選択できるのは25トピックまでです。
トピックは、先頭が英数字で、英数字とダッシュ('-')を使用した35文字以内のものにしてください。
274 行
8.0 KiB
274 行
8.0 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'surprise'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-1-002ce27085d1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0msurprise\u001b[0m \u001b[1;31m# run 'pip install scikit-surprise' to install surprise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'surprise'"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import surprise # run 'pip install scikit-surprise' to install surprise"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class MatrixFacto(surprise.AlgoBase):\n",
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" '''A basic rating prediction algorithm based on matrix factorization.'''\n",
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" \n",
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" def __init__(self, learning_rate, n_epochs, n_factors):\n",
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" \n",
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" self.lr = learning_rate # learning rate for SGD\n",
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" self.n_epochs = n_epochs # number of iterations of SGD\n",
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" self.n_factors = n_factors # number of factors\n",
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" \n",
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" def fit(self, trainset):\n",
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" '''Learn the vectors p_u and q_i with SGD'''\n",
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" \n",
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" print('Fitting data with SGD...')\n",
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" \n",
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" # Randomly initialize the user and item factors.\n",
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" p = np.random.normal(0, .1, (trainset.n_users, self.n_factors))\n",
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" q = np.random.normal(0, .1, (trainset.n_items, self.n_factors))\n",
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" \n",
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" # SGD procedure\n",
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" for _ in range(self.n_epochs):\n",
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" for u, i, r_ui in trainset.all_ratings():\n",
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" err = r_ui - np.dot(p[u], q[i])\n",
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" # Update vectors p_u and q_i\n",
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" p[u] += self.lr * err * q[i]\n",
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" q[i] += self.lr * err * p[u]\n",
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" # Note: in the update of q_i, we should actually use the previous (non-updated) value of p_u.\n",
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" # In practice it makes almost no difference.\n",
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" \n",
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" self.p, self.q = p, q\n",
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" self.trainset = trainset\n",
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"\n",
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" def estimate(self, u, i):\n",
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" '''Return the estmimated rating of user u for item i.'''\n",
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" \n",
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" # return scalar product between p_u and q_i if user and item are known,\n",
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" # else return the average of all ratings\n",
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" if self.trainset.knows_user(u) and self.trainset.knows_item(i):\n",
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" return np.dot(self.p[u], self.q[i])\n",
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" else:\n",
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" return self.trainset.global_mean"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# data loading. We'll use the movielens dataset (https://grouplens.org/datasets/movielens/100k/)\n",
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"# it will be downloaded automatically.\n",
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"data = surprise.Dataset.load_builtin('ml-100k')\n",
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"data.split(2) # split data for 2-folds cross validation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Evaluating RMSE of algorithm MatrixFacto.\n",
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"\n",
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"------------\n",
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"Fold 1\n",
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"Fitting data with SGD...\n",
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"RMSE: 0.9826\n",
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"------------\n",
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"Fold 2\n",
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"Fitting data with SGD...\n",
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"RMSE: 0.9873\n",
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"------------\n",
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"------------\n",
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"Mean RMSE: 0.9849\n",
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"------------\n",
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"------------\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"CaseInsensitiveDefaultDict(list,\n",
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" {'rmse': [0.98263312180825368, 0.9872549391926676]})"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"algo = MatrixFacto(learning_rate=.01, n_epochs=10, n_factors=10)\n",
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"surprise.evaluate(algo, data, measures=['RMSE'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Evaluating RMSE of algorithm KNNBasic.\n",
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"\n",
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"------------\n",
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"Fold 1\n",
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"Computing the msd similarity matrix...\n",
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"Done computing similarity matrix.\n",
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"RMSE: 1.0101\n",
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"------------\n",
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"Fold 2\n",
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"Computing the msd similarity matrix...\n",
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"Done computing similarity matrix.\n",
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"RMSE: 0.9982\n",
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"------------\n",
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"------------\n",
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"Mean RMSE: 1.0042\n",
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"------------\n",
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"------------\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"CaseInsensitiveDefaultDict(list,\n",
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" {'rmse': [1.0101383334175613, 0.99823558896449016]})"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# try a neighborhood-based algorithm (on the same data)\n",
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"algo = surprise.KNNBasic()\n",
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"surprise.evaluate(algo, data, measures=['RMSE'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Evaluating RMSE of algorithm SVD.\n",
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"\n",
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"------------\n",
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"Fold 1\n",
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"RMSE: 0.9604\n",
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"------------\n",
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"Fold 2\n",
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"RMSE: 0.9538\n",
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"------------\n",
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"------------\n",
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"Mean RMSE: 0.9571\n",
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"------------\n",
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"------------\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"CaseInsensitiveDefaultDict(list,\n",
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" {'rmse': [0.96042083843476056,\n",
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" 0.95382688332712151]})"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# try a more sophisticated matrix factorization algorithm (on the same data)\n",
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"algo = surprise.SVD()\n",
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"surprise.evaluate(algo, data, measures=['RMSE'])"
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]
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}
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],
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