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[MRG+1] Fix scipy-dev-wheels build because numpy.core.umath_tests has been renamed #10880

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merged 1 commit into from
Apr 3, 2018
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[MRG+1] Fix scipy-dev-wheels build because numpy.core.umath_tests has been renamed #10880

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5 changes: 2 additions & 3 deletions sklearn/ensemble/weight_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,6 @@
from abc import ABCMeta, abstractmethod

import numpy as np
from numpy.core.umath_tests import inner1d

from .base import BaseEnsemble
from ..base import ClassifierMixin, RegressorMixin, is_regressor, is_classifier
Expand Down Expand Up @@ -522,8 +521,8 @@ def _boost_real(self, iboost, X, y, sample_weight, random_state):

# Boost weight using multi-class AdaBoost SAMME.R alg
estimator_weight = (-1. * self.learning_rate
* (((n_classes - 1.) / n_classes) *
inner1d(y_coding, np.log(y_predict_proba))))
* ((n_classes - 1.) / n_classes)
* (y_coding * np.log(y_predict_proba)).sum(axis=1))
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I found that np.einsum('ij,ij->i', y_coding, np.log(y_predict_proba)) was another possibility but I find it harder to grok (maybe because I don't use einsum very regularly).

Note that (at least in the tests) both y_coding and y_predict_proba shapes are (n, p).

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Can't this be done with np.dot?

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I don't see how but maybe I am missing a numpy trick. In the tests y_coding and y_predict_proba are 2d of shape (n_samples, n_classes) and you want an output that is of shape (n_samples,).

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Right


# Only boost the weights if it will fit again
if not iboost == self.n_estimators - 1:
Expand Down