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metrics.py
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metrics.py
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# -*- coding: utf-8 -*-
"""This file is part of the TPOT library.
TPOT was primarily developed at the University of Pennsylvania by:
- Randal S. Olson ([email protected])
- Weixuan Fu ([email protected])
- Daniel Angell ([email protected])
- and many more generous open source contributors
TPOT is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation, either version 3 of
the License, or (at your option) any later version.
TPOT is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with TPOT. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
from sklearn.metrics import get_scorer, get_scorer_names, make_scorer
def balanced_accuracy(y_true, y_pred):
"""Default scoring function: balanced accuracy.
Balanced accuracy computes each class' accuracy on a per-class basis using a
one-vs-rest encoding, then computes an unweighted average of the class accuracies.
Parameters
----------
y_true: numpy.ndarray {n_samples}
True class labels
y_pred: numpy.ndarray {n_samples}
Predicted class labels by the estimator
Returns
-------
fitness: float
Returns a float value indicating the individual's balanced accuracy
0.5 is as good as chance, and 1.0 is perfect predictive accuracy
"""
all_classes = np.unique(np.append(y_true, y_pred))
all_class_accuracies = []
for this_class in all_classes:
this_class_sensitivity = 0.
this_class_specificity = 0.
if sum(y_true == this_class) != 0:
this_class_sensitivity = \
float(sum((y_pred == this_class) & (y_true == this_class))) /\
float(sum((y_true == this_class)))
if sum(y_true != this_class) != 0:
this_class_specificity = \
float(sum((y_pred != this_class) & (y_true != this_class))) /\
float(sum((y_true != this_class)))
else: # in rase case, y_true has only 1 class then specificity should be 1
this_class_specificity = 1.
this_class_accuracy = (this_class_sensitivity + this_class_specificity) / 2.
all_class_accuracies.append(this_class_accuracy)
return np.mean(all_class_accuracies)
SCORERS = {name: get_scorer(name) for name in get_scorer_names()}
SCORERS['balanced_accuracy'] = make_scorer(balanced_accuracy)