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8ad2469287
workshops_recommender_systems/P5. Graph-based.ipynb
Robert Kwieciński 8ad2469287 Slides added
2020-05-21 16:20:12 +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_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
66489 344 5 Postino, Il (1994) Drama, Romance
15966 344 5 Godfather, The (1972) Action, Crime, Drama
50067 344 5 Mrs. Brown (Her Majesty, Mrs. Brown) (1997) Drama, Romance
65435 344 5 Speed (1994) Action, Romance, Thriller
50396 344 5 Close Shave, A (1995) Animation, Comedy, Thriller
13793 344 5 Local Hero (1983) Comedy
25784 344 5 Dead Man Walking (1995) Drama
15447 344 5 Blade Runner (1982) Film-Noir, Sci-Fi
24693 344 5 One Flew Over the Cuckoo's Nest (1975) Drama
67385 344 5 Good Will Hunting (1997) Drama
55907 344 5 My Life as a Dog (Mitt liv som hund) (1985) Drama
23403 344 5 Eat Drink Man Woman (1994) Comedy, Drama
18500 344 5 Amadeus (1984) Drama, Mystery
23038 344 5 Apt Pupil (1998) Drama, Thriller
57898 344 5 Wrong Trousers, The (1993) Animation, Comedy
user rec_nb title genres
158 344.0 1 Star Wars (1977) Action, Adventure, Romance, Sci-Fi, War
7055 344.0 2 Fargo (1996) Crime, Drama, Thriller
2769 344.0 3 Return of the Jedi (1983) Action, Adventure, Romance, Sci-Fi, War
1078 344.0 4 English Patient, The (1996) Drama, Romance, War
4535 344.0 5 Air Force One (1997) Action, Thriller
6433 344.0 6 Pulp Fiction (1994) Crime, Drama
4851 344.0 7 Titanic (1997) Action, Drama, Romance
5110 344.0 8 Full Monty, The (1997) Comedy
1553 344.0 9 Schindler's List (1993) Drama, War
6646 344.0 10 Empire Strikes Back, The (1980) Action, Adventure, Drama, Romance, Sci-Fi, War

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'