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Replace depreciated logistic_sigmoid function #1

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Replace depreciated logistic_sigmoid function #1

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11 changes: 6 additions & 5 deletions multilayer_perceptron/multilayer_perceptron.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,14 +9,15 @@

from abc import ABCMeta, abstractmethod
from scipy.optimize import fmin_l_bfgs_b
from scipy.special import expit

from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.externals import six
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.utils import atleast2d_or_csr, check_random_state, column_or_1d
from sklearn.utils.extmath import logistic_sigmoid, safe_sparse_dot
from sklearn.utils.extmath import safe_sparse_dot


def _identity(X):
Expand Down Expand Up @@ -108,7 +109,7 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
"""
activation_functions = {
'tanh': _tanh,
'logistic': logistic_sigmoid,
'logistic': expit,
'softmax': _softmax
}
derivative_functions = {
Expand Down Expand Up @@ -213,7 +214,7 @@ def _init_param(self):
self.output_func = _softmax
# output for binary class and multi-label
else:
self.output_func = logistic_sigmoid
self.output_func = expit

def _init_t_eta_(self):
"""Initialize iteration counter attr ``t_``"""
Expand Down Expand Up @@ -751,7 +752,7 @@ def predict(self, X):
scores = self.decision_function(X)

if len(scores.shape) == 1 or self.multi_label is True:
scores = logistic_sigmoid(scores)
scores = expit(scores)
results = (scores > 0.5).astype(np.int)

if self.multi_label:
Expand Down Expand Up @@ -795,7 +796,7 @@ def predict_proba(self, X):
scores = self.decision_function(X)

if len(scores.shape) == 1:
scores = logistic_sigmoid(scores)
scores = expit(scores)
return np.vstack([1 - scores, scores]).T
else:
return _softmax(scores)
Expand Down