import pandas as pd import numpy as np import fasttext to_be_removed_esp = [ 483197,483223,483442,483512,484318,484498,484586,484729,485267,485810, 485903,486030,486154,486295,486525,486749,486773,486786,486791,486811,486814, 486827,486842,486848,486855,486854,486869,486908,486917,486918,486922,486925, 486931,486934,486935,486940,486941,486948,486956,486958,486959,486961,486962, 486966,486968,486973,486974,486978,486981,486983,486987,486992,486999,487001, 487003,487009,487019,487018,487021,487022,487026,487028,487029,487033,487037, 487041,487044,487052,487060,487062,487065,487072,487074,487076,487081,487084, 487085,487088,487092,487094,487095,487099,487103,487105,487106,487109,487110, 487111,487112,487115,487122,487126,487133,487135,487138,487142,487143,487146, 487154,487155,487156,487162,487164,487173,487179,487185,487189,487197,487198, 487199,487204,487207,487210,487211,487216,487219,487225,487229,487233,487234, 487235,487243,487244,487245,487251,487252,487253,487254,487255,487256,487258, 487264,487273,487276,487282,487290,487292,487294,487298,487304,487303,487308, 487318,487321,487323,487326,487327,487329,487330,487331,487334,487337,487346, 487343,487347,487349,487350,487360,487361,487365,487366,487375,487379,487380, 487386,487389,487391,487393,487396,487397,487399,487400,487401,487402,487411, 487412,487414,487416,487417,487419,487421,487424,487425,487427,487433,487435, 487437,487443,487449,487452,487453,487455,487456,487459,487463,487465,487466, 487468,487471,487479,487481,487483,487485,487486,487487,487488,487489,487490, 487493,487494,487496,487498,487500,487501,487502,487505,487506,487512,487517, 487519,487528,487525,487529,487530,487537,487538,487541,487542,487545,487554, 487555,487556,487558,487567,487569,487573,487574,487578,487582,487586,487587, 487592,487596,487602,487603,487604,487607,487608,487609,487612,487613,487616, 487617,487618,487621,487623,487624 ] to_be_removed_por = [ 274310,274300,274299,274294,274287,274281,274265,274259,274256,274255,274232,274225,274226,274219, 274213,274206,274200,274199,274194,274172,274171,274170,274167,274166,274165,274163,274153,274146, 274143,274142,274136,274134,274130,274125,274123,274122,274109,274108,274079,274075,274073,274071, 274068,274057,274054,274044,274043,274042,274030,274029,274019,274018,274017,274015,274014,274011, 273998,273975,273969,273967,273951,273934,273924,273922,273914,273910,273909,273901,273899,273895, 273889,273881,273876,273871,273875,273869,273820,273812,273799,273791,273786,273783,273781,273780, 273779,273772,273768,273754,273750,273741,273739,273736,273732,273731,273727,273715,273703,273674, 273596,273595, ] # countries = ['MLB','MLA','MLM','MLU','MCO','MLC','MLV','MPE'] # esp_countries = ['MLA','MLM','MLU','MCO','MLC','MLV','MPE'] # rates = [1, 2, 3, 4, 5] abbreviations = { 'Hogar / Casa': 'HOGAR', 'Tecnología y electrónica / Tecnologia e electronica': 'TEC', 'Arte y entretenimiento / Arte e Entretenimiento': 'ARTE', 'Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal': 'SALUD', 'Alimentos y Bebidas / Alimentos e Bebidas': 'ALIMENTOS' } inv_abbreviations = {v:k for k,v in abbreviations.items()} def detect_lang_fasttext(df_es,df_pt): ds_es = (df_es['review_content'] + ' ' + df_es['review_title']).astype(str) ds_pt = (df_pt['review_content'] + ' ' + df_pt['review_title']).astype(str) model_predict = fasttext.load_model('../datav2/lid.176.bin').predict def apply_lang_detect(text): return dict(zip(*[('lang','prob'),next(zip(*model_predict(text, k=1)))])) lang_score_es = pd.DataFrame(ds_es.apply(apply_lang_detect).tolist()) lang_score_pt = pd.DataFrame(ds_pt.apply(apply_lang_detect).tolist()) lang_score_es.loc[lang_score_es['lang'] != '__label__es', 'prob'] = 0. df_es['lang_prob'] = lang_score_es['prob'] df_es = df_es.sort_values(by=['lang_prob'],ascending=False).reset_index(drop=True) lang_score_pt.loc[lang_score_pt['lang'] != '__label__pt', 'prob'] = 0. df_pt['lang_prob'] = lang_score_pt['prob'] df_pt = df_pt.sort_values(by=['lang_prob'],ascending=False).reset_index(drop=True) return df_es, df_pt def train_test_split( df, samples, random_seed ): rs = np.random.RandomState(random_seed) test_indices = [] for country in samples.keys(): for cat, n in samples[country].items(): if n == 0: continue # print(country, cat, n) # print(df.loc[ # (df['country'] == country) & (df['category'] == inv_abbreviations[cat]), "review_rate" # ]) idx = df[ (df['country'] == country) & (df['category'] == inv_abbreviations[cat]) ].groupby('review_rate').sample(n=n,random_state=rs).index.tolist() test_indices.extend(idx) df_test = df.loc[ test_indices, ['country','category','review_content','review_title','review_rate'] ].reset_index(drop=True) train_indices = sorted(list(set(range(len(df))) - set(test_indices))) df_train = df.loc[ train_indices, ['country','category','review_content','review_title','review_rate'] ].reset_index(drop=True) return df_train, df_test def main(): # Se leen todos los comentarios descargados df_es = pd.read_csv('./reviews_es_full.csv') df_pt = pd.read_csv('./reviews_pt_full.csv') # Se ordenan por relevancia según idioma df_es, df_pt = detect_lang_fasttext(df_es,df_pt) ## ESPAÑOL # Se eliminan los que están en la lista to_be_removed_esp df_es = df_es.drop(set(to_be_removed_esp)).reset_index(drop=True) # Se extrae el conjunto de test es_country_samples = { 'MLA':{'ALIMENTOS': 3,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315}, 'MLM':{'ALIMENTOS': 4,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315}, 'MLU':{'ALIMENTOS': 4,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315}, 'MCO':{'ALIMENTOS': 4,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315}, 'MLC':{'ALIMENTOS': 4,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315}, 'MLV':{'ALIMENTOS': 2,'ARTE':30,'HOGAR': 156,'SALUD':172,'TEC':353}, 'MPE':{'ALIMENTOS': 2,'ARTE':30,'HOGAR': 156,'SALUD':210,'TEC':315} } df_es_train, df_es_test = train_test_split(df_es,es_country_samples,random_seed=776436538) # Se extrae el conjunto de dev es_country_samples = { 'MLA':{'ALIMENTOS': 10,'ARTE':30,'HOGAR': 200,'SALUD':200,'TEC':300}, 'MLM':{'ALIMENTOS': 10,'ARTE':30,'HOGAR': 200,'SALUD':200,'TEC':300}, 'MLU':{'ALIMENTOS': 10,'ARTE':30,'HOGAR': 200,'SALUD':200,'TEC':300}, 'MCO':{'ALIMENTOS': 10,'ARTE':40,'HOGAR': 200,'SALUD':200,'TEC':300}, 'MLC':{'ALIMENTOS': 20,'ARTE':60,'HOGAR': 200,'SALUD':200,'TEC':300}, 'MLV':{'ALIMENTOS': 0,'ARTE':30,'HOGAR': 20,'SALUD':0,'TEC':250}, 'MPE':{'ALIMENTOS': 0,'ARTE':0,'HOGAR': 1,'SALUD':0,'TEC':1} } df_es_train, df_es_dev = train_test_split(df_es_train,es_country_samples,random_seed=776436538) df_es_train.to_csv('./es/train.csv',index=False) df_es_dev.to_csv('./es/validation.csv',index=False) df_es_test.to_csv('./es/test.csv',index=False) ## PORTUGUÉS # Se eliminan los que están en la lista to_be_removed_por df_pt = df_pt.drop(set(to_be_removed_por)).reset_index(drop=True) # Se extrae el conjunto de test pt_country_samples = {'MLB':{'ALIMENTOS': 23,'ARTE':210,'HOGAR': 1092,'SALUD':1432,'TEC':2243}} df_pt_train, df_pt_test = train_test_split(df_pt,pt_country_samples,random_seed=776436538) # Se extrae el conjunto de dev pt_country_samples = {'MLB':{'ALIMENTOS': 20,'ARTE':200,'HOGAR': 1032,'SALUD':1400,'TEC':1400}} df_pt_train, df_pt_dev = train_test_split(df_pt,pt_country_samples,random_seed=776436538) df_pt_train.to_csv('./pt/train.csv',index=False) df_pt_dev.to_csv('./pt/validation.csv',index=False) df_pt_test.to_csv('./pt/test.csv',index=False) if __name__ == "__main__": main()