WALS_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
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      "text/plain": [
       "   user_id  movie_id  ratings  timestamp\n",
       "0      196       242        3  881250949\n",
       "1      186       302        3  891717742\n",
       "2       22       377        1  878887116\n",
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      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_headers = ['user_id', 'movie_id', 'ratings', 'timestamp']\n",
    "ratings = pd.read_table('u.data', sep='\\t', header=None, names=ratings_headers)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>movie_id</th>\n",
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      ],
      "text/plain": [
       "movie_id  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "user_id                                                               ...    \n",
       "1          5.0   3.0   4.0   3.0   3.0   5.0   4.0   1.0   5.0   3.0  ...    \n",
       "2          4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   2.0  ...    \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "5          4.0   3.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "movie_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \n",
       "user_id                                                               \n",
       "1          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 1682 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp = ratings.pivot_table(columns=['movie_id'],index=['user_id'],values='ratings')\n",
    "rp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "movie_id  1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "user_id                                                               ...    \n",
       "1          5.0   3.0   4.0   3.0   3.0   5.0   4.0   1.0   5.0   3.0  ...    \n",
       "2          4.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   2.0  ...    \n",
       "3          0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    \n",
       "4          0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    \n",
       "5          4.0   3.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    \n",
       "\n",
       "movie_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \n",
       "user_id                                                               \n",
       "1          0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
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       "\n",
       "[5 rows x 1682 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp = rp.fillna(0); # Replace NaN\n",
    "rp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = rp.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = Q>0.1\n",
    "W[W == True] = 1\n",
    "W[W == False] = 0\n",
    "# To be consistent with our Q matrix\n",
    "W = W.astype(np.float64, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_ = 0.06\n",
    "n_factors = 15\n",
    "m, n = Q.shape\n",
    "n_iterations = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.rand(m, n_factors) \n",
    "Y = np.random.rand(n_factors, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "errors = []\n",
    "diff_error = 10\n",
    "for ii in range(n_iterations):\n",
    "    for u, Wu in enumerate(W):\n",
    "        X[u] = np.linalg.solve(np.dot(Y, np.dot(np.diag(Wu), Y.T)) + lambda_ * np.eye(n_factors),np.dot(Y, np.dot(np.diag(Wu), Q[u].T))).T\n",
    "    for i, Wi in enumerate(W.T):\n",
    "        Y[:,i] = np.linalg.solve(np.dot(X.T, np.dot(np.diag(Wi), X)) + lambda_ * np.eye(n_factors),np.dot(X.T, np.dot(np.diag(Wi), Q[:, i])))\n",
    "    errors.append(mean_squared_error(Q,np.dot(X,Y)))\n",
    "    print('{}th iteration is completed'.format(ii))\n",
    "    if(ii>1):\n",
    "        diff_error = abs(errors[ii-2] - errors[ii-1])\n",
    "    if diff_error < 0.0000001:\n",
    "        break;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "now_ratings = np.dot(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(Q,now_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE =  11.756168\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE =  %f\" %mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4287268147332512\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(math.sqrt(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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