lession_4_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lession_4_3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMd6qaoW5CZs69IHoJZOsh6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tlkt/fast_lesson_work/blob/master/lession_4_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hBTMkcxaPbO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "9bf0b2b0-24f5-4d00-e8f9-ab14ddb1e6f1"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZSFHLp8aZ8o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.collab import *\n",
        "from fastai.tabular import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7V13nYmfamsa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user,item,title='userId','movieId','title'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmdFQAtFasjk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53c6e1fa-ec49-467e-ddfc-0a09cef1016a"
      },
      "source": [
        "path = untar_data(URLs.ML_SAMPLE)\n",
        "path"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/root/.fastai/data/movie_lens_sample')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IcFWluNawHe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "54f4e416-2fa4-43d5-eb12-3b4a1d39b72f"
      },
      "source": [
        "ratings = pd.read_csv(path/'ratings.csv')\n",
        "ratings.head()"
      ],
      "execution_count": 13,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UjK9hIca7JD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = CollabDataBunch.from_df(ratings,seed=42)\n",
        "y_rang = [0,5,5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGZm1LljbE1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "07932640-72b0-413a-fcf4-f750549caf35"
      },
      "source": [
        "learn = collab_learner(data,n_factors=50,y_range=y_rang)\n",
        "learn.fit_one_cycle(3,5e-3)"
      ],
      "execution_count": 16,
      "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>2.041571</td>\n",
              "      <td>1.080073</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.933458</td>\n",
              "      <td>0.679021</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.684714</td>\n",
              "      <td>0.670983</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCi7tIXsbNus",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "98374a9c-00f1-4ffa-c1d1-2b81d693673a"
      },
      "source": [
        "! wget http://files.grouplens.org/datasets/movielens/ml-100k.zip"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-01-15 23:02:07--  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",
            "\rml-100k.zip           0%[                    ]       0  --.-KB/s               \rml-100k.zip         100%[===================>]   4.70M  29.3MB/s    in 0.2s    \n",
            "\n",
            "2020-01-15 23:02:07 (29.3 MB/s) - ‘ml-100k.zip’ saved [4924029/4924029]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNYS37QCcXPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mv ml-100k '/root/.fastai/data'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-A90hBSqb05Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path=Config.data_path()/'ml-100k'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92CvfYSWcGvm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ratings = pd.read_csv(path/'u.data',delimiter='\\t',header=None,names=[user,item,'rating','timestamp'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15r1NBn6dF48",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "15fbffec-27d2-426b-d241-dd0cabb2d345"
      },
      "source": [
        "ratings.head()"
      ],
      "execution_count": 31,
      "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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iHjEUkhdHjA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "35273978-f5f0-44ba-fadb-5b2f37987495"
      },
      "source": [
        "movies = pd.read_csv(path/'u.item',delimiter='|',encoding='latin-1',header=None,names=[item,'title','date','N','url',*[f'g{i}' for i in range(19)]])\n",
        "movies.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>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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEh9emt7dzel",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0cc511a7-0a5a-46a4-e82a-fc992a502cd7"
      },
      "source": [
        "len(ratings)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lfzuNSvd3jE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5a923fcb-4dad-4697-b1e8-d020f20617f7"
      },
      "source": [
        "rating_movie = ratings.merge(movies[[item,title]])\n",
        "rating_movie.head()"
      ],
      "execution_count": 34,
      "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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvbS4TdaeCDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d7bf977f-7a04-4fd2-dd0f-cc57acbdf806"
      },
      "source": [
        "data = CollabDataBunch.from_df(rating_movie,seed=42,valid_pct=0.1,item_name=title)\n",
        "data.show_batch()"
      ],
      "execution_count": 35,
      "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>539</td>\n",
              "      <td>Groundhog Day (1993)</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>268</td>\n",
              "      <td>Phantom, The (1996)</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>521</td>\n",
              "      <td>So I Married an Axe Murderer (1993)</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>214</td>\n",
              "      <td>Forrest Gump (1994)</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>727</td>\n",
              "      <td>Wes Craven's New Nightmare (1994)</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFi3E8nyeOoy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_range=[0,5.5]\n",
        "learn = collab_learner(data,n_factors=40,y_range=y_rang,wd=1e-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqjFd23NeY7q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "4a575a09-3207-4031-91cd-0392362de3cc"
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot(skip_end=15)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<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",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='79' class='' max='1406', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      5.62% [79/1406 00:00<00:10 2.1994]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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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vLeKtb7PqNRzatEyuJRhV3aOqy53nhUAGEFelzGE9+S0MB+wbaYyLAgKEs5Oi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\nFcavZ66jtLzC3+H4jZstmDL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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQK1nEqjefL2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f62855d6-3e12-4c0d-a489-17cefb4a59d4"
      },
      "source": [
        "learn.fit_one_cycle(5,5e-3)"
      ],
      "execution_count": 38,
      "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>0.967709</td>\n",
              "      <td>0.960572</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.877084</td>\n",
              "      <td>0.891843</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.798651</td>\n",
              "      <td>0.849343</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.710402</td>\n",
              "      <td>0.820386</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.653508</td>\n",
              "      <td>0.818433</td>\n",
              "      <td>00:09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "My0rhql2emQz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('dotprod')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqqdPAWQe0qQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "e5a754c9-cf4d-4f0a-b5ae-270d4498a731"
      },
      "source": [
        "learn.load('dotprod')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CollabLearner(data=TabularDataBunch;\n",
              "\n",
              "Train: LabelList (90000 items)\n",
              "x: CollabList\n",
              "userId 196; title Kolya (1996); ,userId 63; title Kolya (1996); ,userId 226; title Kolya (1996); ,userId 154; title Kolya (1996); ,userId 306; title Kolya (1996); \n",
              "y: FloatList\n",
              "3.0,3.0,5.0,3.0,5.0\n",
              "Path: .;\n",
              "\n",
              "Valid: LabelList (10000 items)\n",
              "x: CollabList\n",
              "userId 498; title Casino (1995); ,userId 642; title Pocahontas (1995); ,userId 58; title 2001: A Space Odyssey (1968); ,userId 495; title Cat People (1982); ,userId 618; title Philadelphia (1993); \n",
              "y: FloatList\n",
              "3.0,5.0,4.0,3.0,3.0\n",
              "Path: .;\n",
              "\n",
              "Test: None, model=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",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of MSELoss(), metrics=[], true_wd=True, bn_wd=True, wd=0.1, train_bn=True, path=PosixPath('.'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[], layer_groups=[Sequential(\n",
              "  (0): Embedding(944, 40)\n",
              "  (1): Embedding(1654, 40)\n",
              "  (2): Embedding(944, 1)\n",
              "  (3): Embedding(1654, 1)\n",
              ")], add_time=True, silent=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTyVqu_je38Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2be942db-1c0d-48c3-b480-4ad44c40977f"
      },
      "source": [
        "learn.model"
      ],
      "execution_count": 41,
      "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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xiDLc5Pe5i-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7f3a6de9-ca7f-43c2-fc2b-1efb0dfa4be9"
      },
      "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": 42,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_KYVzdvfKo8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ab823d4-f465-4e4b-faa1-71aa7d4a1649"
      },
      "source": [
        "movie_bias = learn.bias(top_movies,is_item=True)\n",
        "movie_bias.shape"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwd_ZQtVfUaC",
        "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": "_TP-C5AjfkOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "item0 = lambda o:o[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdlNDz-ifpxO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "72fc121e-8ec4-4b53-f7c6-a2f5491ad56d"
      },
      "source": [
        "sorted(movie_ratings,key=item0)[:15]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.3317),\n",
              "  'Children of the Corn: The Gathering (1996)',\n",
              "  1.3157894736842106),\n",
              " (tensor(-0.2536),\n",
              "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
              "  1.7142857142857142),\n",
              " (tensor(-0.2121), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
              " (tensor(-0.2105), 'Bio-Dome (1996)', 1.903225806451613),\n",
              " (tensor(-0.2066), 'Cable Guy, The (1996)', 2.339622641509434),\n",
              " (tensor(-0.2059), 'Striptease (1996)', 2.2388059701492535),\n",
              " (tensor(-0.1918), 'Thinner (1996)', 2.4489795918367347),\n",
              " (tensor(-0.1911), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
              " (tensor(-0.1705), \"Joe's Apartment (1996)\", 2.2444444444444445),\n",
              " (tensor(-0.1703), 'Barb Wire (1996)', 1.9333333333333333),\n",
              " (tensor(-0.1662), 'Home Alone 3 (1997)', 1.894736842105263),\n",
              " (tensor(-0.1660),\n",
              "  'Tales from the Crypt Presents: Bordello of Blood (1996)',\n",
              "  2.6363636363636362),\n",
              " (tensor(-0.1659), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),\n",
              " (tensor(-0.1617), 'Beautician and the Beast, The (1997)', 2.313953488372093),\n",
              " (tensor(-0.1567), 'Booty Call (1997)', 2.1666666666666665)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0khE00brfvAe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "aa16abd6-f836-4383-bd98-b39c7ff11bde"
      },
      "source": [
        "sorted(movie_ratings,key=lambda o:o[0],reverse=True)[:15]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.6198), 'L.A. Confidential (1997)', 4.161616161616162),\n",
              " (tensor(0.6124), 'Titanic (1997)', 4.2457142857142856),\n",
              " (tensor(0.5784), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
              " (tensor(0.5773), \"Schindler's List (1993)\", 4.466442953020135),\n",
              " (tensor(0.5693), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
              " (tensor(0.5271), 'Good Will Hunting (1997)', 4.262626262626263),\n",
              " (tensor(0.5216), 'Rear Window (1954)', 4.3875598086124405),\n",
              " (tensor(0.5127), 'Star Wars (1977)', 4.3584905660377355),\n",
              " (tensor(0.5099), 'Apt Pupil (1998)', 4.1),\n",
              " (tensor(0.5034), 'As Good As It Gets (1997)', 4.196428571428571),\n",
              " (tensor(0.4820), 'Casablanca (1942)', 4.45679012345679),\n",
              " (tensor(0.4773), 'Godfather, The (1972)', 4.283292978208232),\n",
              " (tensor(0.4772), \"One Flew Over the Cuckoo's Nest (1975)\", 4.291666666666667),\n",
              " (tensor(0.4697), 'Boot, Das (1981)', 4.203980099502488),\n",
              " (tensor(0.4653), 'Contact (1997)', 3.8035363457760316)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THQdmnAKf26L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movie_w = learn.weight(top_movies,is_item=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4t8pqxp-f_Sq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b0895b0-8096-47f4-aa9a-572ede18da66"
      },
      "source": [
        "movie_w.shape"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 40])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFuJLB_cgA7D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bdeb6639-6872-4713-aeb8-d973d99ff394"
      },
      "source": [
        "movie_pca = movie_w.pca(3)\n",
        "movie_pca.shape"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekWvAIX9gGtU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fac0,fac1,fac2 = movie_pca.t()\n",
        "movie_camp = [(f,i) for f,i in zip(fac0,top_movies)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14aYpTb-gSYe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "877bd110-b186-4586-ec36-a55390251797"
      },
      "source": [
        "sorted(movie_camp,key=itemgetter(0),reverse=True)[:10]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.3725), \"McHale's Navy (1997)\"),\n",
              " (tensor(1.3212), 'Children of the Corn: The Gathering (1996)'),\n",
              " (tensor(1.2841), 'Home Alone 3 (1997)'),\n",
              " (tensor(1.2428), 'Jungle2Jungle (1997)'),\n",
              " (tensor(1.1749), 'D3: The Mighty Ducks (1996)'),\n",
              " (tensor(1.1705), 'Leave It to Beaver (1997)'),\n",
              " (tensor(1.1593), 'Bio-Dome (1996)'),\n",
              " (tensor(1.1377), 'Congo (1995)'),\n",
              " (tensor(1.1274), 'Free Willy 3: The Rescue (1997)'),\n",
              " (tensor(1.1011), 'Mortal Kombat: Annihilation (1997)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTkFsDz1gZcg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "377db2c9-dc1f-4b2d-d85b-f4346aa51f59"
      },
      "source": [
        "sorted(movie_camp,key=itemgetter(0))[:10]"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-1.4961), 'Close Shave, A (1995)'),\n",
              " (tensor(-1.4613), 'Wrong Trousers, The (1993)'),\n",
              " (tensor(-1.4556), 'Casablanca (1942)'),\n",
              " (tensor(-1.2574), 'Godfather, The (1972)'),\n",
              " (tensor(-1.2426), 'Usual Suspects, The (1995)'),\n",
              " (tensor(-1.2320), 'Chinatown (1974)'),\n",
              " (tensor(-1.2061), 'Rear Window (1954)'),\n",
              " (tensor(-1.2025), 'North by Northwest (1959)'),\n",
              " (tensor(-1.1896), \"Schindler's List (1993)\"),\n",
              " (tensor(-1.1894), 'Citizen Kane (1941)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWMOX7qyghtE",
        "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": "DNSgCil0gqNs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "9e42341e-4a96-46f0-d5f1-b77217c3d79e"
      },
      "source": [
        "sorted(movie_comp,key=itemgetter(0),reverse=True)[:10]"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.3421), 'Raiders of the Lost Ark (1981)'),\n",
              " (tensor(1.3048), 'Braveheart (1995)'),\n",
              " (tensor(1.2275), 'Titanic (1997)'),\n",
              " (tensor(1.0924), 'Star Wars (1977)'),\n",
              " (tensor(0.9867), \"Schindler's List (1993)\"),\n",
              " (tensor(0.9679), \"It's a Wonderful Life (1946)\"),\n",
              " (tensor(0.9670), 'Independence Day (ID4) (1996)'),\n",
              " (tensor(0.9657), 'Return of the Jedi (1983)'),\n",
              " (tensor(0.9655), 'Forrest Gump (1994)'),\n",
              " (tensor(0.9554), 'Indiana Jones and the Last Crusade (1989)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKgXSlBPgvSF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4d99434b-75c0-49fd-b096-2f6f621d92fe"
      },
      "source": [
        "sorted(movie_comp,key=itemgetter(0))[:10]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.9195), 'Trainspotting (1996)'),\n",
              " (tensor(-0.9098), 'Ready to Wear (Pret-A-Porter) (1994)'),\n",
              " (tensor(-0.8790), 'Clockwork Orange, A (1971)'),\n",
              " (tensor(-0.8705), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),\n",
              " (tensor(-0.8072), 'Crumb (1994)'),\n",
              " (tensor(-0.7763), 'Serial Mom (1994)'),\n",
              " (tensor(-0.7671), 'Stuart Saves His Family (1995)'),\n",
              " (tensor(-0.7524), 'Keys to Tulsa (1997)'),\n",
              " (tensor(-0.7390), 'Brazil (1985)'),\n",
              " (tensor(-0.7378), 'Sweet Hereafter, The (1997)')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZchC80cg0NK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "outputId": "2e4e4cfd-378c-4f57-e494-892b4eb24660"
      },
      "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": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S0tOOAkW7H+/ZsfeqjTM7Kwd3DzeCQgKx2+3MmzHfUebt601+bp7jWUpff18q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XHXId5/s7vxpUcd/4qcv3KdegFVHnue4TnkGF/SCPFfJyvJhRBCCPHM5W5NFpGYgR5Dgp/ZO6+x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\nKq40HNiE57efMcF7NGbW5rj4lCY+MhaA2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L2L6o3LUrVRGRycs35mJiemEXIvgqadtV8o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\ndM1R9tazq0zfPIJvey2npG3WHVjTVGlYmhQpVB8EQRAEQRAEQfh4xBTlD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Zp2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\nRERERMQl/D+QjDHgmVWDFAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "METtdr33hRiI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}