# variables to log transform
NUMERICALS_LOG_VARS = ['LotFrontage', '1stFlrSF', 'GrLivArea']
# numerical variables with NA in train set
NUMERICAL_VARS_WITH_NA = ['LotFrontage']
# categorical variables to encode
CATEGORICAL_VARS = [
'MSZoning', 'Neighborhood', 'RoofStyle', 'MasVnrType', 'BsmtQual',
'BsmtExposure', 'HeatingQC', 'CentralAir', 'KitchenQual', 'FireplaceQu',
'GarageType', 'GarageFinish', 'PavedDrive'
]
price_pipe = Pipeline([
('categorical_imputer',
pp.CategoricalImputer(variables=CATEGORICAL_VARS_WITH_NA)),
('numerical_inputer',
pp.NumericalImputer(variables=NUMERICAL_VARS_WITH_NA)),
('temporal_variable',
pp.TemporalVariableEstimator(variables=TEMPORAL_VARS,
reference_variable=TEMPORAL_VARS)),
('rare_label_encoder',
pp.RareLabelCategoricalEncoder(tol=0.01, variables=CATEGORICAL_VARS)),
('categorical_encoder', pp.CategoricalEncoder(variables=CATEGORICAL_VARS)),
('log_transformer', pp.LogTransformer(variables=NUMERICALS_LOG_VARS)),
('drop_features',
pp.DropUnecessaryFeatures(variables_to_drop=DROP_FEATURES)),
('scaler', MinMaxScaler()),
('Linear_model', Lasso(alpha=0.005, random_state=0))
])
from sklearn.linear_model import Lasso
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
import preprocessors as pp
import config
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',
pp.LogTransformer(variables=config.NUMERICALS_LOG_VARS)),
('drop_features',
pp.DropUnecessaryFeatures(variables_to_drop=config.DROP_FEATURES)),
('scaler', MinMaxScaler()),
('Linear_model', Lasso(alpha=0.005, random_state=0))
])
