Python TemporalVariableEstimator Examples

Example #1

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        # encode labels
        X = X.copy()
        X = X.drop(self.variables, axis=1)

        return X


from sklearn.linear_model import Lasso
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler

from regression_model.config import config
from regression_model.processing import preprocessors as pp

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.REFERENCE_TEMP_VAR)),
    ('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))
])

Example #2

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))
])

Example #3

File: pipeline.py Project: ChandanVerma/MachineLearning_CI_CD

import logging

_logger = logging.getLogger(__name__)

price_pipe = Pipeline([
    (
        "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),
    ),
    (