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67d6328405
workshops_recommender_systems/P5. Graph-based.ipynb
Robert 67d6328405 Codes added
2020-05-21 13:42:50 +02:00

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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 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 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 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 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
0 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
0 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
0 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
0 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 0.0 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 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 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 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 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 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 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 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 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 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_RP3Beta_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
25689 645 5 Citizen Kane (1941) Drama
46234 645 5 Miller's Crossing (1990) Drama
29481 645 5 Psycho (1960) Horror, Romance, Thriller
29861 645 5 To Kill a Mockingbird (1962) Drama
24699 645 5 One Flew Over the Cuckoo's Nest (1975) Drama
64848 645 5 Taxi Driver (1976) Drama, Thriller
31022 645 5 GoodFellas (1990) Crime, Drama
23585 645 5 Casablanca (1942) Drama, Romance, War
18551 645 5 Amadeus (1984) Drama, Mystery
40333 645 5 Exotica (1994) Drama
42006 645 5 Dr. Strangelove or: How I Learned to Stop Worr... Sci-Fi, War
27477 645 5 Young Frankenstein (1974) Comedy, Horror
43025 645 5 2001: A Space Odyssey (1968) Drama, Mystery, Sci-Fi, Thriller
12217 645 5 Graduate, The (1967) Drama, Romance
42731 645 5 Brazil (1985) Sci-Fi
user rec_nb title genres
284 645.0 1 Star Wars (1977) Action, Adventure, Romance, Sci-Fi, War
7185 645.0 2 Fargo (1996) Crime, Drama, Thriller
620 645.0 3 Raiders of the Lost Ark (1981) Action, Adventure
872 645.0 4 Silence of the Lambs, The (1991) Drama, Thriller
2483 645.0 5 Godfather, The (1972) Action, Crime, Drama
6723 645.0 6 Empire Strikes Back, The (1980) Action, Adventure, Drama, Romance, Sci-Fi, War
1440 645.0 7 Fugitive, The (1993) Action, Thriller
3288 645.0 8 Toy Story (1995) Animation, Children's, Comedy
8416 645.0 9 Indiana Jones and the Last Crusade (1989) Action, Adventure
2062 645.0 10 Back to the Future (1985) Comedy, Sci-Fi

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'

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'