1st Task.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-66699cb8c506>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_ratings = pd.read_csv(\"file\\\\ml-1m\\\\ratings.dat\", sep=\"::\", header=None, names=[\"UserID\", \"MovieID\", \"Rating\", \"Timestamp\"])\n",
      "<ipython-input-4-66699cb8c506>:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_movies = pd.read_csv(\"file\\\\ml-1m\\\\movies.dat\", sep=\"::\", header=None, names=[\"MovieID\", \"Title\", \"Genres\"])\n",
      "<ipython-input-4-66699cb8c506>:4: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_users = pd.read_csv(\"file\\\\ml-1m\\\\users.dat\", sep=\"::\", header=None, names=[\"UserID\", \"Gender\", \"Age\", \"Occupation\", \"Zip-code\"])\n"
     ]
    }
   ],
   "source": [
    "# read data\n",
    "df_ratings = pd.read_csv(\"file\\\\ml-1m\\\\ratings.dat\", sep=\"::\", header=None, names=[\"UserID\", \"MovieID\", \"Rating\", \"Timestamp\"])\n",
    "df_movies = pd.read_csv(\"file\\\\ml-1m\\\\movies.dat\", sep=\"::\", header=None, names=[\"MovieID\", \"Title\", \"Genres\"])\n",
    "df_users = pd.read_csv(\"file\\\\ml-1m\\\\users.dat\", sep=\"::\", header=None, names=[\"UserID\", \"Gender\", \"Age\", \"Occupation\", \"Zip-code\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000209, 4), (3883, 3), (6040, 5))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.shape, df_movies.shape, df_users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp\n",
       "0       1     1193       5  978300760\n",
       "1       1      661       3  978302109\n",
       "2       1      914       3  978301968\n",
       "3       1     3408       4  978300275\n",
       "4       1     2355       5  978824291"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID                               Title                        Genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zip-code</th>\n",
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       "      <td>48067</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   UserID Gender  Age  Occupation Zip-code\n",
       "0       1      F    1          10    48067\n",
       "1       2      M   56          16    70072\n",
       "2       3      M   25          15    55117\n",
       "3       4      M   45           7    02460\n",
       "4       5      M   25          20    55455"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Occupation</th>\n",
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       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
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       "      <td>978302109</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp                                   Title  \\\n",
       "0       1     1193       5  978300760  One Flew Over the Cuckoo's Nest (1975)   \n",
       "1       1      661       3  978302109        James and the Giant Peach (1996)   \n",
       "2       1      914       3  978301968                     My Fair Lady (1964)   \n",
       "3       1     3408       4  978300275                  Erin Brockovich (2000)   \n",
       "4       1     2355       5  978824291                    Bug's Life, A (1998)   \n",
       "\n",
       "                         Genres Gender  Age  Occupation Zip-code  \n",
       "0                         Drama      F    1          10    48067  \n",
       "1  Animation|Children's|Musical      F    1          10    48067  \n",
       "2               Musical|Romance      F    1          10    48067  \n",
       "3                         Drama      F    1          10    48067  \n",
       "4   Animation|Children's|Comedy      F    1          10    48067  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data\n",
    "df_ratings_movies = pd.merge(df_ratings, df_movies, on='MovieID')\n",
    "df = pd.merge(df_ratings_movies, df_users, on=\"UserID\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
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       "      <th>1</th>\n",
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       "      <td>James and the Giant Peach (1996)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   UserID  MovieID  Rating                                   Title\n",
       "0       1     1193       5  One Flew Over the Cuckoo's Nest (1975)\n",
       "1       1      661       3        James and the Giant Peach (1996)\n",
       "2       1      914       3                     My Fair Lady (1964)\n",
       "3       1     3408       4                  Erin Brockovich (2000)\n",
       "4       1     2355       5                    Bug's Life, A (1998)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete unnecessary columns\n",
    "data = df.copy()\n",
    "data = data.drop(['Timestamp','Genres','Gender','Age','Occupation','Zip-code'], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2858                Muppet Treasure Island (1996)\n",
       "260                            Dragonheart (1996)\n",
       "1196    Close Encounters of the Third Kind (1977)\n",
       "1210                   Breakfast Club, The (1985)\n",
       "480                          Shanghai Noon (2000)\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommend 5 most seen movies\n",
    "def recommend_movie(n):\n",
    "    movie_rank = data['MovieID'].value_counts()[:n]\n",
    "    recommend_movies = data.loc[movie_rank.index]\n",
    "    recommend = recommend_movies['Title']\n",
    "    return recommend\n",
    "\n",
    "recommend_movie(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split train, test set\n",
    "x = data.copy()\n",
    "x_train, x_test = train_test_split(x, test_size=0.05)\n",
    "\n",
    "# dRecommend n most popular movies on a dataset\n",
    "def popular_movie(dataset, n):\n",
    "    movie_rank = dataset['MovieID'].value_counts()[:n]\n",
    "    popular_movies = dataset.iloc[movie_rank.index]\n",
    "    return popular_movies\n",
    "\n",
    "# Calculate hitrate@K\n",
    "def hitrate(K):\n",
    "    raw_ranking = popular_movie(data, K)\n",
    "    pred_ranking = popular_movie(x_test, K)\n",
    "    return raw_ranking['MovieID'].isin(pred_ranking['MovieID']).value_counts(normalize=True)[True]\n",
    "\n",
    "hitrate(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MovieID\n",
       "3382                        Sneakers (1992)\n",
       "3172                              Pi (1998)\n",
       "3607                        Candyman (1992)\n",
       "3656                       Junk Mail (1997)\n",
       "3280    Teenage Mutant Ninja Turtles (1990)\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommend 5 movies with high ratings\n",
    "def recommend_movie2(n):\n",
    "    movie_sort = movie_mean.sort_values(ascending=False)[:n]\n",
    "    recommend_movies = data.loc[movie_sort.index]\n",
    "    recommendation = recommend_movies['Title']\n",
    "    return recommendation\n",
    "\n",
    "movie_mean = data.groupby(['MovieID'])['Rating'].mean()\n",
    "recommend_movie2(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7787902185267609\n"
     ]
    }
   ],
   "source": [
    "# Accuracy calculation\n",
    "def RMSE(y_true, y_pred):\n",
    "    return np.sqrt(np.mean((np.array(y_true) - np.array(y_pred))**2))\n",
    "\n",
    "rmse = []\n",
    "for user in set(data.index):\n",
    "    y_true = data.loc[user]['Rating']\n",
    "    y_pred = movie_mean[data.loc[user]['MovieID']]\n",
    "    accuracy = RMSE(y_true, y_pred)\n",
    "    rmse.append(accuracy)\n",
    "print(np.mean(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9765758608443047"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split train, test set\n",
    "x = data.copy()\n",
    "y = data['UserID']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.05,stratify=y)\n",
    "\n",
    "# Accuracy calculation\n",
    "def RMSE(y_true, y_pred):\n",
    "    return np.sqrt(np.mean((np.array(y_true) - np.array(y_pred))**2))\n",
    "\n",
    "# Calculate RMSE by model\n",
    "def score(model):\n",
    "    id_pairs = zip(x_test['UserID'], x_test['MovieID'])\n",
    "    y_pred = np.array([model(user,movie) for (user,movie) in id_pairs])\n",
    "    y_true = np.array(x_test['Rating'])\n",
    "    return RMSE(y_true, y_pred)\n",
    "\n",
    "# Get full matrix with training data\n",
    "rating_matrix = x_train.pivot(index='UserID', columns='MovieID', values='Rating')\n",
    "\n",
    "# The default model for calculating forecasts by the overall mean\n",
    "def best_seller(user_id, movie_id):\n",
    "    try:\n",
    "        rating = train_mean[movie_id]\n",
    "    except:\n",
    "        rating = 3.0\n",
    "    return rating\n",
    "\n",
    "train_mean = x_train.groupby(['MovieID'])['Rating'].mean()\n",
    "score(best_seller)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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