warsztaty2/P7. WRMF (Implicit ALS).ipynb

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
import implicit
import evaluation_measures as ev

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)

alpha = 30
train_ui*=alpha
train_iu=train_ui.transpose().tocsr()

model = implicit.als.AlternatingLeastSquares(factors=200, regularization=0.1, iterations=10)
model.fit(train_iu)

WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading

HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))

def top_k_recommendations(model, user_code_id, item_code_id, topK=10):
    recommendations=[]
    for u in range(train_ui.shape[0]):
        u_recommended_items=model.recommend(u, train_ui, N=10, filter_already_liked_items=True)
        recommendations.append([user_code_id[u]]+list(chain(*u_recommended_items)))
    reco=pd.DataFrame(recommendations)
    reco.iloc[:,1::2]=reco.iloc[:,1::2].applymap(lambda x: item_code_id[x])
    return reco

def estimate(model, 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], 
            model.rank_items(userid=user, user_items=train_ui, selected_items=[item])[0][1]])
    return result

reco=top_k_recommendations(model, user_code_id, item_code_id, topK=10)
reco.to_csv('Recommendations generated/ml-100k/Ready_ImplicitALS_reco.csv', index=False, header=False)

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

import evaluation_measures as ev
import imp
imp.reload(ev)

estimations_df=pd.read_csv('Recommendations generated/ml-100k/Ready_ImplicitALS_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Ready_ImplicitALS_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])
#also you can just type ev.evaluate_all(estimations_df, reco) - I put above values as default

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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	3.274971	3.076091	0.246766	0.180284	0.171181	0.194935	0.16309	0.205416	0.29822	0.166226	0.520157	0.587509	0.878049	0.999788	0.508658	5.756947	0.819903

Hiperparameters tuning

Number of latent factors

from tqdm import tqdm
result=[]
for factors in tqdm([i for i in np.arange(25,400,25)]):
    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)
    
    train_ui*=100
    train_iu=train_ui.transpose().tocsr()
    
    model = implicit.als.AlternatingLeastSquares(factors=factors, regularization=0.1, iterations=10)
    model.fit(train_iu, show_progress=False)
    
    reco=top_k_recommendations(model, user_code_id, item_code_id, topK=10)
    estimations_df=pd.DataFrame(estimate(model, 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, "Factors", factors)
    result.append(to_append)
    
result=pd.concat(result)
result

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	Factors	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	25	2.844919	2.620125	0.112513	0.118879	0.097878	0.100372	0.066738	0.129517	0.125779	0.052940	0.218140	0.556162	0.656416	0.996501	0.709957	6.457332	0.647711
0	50	2.898551	2.678081	0.144645	0.135718	0.116720	0.123745	0.085086	0.147738	0.162899	0.073388	0.281801	0.564729	0.744433	0.999894	0.649351	6.257955	0.706753
0	75	2.946453	2.728472	0.168081	0.149663	0.131850	0.141945	0.103112	0.160522	0.192246	0.090485	0.335233	0.571818	0.797455	0.999788	0.608225	6.053205	0.755007
0	100	2.983337	2.766327	0.186957	0.159899	0.143370	0.156146	0.118670	0.179116	0.213595	0.103269	0.365040	0.577007	0.829268	1.000000	0.561328	5.906052	0.789284
0	125	3.013649	2.798792	0.195440	0.158716	0.144700	0.159963	0.119957	0.174794	0.227569	0.114459	0.399596	0.576467	0.815483	0.999894	0.532468	5.782155	0.813818
0	150	3.042673	2.830717	0.210180	0.164005	0.151327	0.169325	0.135944	0.181649	0.243943	0.125804	0.425139	0.579170	0.847296	1.000000	0.506494	5.746312	0.823647
0	175	3.064257	2.852890	0.215589	0.165439	0.153855	0.172823	0.136052	0.185028	0.254184	0.130538	0.457603	0.579917	0.857900	1.000000	0.503608	5.631072	0.838636
0	200	3.084422	2.874424	0.219618	0.162595	0.153513	0.174288	0.140451	0.186207	0.257191	0.136220	0.449647	0.578527	0.844115	1.000000	0.488456	5.644481	0.840826
0	225	3.097204	2.888454	0.227678	0.168651	0.159966	0.181384	0.150858	0.195783	0.267800	0.143532	0.464535	0.581580	0.861082	0.999788	0.468254	5.566533	0.850247
0	250	3.111146	2.902813	0.232131	0.169147	0.159200	0.181969	0.147854	0.189227	0.268282	0.144025	0.458117	0.581832	0.853659	0.999894	0.466089	5.603329	0.846631
0	275	3.124854	2.916841	0.234571	0.171622	0.162598	0.185026	0.149356	0.185706	0.274896	0.150449	0.474372	0.583088	0.858961	1.000000	0.466811	5.535912	0.852984
0	300	3.135253	2.928828	0.229162	0.164930	0.157395	0.179806	0.149249	0.191109	0.265386	0.142651	0.454700	0.579712	0.848356	1.000000	0.463203	5.546113	0.853711
0	325	3.143012	2.936466	0.236267	0.164353	0.159323	0.184114	0.155579	0.187519	0.275057	0.150313	0.471154	0.579462	0.854719	0.999788	0.462482	5.542926	0.856801
0	350	3.149240	2.943003	0.238494	0.170851	0.161939	0.185788	0.155794	0.195913	0.278565	0.152289	0.479033	0.582719	0.868505	1.000000	0.474026	5.543713	0.854430
0	375	3.158746	2.953578	0.231071	0.157607	0.153557	0.178314	0.148069	0.174575	0.266733	0.144945	0.460581	0.576058	0.846235	1.000000	0.467532	5.522817	0.855222

### import matplotlib.pyplot as plt

metrics=list(result.columns[[i not in ['Factors'] 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[['Factors', metrics[i]]]
    df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

Alpha

from tqdm import tqdm
result=[]
for alpha in tqdm([1, 3, 7]+[i for i in np.arange(10,200,20)]):
    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)
    
    train_ui*=alpha
    train_iu=train_ui.transpose().tocsr()
    
    model = implicit.als.AlternatingLeastSquares(factors=factors, regularization=0.1, iterations=10)
    model.fit(train_iu, show_progress=False)
    
    reco=top_k_recommendations(model, user_code_id, item_code_id, topK=10)
    estimations_df=pd.DataFrame(estimate(model, 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	1	3.667964	3.489818	0.094380	0.070257	0.065728	0.074445	0.051502	0.070147	0.113450	0.049410	0.251617	0.531850	0.569459	0.993955	0.737374	6.690952	0.542274
0	3	3.627294	3.448283	0.148780	0.103792	0.099159	0.114233	0.093455	0.110666	0.181431	0.089470	0.371873	0.548831	0.712619	0.994380	0.711400	6.639888	0.567716
0	7	3.554798	3.373876	0.219936	0.150798	0.145514	0.168680	0.144635	0.171680	0.263177	0.145537	0.472968	0.572621	0.816543	0.997773	0.631313	6.305570	0.698148
0	10	3.510181	3.327658	0.247826	0.166669	0.161966	0.188961	0.161266	0.184196	0.298395	0.172603	0.527587	0.580683	0.845175	0.998091	0.588745	6.108851	0.748855
0	30	3.355511	3.164379	0.275928	0.193550	0.186070	0.214375	0.183262	0.224550	0.332232	0.198190	0.556671	0.594247	0.876988	0.999788	0.479798	5.570110	0.844550
0	50	3.273365	3.076256	0.265005	0.186234	0.180388	0.207058	0.176288	0.219204	0.310446	0.177237	0.521655	0.590528	0.884411	0.999894	0.476912	5.495722	0.854628
0	70	3.220208	3.018475	0.247508	0.174676	0.167792	0.192897	0.162661	0.203997	0.296658	0.167366	0.522179	0.584690	0.869565	1.000000	0.464646	5.532033	0.853659
0	90	3.177784	2.972935	0.244327	0.174771	0.165438	0.190194	0.161266	0.198930	0.289373	0.160604	0.498875	0.584710	0.871686	0.999894	0.461760	5.495266	0.857651
0	110	3.144328	2.937850	0.234995	0.164141	0.158510	0.183029	0.151717	0.185931	0.268423	0.144579	0.450377	0.579336	0.856840	0.999894	0.453824	5.518412	0.857772
0	130	3.112180	2.902455	0.228950	0.164122	0.156619	0.179336	0.147210	0.188450	0.265865	0.142502	0.459092	0.579307	0.856840	1.000000	0.453824	5.475153	0.864239
0	150	3.092145	2.882119	0.219830	0.154060	0.148340	0.171206	0.141416	0.174732	0.256054	0.136146	0.449823	0.574245	0.841994	1.000000	0.461760	5.489169	0.859261
0	170	3.072217	2.860201	0.221951	0.155889	0.149070	0.172317	0.138412	0.167305	0.251275	0.133171	0.426381	0.575125	0.827147	0.999894	0.467532	5.562501	0.853246
0	190	3.056080	2.843538	0.208696	0.145416	0.140349	0.162318	0.134227	0.157974	0.238459	0.122803	0.423420	0.569854	0.818664	0.999894	0.473304	5.438078	0.867098

### import matplotlib.pyplot as plt

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)

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_RP3Beta	3.704589	3.529397	0.286744	0.196524	0.191117	0.221375	0.213948	0.251263	0.344598	0.207836	0.587953	0.595770	0.885472	0.998197	0.193362	4.291821	0.960775
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	1.000000	0.077201	3.875892	0.974947
0	Ready_ImplicitALS	3.274971	3.076091	0.246766	0.180284	0.171181	0.194935	0.163090	0.205416	0.298220	0.166226	0.520157	0.587509	0.878049	0.999788	0.508658	5.756947	0.819903
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