embeddings_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ee04fd5-54fb-4cee-9c84-e4ec76957b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import data_processing\n",
    "from embeddings import perform_nmf, perform_pca, perform_svd\n",
    "import polars as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer\n",
    "from sklearn.decomposition import PCA, TruncatedSVD, NMF\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae612076-fa1c-4775-b77d-94e74777932c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nif not os.path.exists('data/books.csv'):\\n    data_processing.load_books()\\n\\ncreate_ratings = not os.path.exists('data/ratings.csv')\\ncreate_read = not os.path.exists('data/read.csv')\\ncreate_shelved = not os.path.exist('data/shelved.csv')\\n\\nif create_ratings or create_read or create_shelved:\\n    data_processing.load_interactions(ratings=create_ratings,\\n                                      read=create_read,\\n                                      shelved=create_shelved)\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "if not os.path.exists('data/books.csv'):\n",
    "    data_processing.load_books()\n",
    "\n",
    "create_ratings = not os.path.exists('data/ratings.csv')\n",
    "create_read = not os.path.exists('data/read.csv')\n",
    "create_shelved = not os.path.exist('data/shelved.csv')\n",
    "\n",
    "if create_ratings or create_read or create_shelved:\n",
    "    data_processing.load_interactions(ratings=create_ratings,\n",
    "                                      read=create_read,\n",
    "                                      shelved=create_shelved)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc970b44-5bb0-4675-b96b-32d6ef800b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test train split user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be4dfaf6-6618-4f22-b756-8d492043e327",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('data/ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "306540ef-630b-449a-be9e-646afb9d2b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ids = pd.read_csv('data/train_ids.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5243a85-3a02-459a-8758-c62a4b5d67f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = ratings[ratings['user_id'].isin(train_ids['user_id'])].drop(columns=['Unnamed: 0', 'user_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8016c94f-aa0a-4c4e-b812-abf183cabf37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform_pca(ratings, 'user_ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4df6dda9-66f7-45c2-bd4d-3577d3050e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform_svd(ratings, 'user_ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4dc070b-df44-474e-bc70-c27c9f6b15b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_nmf(ratings, 'user_ratings', n_components=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5f93688-e3bb-4acc-bb1b-67b0bdd089b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_info = pd.read_csv('user_ratings_pca_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3ebf22a-436c-4b0b-8243-541beb1fd753",
   "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>explained_variance_ratio</th>\n",
       "      <th>singular_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.047463</td>\n",
       "      <td>4765.914071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.032868</td>\n",
       "      <td>3966.005873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.018308</td>\n",
       "      <td>2959.972051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016084</td>\n",
       "      <td>2774.382229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012774</td>\n",
       "      <td>2472.491357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   explained_variance_ratio  singular_values\n",
       "0                  0.047463      4765.914071\n",
       "1                  0.032868      3966.005873\n",
       "2                  0.018308      2959.972051\n",
       "3                  0.016084      2774.382229\n",
       "4                  0.012774      2472.491357"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0bfa0e3-c29d-43b7-9c02-59a0cd5e5b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_info = pca_info.reset_index().rename(columns={'index': 'pc'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4db0751a-7594-4e86-85ee-41b285bfe9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pc', ylabel='explained_variance_ratio'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=pca_info['pc'], y=np.cumsum(pca_info['explained_variance_ratio']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0cb30608-b20b-4c5c-ad28-1e8f7a9310b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% Explained Variance at PC_538\n"
     ]
    }
   ],
   "source": [
    "pc = 0\n",
    "for x in np.cumsum(pca_info['explained_variance_ratio']):\n",
    "    if x >= 0.80:\n",
    "        print(f'80% Explained Variance at PC_{pc}')\n",
    "        break\n",
    "    pc += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0580e301-a8e2-4eb7-8689-c4ecd7ebe632",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}