from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from regression_model.processing import preprocessors as pp from regression_model.processing import features from regression_model.config import config import logging _logger = logging.getLogger(__name__) price_pipe = Pipeline([ ('categorical_imputer', pp.CategoricalImputer(variables=config.CATEGORICAL_VARS_WITH_NA)), ('numerical_inputer', pp.NumericalImputer(variables=config.NUMERICAL_VARS_WITH_NA)), ('temporal_variable', pp.TemporalVariableEstimator(variables=config.TEMPORAL_VARS, reference_variable=config.DROP_FEATURES)), ('rare_label_encoder', pp.RareLabelCategoricalEncoder(tol=0.01, variables=config.CATEGORICAL_VARS)), ('categorical_encoder', pp.CategoricalEncoder(variables=config.CATEGORICAL_VARS)), ('log_transformer', features.LogTransformer(variables=config.NUMERICALS_LOG_VARS)), ('drop_features', pp.DropUnecessaryFeatures(variables_to_drop=config.DROP_FEATURES)), ('scaler', MinMaxScaler()), ('forest', Lasso(random_state=0)) ])
from sklearn.pipeline import Pipeline import regression_model.preprocessors as pp from sklearn.linear_model import Lasso from sklearn.preprocessing import MinMaxScaler from regression_model.config import config from regression_model.processing import features price_pipe = Pipeline( [("categorical_imputer", pp.CategoricalImputer(variables=config.CATEGORICAL_VARS)), ("numerical_imputer", pp.NumericalImputer(variables=config.NUMERICAL_VARS_WITH_NA)), ("rare_label_encoder", pp.RareLabelCategoricalEncoder(tol=0.01, variables=config.CATEGORICAL_VARS)), ("categorical_encoder", pp.CategoricalEncoder(variables=config.CATEGORICAL_VARS)), ("log_transformer", features.LogTransformer(variables=config.NUMERICAL_LOG_VARS)), ("drop_features", pp.DropUnnecessaryFeatures(variables_to_drop=config.DROP_FEATURES)), ("scaler", MinMaxScaler()), ("linear_model", Lasso(alpha=0.005, random_state=0))])
"categorical_imputer", pp.CategoricalImputer(variables=config.CATEGORICAL_VARS_WITH_NA), ), ( "numerical_imputer", pp.NumericalImputer(variables=config.NUMERICAL_VARS_WITH_NA), ), ( "temporal_varibales", pp.TemporalVariableEstimator(variables=config.TEMPORAL_VARS), ), ( "rare_label_encoder", pp.RareLabelCategoricalEncoder(tol=0.01, variables=config.CATEGORICAL_VARS), ), ( "categorical_encoder", pp.CategoricalEncoder(variables=config.CATEGORICAL_VARS), ), ( "log_transform", fe.LogTransformer(variables=config.NUMERICAL_LOG_VARS), ), ( "drop_features", pp.DropUnecessaryFeatures(variables_to_drop=config.DROP_FEATURES), ), ("scalar", MinMaxScaler()), ("Linear_model", Lasso(alpha=0.005, random_state=0)), ])
from regression_model.processing import features from regression_model.processing import preprocessors as pp from regression_model.config import config import logging _logger = logging.getLogger(__name__) price_pipe = Pipeline([ ( "categorical_imputer", pp.CategoricalImputer(variables_path=config.CATEGORICAL_VARS_FILE), ), ( "numerical_inputer", pp.NumericalImputer(variables_path=config.NUMERICAL_VARS_FILE), ), ( "rare_label_encoder", pp.RareLabelCategoricalEncoder( tol=0.01, variables_path=config.CATEGORICAL_VARS_FILE), ), ("log_transformer", features.LogTransformer(variables_path=config.NUMERICAL_LOG_VARS_FILE)), ( "scaler", MinMaxScaler(), ), ("Linear_model", Lasso(alpha=0.005, random_state=0)), ])