lesson4_collab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of lesson4-collab.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsaEhvbTr6ON",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.collab import *\n",
        "from fastai.tabular import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qI70tiBFr6OV",
        "colab_type": "text"
      },
      "source": [
        "## Collaborative filtering example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACI5oz4Vr6OW",
        "colab_type": "text"
      },
      "source": [
        "`collab` models use data in a `DataFrame` of user, items, and ratings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiyuBZGpr6OY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user,item,title = 'userId','movieId','title'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qB66MkZ4r6Oc",
        "colab_type": "code",
        "outputId": "739f3346-44c3-4a7f-8b90-38d520e71df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "path = untar_data(URLs.ML_SAMPLE)\n",
        "path"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/root/.fastai/data/movie_lens_sample')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKpmBYEor6Ol",
        "colab_type": "code",
        "outputId": "621032e2-d0ed-4819-a8fb-4664299c43e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "ratings = pd.read_csv(path/'ratings.csv')\n",
        "ratings.head()"
      ],
      "execution_count": 4,
      "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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>73</td>\n",
              "      <td>1097</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1255504951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>561</td>\n",
              "      <td>924</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1172695223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>157</td>\n",
              "      <td>260</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1291598691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>358</td>\n",
              "      <td>1210</td>\n",
              "      <td>5.0</td>\n",
              "      <td>957481884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>316</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1138999234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0      73     1097     4.0  1255504951\n",
              "1     561      924     3.5  1172695223\n",
              "2     157      260     3.5  1291598691\n",
              "3     358     1210     5.0   957481884\n",
              "4     130      316     2.0  1138999234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVYpTyxWr6Or",
        "colab_type": "text"
      },
      "source": [
        "That's all we need to create and train a model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Mi7rGX_r6Os",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = CollabDataBunch.from_df(ratings, seed=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5fOO7O4r6Ox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_range = [0,5.5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mELPHKTar6O1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = collab_learner(data, n_factors=50, y_range=y_range)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWfrpESLr6O5",
        "colab_type": "code",
        "outputId": "9d4135bb-0f90-4675-ce65-04478b584b6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "learn.fit_one_cycle(3, 5e-3)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.619386</td>\n",
              "      <td>0.973682</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.846683</td>\n",
              "      <td>0.665450</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.648963</td>\n",
              "      <td>0.655851</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znkXy331r6O9",
        "colab_type": "text"
      },
      "source": [
        "## Movielens 100k"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amiyX2TCr6O-",
        "colab_type": "text"
      },
      "source": [
        "Let's try with the full Movielens 100k data dataset, available from http://files.grouplens.org/datasets/movielens/ml-100k.zip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yGIBCqWtiqI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6f521ce0-ba62-49c5-87d7-2af89733c61f"
      },
      "source": [
        "!wget http://files.grouplens.org/datasets/movielens/ml-100k.zip"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-06-16 14:35:04--  http://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4924029 (4.7M) [application/zip]\n",
            "Saving to: ‘ml-100k.zip’\n",
            "\n",
            "ml-100k.zip         100%[===================>]   4.70M  2.93MB/s    in 1.6s    \n",
            "\n",
            "2019-06-16 14:35:06 (2.93 MB/s) - ‘ml-100k.zip’ saved [4924029/4924029]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4MG0--ytqq1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv ./ml-100k.zip ~/.fastai/data/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LpJ-nrLuSPV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "fp=zipfile.ZipFile('/root/.fastai/data/ml-100k.zip')\n",
        "fp.extractall('/root/.fastai/data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMfpQLE-r6O_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path=Config.data_path()/'ml-100k'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GUVa-Msr6PC",
        "colab_type": "code",
        "outputId": "b51a3588-6fcf-4ec1-de17-a62d7548a4e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
        "                      names=[user,item,'rating','timestamp'])\n",
        "ratings.head()"
      ],
      "execution_count": 29,
      "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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "      <td>881250949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>186</td>\n",
              "      <td>302</td>\n",
              "      <td>3</td>\n",
              "      <td>891717742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>377</td>\n",
              "      <td>1</td>\n",
              "      <td>878887116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>244</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>880606923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>166</td>\n",
              "      <td>346</td>\n",
              "      <td>1</td>\n",
              "      <td>886397596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0     196      242       3  881250949\n",
              "1     186      302       3  891717742\n",
              "2      22      377       1  878887116\n",
              "3     244       51       2  880606923\n",
              "4     166      346       1  886397596"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60EVh3KKr6PG",
        "colab_type": "code",
        "outputId": "bf541bb9-4d14-42aa-d31a-46d852fba3ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1', header=None,\n",
        "                    names=[item, 'title', 'date', 'N', 'url', *[f'g{i}' for i in range(19)]])\n",
        "movies.head()"
      ],
      "execution_count": 30,
      "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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>date</th>\n",
              "      <th>N</th>\n",
              "      <th>url</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>01-Jan-1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <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>1</th>\n",
              "      <td>2</td>\n",
              "      <td>GoldenEye (1995)</td>\n",
              "      <td>01-Jan-1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <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>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Four Rooms (1995)</td>\n",
              "      <td>01-Jan-1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</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",
              "      <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>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Get Shorty (1995)</td>\n",
              "      <td>01-Jan-1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Copycat (1995)</td>\n",
              "      <td>01-Jan-1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</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>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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId              title         date   N  ... g15  g16  g17  g18\n",
              "0        1   Toy Story (1995)  01-Jan-1995 NaN  ...   0    0    0    0\n",
              "1        2   GoldenEye (1995)  01-Jan-1995 NaN  ...   0    1    0    0\n",
              "2        3  Four Rooms (1995)  01-Jan-1995 NaN  ...   0    1    0    0\n",
              "3        4  Get Shorty (1995)  01-Jan-1995 NaN  ...   0    0    0    0\n",
              "4        5     Copycat (1995)  01-Jan-1995 NaN  ...   0    1    0    0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYRdDvFZr6PK",
        "colab_type": "code",
        "outputId": "1ffbd258-517e-4b54-a01b-d60d7d8710e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(ratings)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQgLNPYpr6PO",
        "colab_type": "code",
        "outputId": "b532331f-73eb-4560-ab33-ef2a6fa4dd5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "rating_movie = ratings.merge(movies[[item, title]])\n",
        "rating_movie.head()"
      ],
      "execution_count": 32,
      "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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "      <td>881250949</td>\n",
              "      <td>Kolya (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "      <td>875747190</td>\n",
              "      <td>Kolya (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>226</td>\n",
              "      <td>242</td>\n",
              "      <td>5</td>\n",
              "      <td>883888671</td>\n",
              "      <td>Kolya (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>154</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "      <td>879138235</td>\n",
              "      <td>Kolya (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>306</td>\n",
              "      <td>242</td>\n",
              "      <td>5</td>\n",
              "      <td>876503793</td>\n",
              "      <td>Kolya (1996)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp         title\n",
              "0     196      242       3  881250949  Kolya (1996)\n",
              "1      63      242       3  875747190  Kolya (1996)\n",
              "2     226      242       5  883888671  Kolya (1996)\n",
              "3     154      242       3  879138235  Kolya (1996)\n",
              "4     306      242       5  876503793  Kolya (1996)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozUnBTlAr6PR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = CollabDataBunch.from_df(rating_movie, seed=42, valid_pct=0.1, item_name=title)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELYeW5qXr6PV",
        "colab_type": "code",
        "outputId": "afb4a68b-f62d-46b1-9ecc-441927c42f64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.show_batch()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>userId</th>\n",
              "      <th>title</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>648</td>\n",
              "      <td>Mighty Aphrodite (1995)</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>457</td>\n",
              "      <td>Postino, Il (1994)</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>655</td>\n",
              "      <td>Walking and Talking (1996)</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>222</td>\n",
              "      <td>Aladdin (1992)</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>537</td>\n",
              "      <td>Good, The Bad and The Ugly, The (1966)</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5E1yX4Ir6Pa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_range = [0,5.5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EsHKo0Nr6Pd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = collab_learner(data, n_factors=40, y_range=y_range, wd=1e-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6lG2A6Ir6Pj",
        "colab_type": "code",
        "outputId": "8947c525-f795-441c-c3e4-62dbbc7ad579",
        "colab": {}
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot(skip_end=15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9SRnNyFr6Po",
        "colab_type": "code",
        "outputId": "bb53e988-db96-4a10-b3c3-2b063019f66a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "learn.fit_one_cycle(5, 5e-3)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.935275</td>\n",
              "      <td>0.949416</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.863663</td>\n",
              "      <td>0.884337</td>\n",
              "      <td>00:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.762157</td>\n",
              "      <td>0.839347</td>\n",
              "      <td>00:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.654652</td>\n",
              "      <td>0.815229</td>\n",
              "      <td>00:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.571526</td>\n",
              "      <td>0.814104</td>\n",
              "      <td>00:08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4McoxHZtr6Ps",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('dotprod')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "de7c0lRur6Pu",
        "colab_type": "text"
      },
      "source": [
        "Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91, which corresponds to an MSE of `0.91**2 = 0.83`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhzzGZuIr6Pv",
        "colab_type": "text"
      },
      "source": [
        "## Interpretation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfUf03Edr6Pv",
        "colab_type": "text"
      },
      "source": [
        "### Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B54X8Sknr6Pw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.load('dotprod');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JDK_ofmr6Pz",
        "colab_type": "code",
        "outputId": "5c7e0631-4614-419e-b89a-4bc86ef31e6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "learn.model"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmbeddingDotBias(\n",
              "  (u_weight): Embedding(944, 40)\n",
              "  (i_weight): Embedding(1654, 40)\n",
              "  (u_bias): Embedding(944, 1)\n",
              "  (i_bias): Embedding(1654, 1)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "os9e1kW0r6P2",
        "colab_type": "code",
        "outputId": "a1499e9a-54b2-4fef-9c4d-2013c22fc268",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "g = rating_movie.groupby(title)['rating'].count()\n",
        "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
        "top_movies[:10]"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)', 'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
              "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)', 'Air Force One (1997)',\n",
              "       'Independence Day (ID4) (1996)'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGSI1BPCr6P5",
        "colab_type": "text"
      },
      "source": [
        "### Movie bias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiKgEPHir6P6",
        "colab_type": "code",
        "outputId": "2bf224fe-1237-44b7-fe54-85a853e84ad8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_bias = learn.bias(top_movies, is_item=True)\n",
        "movie_bias.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b65Oe7LBr6P-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_ratings = rating_movie.groupby(title)['rating'].mean()\n",
        "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD8R8xWer6QE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "item0 = lambda o:o[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1omZex3Mr6QH",
        "colab_type": "code",
        "outputId": "319e133d-6172-483d-f25b-9ffc46d81412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "sorted(movie_ratings, key=item0)[:15]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.3759),\n",
              "  'Children of the Corn: The Gathering (1996)',\n",
              "  1.3157894736842106),\n",
              " (tensor(-0.3332),\n",
              "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
              "  1.7142857142857142),\n",
              " (tensor(-0.3026), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
              " (tensor(-0.2617), 'Cable Guy, The (1996)', 2.339622641509434),\n",
              " (tensor(-0.2545), \"McHale's Navy (1997)\", 2.1884057971014492),\n",
              " (tensor(-0.2288), 'Barb Wire (1996)', 1.9333333333333333),\n",
              " (tensor(-0.2275), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),\n",
              " (tensor(-0.2227), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
              " (tensor(-0.2197), 'Grease 2 (1982)', 2.0),\n",
              " (tensor(-0.2175), 'Striptease (1996)', 2.2388059701492535),\n",
              " (tensor(-0.2161), 'Bio-Dome (1996)', 1.903225806451613),\n",
              " (tensor(-0.2147), 'Leave It to Beaver (1997)', 1.8409090909090908),\n",
              " (tensor(-0.2122), \"Joe's Apartment (1996)\", 2.2444444444444445),\n",
              " (tensor(-0.2072), 'Beverly Hills Ninja (1997)', 2.3125),\n",
              " (tensor(-0.2049), 'Free Willy 3: The Rescue (1997)', 1.7407407407407407)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0k8SVMTzr6QK",
        "colab_type": "code",
        "outputId": "88550658-6a0c-4db5-8a61-2e368403b259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.6027), \"Schindler's List (1993)\", 4.466442953020135),\n",
              " (tensor(0.5648), 'Titanic (1997)', 4.2457142857142856),\n",
              " (tensor(0.5488), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
              " (tensor(0.5463), 'L.A. Confidential (1997)', 4.161616161616162),\n",
              " (tensor(0.5431), 'Rear Window (1954)', 4.3875598086124405),\n",
              " (tensor(0.5384), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
              " (tensor(0.5331), 'Star Wars (1977)', 4.3584905660377355),\n",
              " (tensor(0.5016), 'Good Will Hunting (1997)', 4.262626262626263),\n",
              " (tensor(0.4989), 'Close Shave, A (1995)', 4.491071428571429),\n",
              " (tensor(0.4968), 'Casablanca (1942)', 4.45679012345679),\n",
              " (tensor(0.4878), 'Apt Pupil (1998)', 4.1),\n",
              " (tensor(0.4822), 'As Good As It Gets (1997)', 4.196428571428571),\n",
              " (tensor(0.4757), 'Vertigo (1958)', 4.251396648044692),\n",
              " (tensor(0.4717), 'Boot, Das (1981)', 4.203980099502488),\n",
              " (tensor(0.4669), \"One Flew Over the Cuckoo's Nest (1975)\", 4.291666666666667)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3Awfy1Br6QO",
        "colab_type": "text"
      },
      "source": [
        "### Movie weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjBRpRZ7r6QP",
        "colab_type": "code",
        "outputId": "5fe12341-a636-4d65-cf8b-2b292cc51f68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_w = learn.weight(top_movies, is_item=True)\n",
        "movie_w.shape"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 40])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIRgKzbHr6QR",
        "colab_type": "code",
        "outputId": "5420915a-f70d-49e2-f3e6-da5306423007",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_pca = movie_w.pca(3)\n",
        "movie_pca.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qoW6QH_r6QU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fac0,fac1,fac2 = movie_pca.t()\n",
        "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWZ3l8fyr6QW",
        "colab_type": "code",
        "outputId": "7ceab027-5b1f-4e1e-f592-6765a1c3369b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.0498), 'Close Shave, A (1995)'),\n",
              " (tensor(1.0427), 'Lawrence of Arabia (1962)'),\n",
              " (tensor(1.0392), 'Apocalypse Now (1979)'),\n",
              " (tensor(1.0352), 'Wallace & Gromit: The Best of Aardman Animation (1996)'),\n",
              " (tensor(1.0324), 'Wrong Trousers, The (1993)'),\n",
              " (tensor(1.0230), 'Godfather, The (1972)'),\n",
              " (tensor(1.0180), 'Casablanca (1942)'),\n",
              " (tensor(0.9733),\n",
              "  'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)'),\n",
              " (tensor(0.9593), 'Some Folks Call It a Sling Blade (1993)'),\n",
              " (tensor(0.9585), 'Chinatown (1974)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cF83LDTEr6QZ",
        "colab_type": "code",
        "outputId": "92eecd09-10b3-4272-a82a-f6aa95cb0e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-1.2868), 'Home Alone 3 (1997)'),\n",
              " (tensor(-1.2448), 'Jungle2Jungle (1997)'),\n",
              " (tensor(-1.1555), \"McHale's Navy (1997)\"),\n",
              " (tensor(-1.1471), 'Children of the Corn: The Gathering (1996)'),\n",
              " (tensor(-1.1290), 'Leave It to Beaver (1997)'),\n",
              " (tensor(-1.1144), 'Flipper (1996)'),\n",
              " (tensor(-1.1115), 'Congo (1995)'),\n",
              " (tensor(-1.0987), 'Bio-Dome (1996)'),\n",
              " (tensor(-1.0936), 'D3: The Mighty Ducks (1996)'),\n",
              " (tensor(-1.0376), 'Mortal Kombat: Annihilation (1997)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsfLC5j9r6Qb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qONhJZuQr6Qd",
        "colab_type": "code",
        "outputId": "0f58d30f-2ceb-491c-e172-59afd7b37a8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.1540), 'Braveheart (1995)'),\n",
              " (tensor(1.0953), 'Raiders of the Lost Ark (1981)'),\n",
              " (tensor(1.0868), 'Titanic (1997)'),\n",
              " (tensor(0.9410), \"It's a Wonderful Life (1946)\"),\n",
              " (tensor(0.8886), 'Star Wars (1977)'),\n",
              " (tensor(0.8825), 'Independence Day (ID4) (1996)'),\n",
              " (tensor(0.8703), 'Pretty Woman (1990)'),\n",
              " (tensor(0.8571), 'Forrest Gump (1994)'),\n",
              " (tensor(0.8533), 'Top Gun (1986)'),\n",
              " (tensor(0.8301), 'Return of the Jedi (1983)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trGPjeuHr6Qg",
        "colab_type": "code",
        "outputId": "09ee8cd7-2bdf-471c-8033-bdfcd201fb56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.8212), 'Keys to Tulsa (1997)'),\n",
              " (tensor(-0.8137), 'Ready to Wear (Pret-A-Porter) (1994)'),\n",
              " (tensor(-0.8051), 'Clockwork Orange, A (1971)'),\n",
              " (tensor(-0.7698), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),\n",
              " (tensor(-0.7566), 'Brazil (1985)'),\n",
              " (tensor(-0.7471), 'Cable Guy, The (1996)'),\n",
              " (tensor(-0.7336), 'Trainspotting (1996)'),\n",
              " (tensor(-0.7243), 'Stupids, The (1996)'),\n",
              " (tensor(-0.7231), 'Jude (1996)'),\n",
              " (tensor(-0.7178), 'Big Night (1996)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zi5LrAwTr6Qj",
        "colab_type": "code",
        "outputId": "512ec1e5-628b-43f6-8be3-8c653d87d024",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
        "idxs = list(range(50))\n",
        "X = fac0[idxs]\n",
        "Y = fac2[idxs]\n",
        "plt.figure(figsize=(15,15))\n",
        "plt.scatter(X, Y)\n",
        "for i, x, y in zip(top_movies[idxs], X, Y):\n",
        "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rs2Y5AHbtSuaHH27E4TBp2fJ90tIy+emnmzl6NI0OHT5m2LDG+Pp65Bn3pk0JVK0aiJfX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nwdAJN/foocexYTfnrE1e4tt35BmTJVSU9PITVlN1WqXFWs/orIlcdYlnWx+5Cv2NhYa/ny\n/K/xERERkUtL6xGzSconHEeE+PH3M53y2UPkpLS0Q3z91U3c/8BvxZpOfa4tmP8pDoeddu0Lvme1\nyMVgjImzLKt43zBJiWgqtYiIiJTYk92i8fM69V69fl4ePNntzCmrIqcLCAil69VPk3J410Vp38vb\nn1at77oobYvIpUFTqUVERKTETiywdWJV6oohfjzZLfqCLLwlV4aCVrq+EJo3H3TR2haRS4OCsYiI\niJwT/WIiFIRFROSypKnUIiIiIiIi4tYUjEVERERERMStKRiLiIiIiIiIW1MwFhEREREREbemYCwi\nIiIiIiJuTcFYRERERERE3JqCsYiIiIiIiLg1BWMRERERERFxawrGIiIiIiIi4tYUjEVERERERMSt\nKRiLiIiIiIiIW1MwFhEREREREbemYCwiIiIiIiJuTcFYRERERERE3JqCsYiIiIiIiLg1BWMRERER\nERFxawrGIiIiIiIi4tYUjEVERERERMStKRiLiIiIiIiIW1MwFhEREREREbemYCwiIiIiIiJuTcFY\nRERERERE3JqCsYiIiIiIiLg1BWMRERERERFxawrGIiIiIiIi4tYUjEVERERERMStKRiLiIiIiIiI\nW1MwFhEREREREbemYCwiIiIiIiJuTcFYRERERERE3JqCsYiIiIiIiLg1BWMRERERERFxawrGIiIi\nIiIi4tYUjEVERERERMStGcuyLnYf8mWM2Q9sv9j9kCKVBQ5c7E5IiWkcL38awyuDxvHypzG8Mmgc\nL39XyhhWsSyr3MXuhDu4ZIOxXB6MMcsty4q92P2QktE4Xv40hlcGjePlT2N4ZdA4Xv40hnK2NJVa\nRERERERE3JqCsYiIiIiIiLg1BWMpqc8vdgfknNA4Xv40hlcGjePlT2N4ZdA4Xv40hnJWdI2xiIiI\niIiIuDWdMRYRERERERG3pmAsRTLGhBpjZhpjNuX+LF1APbsxZlXuf7/k2R5ljFlijNlsjPnRGON9\n4XovULwxNMY0NsYsMsasM8asMcbclKfsW2PM1jzj2/jCvgL3Zoy5xhizMfc99Ew+5T65763Nue+1\nqnnKns3dvtEY0+1C9ltOKsYYPmaMic99780yxlTJU5bvZ6tceMUYxyHGmP15xuuuPGWDcz+DNxlj\nBl/YnssJxRjDd/OMX4IxJiVPmd6LlwBjzNfGmGRjzD8FlBtjzPu5Y7zGGNMkT5neh1IgTaWWIhlj\n3gQOWZY1Ivd/IqUty3o6n3rHLMsqlc/28cAky7LGGWM+BVZblvXJ+e+5nFCcMTTG1AIsy7I2GWMq\nAnFAHcuyUowx3wLTLMuacME77+aMMR5AAtAV2AUsA262LCs+T537gYaWZQ01xgwArrUs6yZjTF1g\nLNAMqAj8CdSyLMt+oV+HOyvmGHYElliWddwYcx/QwbKsm3LL8v1slQurmOM4BIi1LOvB0/YNBZYD\nsYCF8/O1qWVZhy9M7wWKN4an1R8GxFiWdUfuc70XLwHGmHbAMeA7y7Lq51PeAxgG9ACaA+9ZltVc\n70Mpis4YS3H0BUblPh4F9CvujsYYA3QCTgSqs9pfzpkix9CyrATLsjblPt4NJAO6ofzF1wzYbFlW\nomVZWcA4nOOZV97xnQB0zn3v9QXGWZaVaVnWVmBz7vHkwipyDC3LmmNZ1vHcp4uBShe4j1K04rwX\nC9INmGlZ1qHcP8JnAtecp35Kwc52DG/G+eWiXEIsy5oHHCqkSl+codmyLGsxEGKMqYDeh1IEBWMp\njnDLsvbkPt4LhBdQz9cYs9wYs9gYcyJ4lQFSLMvKyX2+C4g4j32V/BV3DAEwxjQDvIEteTa/mjsl\n6V1jjM956qecKQLYmed5fu8hV53c91oqzvdecfaV8+9sx+FOYHqe5/l9tsqFV9xx7J/7WTnBGBN5\nlvvK+VXscci9nCEKmJ1ns96Ll4eCxlnvQymU58XugFwajDF/AuXzKXo+7xPLsixjTEHz76tYlpVk\njKkGzDbGrMX5B7pcAOdoDMn9VnU0MNiyLEfu5mdxBmpvnLc/eBp4+Vz0W0ROMsbchnOaX/s8m8/4\nbLUsa0v+R5CLbCow1rKsTGPMvThncnS6yH2Sf2cAMOG0S0/0XhS5gikYCwCWZXUpqMwYs88YU8Gy\nrD25oSm5gGMk5f5MNMbMBWKAiTinsHjmnsmqBCSd8xcg52QMjTFBwK/A87nTj04c+8TZ5kxjzDfA\nE+ew61K4JCAyz/P83kMn6uwyxngCwcDBYu4r51+xxsEY0wXnF1ntLcvKPLG9gM9W/TF+4RU5jpZl\nHczz9EvgzTz7djht37nnvIdSlLP5TBwAPJB3g96Ll42CxlnvQymUplJLcfwCnFi5bzAw5fQKxpjS\nJ6bXGmPKAq2BeMu5utsc4PrC9pfzrjhj6A38jPO6nAmnlVXI/WlwXp+c70qQcl4sA2oa5+ru3jj/\nWDt9NdS843s9MDv3vfcLMMA4V62OAmoCSy9Qv+WkIsfQGBMDfAb0sSwrOc/2fD9bL1jPJa/ijGOF\nPE/7AOtzH/8OXJ07nqWBq3O3yYVVnM9TjDG1gdLAojzb9F68fPwCDMpdnboFkJr7Bb/eh1IonTGW\n4hgBjDfG3AlsB24EMMbEAkMty7oLqAN8Zoxx4PzCZUSeVR6fBsYZY4YDK4GvLvQLkGKN4Y1AO6BM\n7sqqAEMsy1oFfG+MKQcYYBUw9AL3321ZlpVjjHkQ5/+8PYCvLctaZ4x5GVhuWdYvON9To40xm3Eu\nSDIgd991uavCxwM5wANakfrCK+YYvgWUAn5yfv/EDsuy+lD4Z6tcQMUcx4eMMX1wvt8OAUNy9z1k\njHkFZzADeNmyrMIWD5LzoJhjCM7P0HHWqbdu0XvxEmGMGYvzzG9ZY8wu+P/27pgIoSCIguAkCEUJ\nNlCCGnT89AggxsDrTje+YKq29npUt6pzzrN69b1I/a6u6v6beYf85bsmAAAAplmlBgAAYJowBgAA\nYJowBgAAYJowBgAAYJowBgAAYJowBgAAYJowBgAAYJowBgAAYNoH1pW8BFPl1dwAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VUA_-IDr6Qn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}