Collaborative filtering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collab flitering using fastai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are done with content based recommendation systems now, we will start with collaborative filtering. This first Notebook on collaborative filtering method will use fastai and deep learning to build a collab filtering system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.collab import *\n",
    "from fastai.tabular import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/Vikas/.fastai/data/movie_lens_sample')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.ML_SAMPLE)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0      73     1097     4.0  1255504951\n",
       "1     561      924     3.5  1172695223\n",
       "2     157      260     3.5  1291598691\n",
       "3     358     1210     5.0   957481884\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CollabDataBunch.from_df(ratings, seed = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_range = [0, 5.5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(data, n_factors=50, y_range = y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>1.597770</td>\n",
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       "      <td>00:29</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>0.676945</td>\n",
       "      <td>00:28</td>\n",
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   ],
   "source": [
    "learn.fit_one_cycle(3, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its that simple with fastai. Everything is implemented already. Now, lets try to implement with full movielens dataset.\n"
   ]
  }
 ],
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