custom oob score function sklearn

oob_patch.py

# -*- coding: utf-8 -*-
"""
Created on Fri Aug 10 21:41:24 2018

@author: goraj
"""
import numpy as np
from sklearn.ensemble import RandomForestClassifier

from warnings import warn
from sklearn.tree._tree import DTYPE, DOUBLE
from sklearn.ensemble.forest import _generate_unsampled_indices
from sklearn.utils import check_array

def custom_oob_score(self, X, y):
    """Compute out-of-bag score"""
    X = check_array(X, dtype=DTYPE, accept_sparse='csr')

    n_classes_ = self.n_classes_
    n_samples = y.shape[0]

    oob_decision_function = []
    oob_score = 0.0
    predictions = []

    for k in range(self.n_outputs_):
        predictions.append(np.zeros((n_samples, n_classes_[k])))

    for estimator in self.estimators_:
        unsampled_indices = _generate_unsampled_indices(
            estimator.random_state, n_samples)
        p_estimator = estimator.predict_proba(X[unsampled_indices, :],
                                              check_input=False)

        if self.n_outputs_ == 1:
            p_estimator = [p_estimator]

        for k in range(self.n_outputs_):
            predictions[k][unsampled_indices, :] += p_estimator[k]

    for k in range(self.n_outputs_):
        if (predictions[k].sum(axis=1) == 0).any():
            warn("Some inputs do not have OOB scores. "
                 "This probably means too few trees were used "
                 "to compute any reliable oob estimates.")

        decision = (predictions[k] /
                    predictions[k].sum(axis=1)[:, np.newaxis])
        print(decision)
        oob_decision_function.append(decision)
        oob_score += np.mean(y[:, k] ==
                             np.argmax(predictions[k], axis=1), axis=0)

    if self.n_outputs_ == 1:
        self.oob_decision_function_ = oob_decision_function[0]
    else:
        self.oob_decision_function_ = oob_decision_function

    self.oob_score_ = oob_score / self.n_outputs_


if __name__ == '__main__':
    m = RandomForestClassifier(oob_score=True)
    m._set_oob_score = custom_oob_score.__get__(m, type(m))
    # etc
    m.fit()