3_training_pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tk_T-ncusxnS"
      },
      "source": [
        "## Install hopsworks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5ZN4YOras7S"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "EQx_j7AdEPwl"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/martinbravodiaz/miniconda3/envs/imdb/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import requests\n",
        "\n",
        "from datetime import datetime, timedelta\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Models\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "import hopsworks\n",
        "import json\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mkAiccOQaxza"
      },
      "source": [
        "## Read data from Hopsworks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTkR9ZR4porK",
        "outputId": "318f008f-cf42-4b0c-f246-a89d9a464d59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2025-01-08 10:39:40,737 INFO: Initializing external client\n",
            "2025-01-08 10:39:40,738 INFO: Base URL: https://c.app.hopsworks.ai:443\n",
            "2025-01-08 10:39:42,338 INFO: Python Engine initialized.\n",
            "\n",
            "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/1169559\n"
          ]
        }
      ],
      "source": [
        "project = hopsworks.login(project=\"dbillLab1\")\n",
        "fs = project.get_feature_store()\n",
        "\n",
        "movie_fg = fs.get_feature_group(\n",
        "    name='tmdb_movies',\n",
        "    version=2,\n",
        ")\n",
        "\n",
        "genres_fg= fs.get_feature_group(\n",
        "    name='tmdb_movie_genres',\n",
        "    version=1,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished: Reading data from Hopsworks, using Hopsworks Feature Query Service (19.13s) \n",
            "Finished: Reading data from Hopsworks, using Hopsworks Feature Query Service (3.49s) \n"
          ]
        },
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>release_year</th>\n",
              "      <th>runtime</th>\n",
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              "      <th>original_language</th>\n",
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              "      <th>music</th>\n",
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              "      <th>romance</th>\n",
              "      <th>science_fiction</th>\n",
              "      <th>tv_movie</th>\n",
              "      <th>thriller</th>\n",
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              "      <th>western</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Moscow Heat</td>\n",
              "      <td>2004</td>\n",
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              "      <td>A Killing Affair</td>\n",
              "      <td>1977</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>6.1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How to Change the World</td>\n",
              "      <td>2014</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
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              "                             title  release_year  runtime  budget  \\\n",
              "0                      Moscow Heat          2004     89.0     0.0   \n",
              "1                 A Killing Affair          1977    100.0     0.0   \n",
              "2                       Top Secret          1952     93.0     0.0   \n",
              "3          How to Change the World          2014    110.0     0.0   \n",
              "4  The Man Who Could Work Miracles          1936     82.0     0.0   \n",
              "\n",
              "   original_language  imdb_rating  first_company  first_producer  first_actor  \\\n",
              "0                 14          2.5             36             387         9712   \n",
              "1                 14          6.1             36            5492        20159   \n",
              "2                 14          6.3             36            8387        16839   \n",
              "3                 14          7.5             36            1547        17375   \n",
              "4                 14          6.9             26             385         6987   \n",
              "\n",
              "       production_countries  ...  history  horror music  mystery  romance  \\\n",
              "0                    Russia  ...        0       0     0        0        0   \n",
              "1  United States of America  ...        0       0     0        0        0   \n",
              "2            United Kingdom  ...        0       0     0        0        0   \n",
              "3    United Kingdom, Canada  ...        1       0     0        0        0   \n",
              "4            United Kingdom  ...        0       0     0        0        0   \n",
              "\n",
              "   science_fiction  tv_movie  thriller  war  western  \n",
              "0                0         0         1    0        0  \n",
              "1                0         1         0    0        0  \n",
              "2                0         0         0    0        0  \n",
              "3                0         0         0    0        0  \n",
              "4                0         0         0    0        0  \n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#TA BORT CELL\n",
        "movie_df=movie_fg.read()\n",
        "genres_df=genres_fg.read()\n",
        "\n",
        "selected_columns = ['title','release_year', 'runtime', 'budget',\n",
        "                                     'original_language', 'imdb_rating', 'first_company','first_producer', 'first_actor',\n",
        "                                     'production_countries', 'imdb_votes', 'id']\n",
        "movie_df = movie_df[selected_columns]\n",
        "\n",
        "merged_df = pd.merge(movie_df, genres_df, on='id', how='inner')\n",
        "\n",
        "merged_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished: Reading data from Hopsworks, using Hopsworks Feature Query Service (5.21s) \n"
          ]
        },
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              "      <td>2003</td>\n",
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              "      <td>Five</td>\n",
              "      <td>1951</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The Manitou</td>\n",
              "      <td>1978</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Cold Feet</td>\n",
              "      <td>1989</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>4.8</td>\n",
              "      <td>36</td>\n",
              "      <td>1936</td>\n",
              "      <td>2138</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           title  release_year  runtime  \\\n",
              "0                                    Moscow Heat          2004     89.0   \n",
              "1                               A Killing Affair          1977    100.0   \n",
              "2                                     Top Secret          1952     93.0   \n",
              "3                The Man Who Could Work Miracles          1936     82.0   \n",
              "4                                      Them Who?          2015     95.0   \n",
              "5                                      She Freak          1967     83.0   \n",
              "6  More Than Meets the Eye: The Joan Brock Story          2003    120.0   \n",
              "7                                           Five          1951     93.0   \n",
              "8                                    The Manitou          1978    104.0   \n",
              "9                                      Cold Feet          1989     94.0   \n",
              "\n",
              "   budget  original_language  imdb_rating  first_company  first_producer  \\\n",
              "0     0.0                 14          2.5             36             387   \n",
              "1     0.0                 14          6.1             36            5492   \n",
              "2     0.0                 14          6.3             36            8387   \n",
              "3     0.0                 14          6.9             26             385   \n",
              "4     0.0                 30          6.4             36           11283   \n",
              "5     0.0                 14          3.6             36            2818   \n",
              "6     0.0                 14          6.4             36            8904   \n",
              "7     0.0                 14          6.3             36             967   \n",
              "8     0.0                 14          5.4             36            2963   \n",
              "9     0.0                 14          4.8             36            1936   \n",
              "\n",
              "   first_actor              production_countries  ...  history  horror music  \\\n",
              "0         9712                            Russia  ...        0       0     0   \n",
              "1        20159          United States of America  ...        0       0     0   \n",
              "2        16839                    United Kingdom  ...        0       0     0   \n",
              "3         6987                    United Kingdom  ...        0       0     0   \n",
              "4         7077                             Italy  ...        0       0     0   \n",
              "5        12125          United States of America  ...        0       1     0   \n",
              "6        14736                            Canada  ...        0       0     0   \n",
              "7        19473          United States of America  ...        0       0     0   \n",
              "8         3706  Canada, United States of America  ...        0       1     0   \n",
              "9         2138          United States of America  ...        0       0     0   \n",
              "\n",
              "   mystery  romance  science_fiction  tv_movie  thriller  war  western  \n",
              "0        0        0                0         0         1    0        0  \n",
              "1        0        0                0         1         0    0        0  \n",
              "2        0        0                0         0         0    0        0  \n",
              "3        0        0                0         0         0    0        0  \n",
              "4        0        0                0         0         0    0        0  \n",
              "5        0        0                0         0         0    0        0  \n",
              "6        0        0                0         1         0    0        0  \n",
              "7        0        0                1         0         0    0        0  \n",
              "8        0        0                1         0         0    0        0  \n",
              "9        0        0                0         0         0    0        0  \n",
              "\n",
              "[10 rows x 32 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "selected_features = movie_fg.select(['title','release_year', 'runtime', 'budget',\n",
        "                                     'original_language', 'imdb_rating', 'first_company','first_producer', 'first_actor',\n",
        "                                     'production_countries', 'imdb_votes']).join(genres_fg.select_all(), on=['id'])\n",
        "\n",
        "selected_features.show(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "feature_view = fs.get_or_create_feature_view(\n",
        "    name='tmdb_fv',\n",
        "    description=\"Movie features with imdb rating as the target\",\n",
        "    version=3,\n",
        "    labels=['imdb_rating'],\n",
        "    query=selected_features,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished: Reading data from Hopsworks, using Hopsworks Feature Query Service (4.49s) \n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = feature_view.train_test_split(test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "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>title</th>\n",
              "      <th>release_year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>budget</th>\n",
              "      <th>original_language</th>\n",
              "      <th>first_company</th>\n",
              "      <th>first_producer</th>\n",
              "      <th>first_actor</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>imdb_votes</th>\n",
              "      <th>...</th>\n",
              "      <th>history</th>\n",
              "      <th>horror</th>\n",
              "      <th>music</th>\n",
              "      <th>mystery</th>\n",
              "      <th>romance</th>\n",
              "      <th>science_fiction</th>\n",
              "      <th>tv_movie</th>\n",
              "      <th>thriller</th>\n",
              "      <th>war</th>\n",
              "      <th>western</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Moscow Heat</td>\n",
              "      <td>2004</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>387</td>\n",
              "      <td>9712</td>\n",
              "      <td>Russia</td>\n",
              "      <td>2438.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Killing Affair</td>\n",
              "      <td>1977</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>5492</td>\n",
              "      <td>20159</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>125.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Top Secret</td>\n",
              "      <td>1952</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>8387</td>\n",
              "      <td>16839</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Man Who Could Work Miracles</td>\n",
              "      <td>1936</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>26</td>\n",
              "      <td>385</td>\n",
              "      <td>6987</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1647.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Them Who?</td>\n",
              "      <td>2015</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "      <td>36</td>\n",
              "      <td>11283</td>\n",
              "      <td>7077</td>\n",
              "      <td>Italy</td>\n",
              "      <td>1911.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25716</th>\n",
              "      <td>The Tenor - Lirico Spinto</td>\n",
              "      <td>2014</td>\n",
              "      <td>114.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>12651</td>\n",
              "      <td>21451</td>\n",
              "      <td>Japan, Serbia, South Korea, United States of A...</td>\n",
              "      <td>151.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25717</th>\n",
              "      <td>Madea's Big Happy Family</td>\n",
              "      <td>2011</td>\n",
              "      <td>106.0</td>\n",
              "      <td>25000000.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>9897</td>\n",
              "      <td>12606</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>8781.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25718</th>\n",
              "      <td>The Mechanic</td>\n",
              "      <td>2011</td>\n",
              "      <td>93.0</td>\n",
              "      <td>40000000.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>11066</td>\n",
              "      <td>20371</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>176443.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25719</th>\n",
              "      <td>Twilight of the Warriors: Walled In</td>\n",
              "      <td>2024</td>\n",
              "      <td>125.0</td>\n",
              "      <td>38400000.0</td>\n",
              "      <td>7</td>\n",
              "      <td>36</td>\n",
              "      <td>10298</td>\n",
              "      <td>3308</td>\n",
              "      <td>China, Hong Kong</td>\n",
              "      <td>6647.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25721</th>\n",
              "      <td>Life</td>\n",
              "      <td>2015</td>\n",
              "      <td>112.0</td>\n",
              "      <td>12000000.0</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>12809</td>\n",
              "      <td>11977</td>\n",
              "      <td>Australia, Canada, Germany, United Kingdom, Un...</td>\n",
              "      <td>9073.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20577 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     title  release_year  runtime      budget  \\\n",
              "0                              Moscow Heat          2004     89.0         0.0   \n",
              "1                         A Killing Affair          1977    100.0         0.0   \n",
              "2                               Top Secret          1952     93.0         0.0   \n",
              "3          The Man Who Could Work Miracles          1936     82.0         0.0   \n",
              "4                                Them Who?          2015     95.0         0.0   \n",
              "...                                    ...           ...      ...         ...   \n",
              "25716            The Tenor - Lirico Spinto          2014    114.0       500.0   \n",
              "25717             Madea's Big Happy Family          2011    106.0  25000000.0   \n",
              "25718                         The Mechanic          2011     93.0  40000000.0   \n",
              "25719  Twilight of the Warriors: Walled In          2024    125.0  38400000.0   \n",
              "25721                                 Life          2015    112.0  12000000.0   \n",
              "\n",
              "       original_language  first_company  first_producer  first_actor  \\\n",
              "0                     14             36             387         9712   \n",
              "1                     14             36            5492        20159   \n",
              "2                     14             36            8387        16839   \n",
              "3                     14             26             385         6987   \n",
              "4                     30             36           11283         7077   \n",
              "...                  ...            ...             ...          ...   \n",
              "25716                 36             36           12651        21451   \n",
              "25717                 14             36            9897        12606   \n",
              "25718                 14             36           11066        20371   \n",
              "25719                  7             36           10298         3308   \n",
              "25721                 14             36           12809        11977   \n",
              "\n",
              "                                    production_countries  imdb_votes  ...  \\\n",
              "0                                                 Russia      2438.0  ...   \n",
              "1                               United States of America       125.0  ...   \n",
              "2                                         United Kingdom       201.0  ...   \n",
              "3                                         United Kingdom      1647.0  ...   \n",
              "4                                                  Italy      1911.0  ...   \n",
              "...                                                  ...         ...  ...   \n",
              "25716  Japan, Serbia, South Korea, United States of A...       151.0  ...   \n",
              "25717                           United States of America      8781.0  ...   \n",
              "25718                           United States of America    176443.0  ...   \n",
              "25719                                   China, Hong Kong      6647.0  ...   \n",
              "25721  Australia, Canada, Germany, United Kingdom, Un...      9073.0  ...   \n",
              "\n",
              "       history horror  music  mystery  romance  science_fiction  tv_movie  \\\n",
              "0            0      0      0        0        0                0         0   \n",
              "1            0      0      0        0        0                0         1   \n",
              "2            0      0      0        0        0                0         0   \n",
              "3            0      0      0        0        0                0         0   \n",
              "4            0      0      0        0        0                0         0   \n",
              "...        ...    ...    ...      ...      ...              ...       ...   \n",
              "25716        0      0      1        0        0                0         0   \n",
              "25717        0      0      0        0        0                0         0   \n",
              "25718        0      0      0        0        0                0         0   \n",
              "25719        0      0      0        0        0                0         0   \n",
              "25721        0      0      0        0        0                0         0   \n",
              "\n",
              "       thriller  war  western  \n",
              "0             1    0        0  \n",
              "1             0    0        0  \n",
              "2             0    0        0  \n",
              "3             0    0        0  \n",
              "4             0    0        0  \n",
              "...         ...  ...      ...  \n",
              "25716         0    0        0  \n",
              "25717         0    0        0  \n",
              "25718         1    0        0  \n",
              "25719         1    0        0  \n",
              "25721         0    0        0  \n",
              "\n",
              "[20577 rows x 31 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "drop_list=[\"id\",\"title\",\"timestamp\", \"production_countries\"]\n",
        "\n",
        "train_features=X_train.drop(drop_list, axis=1)\n",
        "test_features=X_test.drop(drop_list, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['release_year', 'runtime', 'budget', 'original_language',\n",
              "       'first_company', 'first_producer', 'first_actor', 'imdb_votes',\n",
              "       'action', 'adventure', 'animation', 'comedy', 'crime', 'documentary',\n",
              "       'drama', 'family', 'fantasy', 'history', 'horror', 'music', 'mystery',\n",
              "       'romance', 'science_fiction', 'tv_movie', 'thriller', 'war', 'western'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_features.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGUcsTNCHRfi"
      },
      "source": [
        "# Training models\n",
        "XGBoost, RandomForest, Linear Regression, SVR, DecisionTree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "8SfLKG9HHRfi"
      },
      "outputs": [],
      "source": [
        "xgb_model= XGBRegressor()\n",
        "rf_model= RandomForestRegressor(random_state=48)\n",
        "lr_model= LinearRegression()\n",
        "svr_model= SVR()\n",
        "dt_model= DecisionTreeRegressor(random_state=48)\n",
        "\n",
        "models={\n",
        "    \"XGBoost\": xgb_model,\n",
        "    \"Random Forest\": rf_model,\n",
        "    \"Linear Regression\": lr_model,\n",
        "    \"SVR\": svr_model,\n",
        "    \"Decision Tree\": dt_model\n",
        "}\n",
        "\n",
        "model_scores_MSE={\n",
        "    \"XGBoost\": 0,\n",
        "    \"Random Forest\": 0,\n",
        "    \"Linear Regression\": 0,\n",
        "    \"SVR\": 0,\n",
        "    \"Decision Tree\": 0\n",
        "}\n",
        "\n",
        "model_scores_r2={\n",
        "    \"XGBoost\": 0,\n",
        "    \"Random Forest\": 0,\n",
        "    \"Linear Regression\": 0,\n",
        "    \"SVR\": 0,\n",
        "    \"Decision Tree\": 0\n",
        "}"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "TmlL9Lj0HRfi"
      },
      "source": [
        "## With categorical features\n",
        "Removed production countries, but keeping original language and the first_producer, first_actor and first company."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDXcXQeXHRfj",
        "outputId": "5819bad7-0920-4a1c-f86f-0760356dee69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "XGBoost\n",
            "MSE: 0.5094779454232782\n",
            "R squared: 0.6034133063820155\n",
            "_________\n",
            "Random Forest\n",
            "MSE: 0.5416669755102042\n",
            "R squared: 0.5783567928908615\n",
            "_________\n",
            "Linear Regression\n",
            "MSE: 0.8287023240921697\n",
            "R squared: 0.35492337272380636\n",
            "_________\n",
            "SVR\n",
            "MSE: 1.2568727536704811\n",
            "R squared: 0.021627895467420277\n",
            "_________\n",
            "Decision Tree\n",
            "MSE: 1.0861632653061224\n",
            "R squared: 0.15451119722328366\n",
            "_________\n"
          ]
        }
      ],
      "source": [
        "for name, model in models.items():\n",
        "    model.fit(train_features, y_train)\n",
        "    print(name)\n",
        "    y_pred= model.predict(test_features)\n",
        "    mse= mean_squared_error(y_test.iloc[:,0], y_pred)\n",
        "    print(\"MSE:\", mse)\n",
        "    r2 = r2_score(y_test.iloc[:,0], y_pred)\n",
        "    print(\"R squared:\", r2)\n",
        "    print('_________')\n",
        "    models[name]= model\n",
        "    model_scores_MSE[name]= mse\n",
        "    model_scores_r2[name]= r2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtDcyBdkHRfj"
      },
      "source": [
        "## Without categorical features\n",
        "Removed original language as well since half of the dataset had \"en\" (english). Removing this feature led to a lower MSE."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoLcjcOOHRfj",
        "outputId": "659062d8-d3e4-4691-ff96-c468d9b1c9bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dropped: ['original_language'] \n",
            "\n",
            "XGBoost\n",
            "MSE: 0.5705394316544234\n",
            "R squared: 0.5558819595409055\n",
            "_________\n",
            "Random Forest\n",
            "MSE: 0.591078460058309\n",
            "R squared: 0.5398940145513427\n",
            "_________\n",
            "Linear Regression\n",
            "MSE: 0.843571434255214\n",
            "R squared: 0.34334899293057675\n",
            "_________\n",
            "SVR\n",
            "MSE: 1.2568753769359167\n",
            "R squared: 0.02162585347095125\n",
            "_________\n",
            "Decision Tree\n",
            "MSE: 1.1762798833819241\n",
            "R squared: 0.084362819018169\n",
            "_________\n"
          ]
        }
      ],
      "source": [
        "drop_list=['original_language']\n",
        "\n",
        "print(\"Dropped:\", drop_list, \"\\n\")\n",
        "train_features_without = train_features.drop(drop_list, axis=1)\n",
        "test_features_without = test_features.drop(drop_list, axis=1)\n",
        "\n",
        "for name, model in models.items():\n",
        "    model.fit(train_features_without, y_train)\n",
        "    print(name)\n",
        "    y_pred= model.predict(test_features_without)\n",
        "    mse= mean_squared_error(y_test.iloc[:,0], y_pred)\n",
        "    print(\"MSE:\", mse)\n",
        "    r2 = r2_score(y_test.iloc[:,0], y_pred)\n",
        "    print(\"R squared:\", r2)\n",
        "    print('_________')\n",
        "    models[name]= model\n",
        "    model_scores_MSE[name]= mse\n",
        "    model_scores_r2[name]= r2"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Chosen Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MSE: 0.5094779454232782\n",
            "R squared: 0.6034133063820155\n",
            "_________\n"
          ]
        }
      ],
      "source": [
        "#Chosen model:\n",
        "model= XGBRegressor()\n",
        "model.fit(train_features, y_train)\n",
        "y_pred= model.predict(test_features)\n",
        "mse= mean_squared_error(y_test.iloc[:,0], y_pred)\n",
        "print(\"MSE:\", mse)\n",
        "r2 = r2_score(y_test.iloc[:,0], y_pred)\n",
        "print(\"R squared:\", r2)\n",
        "print('_________')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TeQK9jv8HRfj",
        "outputId": "6211ce8d-76a0-4176-b31c-ed562484e135"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from xgboost import plot_importance\n",
        "\n",
        "plot_importance(model)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions for 10 movies\n",
            "Predicted: 5.857948 Actual: 7.1\n",
            "Predicted: 6.3983464 Actual: 6.3\n",
            "Predicted: 5.2628407 Actual: 6.1\n",
            "Predicted: 4.411699 Actual: 4.2\n",
            "Predicted: 5.9354286 Actual: 5.9\n",
            "Predicted: 6.1494517 Actual: 6.1\n",
            "Predicted: 4.789273 Actual: 4.4\n",
            "Predicted: 4.68718 Actual: 4.2\n",
            "Predicted: 4.7766104 Actual: 6.6\n",
            "Predicted: 6.090134 Actual: 6.3\n"
          ]
        }
      ],
      "source": [
        "# check some samples\n",
        "\n",
        "movies = 10\n",
        "\n",
        "print (\"Predictions for\", movies, \"movies\")\n",
        "\n",
        "for i in range(movies):\n",
        "    print(\"Predicted:\", y_pred[i], \"Actual:\", y_test.iloc[i,0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Mz1cTYN-HRfk"
      },
      "outputs": [],
      "source": [
        "from hsml.schema import Schema\n",
        "from hsml.model_schema import ModelSchema\n",
        "\n",
        "# Creating input and output schemas using the 'Schema' class for features (X) and target variable (y)\n",
        "input_schema = Schema(train_features)\n",
        "output_schema = Schema(test_features)\n",
        "\n",
        "# Creating a model schema using 'ModelSchema' with the input and output schemas\n",
        "model_schema = ModelSchema(input_schema=input_schema, output_schema=output_schema)\n",
        "\n",
        "# Converting the model schema to a dictionary representation\n",
        "schema_dict = model_schema.to_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "febGt4kIHRfk"
      },
      "outputs": [],
      "source": [
        "res_dict = {\n",
        "        \"MSE\": str(mse),\n",
        "        \"R squared\": str(r2),\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179,
          "referenced_widgets": [
            "667336e65bb141cfa5be02ba1ca0d1c3",
            "00a1311591934483b27fb6585a91df73",
            "a5df5bcae5454216b38f4a587158f723",
            "664b6540c4414035a4371ac6322b692f",
            "c68f3f1ff0cf453a8d40f95442e39453",
            "f4f682a821404b2e99da5ea20e3b5c97",
            "c57ababe643f4e358579da5d3a2d06a0",
            "803eb8b279d34b1cb2a36816879021f1",
            "5f3a38cd217649d19f3f5bb4a660c386",
            "577e773007a545ecb3605980e230fd7a",
            "19669e8a5224488b97db1907ab28c346",
            "a27cd23c571949c8951083df8df65f8c",
            "d51de9eeb0064cf3ab82f38f188bbe80",
            "e3d1419fe1bc4b4fb763b0b7b50e8b94",
            "90d70af91d8f41c2a3d69ab14e8ad118",
            "46ccae630edd4562bb974ad11aafceb9",
            "946c6ec306434679be1340394d0c4e1b",
            "f894c84537e847afa168875ec40fc456",
            "d647250d07034c33a8c5db96bb099cfc",
            "aed3494aac5e4b2eb3140be371c1d61e",
            "d476be784d974b1f89724ed415f5e550",
            "dadbda9c5e51453c9166d996a0838ee4",
            "ccf5d65c94da49adb84099ea160a47ca",
            "47d389d84a584f95bbc5d307b24330a9",
            "b5f360496da34840ae2e7d330146343f",
            "3f3812ff5dd646b292144410aef365ab",
            "27ec7eefae2d4460b3b40cfe95de0a74",
            "5d398911a3ed4c74b35fc24f5c8c7360",
            "60ec99ce433049dfac72ddef87a5fd8a",
            "30ea9de0453341be986829f26ac9e9f1",
            "1d8cde11caa7494c84f6d97ac1e7b7d0",
            "76d483538ac748409ccb499e6365c48d",
            "b54795696b18424d8d8a7a4f8c06cffc",
            "6c6022b349624526a7c75992da139550",
            "d9690f454616427c834ba01327c23fb0",
            "41572f09d1a44b76b1687058a6092e8e",
            "9658fbf45d544437ad1537d2d720f1b9",
            "c3242a57ec234e208f14bb2b5dcd97e7",
            "1e8b1482e65d43bea563c748c6455b7a",
            "15afe80b211d43bd9df821e5d679edbb",
            "1b73c5466a9e46b99acafe0bbc89c74f",
            "ac65261e0f164660ac67c93fca3aa3cc",
            "5851a52056774f2596f852308a1629e9",
            "f89f1be1c8f04b38a7d93a66abe2a896"
          ]
        },
        "id": "XTBf-0oLHRfl",
        "outputId": "8d934b24-e7b1-4ae2-ebf0-400c03c28a45"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "caa36a83afc546bfac24d76a46545bbe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/6 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "842480466ba84b968a9d79a17958e259",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Uploading: 0.000%|          | 0/593443 elapsed<00:00 remaining<?"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48ff34d7b4d44dfd9eaf393ab4e85c82",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Uploading: 0.000%|          | 0/0 elapsed<00:00 remaining<?"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0d4e6f8617544918c62359944bb0bc5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Uploading: 0.000%|          | 0/201 elapsed<00:00 remaining<?"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e1404d79e4e497f93d26e47e1fdf057",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Uploading: 0.000%|          | 0/3873 elapsed<00:00 remaining<?"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model created, explore it at https://c.app.hopsworks.ai:443/p/1169559/models/movie_rating_model_2/1\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Model(name: 'movie_rating_model_2', version: 1)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mr = project.get_model_registry()\n",
        "\n",
        "model_dir=\"model\"\n",
        "model.save_model(model_dir + \"/model.json\")\n",
        "\n",
        "# Creating a Python model in the model registry named 'imdb_rating_model'\n",
        "\n",
        "movie_rating_pred_model = mr.python.create_model(\n",
        "    name=\"movie_rating_model_2\",\n",
        "    metrics= res_dict,\n",
        "    model_schema=model_schema,\n",
        "    input_example=X_test.sample().values,\n",
        "    description=\"Movie Rating (1 to 10) predictor\",\n",
        ")\n",
        "\n",
        "# Saving the model artifacts to the model registry\n",
        "movie_rating_pred_model.save(model_dir)"
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