Source code for evalml.pipelines.components.estimators.regressors.baseline_regressor

import numpy as np
import pandas as pd

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import _convert_woodwork_types_wrapper, infer_feature_types

[docs]class BaselineRegressor(Estimator): """Regressor that predicts using the specified strategy. This is useful as a simple baseline regressor to compare with other regressors. """ name = "Baseline Regressor" hyperparameter_ranges = {} model_family = ModelFamily.BASELINE supported_problem_types = [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION]
[docs] def __init__(self, strategy="mean", random_seed=0, **kwargs): """Baseline regressor that uses a simple strategy to make predictions. Arguments: strategy (str): Method used to predict. Valid options are "mean", "median". Defaults to "mean". random_seed (int): Seed for the random number generator. Defaults to 0. """ if strategy not in ["mean", "median"]: raise ValueError("'strategy' parameter must equal either 'mean' or 'median'") parameters = {"strategy": strategy} parameters.update(kwargs) self._prediction_value = None self._num_features = None super().__init__(parameters=parameters, component_obj=None, random_seed=random_seed)
[docs] def fit(self, X, y=None): if y is None: raise ValueError("Cannot fit Baseline regressor if y is None") X = infer_feature_types(X) y = infer_feature_types(y) y = _convert_woodwork_types_wrapper(y.to_series()) if self.parameters["strategy"] == "mean": self._prediction_value = y.mean() elif self.parameters["strategy"] == "median": self._prediction_value = y.median() self._num_features = X.shape[1] return self
[docs] def predict(self, X): X = infer_feature_types(X) predictions = pd.Series([self._prediction_value] * len(X)) return infer_feature_types(predictions)
@property def feature_importance(self): """Returns importance associated with each feature. Since baseline regressors do not use input features to calculate predictions, returns an array of zeroes. Returns: np.ndarray (float): An array of zeroes """ return np.zeros(self._num_features)