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
def _check_fitted(self):
if self._mode is None and self._classes is None:
raise RuntimeError("You must fit Baseline classifier before calling predict!")
[docs] def predict(self, X):
self._check_fitted()
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):
self._check_fitted()
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
"""
if self._num_unique is None:
raise RuntimeError("You must fit Baseline classifier before getting feature_importance!")
return np.zeros(self._num_features)