217 lines
8.5 KiB
Python
217 lines
8.5 KiB
Python
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import os
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import sys
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import numpy as np
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import pandas as pd
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import math
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from sklearn.preprocessing import normalize
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from tqdm import tqdm
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from datetime import datetime, date
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import random
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import scipy.sparse as sparse
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from os import listdir
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from os.path import isfile, join
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from collections import defaultdict
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def evaluate(test,
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estimations_df,
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reco,
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super_reactions=[4,5],
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topK=10):
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estimations_df=estimations_df.copy()
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reco=reco.copy()
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test_df=test.copy()
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# prepare testset
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test_df.columns=['user', 'item', 'rating', 'timestamp']
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test_df['user_code'] = test_df['user'].astype("category").cat.codes
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test_df['item_code'] = test_df['item'].astype("category").cat.codes
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user_code_id = dict(enumerate(test_df['user'].astype("category").cat.categories))
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user_id_code = dict((v, k) for k, v in user_code_id.items())
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item_code_id = dict(enumerate(test_df['item'].astype("category").cat.categories))
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item_id_code = dict((v, k) for k, v in item_code_id.items())
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test_ui = sparse.csr_matrix((test_df['rating'], (test_df['user_code'], test_df['item_code'])))
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#prepare estimations
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estimations_df.columns=['user', 'item' ,'score']
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estimations_df['user_code']=[user_id_code[user] for user in estimations_df['user']]
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estimations_df['item_code']=[item_id_code[item] for item in estimations_df['item']]
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estimations=sparse.csr_matrix((estimations_df['score'], (estimations_df['user_code'], estimations_df['item_code'])), shape=test_ui.shape)
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#compute_estimations
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estimations_df=estimations_metrics(test_ui, estimations)
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#prepare reco
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users=reco[:,:1]
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items=reco[:,1::2]
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# Let's use inner ids instead of real ones
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users=np.vectorize(lambda x: user_id_code.setdefault(x, -1))(users) # maybe users we recommend are not in test set
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items=np.vectorize(lambda x: item_id_code.setdefault(x, -1))(items) # maybe items we recommend are not in test set
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# Let's put them into one array
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reco=np.concatenate((users, items), axis=1)
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#compute ranking metrics
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ranking_df=ranking_metrics(test_ui, reco, super_reactions=super_reactions, topK=topK)
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#compute diversity metrics
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diversity_df=diversity_metrics(test_ui, reco, topK)
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result=pd.concat([estimations_df, ranking_df, diversity_df], axis=1)
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return(result)
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def ranking_metrics(test_ui, reco, super_reactions=[], topK=10):
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nb_items=test_ui.shape[1]
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relevant_users, super_relevant_users, prec, rec, F_1, F_05, prec_super, rec_super, ndcg, mAP, MRR, LAUC, HR, H2R=\
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0,0,0,0,0,0,0,0,0,0,0,0,0,0
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cg = (1.0 / np.log2(np.arange(2, topK + 2)))
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cg_sum = np.cumsum(cg)
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for (nb_user, user) in tqdm(enumerate(reco[:,0])):
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u_rated_items=test_ui.indices[test_ui.indptr[user]:test_ui.indptr[user+1]]
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nb_u_rated_items=len(u_rated_items)
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if nb_u_rated_items>0: # skip users with no items in test set (still possible that there will be no super items)
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relevant_users+=1
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u_super_items=u_rated_items[np.vectorize(lambda x: x in super_reactions)\
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(test_ui.data[test_ui.indptr[user]:test_ui.indptr[user+1]])]
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# more natural seems u_super_items=[item for item in u_rated_items if test_ui[user,item] in super_reactions]
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# but accesing test_ui[user,item] is expensive -we should avoid doing it
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if len(u_super_items)>0:
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super_relevant_users+=1
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user_successes=np.zeros(topK)
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nb_user_successes=0
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user_super_successes=np.zeros(topK)
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nb_user_super_successes=0
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# evaluation
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for (item_position,item) in enumerate(reco[nb_user,1:topK+1]):
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if item in u_rated_items:
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user_successes[item_position]=1
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nb_user_successes+=1
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if item in u_super_items:
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user_super_successes[item_position]=1
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nb_user_super_successes+=1
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prec_u=nb_user_successes/topK
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prec+=prec_u
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rec_u=nb_user_successes/nb_u_rated_items
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rec+=rec_u
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F_1+=2*(prec_u*rec_u)/(prec_u+rec_u) if prec_u+rec_u>0 else 0
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F_05+=(0.5**2+1)*(prec_u*rec_u)/(0.5**2*prec_u+rec_u) if prec_u+rec_u>0 else 0
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prec_super+=nb_user_super_successes/topK
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rec_super+=nb_user_super_successes/max(len(u_super_items),1)
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ndcg+=np.dot(user_successes,cg)/cg_sum[min(topK, nb_u_rated_items)-1]
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cumsum_successes=np.cumsum(user_successes)
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mAP+=np.dot(cumsum_successes/np.arange(1,topK+1), user_successes)/min(topK, nb_u_rated_items)
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MRR+=1/(user_successes.nonzero()[0][0]+1) if user_successes.nonzero()[0].size>0 else 0
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LAUC+=(np.dot(cumsum_successes, 1-user_successes)+\
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(nb_user_successes+nb_u_rated_items)/2*((nb_items-nb_u_rated_items)-(topK-nb_user_successes)))/\
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((nb_items-nb_u_rated_items)*nb_u_rated_items)
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HR+=nb_user_successes>0
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H2R+=nb_user_successes>1
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result=[]
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result.append(('precision', prec/relevant_users))
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result.append(('recall', rec/relevant_users))
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result.append(('F_1', F_1/relevant_users))
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result.append(('F_05', F_05/relevant_users))
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result.append(('precision_super', prec_super/super_relevant_users))
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result.append(('recall_super', rec_super/super_relevant_users))
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result.append(('NDCG', ndcg/relevant_users))
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result.append(('mAP', mAP/relevant_users))
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result.append(('MRR', MRR/relevant_users))
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result.append(('LAUC', LAUC/relevant_users))
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result.append(('HR', HR/relevant_users))
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result.append(('H2R', H2R/relevant_users))
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df_result=pd.DataFrame()
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if len(result)>0:
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df_result=(pd.DataFrame(list(zip(*result))[1])).T
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df_result.columns=list(zip(*result))[0]
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return df_result
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def estimations_metrics(test_ui, estimations):
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result=[]
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RMSE=(np.sum((estimations.data-test_ui.data)**2)/estimations.nnz)**(1/2)
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result.append(['RMSE', RMSE])
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MAE=np.sum(abs(estimations.data-test_ui.data))/estimations.nnz
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result.append(['MAE', MAE])
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df_result=pd.DataFrame()
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if len(result)>0:
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df_result=(pd.DataFrame(list(zip(*result))[1])).T
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df_result.columns=list(zip(*result))[0]
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return df_result
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def diversity_metrics(test_ui, reco, topK=10):
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frequencies=defaultdict(int)
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for item in list(set(test_ui.indices)):
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frequencies[item]=0
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for item in reco[:,1:].flat:
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frequencies[item]+=1
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nb_reco_outside_test=frequencies[-1]
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del frequencies[-1]
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frequencies=np.array(list(frequencies.values()))
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nb_rec_items=len(frequencies[frequencies>0])
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nb_reco_inside_test=np.sum(frequencies)
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frequencies=frequencies/np.sum(frequencies)
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frequencies=np.sort(frequencies)
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with np.errstate(divide='ignore'): # let's put zeros we items with 0 frequency and ignore division warning
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log_frequencies=np.nan_to_num(np.log(frequencies), posinf=0, neginf=0)
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result=[]
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result.append(('Reco in test', nb_reco_inside_test/(nb_reco_inside_test+nb_reco_outside_test)))
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result.append(('Test coverage', nb_rec_items/test_ui.shape[1]))
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result.append(('Shannon', -np.dot(frequencies, log_frequencies)))
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result.append(('Gini', np.dot(frequencies, np.arange(1-len(frequencies), len(frequencies), 2))/(len(frequencies)-1)))
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df_result=(pd.DataFrame(list(zip(*result))[1])).T
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df_result.columns=list(zip(*result))[0]
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return df_result
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def evaluate_all(test,
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dir_path="Recommendations generated/ml-100k/",
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super_reactions=[4,5],
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topK=10):
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models = list(set(['_'.join(f.split('_')[:2]) for f in listdir(dir_path)
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if isfile(dir_path+f)]))
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result=[]
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for model in models:
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estimations_df=pd.read_csv('{}{}_estimations.csv'.format(dir_path, model), header=None)
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reco=np.loadtxt('{}{}_reco.csv'.format(dir_path, model), delimiter=',')
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to_append=evaluate(test, estimations_df, reco, super_reactions, topK)
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to_append.insert(0, "Model", model)
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result.append(to_append)
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result=pd.concat(result)
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result=result.sort_values(by='recall', ascending=False)
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return result
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