{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Self made SVD" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import helpers\n", "import pandas as pd\n", "import numpy as np\n", "import scipy.sparse as sparse\n", "from collections import defaultdict\n", "from itertools import chain\n", "import random\n", "\n", "train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None)\n", "test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n", "train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code = helpers.data_to_csr(train_read, test_read)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Done similarly to https://github.com/albertauyeung/matrix-factorization-in-python\n", "from tqdm import tqdm\n", "\n", "class SVD():\n", " \n", " def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):\n", " self.train_ui=train_ui\n", " self.uir=list(zip(*[train_ui.nonzero()[0],train_ui.nonzero()[1], train_ui.data]))\n", " \n", " self.learning_rate=learning_rate\n", " self.regularization=regularization\n", " self.iterations=iterations\n", " self.nb_users, self.nb_items=train_ui.shape\n", " self.nb_ratings=train_ui.nnz\n", " self.nb_factors=nb_factors\n", " \n", " self.Pu=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_users, self.nb_factors))\n", " self.Qi=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_items, self.nb_factors))\n", "\n", " def train(self, test_ui=None):\n", " if test_ui!=None:\n", " self.test_uir=list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))\n", " \n", " self.learning_process=[]\n", " pbar = tqdm(range(self.iterations))\n", " for i in pbar:\n", " pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i>0 else 0}. Training epoch {i+1}...')\n", " np.random.shuffle(self.uir)\n", " self.sgd(self.uir)\n", " if test_ui==None:\n", " self.learning_process.append([i+1, self.RMSE_total(self.uir)])\n", " else:\n", " self.learning_process.append([i+1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])\n", " \n", " def sgd(self, uir):\n", " \n", " for u, i, score in uir:\n", " # Computer prediction and error\n", " prediction = self.get_rating(u,i)\n", " e = (score - prediction)\n", " \n", " # Update user and item latent feature matrices\n", " Pu_update=self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])\n", " Qi_update=self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])\n", " \n", " self.Pu[u] += Pu_update\n", " self.Qi[i] += Qi_update\n", " \n", " def get_rating(self, u, i):\n", " prediction = self.Pu[u].dot(self.Qi[i].T)\n", " return prediction\n", " \n", " def RMSE_total(self, uir):\n", " RMSE=0\n", " for u,i, score in uir:\n", " prediction = self.get_rating(u,i)\n", " RMSE+=(score - prediction)**2\n", " return np.sqrt(RMSE/len(uir))\n", " \n", " def estimations(self):\n", " self.estimations=\\\n", " np.dot(self.Pu,self.Qi.T)\n", "\n", " def recommend(self, user_code_id, item_code_id, topK=10):\n", " \n", " top_k = defaultdict(list)\n", " for nb_user, user in enumerate(self.estimations):\n", " \n", " user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n", " for item, score in enumerate(user):\n", " if item not in user_rated and not np.isnan(score):\n", " top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n", " result=[]\n", " # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n", " for uid, item_scores in top_k.items():\n", " item_scores.sort(key=lambda x: x[1], reverse=True)\n", " result.append([uid]+list(chain(*item_scores[:topK])))\n", " return result\n", " \n", " def estimate(self, user_code_id, item_code_id, test_ui):\n", " result=[]\n", " for user, item in zip(*test_ui.nonzero()):\n", " result.append([user_code_id[user], item_code_id[item], \n", " self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])\n", " return result" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Epoch 39 RMSE: 0.750963575605171. Training epoch 40...: 100%|██████████| 40/40 [01:38<00:00, 2.45s/it] \n" ] } ], "source": [ "model=SVD(train_ui, learning_rate=0.005, regularization=0.02, nb_factors=100, iterations=40)\n", "model.train(test_ui)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "df=pd.DataFrame(model.learning_process).iloc[:,:2]\n", "df.columns=['epoch', 'train_RMSE']\n", "plt.plot('epoch', 'train_RMSE', data=df, color='blue')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "df=pd.DataFrame(model.learning_process[10:], columns=['epoch', 'train_RMSE', 'test_RMSE'])\n", "plt.plot('epoch', 'train_RMSE', data=df, color='blue')\n", "plt.plot('epoch', 'test_RMSE', data=df, color='yellow', linestyle='dashed')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Saving and evaluating recommendations" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.estimations()\n", "\n", "top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))\n", "\n", "top_n.to_csv('Recommendations generated/ml-100k/Self_SVD_reco.csv', index=False, header=False)\n", "\n", "estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))\n", "estimations.to_csv('Recommendations generated/ml-100k/Self_SVD_estimations.csv', index=False, header=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "943it [00:00, 7303.87it/s]\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RMSEMAEprecisionrecallF_1F_05precision_superrecall_superNDCGmAPMRRLAUCHRH2RReco in testTest coverageShannonGini
00.915730.7189210.1022270.0431370.0519810.0688720.0935620.0780570.1048280.0494480.1912430.5182860.4729590.2587490.8592790.139253.831520.973234
\n", "
" ], "text/plain": [ " RMSE MAE precision recall F_1 F_05 \\\n", "0 0.91573 0.718921 0.102227 0.043137 0.051981 0.068872 \n", "\n", " precision_super recall_super NDCG mAP MRR LAUC \\\n", "0 0.093562 0.078057 0.104828 0.049448 0.191243 0.518286 \n", "\n", " HR H2R Reco in test Test coverage Shannon Gini \n", "0 0.472959 0.258749 0.859279 0.13925 3.83152 0.973234 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import evaluation_measures as ev\n", "\n", "estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_SVD_estimations.csv', header=None)\n", "reco=np.loadtxt('Recommendations generated/ml-100k/Self_SVD_reco.csv', delimiter=',')\n", "\n", "ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None),\n", " estimations_df=estimations_df, \n", " reco=reco,\n", " super_reactions=[4,5])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "943it [00:00, 6614.64it/s]\n", "943it [00:00, 6657.91it/s]\n", "943it [00:00, 6616.31it/s]\n", "943it [00:00, 7049.97it/s]\n", "943it [00:00, 7105.27it/s]\n", "943it [00:00, 7296.68it/s]\n", "943it [00:00, 6993.15it/s]\n", "943it [00:00, 7255.64it/s]\n", "943it [00:00, 6724.45it/s]\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ModelRMSEMAEprecisionrecallF_1F_05precision_superrecall_superNDCGmAPMRRLAUCHRH2RReco in testTest coverageShannonGini
0Self_TopPop2.5082582.2179090.1888650.1169190.1187320.1415840.1304720.1374730.2146510.1117070.4009390.5555460.7656420.4920471.0000000.0389613.1590790.987317
0Self_SVD0.9157300.7189210.1022270.0431370.0519810.0688720.0935620.0780570.1048280.0494480.1912430.5182860.4729590.2587490.8592790.1392503.8315200.973234
0Ready_Baseline0.9494590.7524870.0914100.0376520.0460300.0612860.0796140.0564630.0959570.0431780.1981930.5155010.4379640.2396611.0000000.0339112.8365130.991139
0Self_GlobalAvg1.1257600.9435340.0611880.0259680.0313830.0413430.0405580.0321070.0676950.0274700.1711870.5095460.3849420.1421001.0000000.0259742.7117720.992003
0Ready_Random1.5249541.2233520.0455990.0211810.0245850.0315180.0278970.0219310.0481110.0173810.1190050.5070960.3308590.0911980.9881230.1818185.1007920.906866
0Self_TopRatedNaNNaN0.0320250.0126740.0157140.0211830.0284330.0185730.0227410.0053280.0316020.5027640.2375400.0657480.6970310.0144302.2208110.995173
0Self_KNNSurprisetask0.9971060.7841630.0056200.0029210.0034940.0043250.0049360.0034610.0071030.0028330.0214310.4978190.0424180.0095440.4532340.1370852.8663470.982811
0Self_BaselineUI0.9675850.7627400.0009540.0001700.0002780.0004630.0006440.0001890.0007520.0001680.0016770.4964240.0095440.0000000.6005300.0050511.8031260.996380
0Self_IKNN1.0183630.8087930.0003180.0001080.0001400.0001890.0000000.0000000.0002140.0000370.0003680.4963910.0031810.0000000.3921530.1154404.1747410.965327
\n", "
" ], "text/plain": [ " Model RMSE MAE precision recall F_1 \\\n", "0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n", "0 Self_SVD 0.915730 0.718921 0.102227 0.043137 0.051981 \n", "0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n", "0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n", "0 Ready_Random 1.524954 1.223352 0.045599 0.021181 0.024585 \n", "0 Self_TopRated NaN NaN 0.032025 0.012674 0.015714 \n", "0 Self_KNNSurprisetask 0.997106 0.784163 0.005620 0.002921 0.003494 \n", "0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n", "0 Self_IKNN 1.018363 0.808793 0.000318 0.000108 0.000140 \n", "\n", " F_05 precision_super recall_super NDCG mAP MRR \\\n", "0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n", "0 0.068872 0.093562 0.078057 0.104828 0.049448 0.191243 \n", "0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n", "0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n", "0 0.031518 0.027897 0.021931 0.048111 0.017381 0.119005 \n", "0 0.021183 0.028433 0.018573 0.022741 0.005328 0.031602 \n", "0 0.004325 0.004936 0.003461 0.007103 0.002833 0.021431 \n", "0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n", "0 0.000189 0.000000 0.000000 0.000214 0.000037 0.000368 \n", "\n", " LAUC HR H2R Reco in test Test coverage Shannon \\\n", "0 0.555546 0.765642 0.492047 1.000000 0.038961 3.159079 \n", "0 0.518286 0.472959 0.258749 0.859279 0.139250 3.831520 \n", "0 0.515501 0.437964 0.239661 1.000000 0.033911 2.836513 \n", "0 0.509546 0.384942 0.142100 1.000000 0.025974 2.711772 \n", "0 0.507096 0.330859 0.091198 0.988123 0.181818 5.100792 \n", "0 0.502764 0.237540 0.065748 0.697031 0.014430 2.220811 \n", "0 0.497819 0.042418 0.009544 0.453234 0.137085 2.866347 \n", "0 0.496424 0.009544 0.000000 0.600530 0.005051 1.803126 \n", "0 0.496391 0.003181 0.000000 0.392153 0.115440 4.174741 \n", "\n", " Gini \n", "0 0.987317 \n", "0 0.973234 \n", "0 0.991139 \n", "0 0.992003 \n", "0 0.906866 \n", "0 0.995173 \n", "0 0.982811 \n", "0 0.996380 \n", "0 0.965327 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import evaluation_measures as ev\n", "\n", "dir_path=\"Recommendations generated/ml-100k/\"\n", "super_reactions=[4,5]\n", "test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n", "\n", "ev.evaluate_all(test, dir_path, super_reactions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embeddings" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [3, 4]])" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([[0.4472136 , 0.89442719],\n", " [0.6 , 0.8 ]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=np.array([[1,2],[3,4]])\n", "display(x)\n", "x/np.linalg.norm(x, axis=1)[:,None]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
codescoreitem_ididtitlegenres
014551.00000014561456Beat the Devil (1954)Comedy, Drama
115230.99308315241524Kaspar Hauser (1993)Drama
213660.99219513671367Faust (1994)Animation
311680.99213111691169Fresh (1994)Drama
413680.99118313691369Forbidden Christ, The (Cristo proibito, Il) (1...Drama
514500.99074314511451Foreign Correspondent (1940)Thriller
69260.990661927927Flower of My Secret, The (Flor de mi secreto, ...Drama
710670.99004810681068Star Maker, The (Uomo delle stelle, L') (1995)Drama
813990.98984214001400Picture Bride (1995)Drama, Romance
912040.98962512051205Secret Agent, The (1996)Drama
\n", "
" ], "text/plain": [ " code score item_id id \\\n", "0 1455 1.000000 1456 1456 \n", "1 1523 0.993083 1524 1524 \n", "2 1366 0.992195 1367 1367 \n", "3 1168 0.992131 1169 1169 \n", "4 1368 0.991183 1369 1369 \n", "5 1450 0.990743 1451 1451 \n", "6 926 0.990661 927 927 \n", "7 1067 0.990048 1068 1068 \n", "8 1399 0.989842 1400 1400 \n", "9 1204 0.989625 1205 1205 \n", "\n", " title genres \n", "0 Beat the Devil (1954) Comedy, Drama \n", "1 Kaspar Hauser (1993) Drama \n", "2 Faust (1994) Animation \n", "3 Fresh (1994) Drama \n", "4 Forbidden Christ, The (Cristo proibito, Il) (1... Drama \n", "5 Foreign Correspondent (1940) Thriller \n", "6 Flower of My Secret, The (Flor de mi secreto, ... Drama \n", "7 Star Maker, The (Uomo delle stelle, L') (1995) Drama \n", "8 Picture Bride (1995) Drama, Romance \n", "9 Secret Agent, The (1996) Drama " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "item=random.choice(list(set(train_ui.indices)))\n", "\n", "embeddings_norm=model.Qi/np.linalg.norm(model.Qi, axis=1)[:,None] # we do not mean-center here\n", "# omitting normalization also makes sense, but items with a greater magnitude will be recommended more often\n", "\n", "similarity_scores=np.dot(embeddings_norm,embeddings_norm[item].T)\n", "top_similar_items=pd.DataFrame(enumerate(similarity_scores), columns=['code', 'score'])\\\n", ".sort_values(by=['score'], ascending=[False])[:10]\n", "\n", "top_similar_items['item_id']=top_similar_items['code'].apply(lambda x: item_code_id[x])\n", "\n", "items=pd.read_csv('./Datasets/ml-100k/movies.csv')\n", "\n", "result=pd.merge(top_similar_items, items, left_on='item_id', right_on='id')\n", "\n", "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# project task 5: implement SVD on top baseline (as it is in Surprise library)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# making changes to our implementation by considering additional parameters in the gradient descent procedure \n", "# seems to be the fastest option\n", "# please save the output in 'Recommendations generated/ml-100k/Self_SVDBaseline_reco.csv' and\n", "# 'Recommendations generated/ml-100k/Self_SVDBaseline_estimations.csv'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "class SVD_bias():\n", " \n", " def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):\n", " self.train_ui=train_ui\n", " self.uir=list(zip(*[train_ui.nonzero()[0],train_ui.nonzero()[1], train_ui.data]))\n", " \n", " self.learning_rate=learning_rate\n", " self.regularization=regularization\n", " self.iterations=iterations\n", " self.nb_users, self.nb_items=train_ui.shape\n", " self.nb_ratings=train_ui.nnz\n", " self.nb_factors=nb_factors\n", " \n", " self.Pu=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_users, self.nb_factors))\n", " self.Qi=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_items, self.nb_factors))\n", " self.bias_u = np.zeros(self.nb_users)\n", " self.bias_i = np.zeros(self.nb_items)\n", "\n", " def train(self, test_ui=None):\n", " if test_ui!=None:\n", " self.test_uir=list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))\n", " \n", " self.learning_process=[]\n", " pbar = tqdm(range(self.iterations))\n", " for i in pbar:\n", " pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i>0 else 0}. Training epoch {i+1}...')\n", " np.random.shuffle(self.uir)\n", " self.sgd(self.uir)\n", " if test_ui==None:\n", " self.learning_process.append([i+1, self.RMSE_total(self.uir)])\n", " else:\n", " self.learning_process.append([i+1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])\n", " \n", " def sgd(self, uir):\n", " \n", " for u, i, score in uir:\n", " # Computer prediction and error\n", " prediction = self.get_rating(u,i)\n", " e = (score - prediction)\n", " \n", " # Update user and item latent feature matrices\n", " Pu_update=self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])\n", " Qi_update=self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])\n", " bias_u_update=self.learning_rate * (e - self.regularization * self.bias_u[u])\n", " bias_i_update=self.learning_rate * (e - self.regularization * self.bias_i[i])\n", " \n", " self.Pu[u] += Pu_update\n", " self.Qi[i] += Qi_update\n", " self.bias_u[u] += bias_u_update\n", " self.bias_i[i] += bias_i_update\n", " \n", " def get_rating(self, u, i):\n", " prediction = self.bias_u[u] + self.bias_i[i] + self.Pu[u].dot(self.Qi[i].T)\n", " return prediction\n", " \n", " def RMSE_total(self, uir):\n", " RMSE=0\n", " for u,i, score in uir:\n", " prediction = self.get_rating(u,i)\n", " RMSE+=(score - prediction)**2\n", " return np.sqrt(RMSE/len(uir))\n", " \n", " def estimations(self):\n", " self.estimations=\\\n", " self.bias_u[:,np.newaxis] + self.bias_i[np.newaxis:,] + np.dot(self.Pu,self.Qi.T)\n", "\n", " def recommend(self, user_code_id, item_code_id, topK=10):\n", " \n", " top_k = defaultdict(list)\n", " for nb_user, user in enumerate(self.estimations):\n", " \n", " user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n", " for item, score in enumerate(user):\n", " if item not in user_rated and not np.isnan(score):\n", " top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n", " result=[]\n", " # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n", " for uid, item_scores in top_k.items():\n", " item_scores.sort(key=lambda x: x[1], reverse=True)\n", " result.append([uid]+list(chain(*item_scores[:topK])))\n", " return result\n", " \n", " def estimate(self, user_code_id, item_code_id, test_ui):\n", " result=[]\n", " for user, item in zip(*test_ui.nonzero()):\n", " result.append([user_code_id[user], item_code_id[item], \n", " self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ready-made SVD - Surprise implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVD" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating predictions...\n", "Generating top N recommendations...\n", "Generating predictions...\n" ] } ], "source": [ "import helpers\n", "import surprise as sp\n", "import imp\n", "imp.reload(helpers)\n", "\n", "algo = sp.SVD(biased=False) # to use unbiased version\n", "\n", "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVD_reco.csv',\n", " estimations_path='Recommendations generated/ml-100k/Ready_SVD_estimations.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVD biased - on top baseline" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating predictions...\n", "Generating top N recommendations...\n", "Generating predictions...\n" ] } ], "source": [ "import helpers\n", "import surprise as sp\n", "import imp\n", "imp.reload(helpers)\n", "\n", "algo = sp.SVD() # default is biased=True\n", "\n", "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVDBiased_reco.csv',\n", " estimations_path='Recommendations generated/ml-100k/Ready_SVDBiased_estimations.csv')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "943it [00:00, 5926.84it/s]\n", "943it [00:00, 6314.27it/s]\n", "943it [00:00, 5917.48it/s]\n", "943it [00:00, 6138.94it/s]\n", "943it [00:00, 6278.83it/s]\n", "943it [00:00, 6319.68it/s]\n", "943it [00:00, 4892.96it/s]\n", "943it [00:00, 6955.58it/s]\n", "943it [00:00, 4946.53it/s]\n", "943it [00:00, 6823.16it/s]\n", "943it [00:00, 6276.95it/s]\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ModelRMSEMAEprecisionrecallF_1F_05precision_superrecall_superNDCGmAPMRRLAUCHRH2RReco in testTest coverageShannonGini
0Self_TopPop2.5082582.2179090.1888650.1169190.1187320.1415840.1304720.1374730.2146510.1117070.4009390.5555460.7656420.4920471.0000000.0389613.1590790.987317
0Ready_SVD0.9522470.7511850.0941680.0441670.0509190.0653910.0830470.0693300.1042660.0476290.2277190.5187830.4931070.2386000.9950160.2121214.4529470.951495
0Self_SVD0.9157300.7189210.1022270.0431370.0519810.0688720.0935620.0780570.1048280.0494480.1912430.5182860.4729590.2587490.8592790.1392503.8315200.973234
0Ready_Baseline0.9494590.7524870.0914100.0376520.0460300.0612860.0796140.0564630.0959570.0431780.1981930.5155010.4379640.2396611.0000000.0339112.8365130.991139
0Ready_SVDBiased0.9390530.7408400.0838810.0340330.0418620.0558080.0743560.0517530.0921230.0422240.1991650.5136790.4347830.2036060.9965010.1702744.1907390.963349
0Self_GlobalAvg1.1257600.9435340.0611880.0259680.0313830.0413430.0405580.0321070.0676950.0274700.1711870.5095460.3849420.1421001.0000000.0259742.7117720.992003
0Ready_Random1.5249541.2233520.0455990.0211810.0245850.0315180.0278970.0219310.0481110.0173810.1190050.5070960.3308590.0911980.9881230.1818185.1007920.906866
0Self_TopRatedNaNNaN0.0320250.0126740.0157140.0211830.0284330.0185730.0227410.0053280.0316020.5027640.2375400.0657480.6970310.0144302.2208110.995173
0Self_KNNSurprisetask0.9971060.7841630.0056200.0029210.0034940.0043250.0049360.0034610.0071030.0028330.0214310.4978190.0424180.0095440.4532340.1370852.8663470.982811
0Self_BaselineUI0.9675850.7627400.0009540.0001700.0002780.0004630.0006440.0001890.0007520.0001680.0016770.4964240.0095440.0000000.6005300.0050511.8031260.996380
0Self_IKNN1.0183630.8087930.0003180.0001080.0001400.0001890.0000000.0000000.0002140.0000370.0003680.4963910.0031810.0000000.3921530.1154404.1747410.965327
\n", "
" ], "text/plain": [ " Model RMSE MAE precision recall F_1 \\\n", "0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n", "0 Ready_SVD 0.952247 0.751185 0.094168 0.044167 0.050919 \n", "0 Self_SVD 0.915730 0.718921 0.102227 0.043137 0.051981 \n", "0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n", "0 Ready_SVDBiased 0.939053 0.740840 0.083881 0.034033 0.041862 \n", "0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n", "0 Ready_Random 1.524954 1.223352 0.045599 0.021181 0.024585 \n", "0 Self_TopRated NaN NaN 0.032025 0.012674 0.015714 \n", "0 Self_KNNSurprisetask 0.997106 0.784163 0.005620 0.002921 0.003494 \n", "0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n", "0 Self_IKNN 1.018363 0.808793 0.000318 0.000108 0.000140 \n", "\n", " F_05 precision_super recall_super NDCG mAP MRR \\\n", "0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n", "0 0.065391 0.083047 0.069330 0.104266 0.047629 0.227719 \n", "0 0.068872 0.093562 0.078057 0.104828 0.049448 0.191243 \n", "0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n", "0 0.055808 0.074356 0.051753 0.092123 0.042224 0.199165 \n", "0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n", "0 0.031518 0.027897 0.021931 0.048111 0.017381 0.119005 \n", "0 0.021183 0.028433 0.018573 0.022741 0.005328 0.031602 \n", "0 0.004325 0.004936 0.003461 0.007103 0.002833 0.021431 \n", "0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n", "0 0.000189 0.000000 0.000000 0.000214 0.000037 0.000368 \n", "\n", " LAUC HR H2R Reco in test Test coverage Shannon \\\n", "0 0.555546 0.765642 0.492047 1.000000 0.038961 3.159079 \n", "0 0.518783 0.493107 0.238600 0.995016 0.212121 4.452947 \n", "0 0.518286 0.472959 0.258749 0.859279 0.139250 3.831520 \n", "0 0.515501 0.437964 0.239661 1.000000 0.033911 2.836513 \n", "0 0.513679 0.434783 0.203606 0.996501 0.170274 4.190739 \n", "0 0.509546 0.384942 0.142100 1.000000 0.025974 2.711772 \n", "0 0.507096 0.330859 0.091198 0.988123 0.181818 5.100792 \n", "0 0.502764 0.237540 0.065748 0.697031 0.014430 2.220811 \n", "0 0.497819 0.042418 0.009544 0.453234 0.137085 2.866347 \n", "0 0.496424 0.009544 0.000000 0.600530 0.005051 1.803126 \n", "0 0.496391 0.003181 0.000000 0.392153 0.115440 4.174741 \n", "\n", " Gini \n", "0 0.987317 \n", "0 0.951495 \n", "0 0.973234 \n", "0 0.991139 \n", "0 0.963349 \n", "0 0.992003 \n", "0 0.906866 \n", "0 0.995173 \n", "0 0.982811 \n", "0 0.996380 \n", "0 0.965327 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import imp\n", "imp.reload(ev)\n", "\n", "import evaluation_measures as ev\n", "dir_path=\"Recommendations generated/ml-100k/\"\n", "super_reactions=[4,5]\n", "test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n", "\n", "ev.evaluate_all(test, dir_path, super_reactions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 4 }