"""
Weighted averaging model predictions
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_is_fitted
class BlendingClassifier(BaseEstimator, ClassifierMixin):
"""
weighted average prediction from list of modules
"""
def __init__(self, models, weights):
self.models = models
self.weights = weights
def predict(self, X):
# Verify that model has been fit
check_is_fitted(self, ['_clfs'])
return (self.predict_proba(X) > 0.5).astype(int)
def predict_proba(self, X):
# Verify that model has been fit
check_is_fitted(self, ['_clfs'])
y_proba = np.zeros(X.shape[0])
for i, clf in enumerate(self._clfs):
y_proba += clf.predict_proba(X).reshape(-1) * self.weights[i]
return y_proba
def fit(self, X, y):
# # Check that X and y have correct shape
# y = y.values
X, y = check_X_y(X, y, accept_sparse=True)
# fit models
self._clfs = []
for model in self.models:
self._clfs.append(model.fit(X, y))
return self
