Self made RP3-beta

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
import time
import matplotlib.pyplot as plt

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)

class RP3Beta():
    def fit(self, train_ui, alpha, beta):
        """We weight our edges by user's explicit ratings so if user rated movie high we'll follow that path
        with higher probability."""
        self.train_ui=train_ui
        self.train_iu=train_ui.transpose()
        
        self.alpha = alpha
        self.beta = beta
        
        # Define Pui 
        Pui=sparse.csr_matrix(self.train_ui/self.train_ui.sum(axis=1))
        
        # Define Piu
        to_divide=np.vectorize(lambda x: x if x>0 else 1)(self.train_iu.sum(axis=1)) # to avoid dividing by zero
        Piu=sparse.csr_matrix(self.train_iu/to_divide)
        item_orders=(self.train_ui>0).sum(axis=0)
        
        Pui = Pui.power(self.alpha)
        Piu = Piu.power(self.alpha)

        P3=Pui*Piu*Pui
        
        P3/=np.power(np.vectorize(lambda x: x if x>0 else 1)(item_orders), self.beta)
        
        self.estimations=np.array(P3)
    
    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=RP3Beta()
model.fit(train_ui, alpha=1, beta=0)

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

top_n.to_csv('Recommendations generated/ml-100k/Self_P3_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_P3_estimations.csv', index=False, header=False)

import evaluation_measures as ev
estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_P3_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_P3_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	3.702446	3.527273	0.282185	0.192092	0.186749	0.21698	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	1.0	0.077201	3.875892	0.974947

Let's check hiperparameters

Alpha

from tqdm import tqdm
result=[]
for alpha in tqdm([round(i,1) for i in np.arange(0.2,1.6001,0.2)]):
    model=RP3Beta()
    model.fit(train_ui, alpha=alpha, beta=0)
    reco=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
    estimations_df=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
    to_append=ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None),
            estimations_df=estimations_df, 
            reco=np.array(reco),
            super_reactions=[4,5])
    to_append.insert(0, "Alpha", alpha)
    result.append(to_append)
    
result=pd.concat(result)
result

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Alpha	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	Reco in test	Test coverage	Shannon	Gini
0.2	268.177832	211.732649	0.262672	0.166858	0.166277	0.197184	0.187661	0.203252	0.320910	0.196132	0.563378	0.580866	0.850477	1.000000	0.060606	3.669627	0.979636
0.4	10.546689	7.792373	0.268505	0.172669	0.171569	0.202643	0.192489	0.212653	0.326760	0.200172	0.565148	0.583801	0.854719	1.000000	0.064214	3.726996	0.978426
0.6	3.143988	2.948790	0.274655	0.180502	0.177820	0.208730	0.198176	0.222746	0.332872	0.203290	0.568872	0.587738	0.870626	1.000000	0.065657	3.785282	0.977090
0.8	3.670728	3.495735	0.281972	0.189868	0.185300	0.216071	0.203541	0.236751	0.339867	0.206688	0.573729	0.592432	0.874867	1.000000	0.070707	3.832415	0.975998
1.0	3.702446	3.527273	0.282185	0.192092	0.186749	0.216980	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	1.000000	0.077201	3.875892	0.974947
1.2	3.704441	3.529251	0.280912	0.193633	0.187311	0.216872	0.203004	0.240588	0.338049	0.203453	0.571830	0.594313	0.883351	1.000000	0.085859	3.910718	0.974073
1.4	3.704580	3.529388	0.273595	0.190651	0.183874	0.212183	0.199464	0.239118	0.329550	0.195433	0.566171	0.592793	0.871686	1.000000	0.107504	3.961915	0.972674
1.6	3.704591	3.529399	0.263097	0.186255	0.178709	0.205170	0.191094	0.232920	0.317439	0.184917	0.552349	0.590545	0.868505	0.999576	0.156566	4.060156	0.969203

metrics=list(result.columns[[i not in ['Alpha'] for i in result.columns]])

charts_per_row=6
charts_per_column=3

fig, axes = plt.subplots(nrows=charts_per_row, ncols=charts_per_column,figsize=(18, 7*charts_per_row ))
import itertools
to_iter=[i for i in itertools.product(range(charts_per_row), range(charts_per_column))]

for i in range(len(metrics)):
    df=result[['Alpha', metrics[i]]]
    df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

Beta

from tqdm import tqdm
result=[]
for beta in tqdm([round(i,1) for i in np.arange(0,1,0.1)]):
    model=RP3Beta()
    model.fit(train_ui, alpha=1, beta=beta)
    reco=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
    estimations_df=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
    to_append=ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None),
            estimations_df=estimations_df, 
            reco=np.array(reco),
            super_reactions=[4,5])
    to_append.insert(0, "Beta", beta)
    result.append(to_append)
    
result=pd.concat(result)
result

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Beta	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	3.702446	3.527273	0.282185	0.192092	0.186749	0.216980	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	1.000000	0.077201	3.875892	0.974947
0.1	3.703312	3.528128	0.290138	0.197597	0.192259	0.223336	0.210944	0.246153	0.347768	0.212034	0.581038	0.596328	0.884411	1.000000	0.085137	3.957416	0.972784
0.2	3.703825	3.528636	0.297137	0.201202	0.196067	0.228169	0.218026	0.252767	0.355655	0.219909	0.588904	0.598160	0.886532	1.000000	0.094517	4.053212	0.969980
0.3	3.704130	3.528939	0.303499	0.204749	0.199901	0.232829	0.225107	0.260797	0.363757	0.226825	0.599969	0.599964	0.888653	1.000000	0.105339	4.147779	0.966948
0.4	3.704313	3.529120	0.308908	0.208811	0.203854	0.237241	0.229614	0.266918	0.370758	0.232673	0.609385	0.602014	0.895016	0.999894	0.132035	4.259682	0.962989
0.5	3.704422	3.529229	0.314316	0.211411	0.206768	0.240986	0.237124	0.273416	0.378307	0.239297	0.622792	0.603327	0.903499	0.999046	0.168831	4.411281	0.956648
0.6	3.704488	3.529295	0.314634	0.206209	0.204818	0.240159	0.242489	0.273850	0.376438	0.238428	0.622042	0.600721	0.897137	0.996394	0.212843	4.621938	0.945932
0.7	3.704528	3.529335	0.304136	0.187298	0.191990	0.228749	0.238305	0.256201	0.358807	0.226808	0.593897	0.591207	0.868505	0.983033	0.256854	4.898568	0.928065
0.8	3.704552	3.529360	0.266384	0.147571	0.158660	0.194838	0.214485	0.209336	0.299850	0.184356	0.492852	0.571152	0.803818	0.936373	0.341270	5.257397	0.895882
0.9	3.704567	3.529375	0.162354	0.076967	0.089233	0.114583	0.134657	0.113253	0.160868	0.085486	0.243590	0.535405	0.580064	0.800106	0.415584	5.563910	0.857396

### import matplotlib.pyplot as plt

metrics=list(result.columns[[i not in ['Beta'] for i in result.columns]])

charts_per_row=6
charts_per_column=3

fig, axes = plt.subplots(nrows=charts_per_row, ncols=charts_per_column,figsize=(18, 7*charts_per_row ))
import itertools
to_iter=[i for i in itertools.product(range(charts_per_row), range(charts_per_column))]

for i in range(len(metrics)):
    df=result[['Beta', metrics[i]]]
    df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

Check sample recommendations

train=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])
items=pd.read_csv('./Datasets/ml-100k/movies.csv')

user=random.choice(list(set(train['user'])))

train_content=pd.merge(train, items, left_on='item', right_on='id')
display(train_content[train_content['user']==user][['user', 'rating', 'title', 'genres']]\
        .sort_values(by='rating', ascending=False)[:15])

reco = np.loadtxt('Recommendations generated/ml-100k/Self_P3_reco.csv', delimiter=',')
items=pd.read_csv('./Datasets/ml-100k/movies.csv')

# Let's ignore scores - they are not used in evaluation: 
reco_users=reco[:,:1]
reco_items=reco[:,1::2]
# Let's put them into one array
reco=np.concatenate((reco_users, reco_items), axis=1)

# Let's rebuild it user-item dataframe
recommended=[]
for row in reco:
    for rec_nb, entry in enumerate(row[1:]):
        recommended.append((row[0], rec_nb+1, entry))
recommended=pd.DataFrame(recommended, columns=['user','rec_nb', 'item'])

recommended_content=pd.merge(recommended, items, left_on='item', right_on='id')
recommended_content[recommended_content['user']==user][['user', 'rec_nb', 'title', 'genres']].sort_values(by='rec_nb')

	user	rating	title	genres
28480	774	5	Right Stuff, The (1983)	Drama
50423	774	5	Conan the Barbarian (1981)	Action, Adventure
18188	774	5	Clockwork Orange, A (1971)	Sci-Fi
22286	774	5	Groundhog Day (1993)	Comedy, Romance
23943	774	5	Apocalypse Now (1979)	Drama, War
51726	774	5	Star Trek VI: The Undiscovered Country (1991)	Action, Adventure, Sci-Fi
16714	774	5	Man Who Would Be King, The (1975)	Adventure
42312	774	5	Treasure of the Sierra Madre, The (1948)	Adventure
37594	774	4	Nightmare on Elm Street, A (1984)	Horror
16066	774	4	Godfather, The (1972)	Action, Crime, Drama
62624	774	4	Highlander (1986)	Action, Adventure
3611	774	4	Aliens (1986)	Action, Sci-Fi, Thriller, War
57765	774	4	Killing Fields, The (1984)	Drama, War
54287	774	4	12 Angry Men (1957)	Drama
47568	774	4	Alien (1979)	Action, Horror, Sci-Fi, Thriller

	user	rec_nb	title	genres
900	774.0	1	Silence of the Lambs, The (1991)	Drama, Thriller
3335	774.0	2	Toy Story (1995)	Animation, Children's, Comedy
7822	774.0	3	Princess Bride, The (1987)	Action, Adventure, Comedy, Romance
4301	774.0	4	Twelve Monkeys (1995)	Drama, Sci-Fi
8426	774.0	5	Indiana Jones and the Last Crusade (1989)	Action, Adventure
8752	774.0	6	Terminator, The (1984)	Action, Sci-Fi, Thriller
2075	774.0	7	Back to the Future (1985)	Comedy, Sci-Fi
8681	774.0	8	Forrest Gump (1994)	Comedy, Romance, War
7507	774.0	9	Star Trek: First Contact (1996)	Action, Adventure, Sci-Fi
7942	774.0	10	Amadeus (1984)	Drama, Mystery

project task 6: generate recommendations of RP3Beta for hiperparameters found to optimize recall

# use better values than (1,0) for alpha and beta
# if you want you can also modify the model to consider different weights (we took as weights user ratings, maybe take ones or squares of ratings instead)
# save the outptut in 'Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv'
# and 'Recommendations generated/ml-100k/Self_RP3Beta_reco.csv'model=RP3Beta()
model.fit(train_ui, alpha=1.4, beta=0.3)
top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))

top_n.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_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_RP3Beta_estimations.csv', index=False, header=False)
import evaluation_measures as ev
estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_RP3Beta_reco.csv', delimiter=',')

ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='	', header=None),            estimations_df=estimations_df,            reco=reco,            super_reactions=[4,5])

943it [00:00, 9215.29it/s]

	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	3.704589	3.529397	0.286744	0.196524	0.191117	0.221375	0.213948	0.251263	0.344598	0.207836	0.587953	0.59577	0.885472	0.998197	0.193362	4.291821	0.960775

project task 7 (optional): implement graph-based model of your choice

# for example change length of paths in RP3beta
# save the outptut in 'Recommendations generated/ml-100k/Self_GraphTask_estimations.csv'
# and 'Recommendations generated/ml-100k/Self_GraphTask_reco.csv'

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Self made RP3-beta

Let's check hiperparameters

Alpha

Beta

Check sample recommendations

project task 6: generate recommendations of RP3Beta for hiperparameters found to optimize recall

project task 7 (optional): implement graph-based model of your choice

555 KiB

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