Datasets:
Tasks:
Text Classification
License:
File size: 8,249 Bytes
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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() |