Aula02_Exercicios.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y0xocXpOp2QB"
   },
   "source": [
    "### Importar base de dados e instalar framework de recomendação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aYRhN3GYp2QO",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Importar bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [....................................................] 65019041 / 65019041\n",
      "Saved under ml-20m-compact.tar (3).gz\n",
      "dataset/\n",
      "dataset/tags_sample.csv\n",
      "dataset/._.DS_Store\n",
      "dataset/.DS_Store\n",
      "dataset/movies_sample.csv\n",
      "dataset/._genome-tags.csv\n",
      "dataset/genome-tags.csv\n",
      "dataset/._ml-youtube.csv\n",
      "dataset/ml-youtube.csv\n",
      "dataset/._genome-scores.csv\n",
      "dataset/genome-scores.csv\n",
      "dataset/ratings_sample.csv\n"
     ]
    }
   ],
   "source": [
    "import wget\n",
    "!python3 -m wget https://github.com/mmanzato/MBABigData/raw/master/ml-20m-compact.tar.gz\n",
    "!tar -xvzf ml-20m-compact.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: caserecommender in ./recsysintro/lib/python3.10/site-packages (1.1.1)\n",
      "Requirement already satisfied: pandas in ./recsysintro/lib/python3.10/site-packages (from caserecommender) (2.2.2)\n",
      "Requirement already satisfied: numpy in ./recsysintro/lib/python3.10/site-packages (from caserecommender) (1.26.4)\n",
      "Requirement already satisfied: scipy in ./recsysintro/lib/python3.10/site-packages (from caserecommender) (1.14.0)\n",
      "Requirement already satisfied: scikit-learn in ./recsysintro/lib/python3.10/site-packages (from caserecommender) (1.5.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in ./recsysintro/lib/python3.10/site-packages (from pandas->caserecommender) (2024.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./recsysintro/lib/python3.10/site-packages (from pandas->caserecommender) (2024.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in ./recsysintro/lib/python3.10/site-packages (from pandas->caserecommender) (2.9.0.post0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in ./recsysintro/lib/python3.10/site-packages (from scikit-learn->caserecommender) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in ./recsysintro/lib/python3.10/site-packages (from scikit-learn->caserecommender) (3.5.0)\n",
      "Requirement already satisfied: six>=1.5 in ./recsysintro/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->caserecommender) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install caserecommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1651265264070,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "MqgnlqsLp2QP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LQODRAP5p2QY",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Explorar Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1651265269193,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "vraDuP8Hp2QZ",
    "outputId": "b4070cdf-edfc-42b0-be21-d9056d125f36"
   },
   "outputs": [
    {
     "data": {
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       "<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>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>129857</td>\n",
       "      <td>Bright Days Ahead (2013)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>130344</td>\n",
       "      <td>Coast of Death (2013)</td>\n",
       "      <td>Children|Documentary|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>130408</td>\n",
       "      <td>A Rumor Of War (1980)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>130510</td>\n",
       "      <td>Berserk: The Golden Age Arc 3 - Descent (2013)</td>\n",
       "      <td>Action|Animation|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>131239</td>\n",
       "      <td>Three Quarter Moon (2011)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                           title  \\\n",
       "428   129857                        Bright Days Ahead (2013)   \n",
       "429   130344                           Coast of Death (2013)   \n",
       "430   130408                           A Rumor Of War (1980)   \n",
       "431   130510  Berserk: The Golden Age Arc 3 - Descent (2013)   \n",
       "432   131239                       Three Quarter Moon (2011)   \n",
       "\n",
       "                         genres  \n",
       "428               Drama|Romance  \n",
       "429  Children|Documentary|Drama  \n",
       "430          (no genres listed)  \n",
       "431    Action|Animation|Fantasy  \n",
       "432                Comedy|Drama  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('./dataset/movies_sample.csv')\n",
    "movies.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 463,
     "status": "ok",
     "timestamp": 1651265273880,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "Y3GWuHwSp2Qg",
    "outputId": "732f7d86-6431-4ea3-f4ea-5d74dffc313e"
   },
   "outputs": [
    {
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       "<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>11</td>\n",
       "      <td>7481</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1230788624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1046</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1251144145</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>616</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1230782542</td>\n",
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       "      <th>3</th>\n",
       "      <td>11</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1230784884</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>5669</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1230853788</td>\n",
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      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0      11     7481     5.0  1230788624\n",
       "1      11     1046     4.5  1251144145\n",
       "2      11      616     4.0  1230782542\n",
       "3      11     3535     2.0  1230784884\n",
       "4      11     5669     5.0  1230853788"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv('./dataset/ratings_sample.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "executionInfo": {
     "elapsed": 497,
     "status": "ok",
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     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "QdmnQ42Ep2Ql",
    "outputId": "6c629ef0-6232-4112-c575-4b38ed769372"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ratings.rating.value_counts().plot(kind='bar', color=['r', 'g', 'y', 'c', 'b']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 1669,
     "status": "ok",
     "timestamp": 1651265283090,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "1VK8tadsp2Qp",
    "outputId": "63852d92-1b62-4f8a-a6e8-975c2f724a57"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190616</th>\n",
       "      <td>138493</td>\n",
       "      <td>288</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190617</th>\n",
       "      <td>138493</td>\n",
       "      <td>1748</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190618</th>\n",
       "      <td>138493</td>\n",
       "      <td>616</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190619</th>\n",
       "      <td>138493</td>\n",
       "      <td>1597</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190620</th>\n",
       "      <td>138493</td>\n",
       "      <td>7371</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating\n",
       "190616  138493      288     5.0\n",
       "190617  138493     1748     5.0\n",
       "190618  138493      616     4.0\n",
       "190619  138493     1597     4.5\n",
       "190620  138493     7371     5.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ratings[['userId', 'movieId', 'rating']]\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 523,
     "status": "ok",
     "timestamp": 1651265294728,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "qmfmBkdep2Qs",
    "outputId": "bc56067b-e248-4ee3-e286-9cf831a6ce1c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>7481</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Enemy Mine (1985)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1046</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Beautiful Thing (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>616</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Aristocats, The (1970)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>3535</td>\n",
       "      <td>2.0</td>\n",
       "      <td>American Psycho (2000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>5669</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bowling for Columbine (2002)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating                         title\n",
       "0      11     7481     5.0             Enemy Mine (1985)\n",
       "1      11     1046     4.5        Beautiful Thing (1996)\n",
       "2      11      616     4.0        Aristocats, The (1970)\n",
       "3      11     3535     2.0        American Psycho (2000)\n",
       "4      11     5669     5.0  Bowling for Columbine (2002)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(movies[['movieId', 'title']])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vkgi6EY8p2Q0",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Números de usuários e número de itens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 582,
     "status": "ok",
     "timestamp": 1651262596720,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "Oqm9TqFUp2Q0",
    "outputId": "b79494d4-37e4-43fa-de7e-9bf123c17fec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Número de usuários: 11090\n",
      "Número de itens: 417\n",
      "Número de interações: 190621\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "\"\"\"\n",
    "Número de usuários: {}\n",
    "Número de itens: {}\n",
    "Número de interações: {}\n",
    "\"\"\".format(\n",
    "    df.userId.nunique(),\n",
    "    df.movieId.nunique(),\n",
    "    df.shape[0]\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9oUxIUefp2Q4"
   },
   "source": [
    "### Mapeamento em idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 307,
     "status": "ok",
     "timestamp": 1651265300657,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "_xDUH3kbp2Q4"
   },
   "outputs": [],
   "source": [
    "map_users = {user: idx for idx, user in enumerate(df.userId.unique())}\n",
    "map_items = {item: idx for idx, item in enumerate(df.movieId.unique())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1651265302515,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "kqkGY2php2Q8",
    "outputId": "864b78bd-cf41-415f-a0aa-7199a9439616"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Enemy Mine (1985)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Beautiful Thing (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Aristocats, The (1970)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>American Psycho (2000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bowling for Columbine (2002)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating                         title\n",
       "0       0        0     5.0             Enemy Mine (1985)\n",
       "1       0        1     4.5        Beautiful Thing (1996)\n",
       "2       0        2     4.0        Aristocats, The (1970)\n",
       "3       0        3     2.0        American Psycho (2000)\n",
       "4       0        4     5.0  Bowling for Columbine (2002)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['userId'] = df['userId'].map(map_users)\n",
    "df['movieId'] = df['movieId'].map(map_items)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 12498,
     "status": "ok",
     "timestamp": 1651265319355,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "StWvDa5Gp2Q_"
   },
   "outputs": [],
   "source": [
    "map_title = {}\n",
    "\n",
    "for _, row in df.iterrows():\n",
    "    map_title[row.movieId] = row.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6819,
     "status": "ok",
     "timestamp": 1651262789120,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "XLTWg-F9p2RD",
    "outputId": "91b07036-55df-40d1-a532-e341b0d869c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "417\n",
      "Send Me No Flowers (1964)\n"
     ]
    }
   ],
   "source": [
    "print(len(map_title))\n",
    "print(map_title[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "executionInfo": {
     "elapsed": 642,
     "status": "ok",
     "timestamp": 1651262829829,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "35C82h7op2RH",
    "outputId": "3fd426c1-238b-45b9-ebfe-00a5b16d008a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11085</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11086</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11087</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11088</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11089</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11090 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId  rating  title\n",
       "userId                        \n",
       "0            13      13     13\n",
       "1            14      14     14\n",
       "2            15      15     15\n",
       "3            15      15     15\n",
       "4            11      11     11\n",
       "...         ...     ...    ...\n",
       "11085        16      16     16\n",
       "11086        11      11     11\n",
       "11087        13      13     13\n",
       "11088        13      13     13\n",
       "11089        10      10     10\n",
       "\n",
       "[11090 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# qtd interações dos usuários\n",
    "df.groupby('userId').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x8Ln0a1vp2RL"
   },
   "source": [
    "### Divisão do dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 878,
     "status": "ok",
     "timestamp": 1651265328100,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "9MsDESQ_p2RM"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size=.2, random_state=2)\n",
    "train.to_csv('train.txt', index=False, header=False, sep='\\t')\n",
    "test.to_csv('test.txt', index=False, header=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 744,
     "status": "ok",
     "timestamp": 1651262977544,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "n6HTzcwsp2RX",
    "outputId": "831acd8f-11dc-4864-ce08-a8a4fa05daa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 264840\n",
      "-rw-rw-r-- 1 caio caio    19439 ago 18 12:52  Aula02_Exemplos.ipynb\n",
      "-rw-rw-r-- 1 caio caio   121480 ago 18 16:35  Aula02_Exercicios.ipynb\n",
      "drwxr-xr-x 2 caio caio     4096 mar 17  2022  dataset\n",
      "-rw-rw-r-- 1 caio caio 65019041 ago 16 17:24 'ml-20m-compact.tar (1).gz'\n",
      "-rw-rw-r-- 1 caio caio 65019041 ago 18 12:49 'ml-20m-compact.tar (2).gz'\n",
      "-rw-rw-r-- 1 caio caio 65019041 ago 18 16:36 'ml-20m-compact.tar (3).gz'\n",
      "-rw-rw-r-- 1 caio caio 65019041 ago 15 20:57  ml-20m-compact.tar.gz\n",
      "-rw-rw-r-- 1 caio caio  2188294 ago 18 12:50  out_mp_pred_binary.txt\n",
      "-rw-rw-r-- 1 caio caio   647016 ago 18 12:50  out_mp_pred.txt\n",
      "drwxrwxr-x 7 caio caio     4096 ago 15 20:51  recsysintro\n",
      "-rw-rw-r-- 1 caio caio   647016 ago 18 12:50  rp_iknn.txt\n",
      "-rw-rw-r-- 1 caio caio  1491068 ago 18 16:37  test.txt\n",
      "-rw-rw-r-- 1 caio caio  5966209 ago 18 16:37  train.txt\n"
     ]
    }
   ],
   "source": [
    "! ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 689,
     "status": "ok",
     "timestamp": 1651262986486,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "RFZhUjCnp2Rb",
    "outputId": "cdcd5ef0-bc8f-436f-9f20-ab7f5269ba45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. items: 405\n",
      "No. users: 11090\n"
     ]
    }
   ],
   "source": [
    "print('No. items: ' + str(train['movieId'].nunique()))\n",
    "print('No. users: ' + str(train['userId'].nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VVWZRptBp2Re"
   },
   "source": [
    "### Recomendadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ajH3hUALp2Re"
   },
   "source": [
    "## Prever notas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 56,
     "status": "ok",
     "timestamp": 1651263041227,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "foa1ePfOp2Rf"
   },
   "outputs": [],
   "source": [
    "from caserec.recommenders.rating_prediction.most_popular import MostPopular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 74143,
     "status": "ok",
     "timestamp": 1651263246545,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "2ud84_kMp2Rh",
    "outputId": "17aaee62-e965-4a33-875f-51aa9432a3eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Case Recommender: Rating Prediction > Most Popular]\n",
      "\n",
      "train data:: 11090 users and 405 items (152496 interactions) | sparsity:: 96.60%\n",
      "test data:: 10571 users and 331 items (38125 interactions) | sparsity:: 98.91%\n",
      "\n",
      "prediction_time:: 52.837287 sec\n",
      "\n",
      "\n",
      "Eval:: MAE: 0.701356 RMSE: 0.908405 \n"
     ]
    }
   ],
   "source": [
    "MostPopular('train.txt', 'test.txt', 'out_mp_pred.txt').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 2096,
     "status": "ok",
     "timestamp": 1651263311989,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "wESMpB0kp2Rk",
    "outputId": "847827ef-03a9-45dc-cc48-d5ad72bc3e79"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.340482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.462625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3.501614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3.075567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3.654597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>4.070841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2.841837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3.174392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>3.209831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>4.192866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId      pred\n",
       "0       0        5  3.340482\n",
       "1       0        0  3.462625\n",
       "2       1       13  3.501614\n",
       "3       1       16  3.075567\n",
       "4       1       14  3.654597\n",
       "5       1       19  4.070841\n",
       "6       1       24  2.841837\n",
       "7       1       20  3.174392\n",
       "8       2       21  3.209831\n",
       "9       3       33  4.192866"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred = pd.read_csv('out_mp_pred.txt', sep='\\t', names=['userId', 'movieId', 'pred'])\n",
    "df_pred.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "executionInfo": {
     "elapsed": 461,
     "status": "ok",
     "timestamp": 1651263359987,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "aGUl8yVyp2Rn",
    "outputId": "9c0ba999-c3ee-403d-e956-515619cc7f5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sliding Doors (1998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Rush Hour 2 (2001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Go (1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Deep End of the Ocean, The (1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>While You Were Sleeping (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating                              title\n",
       "13       1       13     5.0               Sliding Doors (1998)\n",
       "16       1       16     4.0                 Rush Hour 2 (2001)\n",
       "14       1       14     4.0                          Go (1999)\n",
       "21       1       19     5.0              Reservoir Dogs (1992)\n",
       "26       1       24     3.0  Deep End of the Ocean, The (1999)\n",
       "22       1       20     3.0     While You Were Sleeping (1995)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.userId == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 665,
     "status": "ok",
     "timestamp": 1651263424777,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "x-VuIIi3p2Rp"
   },
   "outputs": [],
   "source": [
    "df_pred = df_pred.merge(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "executionInfo": {
     "elapsed": 922,
     "status": "ok",
     "timestamp": 1651263432823,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "PNYXk7pfp2Rs",
    "outputId": "81838e01-e340-47fe-c513-00eefb0fd778"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred.rating.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "executionInfo": {
     "elapsed": 664,
     "status": "ok",
     "timestamp": 1651263494984,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "WCQJA5q8p2Rv",
    "outputId": "d645310b-f083-441a-a09b-9e74de1df795"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred['pred'] = round(df_pred['pred']*2)/2\n",
    "df_pred.pred.value_counts().plot(kind='bar');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PwB7U14up2Ry"
   },
   "source": [
    "### Top N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 285,
     "status": "ok",
     "timestamp": 1651265412256,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "uOuJBF2lp2Rz"
   },
   "outputs": [],
   "source": [
    "from caserec.recommenders.item_recommendation.most_popular import MostPopular as MPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy<2.0 in ./recsysintro/lib/python3.10/site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install \"numpy<2.0\" # Problema de compatibilidade da lib com numpy > 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7097,
     "status": "ok",
     "timestamp": 1651265426652,
     "user": {
      "displayName": "Marcelo Manzato",
      "userId": "04825752819034907940"
     },
     "user_tz": 180
    },
    "id": "3-ir7MKYp2R4",
    "outputId": "66ee974f-4661-4728-ff8f-4d04534101e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Case Recommender: Item Recommendation > Most Popular]\n",
      "\n",
      "train data:: 11090 users and 405 items (152496 interactions) | sparsity:: 96.60%\n",
      "test data:: 10571 users and 331 items (38125 interactions) | sparsity:: 98.91%\n",
      "\n",
      "prediction_time:: 1.481585 sec\n",
      "\n",
      "\n",
      "Eval:: PREC@1: 0.38407 PREC@3: 0.251159 PREC@5: 0.210936 PREC@10: 0.164895 RECALL@1: 0.124528 RECALL@3: 0.229538 RECALL@5: 0.314706 RECALL@10: 0.481962 MAP@1: 0.38407 MAP@3: 0.454719 MAP@5: 0.46042 MAP@10: 0.437819 NDCG@1: 0.38407 NDCG@3: 0.523129 NDCG@5: 0.547547 NDCG@10: 0.554529 \n"
     ]
    }
   ],
   "source": [
    "MPR('train.txt', 'test.txt', 'out_mp_pred_binary.txt', as_binary=True).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6444.0</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4535.0</td>\n",
       "      <td>Natural Born Killers (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>4484.0</td>\n",
       "      <td>Chasing Amy (1997)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>4356.0</td>\n",
       "      <td>Life Is Beautiful (La Vita è bella) (1997)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>3855.0</td>\n",
       "      <td>North by Northwest (1959)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>I, Robot (2004)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>Conspiracy Theory (1997)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>While You Were Sleeping (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>Crimson Tide (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3392.0</td>\n",
       "      <td>My Best Friend's Wedding (1997)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>6444.0</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4088.0</td>\n",
       "      <td>Bowling for Columbine (2002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4035.0</td>\n",
       "      <td>Dark City (1998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>3855.0</td>\n",
       "      <td>North by Northwest (1959)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>Karate Kid, The (1984)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId   score                                       title\n",
       "0        0       19  6444.0                       Reservoir Dogs (1992)\n",
       "1        0       22  4535.0                 Natural Born Killers (1994)\n",
       "2        0       17  4484.0                          Chasing Amy (1997)\n",
       "3        0       18  4356.0  Life Is Beautiful (La Vita è bella) (1997)\n",
       "4        0       33  3855.0                   North by Northwest (1959)\n",
       "5        0        5  3567.0                             I, Robot (2004)\n",
       "6        0       21  3560.0                    Conspiracy Theory (1997)\n",
       "7        0       20  3495.0              While You Were Sleeping (1995)\n",
       "8        0       23  3482.0                         Crimson Tide (1995)\n",
       "9        0       28  3392.0             My Best Friend's Wedding (1997)\n",
       "10       1       19  6444.0                       Reservoir Dogs (1992)\n",
       "11       1        4  4088.0                Bowling for Columbine (2002)\n",
       "12       1       10  4035.0                            Dark City (1998)\n",
       "13       1       33  3855.0                   North by Northwest (1959)\n",
       "14       1        8  3599.0                      Karate Kid, The (1984)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking = pd.read_csv('out_mp_pred_binary.txt', sep='\\t', names=['userId', 'movieId', 'score'])\n",
    "ranking['title'] = ranking.movieId.map(map_title)\n",
    "ranking.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Case Recommender: Rating Prediction > ItemKNN Algorithm]\n",
      "\n",
      "train data:: 11090 users and 405 items (152496 interactions) | sparsity:: 96.60%\n",
      "test data:: 10571 users and 331 items (38125 interactions) | sparsity:: 98.91%\n",
      "\n",
      "training_time:: 4.146370 sec\n",
      "prediction_time:: 1.712722 sec\n",
      "Eval:: MAE: 0.663504 RMSE: 0.872175 \n"
     ]
    }
   ],
   "source": [
    "from caserec.recommenders.rating_prediction.itemknn import ItemKNN\n",
    "\n",
    "ItemKNN('train.txt', 'test.txt', 'rp_iknn.txt', as_similar_first=True).compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício 01:**\n",
    "\n",
    "a) Encontrar o id do usuário que avaliou mais filmes na base. Informe o id externo (da base dados) e o interno (usado pelo recomendador)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype({\"userId\": int, \"movieId\": int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O usuário com id externo 118205 e id interno 9518 foi o usuário que mais avaliou itens. (163 avaliacoes)\n"
     ]
    }
   ],
   "source": [
    "users_ranked_by_num_of_ratings = (\n",
    "                                    ratings.groupby(\"userId\")\n",
    "                                           .size()\n",
    "                                           .reset_index(name='count')\n",
    "                                           .sort_values(\"count\", ascending=False)\n",
    ")\n",
    "top_rating_user = users_ranked_by_num_of_ratings.iloc[0]\n",
    "\n",
    "user_id = int(top_rating_user[\"userId\"])\n",
    "num_of_ratings = int(top_rating_user[\"count\"])\n",
    "\n",
    "recommender_id = map_users[user_id]\n",
    "\n",
    "print(f\"O usuário com id externo {user_id} e id interno {recommender_id} foi o usuário que mais avaliou itens. ({num_of_ratings} avaliacoes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Encontrar os 5 primeiros usuários que têm maior propensão a dar notas baixas aos filmes (use a média de ratings para isso)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14316/1037652384.py:1: FutureWarning: The provided callable <function mean at 0x7da8d8319630> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  ratings_ranked = df.groupby(\"userId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=True)\n"
     ]
    }
   ],
   "source": [
    "ratings_ranked = df.groupby(\"userId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5 = ratings_ranked.iloc[:5].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9160</td>\n",
       "      <td>0.735294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>172</td>\n",
       "      <td>0.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6728</td>\n",
       "      <td>1.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8266</td>\n",
       "      <td>1.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10835</td>\n",
       "      <td>1.307692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId    rating\n",
       "0    9160  0.735294\n",
       "1     172  0.961538\n",
       "2    6728  1.115385\n",
       "3    8266  1.205882\n",
       "4   10835  1.307692"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os top 5 usuários com menor propensão de dar notas altas a filmes são:\n",
      "Id interno: 9160.0; \t Nota média: 0.7352941176470589\n",
      "Id interno: 172.0; \t Nota média: 0.9615384615384616\n",
      "Id interno: 6728.0; \t Nota média: 1.1153846153846154\n",
      "Id interno: 8266.0; \t Nota média: 1.2058823529411764\n",
      "Id interno: 10835.0; \t Nota média: 1.3076923076923077\n"
     ]
    }
   ],
   "source": [
    "print(\"Os top 5 usuários com menor propensão de dar notas altas a filmes são:\")\n",
    "\n",
    "for idx, row in top_5.iterrows():\n",
    "    userId = row.userId\n",
    "    print(f\"Id interno: {userId}; \\t Nota média: {row.rating}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Encontrar os 5 filmes pior avaliados pelos usuários."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14316/723574192.py:1: FutureWarning: The provided callable <function mean at 0x7da8d8319630> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  ratings_ranked_by_movie = df.groupby(\"movieId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=True).reset_index()\n"
     ]
    }
   ],
   "source": [
    "ratings_ranked_by_movie = df.groupby(\"movieId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>328</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>414</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>409</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>376</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>305</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>167</td>\n",
       "      <td>4.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>268</td>\n",
       "      <td>4.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>12</td>\n",
       "      <td>4.178017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>33</td>\n",
       "      <td>4.199626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>367</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId    rating\n",
       "0        328  1.000000\n",
       "1        414  1.000000\n",
       "2        409  1.000000\n",
       "3        376  1.166667\n",
       "4        305  1.500000\n",
       "..       ...       ...\n",
       "412      167  4.066667\n",
       "413      268  4.107143\n",
       "414       12  4.178017\n",
       "415       33  4.199626\n",
       "416      367  4.500000\n",
       "\n",
       "[417 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_ranked_by_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os top 5 filmes com menor nota média são:\n",
      "Snapshot (1979); \t Nota média: 1.0\n",
      "Heroic Purgatory (Rengoku eroica) (1970); \t Nota média: 1.0\n",
      "Hercules (2005); \t Nota média: 1.0\n",
      "Loaded (2008); \t Nota média: 1.1666666666666667\n",
      "Australian Atomic Confessions (2005); \t Nota média: 1.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Os top 5 filmes com menor nota média são:\")\n",
    "top_5_movies = ratings_ranked_by_movie.iloc[:5]\n",
    "for idx, row in top_5_movies.iterrows():\n",
    "    movie_id = row.movieId\n",
    "    title = map_title[movie_id]\n",
    "    print(f\"{title}; \\t Nota média: {row.rating}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício 02:** Na aula vimos uma abordagem de recomendação não personalizada que utiliza a quantidade de interações dos itens para recomendar filmes mais populares aos usuários. Quando há feedback explícito, entretanto, a mesma abordagem \"Most Popular\" pode fazer uso da média das notas disponíveis, ou seja, recomendam-se os N filmes mais bem avaliados a cada usuário. Desse modo, calcule a recomendação de 5 filmes para um usuário qualquer da base considerando essa estratégia não personalizada. Utilize a base toda como conjunto de treinamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_watched_movies_by_user(userId, df):\n",
    "    movies = df[df[\"userId\"] == userId].movieId\n",
    "    return list(set(movies))\n",
    "\n",
    "\n",
    "def recommend_top_5_most_popular_movies(userId, df, remove_already_seen_movies=True):\n",
    "    \"\"\"\n",
    "        userId -> int; Internal id of the user to recommend a list of movies\n",
    "        df -> dataframe containing columns such as userId, movieId and rating\n",
    "\n",
    "        Recommends the 5 most well rated movies to a given user, excluding the movies that the user has\n",
    "        already rated.\n",
    "\n",
    "        This approach also does not takes into account the number of ratings a movie has.\n",
    "    \"\"\"\n",
    "    watched_movies = get_watched_movies_by_user(userId, df)\n",
    "    if (remove_already_seen_movies):\n",
    "        fresh_movies = df[~df[\"movieId\"].isin(watched_movies)]\n",
    "    else:\n",
    "        fresh_movies = df\n",
    "    \n",
    "    ratings_ranked = fresh_movies.groupby(\"movieId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=False).reset_index()\n",
    "    top_5_movies = ratings_ranked.iloc[:5]\n",
    "    top_5_movies[\"title\"] = top_5_movies[\"movieId\"].apply(lambda r: map_title[r])\n",
    "    \n",
    "    return top_5_movies[[\"title\", \"movieId\", \"rating\"]]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_ids = df.userId.unique()\n",
    "min_id = np.min(user_ids)\n",
    "max_id = np.max(user_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "random_user = randint(min_id, max_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14316/139339595.py:22: FutureWarning: The provided callable <function mean at 0x7da8d8319630> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  ratings_ranked = fresh_movies.groupby(\"movieId\").agg({\"rating\": np.mean}).sort_values(\"rating\", ascending=False).reset_index()\n",
      "/tmp/ipykernel_14316/139339595.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  top_5_movies[\"title\"] = top_5_movies[\"movieId\"].apply(lambda r: map_title[r])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Everybody Street (2013)</td>\n",
       "      <td>367</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Napoléon (1927)</td>\n",
       "      <td>268</td>\n",
       "      <td>4.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Samouraï, Le (Godson, The) (1967)</td>\n",
       "      <td>167</td>\n",
       "      <td>4.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "      <td>19</td>\n",
       "      <td>4.061329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fog of War: Eleven Lessons from the Life of Ro...</td>\n",
       "      <td>80</td>\n",
       "      <td>4.052494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  movieId    rating\n",
       "0                            Everybody Street (2013)      367  4.500000\n",
       "1                                    Napoléon (1927)      268  4.107143\n",
       "2                  Samouraï, Le (Godson, The) (1967)      167  4.066667\n",
       "3                              Reservoir Dogs (1992)       19  4.061329\n",
       "4  Fog of War: Eleven Lessons from the Life of Ro...       80  4.052494"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_top_5_most_popular_movies(random_user, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercício 03:** Com base no código disponível no notebook \"Aula01_Exemplos.ipynb\", implemente uma função que retorna os k filmes mais similares a um outro qualquer passado como parâmetro. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from statistics import mean\n",
    "from math import pow, sqrt\n",
    "\n",
    "\n",
    "def similarity_score(movieId, movieId2, df):\n",
    "    '''\n",
    "    modieId & movieId2 : ids dos dois filmes cuja similaridade será computada\n",
    "    '''\n",
    "            \n",
    "    def get_rating(userId,movieId):\n",
    "        # Checa se existe avaliacao feita pelo usuario userId para o filme movieId\n",
    "        if len(df[(df['userId']==userId)&(df['movieId']==movieId)]) == 0:\n",
    "            return 0\n",
    "        return (df.loc[(df.userId==userId) & (df.movieId == movieId),'rating'].iloc[0])\n",
    "    \n",
    "    def get_ratings(movieId):\n",
    "        if movieId not in df['movieId'].values:\n",
    "            return []\n",
    "        return (df.loc[(df.movieId==movieId),'rating'].tolist())\n",
    "    \n",
    "    def get_movie_mean(movieId):\n",
    "        return np.mean(get_ratings(movieId))\n",
    "    \n",
    "    def get_user_ids(movieId):\n",
    "        if movieId not in df['movieId'].values:\n",
    "            return []\n",
    "        return (df.loc[(df.movieId==movieId),'userId'].tolist())\n",
    "        \n",
    "\n",
    "\n",
    "    # Lista de usuários que avaliaram ambos os filmes\n",
    "    user_list1 = get_user_ids(movieId)\n",
    "    user_list2 = get_user_ids(movieId2)\n",
    "    common_users = list(set(user_list1) & set(user_list2))\n",
    "\n",
    "    if (len(common_users) == 0 ):\n",
    "        return 0\n",
    "    \n",
    "    # Calcular a média de cada usuário\n",
    "    movie1_mean = get_movie_mean(movieId)\n",
    "    movie2_mean = get_movie_mean(movieId2)\n",
    "    \n",
    "    # Cálculo da similaridade.\n",
    "    sim = []\n",
    "    norm1 = []\n",
    "    norm2 = []\n",
    "    for user in common_users:\n",
    "        rating1 = get_rating(user, movieId)\n",
    "        rating2 = get_rating(user, movieId2)\n",
    "        sim.append((rating1 - movie1_mean)*(rating2 - movie2_mean))\n",
    "        norm1.append(pow(rating1 - movie1_mean, 2))\n",
    "        norm2.append(pow(rating2 - movie2_mean, 2))\n",
    "\n",
    "    #  Remover casos que resultaria em uma divisão por zero: isso ocorre por todas as notas feitas pelo\n",
    "    # usuário serem iguais, resultando em uma variância zero. A maneira mais simples de resolver esse problema\n",
    "    # é retornar similaridade zero, para evitar bugs do tipo NaN.\n",
    "\n",
    "    # Poderíamos iterar essa abordagem considerando apenas usuarios que avaliaram mais que X filmes\n",
    "    # ou desconsiderar usuarios cuja variancia de avaliacoes vale zero, mas acredito que foge ao escopo\n",
    "    # do exercicío.\n",
    "    if (sum(norm1) == 0 or sum(norm2) == 0):\n",
    "        return 0\n",
    "    return sum(sim) / (sqrt(sum(norm1)) * sqrt(sum(norm2)))\n",
    "\n",
    "\n",
    "def most_similar_movies(movieId, k, df):\n",
    "    '''\n",
    "    movieId : Targeted movie\n",
    "    k : qtde de vizinhos\n",
    "    '''\n",
    "    # Obter lista de filmes.\n",
    "    movie_ids = df.movieId.unique().tolist()\n",
    "    \n",
    "    # Obter a similaridade entre o filme alvo e os demais filmes\n",
    "    sim = [(similarity_score(movieId, m, df), m) for m in movie_ids if m != movieId]\n",
    "    \n",
    "    # Ordenação inversa.\n",
    "    sim.sort()\n",
    "    sim.reverse()\n",
    "    \n",
    "    # Retornando os filmes mais similares. \n",
    "    return sim[:k]\n",
    "\n",
    "\n",
    "\n",
    "def pretty_print_most_similar(targetMovieId, movie_list):\n",
    "    movie = map_title[targetMovieId]\n",
    "    print(f\"Os filmes mais similares a {movie} são: \")\n",
    "    for sim, movie_id in movie_list:\n",
    "        movie_title = map_title[movie_id]\n",
    "        print(f\"{movie_title}; Similaridade: {sim}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 31s, sys: 54.2 ms, total: 7min 31s\n",
      "Wall time: 7min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "target_movie_id = 12 #empire strikes back\n",
    "m = most_similar_movies(target_movie_id ,5, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.0, 221),\n",
       " (0.9609587727272473, 282),\n",
       " (0.9371138170566475, 363),\n",
       " (0.906631564301307, 285),\n",
       " (0.8918444266938059, 195)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os filmes mais similares a Star Wars: Episode V - The Empire Strikes Back (1980) são: \n",
      "Exists (2014); Similaridade: 1.0\n",
      "Beginner's Guide to Endings, A (2010); Similaridade: 0.9609587727272473\n",
      "Chapiteau-show (Shapito-shou) (2011); Similaridade: 0.9371138170566475\n",
      "Simon Says (2009); Similaridade: 0.906631564301307\n",
      "Fetching Cody (2005); Similaridade: 0.8918444266938059\n"
     ]
    }
   ],
   "source": [
    "pretty_print_most_similar(target_movie_id, m)"
   ]
  }
 ],
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