Source code for evalml.pipelines.components.estimators.classifiers.baseline_classifier


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


[docs]class BaselineClassifier(Estimator): """Classifier that predicts using the specified strategy. This is useful as a simple baseline classifier to compare with other classifiers. """ name = "Baseline Classifier" hyperparameter_ranges = {} model_family = ModelFamily.BASELINE supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
[docs] def __init__(self, strategy="mode", random_state=0, **kwargs): """Baseline classifier that uses a simple strategy to make predictions. Arguments: strategy (str): method used to predict. Valid options are "mode", "random" and "random_weighted". Defaults to "mode". random_state (int, np.random.RandomState): seed for the random number generator """ if strategy not in ["mode", "random", "random_weighted"]: raise ValueError("'strategy' parameter must equal either 'mode', 'random', or 'random_weighted'") parameters = {"strategy": strategy} parameters.update(kwargs) self._classes = None self._percentage_freq = None self._num_features = None self._num_unique = None self._mode = None super().__init__(parameters=parameters, component_obj=None, random_state=random_state)
[docs] def fit(self, X, y=None): if y is None: raise ValueError("Cannot fit Baseline classifier if y is None") if not isinstance(y, pd.Series): y = pd.Series(y) if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X) vals, counts = np.unique(y, return_counts=True) self._classes = list(vals) self._percentage_freq = counts.astype(float) / len(y) self._num_unique = len(self._classes) self._num_features = X.shape[1] if self.parameters["strategy"] == "mode": self._mode = y.mode()[0] return self
[docs] def predict(self, X): strategy = self.parameters["strategy"] if strategy == "mode": return pd.Series([self._mode] * len(X)) elif strategy == "random": return self.random_state.choice(self._classes, len(X)) else: return self.random_state.choice(self._classes, len(X), p=self._percentage_freq)
[docs] def predict_proba(self, X): strategy = self.parameters["strategy"] if strategy == "mode": mode_index = self._classes.index(self._mode) proba_arr = np.array([[1.0 if i == mode_index else 0.0 for i in range(self._num_unique)]] * len(X)) return pd.DataFrame(proba_arr, columns=self._classes) elif strategy == "random": proba_arr = np.array([[1.0 / self._num_unique for i in range(self._num_unique)]] * len(X)) return pd.DataFrame(proba_arr, columns=self._classes) else: proba_arr = np.array([[self._percentage_freq[i] for i in range(self._num_unique)]] * len(X)) return pd.DataFrame(proba_arr, columns=self._classes)
@property def feature_importance(self): """Returns importance associated with each feature. Since baseline classifiers do not use input features to calculate predictions, returns an array of zeroes. Returns: np.array (float): an array of zeroes """ return np.zeros(self._num_features) @property def classes_(self): """Returns class labels. Will return None before fitting. Returns: list(str) or list(float) : class names """ return self._classes