warsztaty2/P4. Matrix Factorization.ipynb

Self made SVD

import helpers
import pandas as pd
import numpy as np
import scipy.sparse as sparse
from collections import defaultdict
from itertools import chain
import random

train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\t', header=None)
test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None)
train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code = helpers.data_to_csr(train_read, test_read)

# Done similarly to https://github.com/albertauyeung/matrix-factorization-in-python
from tqdm import tqdm

class SVD():
    
    def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):
        self.train_ui=train_ui
        self.uir=list(zip(*[train_ui.nonzero()[0],train_ui.nonzero()[1], train_ui.data]))
        
        self.learning_rate=learning_rate
        self.regularization=regularization
        self.iterations=iterations
        self.nb_users, self.nb_items=train_ui.shape
        self.nb_ratings=train_ui.nnz
        self.nb_factors=nb_factors
        
        self.Pu=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_users, self.nb_factors))
        self.Qi=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_items, self.nb_factors))

    def train(self, test_ui=None):
        if test_ui!=None:
            self.test_uir=list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))
            
        self.learning_process=[]
        pbar = tqdm(range(self.iterations))
        for i in pbar:
            pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i>0 else 0}. Training epoch {i+1}...')
            np.random.shuffle(self.uir)
            self.sgd(self.uir)
            if test_ui==None:
                self.learning_process.append([i+1, self.RMSE_total(self.uir)])
            else:
                self.learning_process.append([i+1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])
    
    def sgd(self, uir):
        
        for u, i, score in uir:
            # Computer prediction and error
            prediction = self.get_rating(u,i)
            e = (score - prediction)
            
            # Update user and item latent feature matrices
            Pu_update=self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])
            Qi_update=self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])
            
            self.Pu[u] += Pu_update
            self.Qi[i] += Qi_update
        
    def get_rating(self, u, i):
        prediction = self.Pu[u].dot(self.Qi[i].T)
        return prediction
    
    def RMSE_total(self, uir):
        RMSE=0
        for u,i, score in uir:
            prediction = self.get_rating(u,i)
            RMSE+=(score - prediction)**2
        return np.sqrt(RMSE/len(uir))
    
    def estimations(self):
        self.estimations=\
        np.dot(self.Pu,self.Qi.T)

    def recommend(self, user_code_id, item_code_id, topK=10):
        
        top_k = defaultdict(list)
        for nb_user, user in enumerate(self.estimations):
            
            user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]
            for item, score in enumerate(user):
                if item not in user_rated and not np.isnan(score):
                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))
        result=[]
        # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)
        for uid, item_scores in top_k.items():
            item_scores.sort(key=lambda x: x[1], reverse=True)
            result.append([uid]+list(chain(*item_scores[:topK])))
        return result
    
    def estimate(self, user_code_id, item_code_id, test_ui):
        result=[]
        for user, item in zip(*test_ui.nonzero()):
            result.append([user_code_id[user], item_code_id[item], 
                           self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])
        return result

model=SVD(train_ui, learning_rate=0.005, regularization=0.02, nb_factors=100, iterations=40)
model.train(test_ui)

Epoch 39 RMSE: 0.7503496297849689. Training epoch 40...: 100%|██████████| 40/40 [01:12<00:00,  1.81s/it]

import matplotlib.pyplot as plt

df=pd.DataFrame(model.learning_process).iloc[:,:2]
df.columns=['epoch', 'train_RMSE']
plt.plot('epoch', 'train_RMSE', data=df, color='blue')
plt.legend()

<matplotlib.legend.Legend at 0x1491c088>

import matplotlib.pyplot as plt

df=pd.DataFrame(model.learning_process[10:], columns=['epoch', 'train_RMSE', 'test_RMSE'])
plt.plot('epoch', 'train_RMSE', data=df, color='blue')
plt.plot('epoch', 'test_RMSE', data=df, color='yellow', linestyle='dashed')
plt.legend()

<matplotlib.legend.Legend at 0x40e64d8>

Saving and evaluating recommendations

model.estimations()

top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))

top_n.to_csv('Recommendations generated/ml-100k/Self_SVD_reco.csv', index=False, header=False)

estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
estimations.to_csv('Recommendations generated/ml-100k/Self_SVD_estimations.csv', index=False, header=False)

import evaluation_measures as ev

estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_SVD_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_SVD_reco.csv', delimiter=',')

ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None),
            estimations_df=estimations_df, 
            reco=reco,
            super_reactions=[4,5])

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	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	Reco in test	Test coverage	Shannon	Gini
0	0.91633	0.720153	0.103393	0.044455	0.053177	0.070073	0.093884	0.079366	0.107792	0.051281	0.20021	0.518957	0.47508	0.853022	0.147186	3.911356	0.971196

import evaluation_measures as ev

dir_path="Recommendations generated/ml-100k/"
super_reactions=[4,5]
test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None)

ev.evaluate_all(test, dir_path, super_reactions)

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	Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	Reco in test	Test coverage	Shannon	Gini
0	Self_TopPop	2.508258	2.217909	0.188865	0.116919	0.118732	0.141584	0.130472	0.137473	0.214651	0.111707	0.400939	0.555546	0.765642	1.000000	0.038961	3.159079	0.987317
0	Self_SVD	0.916330	0.720153	0.103393	0.044455	0.053177	0.070073	0.093884	0.079366	0.107792	0.051281	0.200210	0.518957	0.475080	0.853022	0.147186	3.911356	0.971196
0	Ready_Baseline	0.949459	0.752487	0.091410	0.037652	0.046030	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964	1.000000	0.033911	2.836513	0.991139
0	Self_GlobalAvg	1.125760	0.943534	0.061188	0.025968	0.031383	0.041343	0.040558	0.032107	0.067695	0.027470	0.171187	0.509546	0.384942	1.000000	0.025974	2.711772	0.992003
0	Ready_Random	1.510030	1.211848	0.050053	0.022367	0.025984	0.033727	0.030687	0.023255	0.055392	0.021602	0.137690	0.507713	0.338282	0.987911	0.187590	5.111878	0.906685
0	Ready_I-KNN	1.030386	0.813067	0.026087	0.006908	0.010593	0.016046	0.021137	0.009522	0.024214	0.008958	0.048068	0.499885	0.154825	0.402333	0.434343	5.133650	0.877999
0	Ready_I-KNNWithZScore	0.957701	0.752387	0.003712	0.001994	0.002380	0.002919	0.003433	0.002401	0.005137	0.002158	0.016458	0.497349	0.027572	0.389926	0.067821	2.475747	0.992793
0	Ready_I-KNNWithMeans	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.482821	0.059885	2.232578	0.994487
0	Ready_I-KNNBaseline	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.482821	0.059885	2.232578	0.994487
0	Ready_U-KNN	1.023495	0.807913	0.000742	0.000205	0.000305	0.000449	0.000536	0.000198	0.000845	0.000274	0.002744	0.496441	0.007423	0.602121	0.010823	2.089186	0.995706
0	Self_TopRated	2.508258	2.217909	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.699046	0.005051	1.945910	0.995669
0	Self_BaselineUI	0.967585	0.762740	0.000954	0.000170	0.000278	0.000463	0.000644	0.000189	0.000752	0.000168	0.001677	0.496424	0.009544	0.600530	0.005051	1.803126	0.996380
0	Self_IKNN	1.018363	0.808793	0.000318	0.000108	0.000140	0.000189	0.000000	0.000000	0.000214	0.000037	0.000368	0.496391	0.003181	0.392153	0.115440	4.174741	0.965327

Embeddings

x=np.array([[1,2],[3,4]])
display(x)
x/np.linalg.norm(x, axis=1)[:,None]

array([[1, 2],
       [3, 4]])

array([[0.4472136 , 0.89442719],
       [0.6       , 0.8       ]])

item=random.choice(list(set(train_ui.indices)))

embeddings_norm=model.Qi/np.linalg.norm(model.Qi, axis=1)[:,None] # we do not mean-center here
# omitting normalization also makes sense, but items with a greater magnitude will be recommended more often

similarity_scores=np.dot(embeddings_norm,embeddings_norm[item].T)
top_similar_items=pd.DataFrame(enumerate(similarity_scores), columns=['code', 'score'])\
.sort_values(by=['score'], ascending=[False])[:10]

top_similar_items['item_id']=top_similar_items['code'].apply(lambda x: item_code_id[x])

items=pd.read_csv('./Datasets/ml-100k/movies.csv')

result=pd.merge(top_similar_items, items, left_on='item_id', right_on='id')

result

	code	score	item_id	id	title	genres
0	1311	1.000000	1312	1312	Pompatus of Love, The (1996)	Comedy, Drama
1	1395	0.992638	1396	1396	Stonewall (1995)	Drama
2	1448	0.991954	1449	1449	Pather Panchali (1955)	Drama
3	1201	0.991726	1202	1202	Maybe, Maybe Not (Bewegte Mann, Der) (1994)	Comedy
4	1405	0.991419	1406	1406	When Night Is Falling (1995)	Drama, Romance
5	1250	0.991108	1251	1251	A Chef in Love (1996)	Comedy
6	1263	0.991100	1264	1264	Nothing to Lose (1994)	Drama
7	1123	0.991069	1124	1124	Farewell to Arms, A (1932)	Romance, War
8	1110	0.991040	1111	1111	Double Happiness (1994)	Drama
9	1124	0.990889	1125	1125	Innocents, The (1961)	Thriller

# project task 5:  implement SVD on top baseline (as it is in Surprise library)from tqdm import tqdm

class SVDBaseline():

    def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):
        self.train_ui=train_ui
        self.uir=list(zip(*[train_ui.nonzero()[0],train_ui.nonzero()[1], train_ui.data]))

        self.learning_rate=learning_rate
        self.regularization=regularization
        self.iterations=iterations
        self.nb_users, self.nb_items=train_ui.shape
        self.nb_ratings=train_ui.nnz
        self.nb_factors=nb_factors

        self.Pu=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_users, self.nb_factors))
        self.Qi=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_items, self.nb_factors))

        self.b_u = np.zeros(self.nb_users)
        self.b_i = np.zeros(self.nb_items)

    def train(self, test_ui=None):
        if test_ui!=None:
            self.test_uir=list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))

        self.learning_process=[]
        pbar = tqdm(range(self.iterations))
        for i in pbar:
            pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i>0 else 0}. Training epoch {i+1}...')
            np.random.shuffle(self.uir)
            self.sgd(self.uir)
            if test_ui==None:
                self.learning_process.append([i+1, self.RMSE_total(self.uir)])
            else:
                self.learning_process.append([i+1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])

    def sgd(self, uir):

        for u, i, score in uir:
            prediction = self.get_rating(u,i)
            e = (score - prediction)
            
            
            b_u_update = self.learning_rate * (e - self.regularization * self.b_u[u])
            b_i_update = self.learning_rate * (e - self.regularization * self.b_i[i])
            
            self.b_u[u] += b_u_update
            self.b_i[i] += b_i_update
            Pu_update=self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])
            Qi_update=self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])
            
            self.Pu[u] += Pu_update
            self.Qi[i] += Qi_update
        
    def get_rating(self, u, i):
        prediction = self.b_u[u] + self.b_i[i] + self.Pu[u].dot(self.Qi[i].T)
        return prediction
    
    def RMSE_total(self, uir):
        RMSE=0
        for u,i, score in uir:
            prediction = self.get_rating(u,i)
            RMSE+=(score - prediction)**2
        return np.sqrt(RMSE/len(uir))
    
    def estimations(self):
        self.estimations= self.b_u[:,np.newaxis] + self.b_i[np.newaxis:,] + np.dot(self.Pu,self.Qi.T)

    def recommend(self, user_code_id, item_code_id, topK=10):
        
        top_k = defaultdict(list)
        for nb_user, user in enumerate(self.estimations):
            
            user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]
            for item, score in enumerate(user):
                if item not in user_rated and not np.isnan(score):
                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))
        result=[]
        for uid, item_scores in top_k.items():
            item_scores.sort(key=lambda x: x[1], reverse=True)
            result.append([uid]+list(chain(*item_scores[:topK])))
        return result
    
    def estimate(self, user_code_id, item_code_id, test_ui):
        result=[]
        for user, item in zip(*test_ui.nonzero()):
            result.append([user_code_id[user], item_code_id[item], 
                           self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])
        return result

# making changes to our implementation by considering additional parameters in the gradient descent procedure 
# seems to be the fastest option
# please save the output in 'Recommendations generated/ml-100k/Self_SVDBaseline_reco.csv' and
# 'Recommendations generated/ml-100k/Self_SVDBaseline_estimations.csv'

Ready-made SVD - Surprise implementation

SVD

import helpers
import surprise as sp
import imp
imp.reload(helpers)

algo = sp.SVD(biased=False) # to use unbiased version

helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVD_reco.csv',
          estimations_path='Recommendations generated/ml-100k/Ready_SVD_estimations.csv')

Generating predictions...
Generating top N recommendations...
Generating predictions...

SVD biased - on top baseline

import helpers
import surprise as sp
import imp
imp.reload(helpers)

algo = sp.SVD() # default is biased=True

helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVDBiased_reco.csv',
          estimations_path='Recommendations generated/ml-100k/Ready_SVDBiased_estimations.csv')

Generating predictions...
Generating top N recommendations...
Generating predictions...

import imp
imp.reload(ev)

import evaluation_measures as ev
dir_path="Recommendations generated/ml-100k/"
super_reactions=[4,5]
test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None)

ev.evaluate_all(test, dir_path, super_reactions)

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	Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	Reco in test	Test coverage	Shannon	Gini
0	Self_TopPop	2.508258	2.217909	0.188865	0.116919	0.118732	0.141584	0.130472	0.137473	0.214651	0.111707	0.400939	0.555546	0.765642	1.000000	0.038961	3.159079	0.987317
0	Ready_SVD	0.949165	0.746667	0.093955	0.044969	0.051197	0.065474	0.083906	0.073996	0.104672	0.048211	0.220757	0.519187	0.483563	0.997985	0.204906	4.408913	0.954288
0	Self_SVD	0.916330	0.720153	0.103393	0.044455	0.053177	0.070073	0.093884	0.079366	0.107792	0.051281	0.200210	0.518957	0.475080	0.853022	0.147186	3.911356	0.971196
0	Ready_Baseline	0.949459	0.752487	0.091410	0.037652	0.046030	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964	1.000000	0.033911	2.836513	0.991139
0	Ready_SVDBiased	0.938146	0.739917	0.086532	0.037067	0.044832	0.058877	0.078004	0.057865	0.094583	0.043013	0.202391	0.515202	0.433722	0.996076	0.166667	4.168354	0.964092
0	Self_GlobalAvg	1.125760	0.943534	0.061188	0.025968	0.031383	0.041343	0.040558	0.032107	0.067695	0.027470	0.171187	0.509546	0.384942	1.000000	0.025974	2.711772	0.992003
0	Ready_Random	1.510030	1.211848	0.050053	0.022367	0.025984	0.033727	0.030687	0.023255	0.055392	0.021602	0.137690	0.507713	0.338282	0.987911	0.187590	5.111878	0.906685
0	Ready_I-KNN	1.030386	0.813067	0.026087	0.006908	0.010593	0.016046	0.021137	0.009522	0.024214	0.008958	0.048068	0.499885	0.154825	0.402333	0.434343	5.133650	0.877999
0	Ready_I-KNNWithZScore	0.957701	0.752387	0.003712	0.001994	0.002380	0.002919	0.003433	0.002401	0.005137	0.002158	0.016458	0.497349	0.027572	0.389926	0.067821	2.475747	0.992793
0	Ready_I-KNNWithMeans	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.482821	0.059885	2.232578	0.994487
0	Ready_I-KNNBaseline	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.482821	0.059885	2.232578	0.994487
0	Ready_U-KNN	1.023495	0.807913	0.000742	0.000205	0.000305	0.000449	0.000536	0.000198	0.000845	0.000274	0.002744	0.496441	0.007423	0.602121	0.010823	2.089186	0.995706
0	Self_TopRated	2.508258	2.217909	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.699046	0.005051	1.945910	0.995669
0	Self_BaselineUI	0.967585	0.762740	0.000954	0.000170	0.000278	0.000463	0.000644	0.000189	0.000752	0.000168	0.001677	0.496424	0.009544	0.600530	0.005051	1.803126	0.996380
0	Self_IKNN	1.018363	0.808793	0.000318	0.000108	0.000140	0.000189	0.000000	0.000000	0.000214	0.000037	0.000368	0.496391	0.003181	0.392153	0.115440	4.174741	0.965327