warsztaty-B/P5. Graph-based.ipynb

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	F_2	Whole_average	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	0.181702	0.340803	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	F_2	Whole_average	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	0.159293	0.321247	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	0.164747	0.326323	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	0.171652	0.333140	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	0.179823	0.340076	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	0.181702	0.340803	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	0.182776	0.341341	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	0.179766	0.336190	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	0.175419	0.328868	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)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-8-e01206177978> in <module>
     10 for i in range(len(metrics)):
     11     df=result[['Alpha', metrics[i]]]
---> 12     df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

IndexError: list index out of range

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	F_2	Whole_average	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	0.181702	0.340803	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	0.187030	0.347420	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	0.190538	0.352756	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	0.194073	0.358344	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	0.197981	0.363598	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	0.200572	0.369318	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	0.197320	0.367854	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	0.182056	0.352330	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	0.146414	0.307476	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	0.078906	0.197947	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)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-10-8f1dc184fb30> in <module>
     12 for i in range(len(metrics)):
     13     df=result[['Beta', metrics[i]]]
---> 14     df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

IndexError: list index out of range

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
57566	734	5	Emma (1996)	Drama, Romance
50942	734	5	It's a Wonderful Life (1946)	Drama
48429	734	5	Rebecca (1940)	Romance, Thriller
22622	734	5	My Fair Lady (1964)	Musical, Romance
22461	734	5	Sound of Music, The (1965)	Musical
35119	734	4	Much Ado About Nothing (1993)	Comedy, Romance
43403	734	4	Snow White and the Seven Dwarfs (1937)	Animation, Children's, Musical
2447	734	4	Back to the Future (1985)	Comedy, Sci-Fi
36121	734	4	Silence of the Lambs, The (1991)	Drama, Thriller
37465	734	4	Aladdin (1992)	Animation, Children's, Comedy, Musical
38209	734	4	Mary Poppins (1964)	Children's, Comedy, Musical
39918	734	4	Rear Window (1954)	Mystery, Thriller
40514	734	4	Titanic (1997)	Action, Drama, Romance
42508	734	4	Jurassic Park (1993)	Action, Adventure, Sci-Fi
45160	734	4	Tomorrow Never Dies (1997)	Action, Romance, Thriller

	user	rec_nb	title	genres
2936	734.0	1	Return of the Jedi (1983)	Action, Adventure, Romance, Sci-Fi, War
7231	734.0	2	Fargo (1996)	Crime, Drama, Thriller
3322	734.0	3	Toy Story (1995)	Animation, Children's, Comedy
2519	734.0	4	Godfather, The (1972)	Action, Crime, Drama
5958	734.0	5	Contact (1997)	Drama, Sci-Fi
1621	734.0	6	Schindler's List (1993)	Drama, War
1248	734.0	7	English Patient, The (1996)	Drama, Romance, War
1455	734.0	8	Fugitive, The (1993)	Action, Thriller
3542	734.0	9	Jerry Maguire (1996)	Drama, Romance
8124	734.0	10	Monty Python and the Holy Grail (1974)	Comedy

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'

## SOLUTION TASK 6

import evaluation_measures as ev

model = RP3Beta()
model.fit(train_ui, alpha = 0.8, beta = 0.6)

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)
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 = '\t', header = None),
            estimations_df = estimations_df, 
            reco = reco,
            super_reactions = [4, 5])

943it [00:00, 4555.12it/s]

	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	F_2	Whole_average	Reco in test	Test coverage	Shannon	Gini
0	3.702928	3.527713	0.322694	0.216069	0.212152	0.247538	0.245279	0.284983	0.388271	0.248239	0.636318	0.605683	0.910923	0.20545	0.376967	0.999788	0.178932	4.549663	0.950182

result.sort_values(["recall"])

	Beta	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	F_2	Whole_average	Reco in test	Test coverage	Shannon	Gini
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	0.078906	0.197947	0.800106	0.415584	5.563910	0.857396
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	0.146414	0.307476	0.936373	0.341270	5.257397	0.895882
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	0.182056	0.352330	0.983033	0.256854	4.898568	0.928065
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	0.181702	0.340803	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	0.187030	0.347420	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	0.190538	0.352756	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	0.194073	0.358344	1.000000	0.105339	4.147779	0.966948
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	0.197320	0.367854	0.996394	0.212843	4.621938	0.945932
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	0.197981	0.363598	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	0.200572	0.369318	0.999046	0.168831	4.411281	0.956648

So Beta 0.6 and alpha 0.8 seems to maximze recall

model=RP3Beta()
model.fit(train_ui, alpha=0.8, beta=0.6)
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))
reco.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_reco.csv', index=False, header=False)
estimations_df.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv', index=False, header=False)

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)

943it [00:00, 5460.13it/s]
943it [00:00, 4803.05it/s]
943it [00:00, 5055.06it/s]
943it [00:00, 5345.09it/s]
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943it [00:00, 4987.44it/s]

	Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	F_2	Whole_average	Reco in test	Test coverage	Shannon	Gini
0	Self_RP3Beta	3.702928	3.527713	0.322694	0.216069	0.212152	0.247538	0.245279	0.284983	0.388271	0.248239	0.636318	0.605683	0.910923	0.205450	0.376967	0.999788	0.178932	4.549663	0.950182
0	Self_P3	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	0.181702	0.340803	1.000000	0.077201	3.875892	0.974947
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	0.112750	0.249607	1.000000	0.038961	3.159079	0.987317
0	Self_SVDBaseline	3.642359	3.476956	0.135949	0.079751	0.082423	0.099673	0.106545	0.104164	0.160100	0.079313	0.328798	0.536764	0.629905	0.077617	0.201750	1.000000	0.282828	5.130008	0.909760
0	Ready_SVD	0.951186	0.750553	0.094910	0.044564	0.051182	0.065639	0.084549	0.074410	0.106164	0.049263	0.228326	0.518988	0.477200	0.045601	0.153400	0.996925	0.219336	4.494800	0.949844
0	Self_SVD	0.914024	0.717181	0.104454	0.043836	0.053331	0.070716	0.094528	0.076751	0.106711	0.050532	0.194366	0.518647	0.479321	0.045941	0.153261	0.853765	0.148629	3.836334	0.973007
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	0.039549	0.141900	1.000000	0.033911	2.836513	0.991139
0	Ready_SVDBiased	0.939359	0.740564	0.086850	0.036359	0.043933	0.058123	0.076395	0.056913	0.094528	0.043830	0.203204	0.514846	0.443266	0.038036	0.141357	0.994804	0.179654	4.199699	0.962848
0	Self_KNNSurprisetask	0.946255	0.745209	0.083457	0.032848	0.041227	0.055493	0.074785	0.048890	0.089577	0.040902	0.189057	0.513076	0.417815	0.034996	0.135177	0.888547	0.130592	3.611806	0.978659
0	Self_TopRated	2.508258	2.217909	0.079321	0.032667	0.039983	0.053170	0.068884	0.048582	0.070766	0.027602	0.114790	0.512943	0.411453	0.034385	0.124546	1.000000	0.024531	2.761238	0.991660
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	0.027213	0.118383	1.000000	0.025974	2.711772	0.992003
0	Ready_Random	1.525633	1.225714	0.047720	0.022049	0.025494	0.032845	0.029077	0.025015	0.051757	0.019242	0.128181	0.507543	0.327678	0.022628	0.103269	0.987275	0.184704	5.105122	0.906561
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	0.008007	0.069521	0.402333	0.434343	5.133650	0.877999
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	0.000862	0.045379	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	0.000235	0.042533	0.602121	0.010823	2.089186	0.995706
0	Self_BaselineIU	0.958136	0.754051	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.000220	0.042809	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	0.000201	0.042622	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	0.000118	0.041755	0.392153	0.115440	4.174741	0.965327

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'