ALS_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>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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     "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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      ],
      "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",
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      ]
     },
     "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": [
    "lambda_ = 0.06\n",
    "n_factors = 15\n",
    "m, n = Q.shape\n",
    "n_iterations = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.rand(m, n_factors) \n",
    "Y = np.random.rand(n_factors, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0th iteration is completed\n",
      "1th iteration is completed\n",
      "2th iteration is completed\n",
      "3th iteration is completed\n",
      "4th iteration is completed\n",
      "5th iteration is completed\n",
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      "10th iteration is completed\n",
      "11th iteration is completed\n",
      "12th iteration is completed\n",
      "13th iteration is completed\n",
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      "15th iteration is completed\n",
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      "18th iteration is completed\n",
      "19th iteration is completed\n",
      "20th iteration is completed\n",
      "21th iteration is completed\n",
      "22th iteration is completed\n",
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      "24th iteration is completed\n",
      "25th iteration is completed\n",
      "26th iteration is completed\n",
      "27th iteration is completed\n",
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      "34th iteration is completed\n",
      "35th iteration is completed\n",
      "36th iteration is completed\n",
      "37th iteration is completed\n",
      "38th iteration is completed\n",
      "39th iteration is completed\n",
      "40th iteration is completed\n",
      "41th iteration is completed\n",
      "42th iteration is completed\n",
      "43th iteration is completed\n",
      "44th iteration is completed\n",
      "45th iteration is completed\n",
      "46th iteration is completed\n",
      "47th iteration is completed\n",
      "48th iteration is completed\n",
      "49th iteration is completed\n",
      "50th iteration is completed\n",
      "51th iteration is completed\n",
      "52th iteration is completed\n",
      "53th iteration is completed\n",
      "54th iteration is completed\n",
      "55th iteration is completed\n",
      "56th iteration is completed\n",
      "57th iteration is completed\n",
      "58th iteration is completed\n",
      "59th iteration is completed\n",
      "60th iteration is completed\n",
      "61th iteration is completed\n",
      "62th iteration is completed\n",
      "63th iteration is completed\n",
      "64th iteration is completed\n",
      "65th iteration is completed\n",
      "66th iteration is completed\n"
     ]
    }
   ],
   "source": [
    "errors = []\n",
    "diff_error = 10\n",
    "for ii in range(n_iterations):\n",
    "    X = np.linalg.solve(np.dot(Y, Y.T) + lambda_ * np.eye(n_factors), np.dot(Y, Q.T)).T\n",
    "    Y = np.linalg.solve(np.dot(X.T, X) + lambda_ * np.eye(n_factors),np.dot(X.T, Q))\n",
    "    print('{}th iteration is completed'.format(ii))\n",
    "    errors.append(mean_squared_error(Q,np.dot(X,Y)))\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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "now_ratings = np.dot(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(Q,now_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE =  0.435837\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE =  %f\" %mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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