      <td>3.17733</td>\n",
              "      <td>3.076302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user  movie      GAvg  sur1  ...  rating     bslpr  knn_bsl_u  knn_bsl_m\n",
              "0  53406     33  3.581679   4.0  ...       4  3.898982    3.93002   3.867958\n",
              "1  99540     33  3.581679   5.0  ...       3  3.371403    3.17733   3.076302\n",
              "\n",
              "[2 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj3zwPCOzl1M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "2ac78853-32f1-44f0-8c46-9a4270cf3ae6"
      },
      "source": [
        "reg_test_df['knn_bsl_u'] = models_evaluation_test['knn_bsl_u']['predictions']\n",
        "reg_test_df['knn_bsl_m'] = models_evaluation_test['knn_bsl_m']['predictions']\n",
        "\n",
        "reg_test_df.head(2)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "      <th>knn_bsl_u</th>\n",
              "      <th>knn_bsl_m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>3.581679</td>\n",
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              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>941866</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user  movie      GAvg      sur1  ...  rating     bslpr  knn_bsl_u  knn_bsl_m\n",
              "0  808635     71  3.581679  3.581679  ...       5  3.581679   3.581679   3.581679\n",
              "1  941866     71  3.581679  3.581679  ...       4  3.581679   3.581679   3.581679\n",
              "\n",
              "[2 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cfSMIOpzoGS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "35f1bf0a-f7ec-4539-e8b7-8bae419540ca"
      },
      "source": [
        "# prepare the train data....\n",
        "x_train = reg_train.drop(['user', 'movie', 'rating'], axis=1)\n",
        "y_train = reg_train['rating']\n",
        "\n",
        "# prepare the train data....\n",
        "x_test = reg_test_df.drop(['user','movie','rating'], axis=1)\n",
        "y_test = reg_test_df['rating']\n",
        "\n",
        "# declare the model\n",
        "xgb_knn_bsl = xgb.XGBRegressor(n_jobs=10, random_state=15)\n",
        "train_results, test_results = run_xgboost(xgb_knn_bsl, x_train, y_train, x_test, y_test)\n",
        "\n",
        "# store the results in models_evaluations dictionaries\n",
        "models_evaluation_train['xgb_knn_bsl'] = train_results\n",
        "models_evaluation_test['xgb_knn_bsl'] = test_results\n",
        "\n",
        "\n",
        "xgb.plot_importance(xgb_knn_bsl)\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model..\n",
            "[13:55:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done. Time taken : 0:00:05.338920\n",
            "\n",
            "Done \n",
            "\n",
            "Evaluating the model with TRAIN data...\n",
            "Evaluating Test data\n",
            "\n",
            "TEST DATA\n",
            "------------------------------\n",
            "RMSE :  1.0767793575625662\n",
            "MAPE :  34.44745951378593\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": "markdown",
      "metadata": {
        "id": "Qtnpv7JOzuT6",
        "colab_type": "text"
      },
      "source": [
        "## Matrix Factorization Techniques"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhRpaUcZzxne",
        "colab_type": "text"
      },
      "source": [
        "### SVD Matrix Factorization User Movie intractions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcwQed5kzqM2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from surprise import SVD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQia_R3Lxrr3",
        "colab_type": "text"
      },
      "source": [
        "- __ Predicted Rating : __\n",
        "    - \n",
        "    - $ \\large  \\hat r_{ui} = \\mu + b_u + b_i + q_i^Tp_u $\n",
        "    \n",
        "        - $\\pmb q_i$ - Representation of item(movie) in latent factor space\n",
        "        \n",
        "        - $\\pmb p_u$ - Representation of user in new latent factor space\n",
        "        \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4djzeu0xrr3",
        "colab_type": "text"
      },
      "source": [
        "- __Optimization problem with user item interactions and regularization (to avoid overfitting)__\n",
        "    - \n",
        "    - $\\large \\sum_{r_{ui} \\in R_{train}} \\left(r_{ui} - \\hat{r}_{ui} \\right)^2 +\n",
        "\\lambda\\left(b_i^2 + b_u^2 + ||q_i||^2 + ||p_u||^2\\right) $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yd0eI1hOz0zL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "outputId": "8dc085a3-9ea3-499a-c87f-42676122908c"
      },
      "source": [
        "# initiallize the model\n",
        "svd = SVD(n_factors=100, biased=True, random_state=15, verbose=True)\n",
        "svd_train_results, svd_test_results = run_surprise(svd, trainset, testset, verbose=True)\n",
        "\n",
        "# Just store these error metrics in our models_evaluation datastructure\n",
        "models_evaluation_train['svd'] = svd_train_results \n",
        "models_evaluation_test['svd'] = svd_test_results"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model...\n",
            "Processing epoch 0\n",
            "Processing epoch 1\n",
            "Processing epoch 2\n",
            "Processing epoch 3\n",
            "Processing epoch 4\n",
            "Processing epoch 5\n",
            "Processing epoch 6\n",
            "Processing epoch 7\n",
            "Processing epoch 8\n",
            "Processing epoch 9\n",
            "Processing epoch 10\n",
            "Processing epoch 11\n",
            "Processing epoch 12\n",
            "Processing epoch 13\n",
            "Processing epoch 14\n",
            "Processing epoch 15\n",
            "Processing epoch 16\n",
            "Processing epoch 17\n",
            "Processing epoch 18\n",
            "Processing epoch 19\n",
            "Done. time taken : 0:00:07.737045 \n",
            "\n",
            "Evaluating the model with train data..\n",
            "time taken : 0:00:01.298166\n",
            "---------------\n",
            "Train Data\n",
            "---------------\n",
            "RMSE : 0.6574721240954099\n",
            "\n",
            "MAPE : 19.704901088660474\n",
            "\n",
            "adding train results in the dictionary..\n",
            "\n",
            "Evaluating for test data...\n",
            "time taken : 0:00:00.062797\n",
            "---------------\n",
            "Test Data\n",
            "---------------\n",
            "RMSE : 1.0726046873826458\n",
            "\n",
            "MAPE : 35.01953535988152\n",
            "\n",
            "storing the test results in test dictionary...\n",
            "\n",
            "---------------------------------------------\n",
            "Total time taken to run this algorithm : 0:00:09.099265\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okyPiS6Rz_Qe",
        "colab_type": "text"
      },
      "source": [
        "### SVD Matrix Factorization with implicit feedback from user ( user rated movies )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqs5BidYz8CI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from surprise import SVDpp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbgkdFWUxrr-",
        "colab_type": "text"
      },
      "source": [
        "- __ Predicted Rating : __\n",
        "    - \n",
        "    - $ \\large \\hat{r}_{ui} = \\mu + b_u + b_i + q_i^T\\left(p_u +\n",
        "    |I_u|^{-\\frac{1}{2}} \\sum_{j \\in I_u}y_j\\right) $ "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACwlEGXixrr-",
        "colab_type": "text"
      },
      "source": [
        " - $ \\pmb{I_u}$ --- the set of all items rated by user u\n",
        "\n",
        "- $\\pmb{y_j}$ --- Our new set of item factors that capture implicit ratings.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCAxmVdfxrr_",
        "colab_type": "text"
      },
      "source": [
        "- __Optimization problem with user item interactions and regularization (to avoid overfitting)__\n",
        "    - \n",
        "    - $ \\large \\sum_{r_{ui} \\in R_{train}} \\left(r_{ui} - \\hat{r}_{ui} \\right)^2 +\n",
        "\\lambda\\left(b_i^2 + b_u^2 + ||q_i||^2 + ||p_u||^2 + ||y_j||^2\\right) $ "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VrayPYm0Cr1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "outputId": "af8826c6-805c-4e45-fadd-392ddf95ef97"
      },
      "source": [
        "# initiallize the model\n",
        "svdpp = SVDpp(n_factors=50, random_state=15, verbose=True)\n",
        "svdpp_train_results, svdpp_test_results = run_surprise(svdpp, trainset, testset, verbose=True)\n",
        "\n",
        "# Just store these error metrics in our models_evaluation datastructure\n",
        "models_evaluation_train['svdpp'] = svdpp_train_results \n",
        "models_evaluation_test['svdpp'] = svdpp_test_results\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model...\n",
            " processing epoch 0\n",
            " processing epoch 1\n",
            " processing epoch 2\n",
            " processing epoch 3\n",
            " processing epoch 4\n",
            " processing epoch 5\n",
            " processing epoch 6\n",
            " processing epoch 7\n",
            " processing epoch 8\n",
            " processing epoch 9\n",
            " processing epoch 10\n",
            " processing epoch 11\n",
            " processing epoch 12\n",
            " processing epoch 13\n",
            " processing epoch 14\n",
            " processing epoch 15\n",
            " processing epoch 16\n",
            " processing epoch 17\n",
            " processing epoch 18\n",
            " processing epoch 19\n",
            "Done. time taken : 0:02:06.838351 \n",
            "\n",
            "Evaluating the model with train data..\n",
            "time taken : 0:00:06.475928\n",
            "---------------\n",
            "Train Data\n",
            "---------------\n",
            "RMSE : 0.6032438403305899\n",
            "\n",
            "MAPE : 17.49285063490268\n",
            "\n",
            "adding train results in the dictionary..\n",
            "\n",
            "Evaluating for test data...\n",
            "time taken : 0:00:00.062488\n",
            "---------------\n",
            "Test Data\n",
            "---------------\n",
            "RMSE : 1.0728491944183447\n",
            "\n",
            "MAPE : 35.03817913919887\n",
            "\n",
            "storing the test results in test dictionary...\n",
            "\n",
            "---------------------------------------------\n",
            "Total time taken to run this algorithm : 0:02:13.378147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEfM1xkb0O0S",
        "colab_type": "text"
      },
      "source": [
        "## XgBoost with 13 features + Surprise Baseline + Surprise KNNbaseline + MF Techniques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYvpsb5q0KxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "7c2e6685-994b-46e5-9c6c-7ba972c6ab9b"
      },
      "source": [
        "# add the predicted values from both knns to this dataframe\n",
        "reg_train['svd'] = models_evaluation_train['svd']['predictions']\n",
        "reg_train['svdpp'] = models_evaluation_train['svdpp']['predictions']\n",
        "\n",
        "reg_train.head(2) "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "      <th>knn_bsl_u</th>\n",
              "      <th>knn_bsl_m</th>\n",
              "      <th>svd</th>\n",
              "      <th>svdpp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>53406</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.370370</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>4</td>\n",
              "      <td>3.898982</td>\n",
              "      <td>3.93002</td>\n",
              "      <td>3.867958</td>\n",
              "      <td>4.303764</td>\n",
              "      <td>3.621664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99540</td>\n",
              "      <td>33</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.092437</td>\n",
              "      <td>3</td>\n",
              "      <td>3.371403</td>\n",
              "      <td>3.17733</td>\n",
              "      <td>3.076302</td>\n",
              "      <td>3.278967</td>\n",
              "      <td>3.538247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user  movie      GAvg  sur1  ...  knn_bsl_u  knn_bsl_m       svd     svdpp\n",
              "0  53406     33  3.581679   4.0  ...    3.93002   3.867958  4.303764  3.621664\n",
              "1  99540     33  3.581679   5.0  ...    3.17733   3.076302  3.278967  3.538247\n",
              "\n",
              "[2 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEM6RRiC0R33",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "5aa8d891-5a54-47f4-eb98-12ae165b043c"
      },
      "source": [
        "reg_test_df['svd'] = models_evaluation_test['svd']['predictions']\n",
        "reg_test_df['svdpp'] = models_evaluation_test['svdpp']['predictions']\n",
        "\n",
        "reg_test_df.head(2) "
      ],
      "execution_count": 51,
      "outputs": [
        {
          "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>user</th>\n",
              "      <th>movie</th>\n",
              "      <th>GAvg</th>\n",
              "      <th>sur1</th>\n",
              "      <th>sur2</th>\n",
              "      <th>sur3</th>\n",
              "      <th>sur4</th>\n",
              "      <th>sur5</th>\n",
              "      <th>smr1</th>\n",
              "      <th>smr2</th>\n",
              "      <th>smr3</th>\n",
              "      <th>smr4</th>\n",
              "      <th>smr5</th>\n",
              "      <th>UAvg</th>\n",
              "      <th>MAvg</th>\n",
              "      <th>rating</th>\n",
              "      <th>bslpr</th>\n",
              "      <th>knn_bsl_u</th>\n",
              "      <th>knn_bsl_m</th>\n",
              "      <th>svd</th>\n",
              "      <th>svdpp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>808635</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>5</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>941866</td>\n",
              "      <td>71</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>4</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "      <td>3.581679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user  movie      GAvg      sur1  ...  knn_bsl_u  knn_bsl_m       svd     svdpp\n",
              "0  808635     71  3.581679  3.581679  ...   3.581679   3.581679  3.581679  3.581679\n",
              "1  941866     71  3.581679  3.581679  ...   3.581679   3.581679  3.581679  3.581679\n",
              "\n",
              "[2 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh31EuEk0UXJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "ecdaccbe-16ca-42aa-9b20-6a85556852c0"
      },
      "source": [
        "# prepare x_train and y_train\n",
        "x_train = reg_train.drop(['user', 'movie', 'rating',], axis=1)\n",
        "y_train = reg_train['rating']\n",
        "\n",
        "# prepare test data\n",
        "x_test = reg_test_df.drop(['user', 'movie', 'rating'], axis=1)\n",
        "y_test = reg_test_df['rating']\n",
        "\n",
        "\n",
        "\n",
        "xgb_final = xgb.XGBRegressor(n_jobs=10, random_state=15)\n",
        "train_results, test_results = run_xgboost(xgb_final, x_train, y_train, x_test, y_test)\n",
        "\n",
        "# store the results in models_evaluations dictionaries\n",
        "models_evaluation_train['xgb_final'] = train_results\n",
        "models_evaluation_test['xgb_final'] = test_results\n",
        "\n",
        "\n",
        "xgb.plot_importance(xgb_final)\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model..\n",
            "[13:59:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done. Time taken : 0:00:05.821689\n",
            "\n",
            "Done \n",
            "\n",
            "Evaluating the model with TRAIN data...\n",
            "Evaluating Test data\n",
            "\n",
            "TEST DATA\n",
            "------------------------------\n",
            "RMSE :  1.0769599573828592\n",
            "MAPE :  34.431788329400995\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": "markdown",
      "metadata": {
        "id": "TkGefU1P0ZLc",
        "colab_type": "text"
      },
      "source": [
        "### XgBoost with Surprise Baseline + Surprise KNNbaseline + MF Techniques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qISxAop00Wh8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "5936e981-c052-4a94-89e7-0cc682a3ef7e"
      },
      "source": [
        "# prepare train data\n",
        "x_train = reg_train[['knn_bsl_u', 'knn_bsl_m', 'svd', 'svdpp']]\n",
        "y_train = reg_train['rating']\n",
        "\n",
        "# test data\n",
        "x_test = reg_test_df[['knn_bsl_u', 'knn_bsl_m', 'svd', 'svdpp']]\n",
        "y_test = reg_test_df['rating']\n",
        "\n",
        "\n",
        "xgb_all_models = xgb.XGBRegressor(n_jobs=10, random_state=15)\n",
        "train_results, test_results = run_xgboost(xgb_all_models, x_train, y_train, x_test, y_test)\n",
        "\n",
        "# store the results in models_evaluations dictionaries\n",
        "models_evaluation_train['xgb_all_models'] = train_results\n",
        "models_evaluation_test['xgb_all_models'] = test_results\n",
        "\n",
        "xgb.plot_importance(xgb_all_models)\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training the model..\n",
            "[13:59:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Done. Time taken : 0:00:03.649248\n",
            "\n",
            "Done \n",
            "\n",
            "Evaluating the model with TRAIN data...\n",
            "Evaluating Test data\n",
            "\n",
            "TEST DATA\n",
            "------------------------------\n",
            "RMSE :  1.0753047860953797\n",
            "MAPE :  35.07058962951319\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": "markdown",
      "metadata": {
        "id": "OQd-k3Ry0fvm",
        "colab_type": "text"
      },
      "source": [
        "# Comparision between all models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkctU5AV0c3N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "16f7f253-eb26-47ea-896a-d95bce56d08a"
      },
      "source": [
        "# Saving our TEST_RESULTS into a dataframe so that you don't have to run it again\n",
        "pd.DataFrame(models_evaluation_test).to_csv('/content/drive/My Drive/AI projects/Netflix /small_sample_results.csv')\n",
        "models = pd.read_csv('/content/drive/My Drive/AI projects/Netflix /small_sample_results.csv', index_col=0)\n",
        "models.loc['rmse'].sort_values()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "svd               1.0726046873826458\n",
              "knn_bsl_u         1.0726493739667242\n",
              "knn_bsl_m          1.072758832653683\n",
              "svdpp             1.0728491944183447\n",
              "bsl_algo          1.0730330260516174\n",
              "xgb_all_models    1.0753047860953797\n",
              "first_algo         1.076373581778953\n",
              "xgb_bsl           1.0765603714651855\n",
              "xgb_knn_bsl       1.0767793575625662\n",
              "xgb_final         1.0769599573828592\n",
              "Name: rmse, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKNG_k700wc6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}