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)),
])