main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Libraries**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm as tq\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Load files**  ▓▓▓▓▓▓▓▓░░░░░░░░░░░░  100%\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **25M data file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Data/ds100k/ratings.csv')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "movie_dir = next(Path(\"Data/ds100k/\").glob(\"*movie*.csv\"))\n",
    "ratings_dir = next(Path(\"Data/ds100k/\").glob(\"*rating*.csv\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = pd.read_csv(ratings_dir)\n",
    "df_movie = pd.read_csv(movie_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1493848402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493850091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1494273047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493846352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1493846415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "\n",
    "class SparseRatingMatrix:\n",
    "    def __init__(self, csv_file):\n",
    "        self.user_ratings = defaultdict(dict)\n",
    "        self.user_to_index = {}\n",
    "        self.index_to_user = []\n",
    "        self.movie_to_index = {}\n",
    "        self.index_to_movie = []\n",
    "        self.row_indices = []\n",
    "        self.col_indices = []\n",
    "        self.data = []\n",
    "\n",
    "\n",
    "        self._load_data(csv_file)\n",
    "\n",
    "    def _load_data(self, csv_file):\n",
    "        df = pd.read_csv(csv_file).drop(columns=\"timestamp\")\n",
    "        \n",
    "        for _, row in df.iterrows():\n",
    "            user_id = row['userId']\n",
    "            movie_id = row['movieId']\n",
    "            rating = row['rating']\n",
    "            self.add_rating(user_id, movie_id, rating)\n",
    "\n",
    "    def add_rating(self, user_id, movie_id, rating):\n",
    "        if user_id not in self.user_to_index:\n",
    "            user_index = len(self.index_to_user)\n",
    "            self.user_to_index[user_id] = user_index\n",
    "            self.index_to_user.append(user_id)\n",
    "\n",
    "        if movie_id not in self.movie_to_index:\n",
    "            movie_index = len(self.index_to_movie)\n",
    "            self.movie_to_index[movie_id] = movie_index\n",
    "            self.index_to_movie.append(movie_id)\n",
    "\n",
    "        user_index = self.user_to_index[user_id]\n",
    "        movie_index = self.movie_to_index[movie_id]\n",
    "\n",
    "        # Store the rating\n",
    "        self.user_ratings[user_index][movie_index] = rating\n",
    "\n",
    "        # Update CSR components\n",
    "        self.row_indices.append(user_index)\n",
    "        self.col_indices.append(movie_index)\n",
    "        self.data.append(rating)\n",
    "\n",
    "    def build_csr(self):\n",
    "        self.row_indices = np.array(self.row_indices)\n",
    "        self.col_indices = np.array(self.col_indices)\n",
    "        self.data = np.array(self.data)\n",
    "\n",
    "    def get_rating(self, user_id, movie_id):\n",
    "        user_index = self.user_to_index.get(user_id)\n",
    "        movie_index = self.movie_to_index.get(movie_id)\n",
    "\n",
    "        if user_index is not None and movie_index is not None:\n",
    "            return self.user_ratings[user_index].get(movie_index, None)\n",
    "        return None\n",
    "\n",
    "    def get_user_ratings(self, user_id):\n",
    "        user_index = self.user_to_index.get(user_id)\n",
    "        if user_index is not None:\n",
    "            return self.user_ratings[user_index]\n",
    "        return {}\n",
    "\n",
    "    def get_movie_ratings(self, movie_id):\n",
    "        movie_index = self.movie_to_index.get(movie_id)\n",
    "        ratings = {}\n",
    "        for user_index, movies in self.user_ratings.items():\n",
    "            if movie_index in movies:\n",
    "                ratings[self.index_to_user[user_index]] = movies[movie_index]\n",
    "        return ratings\n",
    "\n",
    "# Example usage\n",
    "if __name__ == \"__main__\":\n",
    "    # Replace 'ratings.csv' with your actual CSV file path\n",
    "    sparse_matrix = SparseRatingMatrix(ratings_dir)\n",
    "    sparse_matrix.build_csr()\n",
    "\n",
    "    # # Example queries\n",
    "    # print(\"User 1 Ratings:\", sparse_matrix.get_user_ratings(1))\n",
    "    # print(\"Movie 101 Ratings:\", sparse_matrix.get_movie_ratings(101))\n",
    "    # print(\"Rating of User 1 for Movie 102:\", sparse_matrix.get_rating(1, 102))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trainor:\n",
    "    \"\"\" Matrix factorisation using ALS algorithm \"\"\"\n",
    "    def __init__(self, sparse_matrix, latent_d=5, lamda=0.001, tau=0.01, gamma=0.001):\n",
    "        # Hyper_parameters\n",
    "        self.lamda, self.tau, self.gamma = lamda, tau, gamma\n",
    "        self.latent_d = latent_d\n",
    "\n",
    "        # Extract user and item mappings from the sparse matrix\n",
    "        self.user_data = sparse_matrix.user_ratings  # Access user ratings\n",
    "        self.item_data = sparse_matrix.user_ratings  # Access item ratings as the same dictionary\n",
    "        self.test_data = self.user_data  # Adjust this as per your testing data requirements\n",
    "\n",
    "        # Initialize matrices and biases\n",
    "        self.user_matrix = np.random.normal(0, 1 / np.sqrt(latent_d), size=(len(sparse_matrix.index_to_user), latent_d))\n",
    "        self.item_matrix = np.random.normal(0, 1 / np.sqrt(latent_d), size=(len(sparse_matrix.index_to_movie), latent_d))\n",
    "        self.user_bias = np.zeros(len(sparse_matrix.index_to_user))\n",
    "        self.item_bias = np.zeros(len(sparse_matrix.index_to_movie))\n",
    "\n",
    "        # Loss and RMSE lists\n",
    "        self.loss = []\n",
    "        self.rmse = []\n",
    "        self.loss_test = []\n",
    "        self.rmse_test = []\n",
    "\n",
    "    def _update_user(self):\n",
    "        for m in range(len(self.user_data)):\n",
    "            if not self.user_data[m]:\n",
    "                continue\n",
    "            # Set the biases\n",
    "            bm = 0\n",
    "            user_counter = 0\n",
    "\n",
    "            for n, r in self.user_data[m].items():  # Access ratings directly\n",
    "                inner = np.dot(self.user_matrix[m, :], self.item_matrix[n, :])\n",
    "                bm += self.lamda * (r - (inner + self.item_bias[n]))\n",
    "                user_counter += 1\n",
    "\n",
    "            self.user_bias[m] = bm / (self.lamda * user_counter + self.gamma)\n",
    "\n",
    "            # Set the trait vectors\n",
    "            b_matrix = 0\n",
    "            inv_matrix = 0\n",
    "\n",
    "            for n, r in self.user_data[m].items():\n",
    "                b_matrix += self.lamda * self.item_matrix[n, :] * (r - (self.user_bias[m] + self.item_bias[n]))\n",
    "                inv_matrix += self.lamda * np.outer(self.item_matrix[n, :], self.item_matrix[n, :])\n",
    "\n",
    "            inv_matrix += self.tau * np.identity(self.latent_d)\n",
    "\n",
    "            self.user_matrix[m, :] = np.dot(np.linalg.inv(inv_matrix), b_matrix)\n",
    "\n",
    "    def _update_item(self):\n",
    "        for n in range(len(self.item_data)):\n",
    "            if not self.item_data[n]:\n",
    "                continue\n",
    "            # Set the biases\n",
    "            bn = 0\n",
    "            counter_bias = 0\n",
    "\n",
    "            for m, r in self.item_data[n].items():  # Access ratings directly\n",
    "                inner = np.dot(self.user_matrix[m, :], self.item_matrix[n, :])\n",
    "                bn += self.lamda * (r - (inner + self.user_bias[m]))\n",
    "                counter_bias += 1\n",
    "\n",
    "            self.item_bias[n] = bn / (self.lamda * counter_bias + self.gamma)\n",
    "\n",
    "            # Set the trait vectors\n",
    "            b = 0\n",
    "            outer = 0\n",
    "\n",
    "            for m, r in self.item_data[n].items():\n",
    "                b += self.lamda * self.user_matrix[m, :] * (r - (self.user_bias[m] + self.item_bias[n]))\n",
    "                outer += self.lamda * np.outer(self.user_matrix[m, :], self.user_matrix[m, :])\n",
    "\n",
    "            outer += self.tau * np.identity(self.latent_d)\n",
    "            self.item_matrix[n, :] = np.dot(np.linalg.inv(outer), b)\n",
    "\n",
    "    def train(self, epoch=20, showplt=True):\n",
    "        for epoch in range(epoch):\n",
    "            self._update_user()\n",
    "            self._update_item()\n",
    "\n",
    "            # Computing metrics\n",
    "            l, r = self.compute_cost(data=self.user_data)\n",
    "            l_test, r_test = self.compute_cost(data=self.test_data)\n",
    "            print(f'epoch: {epoch + 1}  rmse train: {r} | rmse test: {r_test}')\n",
    "            self.loss.append(l)\n",
    "            self.rmse.append(r)\n",
    "            self.loss_test.append(l_test)\n",
    "            self.rmse_test.append(r_test)\n",
    "\n",
    "            if showplt:\n",
    "                self.plot_loss(self.loss, self.loss_test)\n",
    "                self.plot_rmse(self.rmse, self.rmse_test)\n",
    "\n",
    "    def compute_cost(self, data):\n",
    "        loss = 0\n",
    "        rmse = 0\n",
    "        count = 0\n",
    "        for m in range(len(data)):\n",
    "            for n, r in data[m].items():\n",
    "                error = (r - (np.dot(self.user_matrix[m, :], self.item_matrix[n, :]) + self.user_bias[m] + self.item_bias[n])) ** 2\n",
    "                loss += self.lamda / 2 * error\n",
    "                rmse += error\n",
    "                count += 1\n",
    "\n",
    "        loss += (self.gamma / 2) * np.dot(self.item_bias, self.item_bias) + (self.gamma / 2) * np.dot(self.user_bias, self.user_bias)\n",
    "\n",
    "        for n in range(len(self.item_matrix)):\n",
    "            loss += (self.tau / 2) * np.dot(self.item_matrix[n, :], self.item_matrix[n, :])\n",
    "\n",
    "        for m in range(len(self.user_matrix)):\n",
    "            loss += (self.tau / 2) * np.dot(self.user_matrix[m, :], self.user_matrix[m, :])\n",
    "\n",
    "        return loss, np.sqrt(rmse / count)\n",
    "\n",
    "    def plot_loss(self, train_loss, test_loss):\n",
    "        # Implement your plotting logic here\n",
    "        pass\n",
    "\n",
    "    def plot_rmse(self, train_rmse, test_rmse):\n",
    "        # Implement your plotting logic here\n",
    "        pass\n",
    "\n",
    "    # The predictor method can remain unchanged.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 610 is out of bounds for axis 0 with size 610",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m     \u001b[38;5;66;03m# Create an instance of the Trainor\u001b[39;00m\n\u001b[1;32m      4\u001b[0m     trainer \u001b[38;5;241m=\u001b[39m Trainor(sparse_matrix)\n\u001b[0;32m----> 5\u001b[0m     \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[19], line 81\u001b[0m, in \u001b[0;36mTrainor.train\u001b[0;34m(self, epoch, showplt)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epoch):\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_user()\n\u001b[0;32m---> 81\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;66;03m# Computing metrics\u001b[39;00m\n\u001b[1;32m     84\u001b[0m     l, r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_cost(data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_data)\n",
      "Cell \u001b[0;32mIn[19], line 61\u001b[0m, in \u001b[0;36mTrainor._update_item\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     58\u001b[0m counter_bias \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m m, r \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_data[n]\u001b[38;5;241m.\u001b[39mitems():  \u001b[38;5;66;03m# Access ratings directly\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m     inner \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muser_matrix\u001b[49m\u001b[43m[\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_matrix[n, :])\n\u001b[1;32m     62\u001b[0m     bn \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlamda \u001b[38;5;241m*\u001b[39m (r \u001b[38;5;241m-\u001b[39m (inner \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_bias[m]))\n\u001b[1;32m     63\u001b[0m     counter_bias \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 610 is out of bounds for axis 0 with size 610"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "\n",
    "    # Create an instance of the Trainor\n",
    "    trainer = Trainor(sparse_matrix)\n",
    "    trainer.train(epoch=10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **100K Df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#100k data file\n",
    "path = \"/home/oepeng/Documents/Python/my jupter/100k_csv/\"\n",
    "ratings_df = pd.read_csv( path + \"ratings.csv\").drop(columns='timestamp').to_numpy()\n",
    "movies_df = pd.read_csv( path + \"movies.csv\").to_numpy()\n",
    "\n",
    "dummy_user = {\n",
    "    'userId' : 999, #.\n",
    "    'movieId': 2640,  # superman\n",
    "    'rating': 5 \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datastructures import Sparser \n",
    "sparse = Sparser(df_25M, movies_df_25M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Plots of data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sparse.plot_ratings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sparse.plot_scatter()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ```Create sparse matrices```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data files\n",
    "\n",
    "train_test_data = sparse.data_by_user_train, sparse.data_by_movie_train, sparse.data_by_user_test \n",
    "features_data = sparse.movie_to_system_mov_title_dict, sparse.item_to_features, sparse.features_to_item, sparse.genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(no genres listed)', 'Action', 'Adventure', 'Animation',\n",
       "       'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
       "       'Film-Noir', 'Horror', 'IMAX', 'Musical', 'Mystery', 'Romance',\n",
       "       'Sci-Fi', 'Thriller', 'War', 'Western'], dtype='<U18')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps = sparse.system_user_to_user_dict, sparse.user_to_system_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24054"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps[0][23]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    " #pickle and load\n",
    "sparse_data = {'features_data':features_data , 'train_test_data': train_test_data}\n",
    "\n",
    "with open( \"25M_data_normal.pkl\", \"wb\") as f:\n",
    "    pickle.dump(sparse_data , f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "with open(\"25M_data_normal.pkl\", \"rb\") as f:\n",
    "    loaded_sparse_data = pickle.load(f)\n",
    "\n",
    "features_data = loaded_sparse_data[\"features_data\"]\n",
    "train_test_data = loaded_sparse_data[\"train_test_data\"]\n",
    "movie_to_system_mov_title_dict = features_data[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [03:46<34:02, 226.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1   rmse train: 1.101921803624593  | rmse test: 1.136399740065903\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 2/10 [07:33<30:15, 226.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2   rmse train: 0.9074086021075621  | rmse test: 0.9282505672403023\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 3/10 [11:23<26:35, 227.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3   rmse train: 0.8612765992230478  | rmse test: 0.8823934634602372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 4/10 [15:05<22:35, 225.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 4   rmse train: 0.8482296881798328  | rmse test: 0.8695291711864426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 5/10 [18:49<18:45, 225.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5   rmse train: 0.8405116306637949  | rmse test: 0.8618130945239313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 6/10 [22:24<14:47, 221.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 6   rmse train: 0.8347388079394509  | rmse test: 0.8560900550797574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 7/10 [26:01<11:00, 220.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 7   rmse train: 0.8295364636341148  | rmse test: 0.8509780516374394\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 8/10 [29:39<07:19, 219.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 8   rmse train: 0.8246714849270645  | rmse test: 0.8462018772805479\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 9/10 [33:16<03:38, 218.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9   rmse train: 0.8202936368860613  | rmse test: 0.8419831391548128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [36:54<00:00, 221.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10   rmse train: 0.8170494493394617  | rmse test: 0.8389386154664006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train the model\n",
    "\n",
    "from model import Trainor\n",
    "trainor = Trainor(data = train_test_data, latent_d=2, lamda= 0.01, tau=0.01, gamma=1)\n",
    "trainor.train() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    " #pickle and load\n",
    "data_trained_data = trainor.user_matrix , trainor.item_matrix ,trainor.user_bias ,trainor.item_bias \n",
    "with open( \"25M_data_trained_data.pkl\", \"wb\") as f:\n",
    "    pickle.dump(data_trained_data , f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "with open(\"25M_data_trained_data.pkl\", \"rb\") as f:\n",
    "    loaded_data_trained_data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ```evalute performance of model```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train_and_plot_rmse_vs_latent_d(data, max_latent_d=20, show_plot=True):\n",
    "    rmse_train_list = []\n",
    "    rmse_test_list = []\n",
    "    latent_d_values = [] \n",
    "    percentage_diff = [] # x-axis values\n",
    "    \n",
    "    for latent_d in range(1 , max_latent_d + 1):\n",
    "        if latent_d <= 10 or latent_d % 2 == 0:\n",
    "            print(\"Training with latent_d =\", latent_d )\n",
    "            model = Trainor(data, latent_d=latent_d)\n",
    "            model.train(epoch=10, showplt = False)\n",
    "            r = model.rmse[-1]  # Get the last RMSE from the training\n",
    "            rt = model.rmse_test[-1]\n",
    "            var =  100*(rt - r)/r\n",
    "            percentage_diff.append(var)\n",
    "            rmse_train_list.append(r) \n",
    "            rmse_test_list.append(rt)  \n",
    "            latent_d_values.append(latent_d)  \n",
    "    \n",
    "    # Plots \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(latent_d_values, rmse_train_list, label='RMSE Train', marker='o')\n",
    "    plt.plot(latent_d_values, rmse_test_list, label='RMSE Test', marker='o')\n",
    "    plt.xlabel('Latent Dimensions k ')\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.title('RMSEs vs. Latent Dimension')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    \n",
    "  \n",
    "    plt.savefig('rmse_vs_latent_d.svg')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.plot(latent_d_values, percentage_diff, marker='o')\n",
    "    plt.xlabel('Latent Dimensions k ')\n",
    "    plt.ylabel('difference in RMSE')\n",
    "    plt.title('varince of RMSEs vs. Latent Dimension')\n",
    "    plt.grid()\n",
    "    plt.savefig('latent dimesions k.svg')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "train_and_plot_rmse_vs_latent_d(train_test_data,max_latent_d=20, show_plot=False)  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Grid search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search(data, lambda_values, gamma_values, tau_values):\n",
    "    best_params = 0\n",
    "    best_rmse = 10000000\n",
    "\n",
    "    for lam in lambda_values:\n",
    "        for g in gamma_values:\n",
    "            for t in tau_values:\n",
    "                trainor = Trainor(data, lamda=lam, gamma=g, tau=t)\n",
    "                trainor.train(showplt=False)\n",
    "                test_loss, test_rmse = trainor.compute_cost(trainor.test_data)\n",
    "\n",
    "                if test_rmse < best_rmse:\n",
    "                    best_rmse = test_rmse\n",
    "                    best_params = (lambda_, gamma, beta)\n",
    "\n",
    "    return best_params, best_rmse\n",
    "\n",
    "\n",
    "\n",
    "lambda_values = [0.001, 0.01, 0.1]\n",
    "gamma_values = [0.001, 0.01, 0.1]\n",
    "beta_values = [0.01, 0.1, 1.0]\n",
    "\n",
    "best_params, best_rmse = grid_search(train_test_data, lambda_values, gamma_values, beta_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.01, 0.01, 1.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Predictor_Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AI recommnds:........ \n",
      "\t 1 Sting, The (1973)\n",
      "\t 2 Vrijdag\n",
      "\t 3 Dark Knight, The (2008)\n",
      "\t 4 Princess Bride, The (1987)\n",
      "\t 5 Lord of the Rings: The Return of the King, The (2003)\n",
      "\t 6 Lord of the Rings: The Fellowship of the Ring, The (2001)\n",
      "\t 7 Lord of the Rings: The Two Towers, The (2002)\n",
      "\t 8 Inception (2010)\n",
      "\t 9 Lethal Weapon (1987)\n",
      "\t 10 Pirates of the Caribbean: The Curse of the Black Pearl (2003)\n"
     ]
    }
   ],
   "source": [
    "trainor.predictor(features_data[0], train_test_data[0] , user_indx = 24054, dummy = True, weight = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Create a scatter plot\n",
    "latent_vectors = trainor.user_matrix  \n",
    "\n",
    "\n",
    "plt.scatter(latent_vectors[:, 0], latent_vectors[:, 1])\n",
    "plt.title(\"Scatter Plot of Latent Vectors\")\n",
    "plt.xlabel(\"Latent Dimension 1\")\n",
    "plt.ylabel(\"Latent Dimension 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "for feature_index, mids in enumerate(features_data[2]):\n",
    "    if len(mids) >= 3:\n",
    "        random_movies = np.random.choice(mids, 2, replace=False).tolist()\n",
    "        for movie in random_movies:\n",
    "            plt.scatter(data_trained_data[1][movie, 0], data_trained_data[1][movie, 1], alpha=0.5)\n",
    "\n",
    "plt.autoscale()\n",
    "plt.legend(features_data[3], loc=\"upper right\")\n",
    "\n",
    "plt.savefig(\"ebeddings.svg\")\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Data/ds100')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pathlib as p\n",
    "\n",
    "\n",
    "\n",
    "p.Path(\"Data\") / \"ds100\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/soot/Code/MovieRecommender\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}