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May 20, 2018 10:17
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import random" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# MF" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"class MatrixFactorization(object):\n", | |
"\n", | |
" def __init__(self, K=20, alpha=1e-6, beta = 0.0):\n", | |
" self.K = K \n", | |
" self.alpha = alpha\n", | |
" self.beta = beta\n", | |
"\n", | |
"\n", | |
" def fit(self, X, n_user, n_item, n_iter = 100):\n", | |
" self.R = X.copy()\n", | |
" self.samples = X.copy()\n", | |
"\n", | |
" self.user_factors = np.random.rand(n_user, self.K)\n", | |
" self.item_factors = np.random.rand(n_item, self.K)\n", | |
" \n", | |
" #stochastic gradient descent \n", | |
" self.loss = []\n", | |
" for i in range(n_iter):\n", | |
" self.sgd()\n", | |
" mse = self.mse()\n", | |
" self.loss.append((i, mse)) \n", | |
" \n", | |
" def sgd(self):\n", | |
" np.random.shuffle(self.samples)\n", | |
" for user, item, rating in self.samples:\n", | |
" err = rating - self.predict_pair(user, item) \n", | |
" \n", | |
" # update parameter\n", | |
" self.user_factors[user] += self.alpha * (err * self.item_factors[item] - self.beta * self.user_factors[user])\n", | |
" self.item_factors[item] += self.alpha * (err * self.user_factors[user] - self.beta * self.item_factors[item]) \n", | |
" \n", | |
" def mse(self):\n", | |
" predicted = self.predict(self.R)\n", | |
" error = np.hstack((self.R, np.array(predicted).reshape(-1, 1)))\n", | |
" error = np.sqrt(pow((error[:, 2] - error[:, 3]), 2).mean())\n", | |
" return error\n", | |
" \n", | |
" def predict_pair(self, user, item):\n", | |
" return np.inner(self.user_factors[user], self.item_factors[item])\n", | |
" \n", | |
" def predict(self, X):\n", | |
" rate = []\n", | |
" for row in X:\n", | |
" rate.append(self.predict_pair(row[0], row[1])) \n", | |
" return rate\n", | |
" \n", | |
" def get_full_matrix(self):\n", | |
" return np.inner(self.user_factors, self.item_factors)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Bias MF" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"class BiasMatrixFactorization(object):\n", | |
"\n", | |
" def __init__(self, K=20, alpha=1e-6, beta = 0.0):\n", | |
" self.K = K \n", | |
" self.alpha = alpha\n", | |
" self.beta = beta\n", | |
"\n", | |
" \n", | |
" def fit(self, X, n_user, n_item, n_iter = 100):\n", | |
" self.R = X.copy()\n", | |
" self.samples = X.copy()\n", | |
"\n", | |
" self.user_factors = np.random.rand(n_user, self.K)\n", | |
" self.item_factors = np.random.rand(n_item, self.K)\n", | |
" \n", | |
" self.bias_u = np.zeros(n_user)\n", | |
" self.bias_i = np.zeros(n_item)\n", | |
" self.bias = np.mean(X[:, 2])\n", | |
" \n", | |
" #stochastic gradient descent \n", | |
" self.loss = []\n", | |
" for i in range(n_iter):\n", | |
" self.sgd()\n", | |
" mse = self.mse()\n", | |
" self.loss.append((i, mse))\n", | |
" \n", | |
" def sgd(self):\n", | |
" np.random.shuffle(self.samples)\n", | |
" for user, item, rating in self.samples:\n", | |
" err = rating - self.predict_pair(user, item)\n", | |
" \n", | |
" # update parameter\n", | |
" self.bias_u[user] += self.alpha * (err - self.beta * self.bias_u[user])\n", | |
" self.bias_i[item] += self.alpha * (err - self.beta * self.bias_i[item])\n", | |
" \n", | |
" self.user_factors[user] += self.alpha * (err * self.item_factors[item] - self.beta * self.user_factors[user])\n", | |
" self.item_factors[item] += self.alpha * (err * self.user_factors[user] - self.beta * self.item_factors[item]) \n", | |
" \n", | |
" def mse(self):\n", | |
" predicted = self.predict(self.R)\n", | |
" error = np.hstack((self.R, np.array(predicted).reshape(-1, 1)))\n", | |
" error = np.sqrt(pow((error[:, 2] - error[:, 3]), 2).mean())\n", | |
" return error\n", | |
" \n", | |
" def predict_pair(self, user, item):\n", | |
" return self.bias + self.bias_u[user] + self.bias_i[item] + np.inner(self.user_factors[user], self.item_factors[item])\n", | |
" \n", | |
" def predict(self, X):\n", | |
" rate = []\n", | |
" for row in X:\n", | |
" rate.append(self.predict_pair(row[0], row[1])) \n", | |
" return rate\n", | |
" \n", | |
" def get_full_matrix(self):\n", | |
" return self.bias + self.bias_u.reshape(-1, 1) + self.bias_i + np.inner(self.user_factors, self.item_factors)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# load data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"\n", | |
"def load_ml100k():\n", | |
" samples = pd.read_csv('data/ml-100k/u.data', sep = '\\t', header=None)\n", | |
" \n", | |
" samples = samples.iloc[:, :3]\n", | |
" samples.columns = ['user', 'item', 'rate']\n", | |
" \n", | |
" samples['user'] = samples['user'] - 1\n", | |
" samples['item'] = samples['item'] - 1\n", | |
" \n", | |
" return samples" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# main" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"df = np.array(load_ml100k())\n", | |
"\n", | |
"n_user = np.unique(df[:, 0]).max() + 1\n", | |
"n_item = np.unique(df[:, 1]).max() + 1\n", | |
"n_rate = np.unique(df[:, 2]).max()\n", | |
"\n", | |
"random.shuffle(df)\n", | |
"train_size = int(df.shape[0] * 0.8)\n", | |
"train_df = df[:train_size]\n", | |
"test_df = df[train_size:]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"#Matrix Factorization\n", | |
"MF = MatrixFactorization(K = 20, alpha = 0.01, beta = 0.5)\n", | |
"MF.fit(train_df, n_user, n_item, n_iter = 10)\n", | |
"\n", | |
"pre = MF.predict(test_df)\n", | |
"ret1 = np.hstack((test_df, np.array(pre).reshape(-1, 1)))\n", | |
"np.sqrt(pow((ret1[:, 2] - ret1[:, 3]), 2).mean())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"#Bias Matrix Factorization\n", | |
"BMF = BiasMatrixFactorization(K=20, alpha = 0.01, beta = 0.5)\n", | |
"BMF.fit(train_df, n_user, n_item, n_iter = 10)\n", | |
"\n", | |
"pre2 = BMF.predict(test_df[:, :2])\n", | |
"ret2 =np.hstack((test_df, np.array(pre2).reshape(-1, 1)))\n", | |
"np.sqrt(pow((ret2[:, 2] - ret2[:, 3]), 2).mean())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"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": "3.5.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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