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!_TAG_FILE_FORMAT 2 /extended format; --format=1 will not append ;" to lines/
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!_TAG_FILE_SORTED 1 /0=unsorted, 1=sorted, 2=foldcase/
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!_TAG_OUTPUT_EXCMD mixed /number, pattern, mixed, or combineV2/
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!_TAG_OUTPUT_FILESEP slash /slash or backslash/
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!_TAG_OUTPUT_MODE u-ctags /u-ctags or e-ctags/
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!_TAG_PATTERN_LENGTH_LIMIT 96 /0 for no limit/
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!_TAG_PROC_CWD /home/matias/repos/encuestador-de-hogares/ //
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!_TAG_PROGRAM_AUTHOR Universal Ctags Team //
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!_TAG_PROGRAM_NAME Universal Ctags /Derived from Exuberant Ctags/
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!_TAG_PROGRAM_URL https://ctags.io/ /official site/
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!_TAG_PROGRAM_VERSION 5.9.0 //
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AGLO_Region codigo/routines/preprocesar_datos.py /^AGLO_Region = AGLO_Region.loc[~((AGLO_Region.AGLOMERADO == 33) & (AGLO_Region.Region == 'Pampean/;" v
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AGLO_Region codigo/routines/preprocesar_datos.py /^AGLO_Region = AGLO_Region.loc[~((AGLO_Region.AGLOMERADO == 93) & (AGLO_Region.Region == 'Pampean/;" v
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AGLO_Region codigo/routines/preprocesar_datos.py /^AGLO_Region = pd.read_csv('.\/data\/info\/radio_ref.csv', usecols = ['AGLOMERADO', 'Region']).dr/;" v
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AGLO_rk codigo/routines/preprocesar_datos.py /^ AGLO_rk = AGLO_rk.sort_values('P47T').reset_index()$/;" v
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AGLO_rk codigo/routines/preprocesar_datos.py /^ AGLO_rk = training.loc[(training.CAT_OCUP == 3) & (training.P47T >= 100)].groupb/;" v
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Actualizaciones Periodicas: README.md /^## Actualizaciones Periodicas:$/;" s chapter:encuestador-de-hogares
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Datos README.md /^## Datos$/;" s chapter:encuestador-de-hogares
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EPH codigo/routines/preprocesar_datos.py /^ EPH = EPH.merge(AGLO_Region)$/;" v
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EPH codigo/routines/preprocesar_datos.py /^ EPH = hogar.merge(indiv_table, on=['CODUSU', 'ANO4', 'TRIMESTRE', 'AGLOMERADO'])$/;" v
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EPH codigo/routines/preprocesar_datos.py /^ EPH = pd.concat([EPH, EPH_sampled]).reset_index(drop=True)$/;" v
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EPH_sampled codigo/routines/preprocesar_datos.py /^ EPH_sampled = EPH.sample(n=sample_size, replace=True, random_state=42)$/;" v
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Ejemplos: README.md /^## Ejemplos:$/;" s chapter:encuestador-de-hogares
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Metodologia: README.md /^## Metodologia:$/;" s chapter:encuestador-de-hogares
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Modelos README.md /^## Modelos$/;" s chapter:encuestador-de-hogares
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Reg_rk codigo/routines/preprocesar_datos.py /^ Reg_rk = Reg_rk.sort_values('P47T').reset_index()$/;" v
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Reg_rk codigo/routines/preprocesar_datos.py /^ Reg_rk = training.loc[(training.CAT_OCUP == 3) & (training.P47T >= 100)].groupby/;" v
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[markdown] codigo/routines/recalcular_rankings.py /^# %% [markdown]$/;" c
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aglo_list codigo/routines/recalcular_rankings.py /^aglo_list = []$/;" v
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aglo_rk codigo/routines/recalcular_rankings.py /^ aglo_rk = pd.concat(aglo_list)$/;" v
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args codigo/routines/entrenar_modelos.py /^args = parser.parse_args()$/;" v
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args codigo/routines/preprocesar_datos.py /^args = parser.parse_args()$/;" v
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cleanup_temp_dir codigo/routines/preprocesar_datos.py /^def cleanup_temp_dir():$/;" f
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col_mon codigo/routines/preprocesar_datos.py /^col_mon = [u'P21', u'P47T', u'PP08D1', u'TOT_P12', u'T_VI', u'V12_M', u'V2_M', u'V3_M', u'V5_M']$/;" v
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columnas_pesos codigo/routines/entrenar_modelos.py /^columnas_pesos = [u'P21', u'P47T', u'PP08D1', u'TOT_P12', u'T_VI', u'V12_M', u'V2_M', u'V3_M', u/;" v
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columnas_pesos data/info/variables.py /^columnas_pesos = [u'P21', u'P47T', u'PP08D1', u'TOT_P12', u'T_VI', u'V12_M', u'V2_M', u'V3_M', u/;" v
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cpi codigo/routines/preprocesar_datos.py /^cpi = cpi['2003':]$/;" v
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cpi codigo/routines/preprocesar_datos.py /^cpi = pd.read_csv(cpi_url, index_col=0)$/;" v
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cpi_M codigo/routines/preprocesar_datos.py /^cpi_M = cpi_M['2003':]$/;" v
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cpi_M codigo/routines/preprocesar_datos.py /^cpi_M = pd.read_csv(cpi_M_url, index_col=0)[['index']]$/;" v
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cpi_M_url codigo/routines/preprocesar_datos.py /^cpi_M_url = 'https:\/\/raw.githubusercontent.com\/matuteiglesias\/IPC-Argentina\/main\/data\/inf/;" v
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cpi_url codigo/routines/preprocesar_datos.py /^cpi_url = 'https:\/\/raw.githubusercontent.com\/matuteiglesias\/IPC-Argentina\/main\/data\/info\//;" v
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df codigo/routines/preprocesar_datos.py /^ df = df.rename(columns={'ESTADO': 'CONDACT'})$/;" v
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df codigo/routines/preprocesar_datos.py /^ df = pd.read_csv(file_, delimiter=';', usecols=['CODUSU', 'ANO4', 'TRIMESTRE/;" v
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df codigo/routines/preprocesar_datos.py /^ df = pd.read_csv(file_, index_col=None, header=0, delimiter=';', usecols=['CODUS/;" v
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encuestador-de-hogares README.md /^# encuestador-de-hogares$/;" c
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endyr codigo/routines/entrenar_modelos.py /^endyr = args.years[1]$/;" v
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endyr codigo/routines/preprocesar_datos.py /^endyr = args.years[1] + 1 # Include the end year in the range$/;" v
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endyr codigo/routines/recalcular_rankings.py /^endyr = 2025$/;" v
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file_path codigo/routines/preprocesar_datos.py /^ file_path = os.path.join(temp_dir, os.path.basename(url))$/;" v
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fit_model codigo/routines/entrenar_modelos.py /^def fit_model(train_data, x_cols, y_cols, out_filename,$/;" f
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hogar codigo/routines/preprocesar_datos.py /^ hogar = hogar.drop_duplicates()$/;" v
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hogar codigo/routines/preprocesar_datos.py /^ hogar = hogar.loc[~hogar.IV1.isin([9])]$/;" v
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hogar codigo/routines/preprocesar_datos.py /^ hogar = pd.concat(list_).drop_duplicates()$/;" v
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hogar_files codigo/routines/preprocesar_datos.py /^ hogar_files = []$/;" v
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hogar_urls codigo/routines/preprocesar_datos.py /^ hogar_urls = [f'{repo_base_url}\/hogar\/usu_hogar_t{quarter:01}{yr}.txt' for quarter in /;" v
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indiv codigo/routines/preprocesar_datos.py /^ indiv = indiv.dropna(subset = ['P47T'])$/;" v
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indiv codigo/routines/preprocesar_datos.py /^ indiv = pd.concat(list_).dropna(subset=['P47T']).drop_duplicates()$/;" v
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indiv_files codigo/routines/preprocesar_datos.py /^ indiv_files = []$/;" v
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indiv_table codigo/routines/preprocesar_datos.py /^ indiv_table = indiv[list(indiv.columns.difference(hogar.columns)) + ['CODUSU', '/;" v
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indiv_urls codigo/routines/preprocesar_datos.py /^ indiv_urls = [f'{repo_base_url}\/individual\/usu_individual_t{quarter:01}{yr}.txt' for q/;" v
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ix codigo/routines/preprocesar_datos.py /^ix = cpi_M.loc['2016-01'].values[0][0]$/;" v
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list_ codigo/routines/preprocesar_datos.py /^ list_ = []$/;" v
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list_ codigo/routines/preprocesar_datos.py /^ list_ = []$/;" v
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names_EPH codigo/routines/preprocesar_datos.py /^names_EPH = ['IX_TOT','CH04','CH06','CONDACT', 'AGLOMERADO',$/;" v
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names_censo codigo/routines/preprocesar_datos.py /^names_censo = ['IX_TOT', 'P02', 'P03', 'CONDACT', 'AGLOMERADO',$/;" v
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out codigo/routines/entrenar_modelos.py /^ out = '.\/modelos\/clf4_'+q+'_ARG'$/;" v
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out codigo/routines/entrenar_modelos.py /^ out = '.\/modelos\/clf1_'+yr+'_ARG'$/;" v
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out codigo/routines/entrenar_modelos.py /^ out = '.\/modelos\/clf2_'+yr+'_ARG'$/;" v
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out codigo/routines/entrenar_modelos.py /^ out = '.\/modelos\/clf3_'+yr+'_ARG'$/;" v
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overwrite codigo/routines/entrenar_modelos.py /^overwrite = args.overwrite$/;" v
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overwrite codigo/routines/preprocesar_datos.py /^overwrite = args.overwrite$/;" v
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parser codigo/routines/entrenar_modelos.py /^parser = argparse.ArgumentParser(description='A script to process data for a range of years')$/;" v
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parser codigo/routines/preprocesar_datos.py /^parser = argparse.ArgumentParser()$/;" v
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pd codigo/routines/entrenar_modelos.py /^import pandas as pd$/;" I nameref:module:pandas
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pd codigo/routines/preprocesar_datos.py /^import pandas as pd$/;" I nameref:module:pandas
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pd codigo/routines/recalcular_rankings.py /^import pandas as pd$/;" I nameref:module:pandas
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predecir1 codigo/routines/entrenar_modelos.py /^predecir1 = ['CAT_OCUP', 'CAT_INAC', 'CH07']$/;" v
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predecir2 codigo/routines/entrenar_modelos.py /^predecir2 = ['INGRESO', 'INGRESO_NLB', 'INGRESO_JUB', 'INGRESO_SBS']$/;" v
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predecir3 codigo/routines/entrenar_modelos.py /^predecir3 = ['PP07G1','PP07G_59', 'PP07I', 'PP07J', 'PP07K']$/;" v
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predecir4 codigo/routines/entrenar_modelos.py /^predecir4 = columnas_pesos$/;" v
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regiones codigo/routines/recalcular_rankings.py /^ regiones = {$/;" v
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regs_list codigo/routines/recalcular_rankings.py /^regs_list = []$/;" v
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regs_rk codigo/routines/recalcular_rankings.py /^ regs_rk = pd.concat(regs_list)$/;" v
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regs_table codigo/routines/recalcular_rankings.py /^ regs_table = pd.read_csv(training_file, usecols=['ANO4', 'Region', 'Reg_rk']).drop_dupli/;" v
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repo_base_url codigo/routines/preprocesar_datos.py /^repo_base_url = 'https:\/\/raw.githubusercontent.com\/matuteiglesias\/microdatos-EPH-INDEC\/mast/;" v
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response codigo/routines/preprocesar_datos.py /^ response = requests.get(url)$/;" v
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sample_size codigo/routines/preprocesar_datos.py /^ sample_size = int(len(EPH) * 0.05)$/;" v
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startyr codigo/routines/entrenar_modelos.py /^startyr = args.years[0]$/;" v
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startyr codigo/routines/preprocesar_datos.py /^startyr = args.years[0]$/;" v
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startyr codigo/routines/recalcular_rankings.py /^startyr = 2023$/;" v
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temp_dir codigo/routines/preprocesar_datos.py /^temp_dir = '.\/temp_data\/'$/;" v
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train_q codigo/routines/entrenar_modelos.py /^ train_q = train_data.loc[train_data.Q == q]$/;" v
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training codigo/routines/preprocesar_datos.py /^ training = training.loc[training[col] != 9]$/;" v
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training codigo/routines/preprocesar_datos.py /^ training = EPH.rename(columns = dict(zip(names_EPH, names_censo)))$/;" v
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training codigo/routines/preprocesar_datos.py /^ training = training.loc[training.P47T >= -0.001].fillna(0)$/;" v
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training codigo/routines/preprocesar_datos.py /^ training = training.merge(AGLO_rk[['ANO4', 'AGLOMERADO', 'AGLO_rk']]).merge(Reg_/;" v
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training codigo/routines/preprocesar_datos.py /^ training = training.sort_values('CODUSU')$/;" v
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training_file codigo/routines/preprocesar_datos.py /^ training_file = '.\/data\/training\/EPHARG_train_'+str(yr)+'.csv'$/;" v
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training_file codigo/routines/recalcular_rankings.py /^ training_file = f'.\/data\/training\/EPHARG_train_{yr}.csv'$/;" v
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x_cols1 codigo/routines/entrenar_modelos.py /^x_cols1 = ['IX_TOT', 'P02', 'P03', 'AGLO_rk', 'Reg_rk', 'V01', 'H05', 'H06',$/;" v
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x_cols1 data/info/variables.py /^x_cols1 = ['IX_TOT', 'P02', 'P03', 'AGLO_rk', 'Reg_rk', 'V01', 'H05', 'H06',$/;" v
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x_cols2 codigo/routines/entrenar_modelos.py /^x_cols2 = x_cols1 + predecir1$/;" v
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x_cols2 data/info/variables.py /^x_cols2 = x_cols1 + y_cols1$/;" v
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x_cols3 codigo/routines/entrenar_modelos.py /^x_cols3 = x_cols2 + predecir2$/;" v
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x_cols3 data/info/variables.py /^x_cols3 = x_cols2 + y_cols2$/;" v
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x_cols4 codigo/routines/entrenar_modelos.py /^x_cols4 = x_cols3 + predecir3$/;" v
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x_cols4 data/info/variables.py /^x_cols4 = x_cols3 + y_cols3$/;" v
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y_cols codigo/routines/entrenar_modelos.py /^y_cols = ['CAT_OCUP', 'P47T', 'PP10E', 'PP10D', 'PP07K', 'PP07I', 'V3_M', 'PP07G4', 'CH16', 'T_V/;" v
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y_cols data/info/variables.py /^y_cols = ['CAT_OCUP', 'P47T', 'PP10E', 'PP10D', 'PP07K', 'PP07I', 'V3_M', 'PP07G4', 'CH16', 'T_V/;" v
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y_cols1 data/info/variables.py /^y_cols1 = ['CAT_OCUP', 'CAT_INAC', 'CH07']$/;" v
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y_cols2 data/info/variables.py /^y_cols2 = ['INGRESO', 'INGRESO_NLB', 'INGRESO_JUB', 'INGRESO_SBS']$/;" v
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y_cols3 data/info/variables.py /^y_cols3 = ['PP07G1','PP07G_59', 'PP07I', 'PP07J', 'PP07K']$/;" v
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y_cols4 codigo/routines/entrenar_modelos.py /^y_cols4 = predecir4$/;" v
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y_cols4 data/info/variables.py /^y_cols4 = columnas_pesos$/;" v
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yr codigo/routines/preprocesar_datos.py /^ yr = str(y)[2:]$/;" v
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yr codigo/routines/recalcular_rankings.py /^ yr = str(y)[2:]$/;" v

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