Galera_David_Sistemes_recomanadors.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "QsneMaH70ViE"
      },
      "source": [
        "# Movielens dataset\n",
        "\n",
        "David Galera"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXUqzNOE01Su",
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      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "ratings = pd.read_csv(\"/content/drive/MyDrive/Advanced ML/ratings.csv\")\n",
        "print(ratings.shape)\n",
        "ratings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100836, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "movies = pd.read_csv(\"/content/drive/MyDrive/Advanced ML/movies.csv\")\n",
        "print(movies.shape)\n",
        "movies.head()"
      ],
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        "colab": {
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          "height": 223
        },
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9742, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Primer calculem el *rating* promig de cada pel·lícula:"
      ],
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      }
    },
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      "source": [
        "ratings_mean=ratings.groupby('movieId')['rating'].mean().rename('Mean_rating').reset_index()\n",
        "ratings_mean.head()"
      ],
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      "execution_count": 5,
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              "   movieId  Mean_rating\n",
              "0        1     3.920930\n",
              "1        2     3.431818\n",
              "2        3     3.259615\n",
              "3        4     2.357143\n",
              "4        5     3.071429"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculem la mitjana amortida del *rating* de cada pel·lícula amb alfa=5:"
      ],
      "metadata": {
        "id": "0lMD66Ei1lp8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alfa=5"
      ],
      "metadata": {
        "id": "cmvahSPy11vb"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primer calculem el sumatori de tots els *ratings* per a cada pel·lícula i posteriorment el mateix sumatori + alpha vegades el promig total de *ratings* (el dividend de l'equació de la mitjana amortida):"
      ],
      "metadata": {
        "id": "v6p64i2B2fZb"
      }
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    {
      "cell_type": "code",
      "source": [
        "ratings_sum=ratings.groupby('movieId')['rating'].sum().rename('sum_rating').reset_index()\n",
        "ratings_sum['sum_rating_factor']=ratings_sum['sum_rating']+alfa*(ratings['rating'].mean())\n",
        "ratings_sum.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "_-aggLyv3Zn7",
        "outputId": "11ad7920-cfa6-4cd2-e87b-df363846cb3a"
      },
      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   movieId  sum_rating  sum_rating_factor\n",
              "0        1       843.0         860.507785\n",
              "1        2       377.5         395.007785\n",
              "2        3       169.5         187.007785\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuació calculem el número de vegades que s'ha puntuat cada pel·lícula i repetim la operació sumant-hi alfa (el divisor de l'equació de la mitjana amortida):"
      ],
      "metadata": {
        "id": "9xOSokOM3vkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_count=ratings.groupby('movieId')['rating'].count().rename('count_rating').reset_index()\n",
        "ratings_count['count_rating_factor']=ratings_count['count_rating']+alfa\n",
        "ratings_count.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fD_VwUHF4MdN",
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      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId  count_rating  count_rating_factor\n",
              "0        1           215                  220\n",
              "1        2           110                  115\n",
              "2        3            52                   57\n",
              "3        4             7                   12\n",
              "4        5            49                   54"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Procedim a calcular la mitjana amortida: \n",
        "\n",
        "*sum_rating_factor*/*count_rating_factor*"
      ],
      "metadata": {
        "id": "HaLkGODF4VuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_damped=pd.merge(ratings_sum,ratings_count,on=['movieId'],how='left')\n",
        "ratings_damped['Damped_mean_rating']=ratings_damped['sum_rating_factor']/ratings_damped['count_rating_factor']\n",
        "ratings_damped.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "-gcS1R2O4s36",
        "outputId": "c6a4cdac-13c6-4c25-e9f7-93868e4f5349"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId  sum_rating  sum_rating_factor  count_rating  count_rating_factor  \\\n",
              "0        1       843.0         860.507785           215                  220   \n",
              "1        2       377.5         395.007785           110                  115   \n",
              "2        3       169.5         187.007785            52                   57   \n",
              "3        4        16.5          34.007785             7                   12   \n",
              "4        5       150.5         168.007785            49                   54   \n",
              "\n",
              "   Damped_mean_rating  \n",
              "0            3.911399  \n",
              "1            3.434850  \n",
              "2            3.280838  \n",
              "3            2.833982  \n",
              "4            3.111255  "
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              "      <td>150.5</td>\n",
              "      <td>168.007785</td>\n",
              "      <td>49</td>\n",
              "      <td>54</td>\n",
              "      <td>3.111255</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fusionem el DataFrame de la mitjana normal amb el de la mitjana amortida."
      ],
      "metadata": {
        "id": "hMYT9Yvs763T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_mean_dampmean=pd.merge(ratings_mean,ratings_damped[['movieId','Damped_mean_rating']],on=['movieId'],how='left')\n",
        "ratings_mean_dampmean.head()"
      ],
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          "height": 206
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        "id": "MWTqrU4_40sC",
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      "execution_count": 15,
      "outputs": [
        {
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            "text/plain": [
              "   movieId  Mean_rating  Damped_mean_rating\n",
              "0        1     3.920930            3.911399\n",
              "1        2     3.431818            3.434850\n",
              "2        3     3.259615            3.280838\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
          "execution_count": 15
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inserim el títol i gènere de les pel·lícules:"
      ],
      "metadata": {
        "id": "J-bEu5sa5EKx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_movies = ratings_mean_dampmean.merge(movies,on=\"movieId\")\n",
        "ratings_movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "I82TQHWH5Oej",
        "outputId": "d5a33970-8e57-4474-b03d-68521be66d51"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId  Mean_rating  Damped_mean_rating  \\\n",
              "0        1     3.920930            3.911399   \n",
              "1        2     3.431818            3.434850   \n",
              "2        3     3.259615            3.280838   \n",
              "3        4     2.357143            2.833982   \n",
              "4        5     3.071429            3.111255   \n",
              "\n",
              "                                title  \\\n",
              "0                    Toy Story (1995)   \n",
              "1                      Jumanji (1995)   \n",
              "2             Grumpier Old Men (1995)   \n",
              "3            Waiting to Exhale (1995)   \n",
              "4  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
            ],
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              "      <td>Comedy|Romance</td>\n",
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              "      <td>Waiting to Exhale (1995)</td>\n",
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              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ordenem el dataset segons la mitjana amortida"
      ],
      "metadata": {
        "id": "i34lF4Pn8aFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_movies.sort_values(\"Damped_mean_rating\",ascending=False,inplace=True)\n",
        "ratings_movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NXB-ms0R6Gd0",
        "outputId": "0328e564-2143-404b-8ceb-446ecaf75406"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movieId  Mean_rating  Damped_mean_rating  \\\n",
              "277       318     4.429022            4.414620   \n",
              "840      1104     4.475000            4.280311   \n",
              "9600   177593     4.750000            4.269830   \n",
              "659       858     4.289062            4.269075   \n",
              "2224     2959     4.272936            4.255640   \n",
              "\n",
              "                                                 title  \\\n",
              "277                   Shawshank Redemption, The (1994)   \n",
              "840                   Streetcar Named Desire, A (1951)   \n",
              "9600  Three Billboards Outside Ebbing, Missouri (2017)   \n",
              "659                              Godfather, The (1972)   \n",
              "2224                                 Fight Club (1999)   \n",
              "\n",
              "                           genres  \n",
              "277                   Crime|Drama  \n",
              "840                         Drama  \n",
              "9600                  Crime|Drama  \n",
              "659                   Crime|Drama  \n",
              "2224  Action|Crime|Drama|Thriller  "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quines són les *N* millors pelicules segons mitjana amortida?\n",
        "\n",
        "Imprimim els 5 primers títols."
      ],
      "metadata": {
        "id": "MbuSUlt-5kiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N=5\n",
        "for j in list(ratings_movies.head(N).title): print (j)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBRDu-Wo6oKX",
        "outputId": "2fa19970-752a-406d-f11c-b0a9f929d12e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shawshank Redemption, The (1994)\n",
            "Streetcar Named Desire, A (1951)\n",
            "Three Billboards Outside Ebbing, Missouri (2017)\n",
            "Godfather, The (1972)\n",
            "Fight Club (1999)\n"
          ]
        }
      ]
    }
  ],
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}