Galera_David_Sistemes_recomanadors_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Movielens dataset: Regles d'associació\n",
        "\n",
        "David Galera"
      ],
      "metadata": {
        "id": "2fse14vbsUSe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AU27CCUWDpMV",
        "outputId": "8801093f-31ff-4c50-d714-50bc0352e954"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
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        "id": "GK5V2foz9nRN",
        "outputId": "92956487-0beb-480d-b058-b054555194fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ratings shape: (100836, 4)\n",
            "Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "   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",
              "3       1       47     5.0  964983815\n",
              "4       1       50     5.0  964982931"
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "ratings = pd.read_csv(\"/content/drive/MyDrive/Advanced ML/ratings.csv\")\n",
        "print('ratings shape:',ratings.shape)\n",
        "print(ratings.columns)\n",
        "ratings.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies = pd.read_csv(\"/content/drive/MyDrive/Advanced ML/movies.csv\")\n",
        "print('movies shape:',movies.shape)\n",
        "print(movies.columns)\n",
        "movies.head()"
      ],
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        "colab": {
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      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "movies shape: (9742, 3)\n",
            "Index(['movieId', 'title', 'genres'], dtype='object')\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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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Inserim una columna adicional amb el títol de la pel·lícula"
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      }
    },
    {
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      "source": [
        "ratings = pd.merge(ratings,movies[['movieId','title']],on=['movieId'],how='left')\n",
        "#print(ratings.dtypes)\n",
        "ratings.head()"
      ],
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        },
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      "execution_count": 31,
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              "   userId  movieId  rating  timestamp                        title\n",
              "0       1        1     4.0  964982703             Toy Story (1995)\n",
              "1       1        3     4.0  964981247      Grumpier Old Men (1995)\n",
              "2       1        6     4.0  964982224                  Heat (1995)\n",
              "3       1       47     5.0  964983815  Seven (a.k.a. Se7en) (1995)\n",
              "4       1       50     5.0  964982931   Usual Suspects, The (1995)"
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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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLL1khde9nRO",
        "outputId": "708e056d-5ff0-483d-8dc1-b011d057362e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número d'usuaris: 610\n",
            "Número de pel·lícules: 9719\n"
          ]
        }
      ],
      "source": [
        "print(\"Número d'usuaris:\",len(ratings.userId.unique()))#610 usuaris\n",
        "print(\"Número de pel·lícules:\",len(ratings.title.unique()))#9719 pel·lícules"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creem un DataFrame on cada instància representa un usuari, cada columna representa una pel·lícula i els valors corresponen als ratings dels l'usuaris a les pel·lícules. Les pel·lícules no puntuades pels usuaris les omplim amb un valor de -1."
      ],
      "metadata": {
        "id": "oR1zolur71SD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pivot=ratings.pivot_table(values='rating',index='userId',columns='title',fill_value=-1)\n",
        "pivot.reset_index(drop=True,inplace=True)\n",
        "pivot.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "4hjdpkPR7t8N",
        "outputId": "cb74ae1b-06c3-4f4c-c15a-0e32ab8047fc"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title  '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
              "0              -1                                       -1   \n",
              "1              -1                                       -1   \n",
              "2              -1                                       -1   \n",
              "3              -1                                       -1   \n",
              "4              -1                                       -1   \n",
              "\n",
              "title  'Round Midnight (1986)  'Salem's Lot (2004)  'Til There Was You (1997)  \\\n",
              "0                        -1.0                   -1                         -1   \n",
              "1                        -1.0                   -1                         -1   \n",
              "2                        -1.0                   -1                         -1   \n",
              "3                        -1.0                   -1                         -1   \n",
              "4                        -1.0                   -1                         -1   \n",
              "\n",
              "title  'Tis the Season for Love (2015)  'burbs, The (1989)  \\\n",
              "0                                 -1.0                -1.0   \n",
              "1                                 -1.0                -1.0   \n",
              "2                                 -1.0                -1.0   \n",
              "3                                 -1.0                -1.0   \n",
              "4                                 -1.0                -1.0   \n",
              "\n",
              "title  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
              "0                        -1                         -1.0   \n",
              "1                        -1                         -1.0   \n",
              "2                        -1                         -1.0   \n",
              "3                        -1                         -1.0   \n",
              "4                        -1                         -1.0   \n",
              "\n",
              "title  *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
              "0                                -1.0  ...         -1.0          -1.0   \n",
              "1                                -1.0  ...         -1.0          -1.0   \n",
              "2                                -1.0  ...         -1.0          -1.0   \n",
              "3                                -1.0  ...         -1.0          -1.0   \n",
              "4                                -1.0  ...         -1.0          -1.0   \n",
              "\n",
              "title  [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
              "0               -1.0                       -1   \n",
              "1               -1.0                       -1   \n",
              "2               -1.0                       -1   \n",
              "3               -1.0                       -1   \n",
              "4               -1.0                       -1   \n",
              "\n",
              "title  anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
              "0                                                     -1        \n",
              "1                                                     -1        \n",
              "2                                                     -1        \n",
              "3                                                     -1        \n",
              "4                                                     -1        \n",
              "\n",
              "title  eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
              "0                 -1.0        -1.0                            -1.0   \n",
              "1                 -1.0        -1.0                            -1.0   \n",
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              "3                 -1.0        -1.0                            -1.0   \n",
              "4                 -1.0        -1.0                            -1.0   \n",
              "\n",
              "title  ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
              "0                        4.0                                         -1  \n",
              "1                       -1.0                                         -1  \n",
              "2                       -1.0                                         -1  \n",
              "3                       -1.0                                         -1  \n",
              "4                       -1.0                                         -1  \n",
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              "\n",
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              "          document.querySelector('#df-656d32b6-fb17-44da-a2df-08dfe1167498 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-656d32b6-fb17-44da-a2df-08dfe1167498');\n",
              "          const dataTable =\n",
              "            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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podem veure per exemple com el primer usuari ha votat 232 pel·lícules amb un *rating* mitjà de 4.36"
      ],
      "metadata": {
        "id": "MHngllEm9pim"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(pivot.iloc[0,:][pivot.iloc[0,:]>=0].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WpCNGbo8YBq",
        "outputId": "addbbbc2-559c-4e5d-ce29-4ab5d0f3b987"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    232.000000\n",
            "mean       4.366379\n",
            "std        0.800048\n",
            "min        1.000000\n",
            "25%        4.000000\n",
            "50%        5.000000\n",
            "75%        5.000000\n",
            "max        5.000000\n",
            "Name: 0, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculem el rating promig de tot el DataFrame."
      ],
      "metadata": {
        "id": "R2D3nhuz99Uh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.rating[ratings.rating>=0].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQmHvUSQ-SRc",
        "outputId": "3db531d5-02c4-4123-98d5-ce79945765fd"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    100836.000000\n",
              "mean          3.501557\n",
              "std           1.042529\n",
              "min           0.500000\n",
              "25%           3.000000\n",
              "50%           3.500000\n",
              "75%           4.000000\n",
              "max           5.000000\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Establim un rating mínim de 2.5 (equivalent a 5 sobre 10) per classificar la pel·lícula com *puntuada favorablement* per l'usuari. És a dir, si cada usuari representa una transacció, la pel·lícula en qüestió forma part de la transacció si ha estat puntuada amb un rating>=2.5."
      ],
      "metadata": {
        "id": "o4JLSnXD-ZU3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "VnvRZFot9nRP",
        "outputId": "c6d2c105-d2fb-40cc-dbcc-68f7c6b069c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "226\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title  '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
              "0           False                                    False   \n",
              "1           False                                    False   \n",
              "2           False                                    False   \n",
              "3           False                                    False   \n",
              "4           False                                    False   \n",
              "\n",
              "title  'Round Midnight (1986)  'Salem's Lot (2004)  'Til There Was You (1997)  \\\n",
              "0                       False                False                      False   \n",
              "1                       False                False                      False   \n",
              "2                       False                False                      False   \n",
              "3                       False                False                      False   \n",
              "4                       False                False                      False   \n",
              "\n",
              "title  'Tis the Season for Love (2015)  'burbs, The (1989)  \\\n",
              "0                                False               False   \n",
              "1                                False               False   \n",
              "2                                False               False   \n",
              "3                                False               False   \n",
              "4                                False               False   \n",
              "\n",
              "title  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
              "0                     False                        False   \n",
              "1                     False                        False   \n",
              "2                     False                        False   \n",
              "3                     False                        False   \n",
              "4                     False                        False   \n",
              "\n",
              "title  *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
              "0                               False  ...        False         False   \n",
              "1                               False  ...        False         False   \n",
              "2                               False  ...        False         False   \n",
              "3                               False  ...        False         False   \n",
              "4                               False  ...        False         False   \n",
              "\n",
              "title  [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
              "0              False                    False   \n",
              "1              False                    False   \n",
              "2              False                    False   \n",
              "3              False                    False   \n",
              "4              False                    False   \n",
              "\n",
              "title  anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
              "0                                                  False        \n",
              "1                                                  False        \n",
              "2                                                  False        \n",
              "3                                                  False        \n",
              "4                                                  False        \n",
              "\n",
              "title  eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
              "0                False       False                           False   \n",
              "1                False       False                           False   \n",
              "2                False       False                           False   \n",
              "3                False       False                           False   \n",
              "4                False       False                           False   \n",
              "\n",
              "title  ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
              "0                       True                                      False  \n",
              "1                      False                                      False  \n",
              "2                      False                                      False  \n",
              "3                      False                                      False  \n",
              "4                      False                                      False  \n",
              "\n",
              "[5 rows x 9719 columns]"
            ],
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              "      <th>'71 (2014)</th>\n",
              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "llindar = 2.5\n",
        "pivot = pivot.transform(lambda x: x >= llindar)\n",
        "print(pivot.iloc[0,:].sum())\n",
        "pivot.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extraiem els itemsets frequents del DataFrame que apareixen com a mínim en un 20% de les transaccions utilitzant l'algorisme *apriori*"
      ],
      "metadata": {
        "id": "wOlaP62S_kjD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQr-OPwP9nRP",
        "outputId": "253633bd-2a9e-478a-d282-5d4a1723ad8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      support                                           itemsets\n",
            "220  0.204918  (Usual Suspects, The (1995), Forrest Gump (199...\n",
            "145  0.204918  (Jurassic Park (1993), Star Wars: Episode IV -...\n",
            "150  0.204918  (Lord of the Rings: The Fellowship of the Ring...\n",
            "84   0.203279              (Batman (1989), Jurassic Park (1993))\n",
            "78   0.203279           (Jurassic Park (1993), Apollo 13 (1995))\n",
            "101  0.203279  (Fight Club (1999), Lord of the Rings: The Fel...\n",
            "141  0.203279  (Godfather, The (1972), Star Wars: Episode IV ...\n",
            "200  0.203279  (Terminator 2: Judgment Day (1991), Star Wars:...\n",
            "212  0.203279  (Forrest Gump (1994), Jurassic Park (1993), Si...\n",
            "23   0.203279                                (Goodfellas (1990))\n",
            "140  0.203279  (Godfather, The (1972), Silence of the Lambs, ...\n",
            "61   0.201639                                  (Stargate (1994))\n",
            "106  0.201639  (Fight Club (1999), Star Wars: Episode IV - A ...\n",
            "137  0.201639        (Matrix, The (1999), Godfather, The (1972))\n",
            "226  0.201639  (Matrix, The (1999), Lord of the Rings: The Fe...\n",
            "207  0.201639  (Fight Club (1999), Forrest Gump (1994), Shaws...\n",
            "237  0.201639  (Star Wars: Episode V - The Empire Strikes Bac...\n",
            "179  0.201639  (Saving Private Ryan (1998), Shawshank Redempt...\n",
            "239  0.200000  (Star Wars: Episode IV - A New Hope (1977), St...\n",
            "178  0.200000  (Star Wars: Episode VI - Return of the Jedi (1...\n",
            "158  0.200000      (Matrix, The (1999), Schindler's List (1993))\n",
            "76   0.200000              (Braveheart (1995), Apollo 13 (1995))\n",
            "136  0.200000  (Godfather: Part II, The (1974), Godfather, Th...\n",
            "81   0.200000  (Apollo 13 (1995), Silence of the Lambs, The (...\n",
            "204  0.200000  (Forrest Gump (1994), Braveheart (1995), Silen...\n",
            "128  0.200000            (Forrest Gump (1994), True Lies (1994))\n",
            "113  0.200000  (Lord of the Rings: The Two Towers, The (2002)...\n",
            "98   0.200000   (Fargo (1996), Shawshank Redemption, The (1994))\n",
            "180  0.200000  (Star Wars: Episode IV - A New Hope (1977), Sa...\n",
            "229  0.200000  (Raiders of the Lost Ark (Indiana Jones and th...\n",
            "(243, 2)\n"
          ]
        }
      ],
      "source": [
        "from mlxtend.frequent_patterns import apriori\n",
        "freq_itemsets = apriori(pivot,min_support=0.2,use_colnames=True)\n",
        "print(freq_itemsets.sort_values('support',ascending=False).tail(30))\n",
        "print(freq_itemsets.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definim les regles d'associació d'interés quan la confiança>=0.5"
      ],
      "metadata": {
        "id": "4Z-IZXxogi1V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import association_rules\n",
        "asrules = association_rules(freq_itemsets,metric = \"confidence\", min_threshold = 0.5)\n",
        "print(asrules.shape)\n",
        "asrules.head(12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "w1zplxx4AF2-",
        "outputId": "a6852042-c0da-480a-dc71-21df1d1b45dc"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(410, 9)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 antecedents                         consequents  \\\n",
              "0           (Aladdin (1992))               (Forrest Gump (1994))   \n",
              "1        (Fight Club (1999))            (American Beauty (1999))   \n",
              "2   (American Beauty (1999))                 (Fight Club (1999))   \n",
              "3   (American Beauty (1999))               (Forrest Gump (1994))   \n",
              "4       (Matrix, The (1999))            (American Beauty (1999))   \n",
              "5   (American Beauty (1999))                (Matrix, The (1999))   \n",
              "6   (American Beauty (1999))               (Pulp Fiction (1994))   \n",
              "7   (American Beauty (1999))  (Shawshank Redemption, The (1994))   \n",
              "8   (American Beauty (1999))  (Silence of the Lambs, The (1991))   \n",
              "9        (Braveheart (1995))                  (Apollo 13 (1995))   \n",
              "10        (Apollo 13 (1995))                 (Braveheart (1995))   \n",
              "11        (Apollo 13 (1995))               (Forrest Gump (1994))   \n",
              "\n",
              "    antecedent support  consequent support   support  confidence      lift  \\\n",
              "0             0.280328            0.521311  0.216393    0.771930  1.480746   \n",
              "1             0.342623            0.314754  0.208197    0.607656  1.930572   \n",
              "2             0.314754            0.342623  0.208197    0.661458  1.930572   \n",
              "3             0.314754            0.521311  0.211475    0.671875  1.288817   \n",
              "4             0.432787            0.314754  0.222951    0.515152  1.636679   \n",
              "5             0.314754            0.432787  0.222951    0.708333  1.636679   \n",
              "6             0.314754            0.475410  0.224590    0.713542  1.500898   \n",
              "7             0.314754            0.513115  0.209836    0.666667  1.299255   \n",
              "8             0.314754            0.442623  0.206557    0.656250  1.482639   \n",
              "9             0.367213            0.308197  0.200000    0.544643  1.767192   \n",
              "10            0.308197            0.367213  0.200000    0.648936  1.767192   \n",
              "11            0.308197            0.521311  0.252459    0.819149  1.571323   \n",
              "\n",
              "    leverage  conviction  \n",
              "0   0.070255    2.098865  \n",
              "1   0.100355    1.746541  \n",
              "2   0.100355    1.941791  \n",
              "3   0.047390    1.458860  \n",
              "4   0.086729    1.413320  \n",
              "5   0.086729    1.944731  \n",
              "6   0.074953    1.831297  \n",
              "7   0.048331    1.460656  \n",
              "8   0.067240    1.621461  \n",
              "9   0.086826    1.519254  \n",
              "10  0.086826    1.802484  \n",
              "11  0.091793    2.646866  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Aladdin (1992))</td>\n",
              "      <td>(Forrest Gump (1994))</td>\n",
              "      <td>0.280328</td>\n",
              "      <td>0.521311</td>\n",
              "      <td>0.216393</td>\n",
              "      <td>0.771930</td>\n",
              "      <td>1.480746</td>\n",
              "      <td>0.070255</td>\n",
              "      <td>2.098865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Fight Club (1999))</td>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>0.342623</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.208197</td>\n",
              "      <td>0.607656</td>\n",
              "      <td>1.930572</td>\n",
              "      <td>0.100355</td>\n",
              "      <td>1.746541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Fight Club (1999))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.342623</td>\n",
              "      <td>0.208197</td>\n",
              "      <td>0.661458</td>\n",
              "      <td>1.930572</td>\n",
              "      <td>0.100355</td>\n",
              "      <td>1.941791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Forrest Gump (1994))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.521311</td>\n",
              "      <td>0.211475</td>\n",
              "      <td>0.671875</td>\n",
              "      <td>1.288817</td>\n",
              "      <td>0.047390</td>\n",
              "      <td>1.458860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Matrix, The (1999))</td>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>0.432787</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.222951</td>\n",
              "      <td>0.515152</td>\n",
              "      <td>1.636679</td>\n",
              "      <td>0.086729</td>\n",
              "      <td>1.413320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Matrix, The (1999))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.432787</td>\n",
              "      <td>0.222951</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>1.636679</td>\n",
              "      <td>0.086729</td>\n",
              "      <td>1.944731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Pulp Fiction (1994))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.475410</td>\n",
              "      <td>0.224590</td>\n",
              "      <td>0.713542</td>\n",
              "      <td>1.500898</td>\n",
              "      <td>0.074953</td>\n",
              "      <td>1.831297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Shawshank Redemption, The (1994))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.513115</td>\n",
              "      <td>0.209836</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.299255</td>\n",
              "      <td>0.048331</td>\n",
              "      <td>1.460656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(American Beauty (1999))</td>\n",
              "      <td>(Silence of the Lambs, The (1991))</td>\n",
              "      <td>0.314754</td>\n",
              "      <td>0.442623</td>\n",
              "      <td>0.206557</td>\n",
              "      <td>0.656250</td>\n",
              "      <td>1.482639</td>\n",
              "      <td>0.067240</td>\n",
              "      <td>1.621461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Braveheart (1995))</td>\n",
              "      <td>(Apollo 13 (1995))</td>\n",
              "      <td>0.367213</td>\n",
              "      <td>0.308197</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.544643</td>\n",
              "      <td>1.767192</td>\n",
              "      <td>0.086826</td>\n",
              "      <td>1.519254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(Apollo 13 (1995))</td>\n",
              "      <td>(Braveheart (1995))</td>\n",
              "      <td>0.308197</td>\n",
              "      <td>0.367213</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.648936</td>\n",
              "      <td>1.767192</td>\n",
              "      <td>0.086826</td>\n",
              "      <td>1.802484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(Apollo 13 (1995))</td>\n",
              "      <td>(Forrest Gump (1994))</td>\n",
              "      <td>0.308197</td>\n",
              "      <td>0.521311</td>\n",
              "      <td>0.252459</td>\n",
              "      <td>0.819149</td>\n",
              "      <td>1.571323</td>\n",
              "      <td>0.091793</td>\n",
              "      <td>2.646866</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recomanació de pel·lícules donat el títol d'una o de vàries pel·lícules."
      ],
      "metadata": {
        "id": "E3GxLHaVBGYz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defineixo una funció que retorna una llista amb els títols introduïts per l'usuari dels quals vol recomanacions similars. En l'exemple, busquem pel·lícules similars a *American Beauty (1999)*"
      ],
      "metadata": {
        "id": "dWrqbCwWBQaA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pelicules_similars():\n",
        "  print('Pel·lícules similars a:')\n",
        "  print(\"-------------------------\")\n",
        "  titols_similars_a = input(\"Introdueix una o vàries pel·lícules separades per coma:\")\n",
        "  #American Beauty (1999)\n",
        "  my_list = titols_similars_a.split(\",\")\n",
        "  if all(x in list(movies.title) for x in my_list) == False:\n",
        "      print('Títol/s erroni')\n",
        "  else:\n",
        "      return my_list\n",
        "\n",
        "similar_titles_to = pelicules_similars()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V00v1wNFBU5A",
        "outputId": "fb8166c8-1c82-4706-fedb-d7088891f1c8"
      },
      "execution_count": 48,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pel·lícules similars a:\n",
            "-------------------------\n",
            "Introdueix una o vàries pel·lícules separades per coma:American Beauty (1999)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Busquem dins el DataFrame de regles d'associació les regles on tots els *antecedents* formen part de la llista **similar_titles_to** i recomanem els títols de la columna *consequents* sempre i quan aquests no figurin a la llista **similar_titles_to**."
      ],
      "metadata": {
        "id": "KdTp0j89Cy_U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def troba_titols_per_recomanar(asrules, titles_list):\n",
        "    titols = []\n",
        "    for i in asrules.index:#itera entre totes les regles d'associació\n",
        "        if asrules[\"antecedents\"][i].issubset(frozenset(similar_titles_to)):#filtra les regles d'associació on TOTS els antecedents són un subset de la llista similar_titles_to\n",
        "            titols.extend(list(asrules[\"consequents\"][i]))#afegeix els títols conseqüents a la regla     \n",
        "    return set(titols).difference(set(similar_titles_to))#retorna els títols que són conseqüents i que no ha introduït l'usuari\n",
        "titols_recomanats = troba_titols_per_recomanar(asrules, similar_titles_to)\n",
        "print(len(titols_recomanats))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zc0wSP29BzJh",
        "outputId": "0fa2c2ab-f468-42e3-87a6-d53a638cb39d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imprimim els títols recomanats a l'usuari."
      ],
      "metadata": {
        "id": "ZhaYwHF-D0jm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def escriu_titols(llista_titols):\n",
        "    for titol in llista_titols:\n",
        "        print(titol)\n",
        "        print(\"-------------------------\")\n",
        "\n",
        "escriu_titols(titols_recomanats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7hU2HkKD79J",
        "outputId": "556160b9-69a5-46d4-eee2-c9f8ed71c131"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fight Club (1999)\n",
            "-------------------------\n",
            "Forrest Gump (1994)\n",
            "-------------------------\n",
            "Silence of the Lambs, The (1991)\n",
            "-------------------------\n",
            "Shawshank Redemption, The (1994)\n",
            "-------------------------\n",
            "Matrix, The (1999)\n",
            "-------------------------\n",
            "Pulp Fiction (1994)\n",
            "-------------------------\n"
          ]
        }
      ]
    }
  ],
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