def missing_state_and_imputing(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
imp = SimpleImputer(missing_values = '0', strategy="most_frequent")
zero_values = list(dataset.columns[dataset.eq('0').mean().mean() > 0])[0]
print(zero_values)
for feature in zero_values:
print(feature)
dataset[f'{feature}_is_missing'] = dataset[feature].apply(lambda value: 0 if value == '0' else 1)
dataset[zero_values] = imp.fit_transform(dataset[zero_values])
dtypes_df = dataset.dtypes.to_frame(name = 'dtype')
dtypes_df = dtypes_df[dtypes_df['dtype'] == 'object']
non_numeric_features = list(dtypes_df.index)
dataset[non_numeric_features] = dataset[non_numeric_features].applymap(str)
dataset[non_numeric_features] = label_encode(dataset[non_numeric_features])
dataset.to_csv("datasets/experiments/missing_state_and_imputing.csv", index = False)
log_artifact("datasets/experiments/missing_state_and_imputing.csv")
def clean_dataset(path):
dataset = data_utils.get_dataset()
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset.to_csv(path, index=False)
def risk_cases_encoder(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
mot_esp_cases = ['referencia', 'contra_referencia', 'urgencias', 'entrega de medicamentos', 'citas de consulta medica especializada', 'procedimientos y/o servicios', 'enfermedades raras o hu']
afec_edad_cases = ['de 6 a 12 años', 'de 0 a 5 años', 'de 13 a 17 años', 'mayor de 63 años']
cie10_cases = ['vih', 'tumores malignos', 'maternas', 'trasplantados']
dataset['CASO_RIESGO'] = dataset['MOTIVO_ESPECIFICO'].apply(lambda value: contains(value, mot_esp_cases))
dataset['POBESPECIAL'] = dataset['AFEC_POBESPECIAL'].apply(lambda value: False if value == 'no aplica' else True)
dataset['EDAD_RIESGO'] = dataset['AFEC_EDADR'].apply(lambda value: contains(value, afec_edad_cases))
dataset['CIE10_RIESGO'] = dataset['CIE_10'].apply(lambda value: contains(value, cie10_cases))
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
labels = dataset[['RIESGO_VIDA']]
features = dataset.drop(['RIESGO_VIDA'], axis = 1)
encoded_features = data_utils.encode_features(features, labels)
encoded_features['RIESGO_VIDA'] = labels
encoded_features.to_csv("datasets/experiments/risk_cases_encoder.csv", index = False)
log_artifact("datasets/experiments/risk_cases_encoder.csv")
def basic(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset.drop(['index'], axis = 1)
dataset = label_encode(dataset)
dataset.to_csv("datasets/experiments/basic.csv", index = False)
log_artifact("datasets/experiments/basic.csv")
def target_encoder(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
labels = dataset[['RIESGO_VIDA']]
features = dataset.drop(['RIESGO_VIDA'], axis = 1)
encoded_features = data_utils.encode_features(features, labels)
encoded_features['RIESGO_VIDA'] = labels
encoded_features.to_csv("datasets/experiments/target_encoder.csv", index = False)
def imputing(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
zero_values = list(dataset.columns[dataset.eq('0').mean() > 0])
dataset[zero_values] = dataset[zero_values].apply(lambda col: col.fillna(col.mode()[0]), axis=0)
dataset = dataset.applymap(str)
dataset = label_encode(dataset)
dataset.to_csv("datasets/experiments/imputing.csv", index = False)
log_artifact("datasets/experiments/imputing.csv")
def missing_state(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
zero_values = set(dataset.columns[dataset.eq('0').mean() > 0])
for feature in zero_values:
dataset[f'{feature}_is_missing'] = dataset[feature].apply(lambda f: 1 if f == '0' else 0)
features_columns = [column for column in dataset.columns if '_is_missing' not in column]
dataset[features_columns] = label_encode(dataset[features_columns])
dataset.to_csv("datasets/experiments/missing_state.csv", index = False)
log_artifact("datasets/experiments/missing_state.csv")
def target_encoder_only_complains(dataset):
dataset = dataset[
(dataset['PQR_TIPOPETICION'] != 'peticion de informacion') &
(dataset['PQR_TIPOPETICION'] != 'consulta y/o solicitud de informacion')
]
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
labels = dataset[['RIESGO_VIDA']]
features = dataset.drop(['RIESGO_VIDA'], axis = 1)
encoded_features = data_utils.encode_features(features, labels)
encoded_features['RIESGO_VIDA'] = labels
encoded_features.to_csv("datasets/experiments/target_encoder_only_complains.csv", index = False)
log_artifact("datasets/experiments/target_encoder_only_complains.csv")
def normalizing(dataset):
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.clean_cie_10(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
dataset = label_encode(dataset)
scaler = MinMaxScaler()
features = dataset.drop(['RIESGO_VIDA'], axis = 1)
labels = dataset[['RIESGO_VIDA']]
features[features.columns] = features[features.columns].apply(lambda x: np.log(x + 1))
features[features.columns] = scaler.fit_transform(features[features.columns])
dataset = features
dataset['RIESGO_VIDA'] = labels.values
dataset.to_csv("datasets/experiments/normalizing.csv", index = False)
log_artifact("datasets/experiments/normalizing.csv")
def naive(dataset):
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = dataset.drop(['PQR_ESTADO'], axis = 1)
dataset = label_encode(dataset)
dataset.to_csv("datasets/experiments/naive.csv", index = False)
log_artifact("datasets/experiments/naive.csv")
def cie10_only_complains(dataset):
dataset = dataset[
(dataset['PQR_TIPOPETICION'] != 'peticion de informacion') &
(dataset['PQR_TIPOPETICION'] != 'consulta y/o solicitud de informacion')
]
cie10_df = pd.read_csv('datasets/CIE10.csv', sep = ';')
cie10_df['DESCRIPCION_COD_CIE_10_04'] = cie10_df['DESCRIPCION_COD_CIE_10_04'].apply(lambda value: value.lower())
dataset = pd.merge(left = dataset, right = cie10_df, how = 'left', left_on='CIE_10', right_on='DESCRIPCION_COD_CIE_10_04')
dataset = dataset.drop(['CIE_10', 'NOMBRE_CAPITULO', 'DESCRIPCION_COD_CIE_10_03', 'DESCRIPCION_COD_CIE_10_04'], axis = 1)
cie10_columns = [
'CAPITULO',
'COD_CIE_10_03',
'COD_CIE_10_04',
'SEXO',
'LIMITE_INFERIOR_EDAD',
'LIMITE_SUPERIOR_EDAD']
dataset[cie10_columns] = dataset[cie10_columns].replace(np.nan, 'no_cie10', regex=True)
dataset = dataset[dataset['CAPITULO'] != 'no_cie10']
dataset['CIE10_SEXO'] = dataset['SEXO'].apply(cie10_sexo)
dataset['LIMITE_INFERIOR_EDAD_Y'] = dataset['LIMITE_INFERIOR_EDAD'].apply(to_year)
dataset['LIMITE_SUPERIOR_EDAD_Y'] = dataset['LIMITE_SUPERIOR_EDAD'].apply(to_year)
dataset['AFEC_EDADR_INF'] = dataset['AFEC_EDADR'].apply(get_edad_inf)
dataset['AFEC_EDADR_SUP'] = dataset['AFEC_EDADR'].apply(get_edad_sup)
dataset['CIE10_RANGO_EDAD'] = dataset.apply(in_range, axis=1)
dataset = dataset.drop(
[
'SEXO',
'LIMITE_INFERIOR_EDAD',
'LIMITE_SUPERIOR_EDAD',
'LIMITE_INFERIOR_EDAD_Y',
'LIMITE_SUPERIOR_EDAD_Y',
'AFEC_EDADR'
], axis = 1
)
dataset[
[
'AFEC_GENERO',
'CIE10_SEXO',
'CIE10_RANGO_EDAD',
'AFEC_EDADR_INF',
'AFEC_EDADR_SUP',
]
].head()
dataset = data_utils.clean_afec_dpto(dataset)
dataset = data_utils.clean_riesgo_vida(dataset)
dataset = data_utils.remove_features(dataset)
dataset = dataset.reset_index()
dataset = dataset.drop(['index'], axis = 1)
labels = dataset[['RIESGO_VIDA']]
features = dataset.drop(['RIESGO_VIDA'], axis = 1)
encoded_features = data_utils.encode_features(features, labels)
encoded_features['RIESGO_VIDA'] = labels
encoded_features.to_csv("datasets/experiments/cie10_only_complains.csv", index = False)
log_artifact("datasets/experiments/cie10_only_complains.csv")
