from evalml.model_types import ModelTypes
from evalml.pipelines import PipelineBase
from evalml.pipelines.components import (
LinearRegressor,
OneHotEncoder,
SimpleImputer,
StandardScaler
)
from evalml.problem_types import ProblemTypes
[docs]class LinearRegressionPipeline(PipelineBase):
"""Linear Regression Pipeline for regression problems"""
name = "Linear Regressor w/ One Hot Encoder + Simple Imputer + Standard Scaler"
model_type = ModelTypes.LINEAR_MODEL
problem_types = [ProblemTypes.REGRESSION]
hyperparameters = {
'impute_strategy': ['most_frequent', 'mean', 'median'],
'normalize': [False, True],
'fit_intercept': [False, True]
}
[docs] def __init__(self, objective, random_state, number_features, impute_strategy, normalize=False, fit_intercept=True, n_jobs=-1):
imputer = SimpleImputer(impute_strategy=impute_strategy)
enc = OneHotEncoder()
scaler = StandardScaler()
estimator = LinearRegressor(normalize=normalize,
fit_intercept=fit_intercept,
n_jobs=-1)
super().__init__(objective=objective,
component_list=[enc, imputer, scaler, estimator],
n_jobs=n_jobs,
random_state=random_state)