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Commit 81c4aa49 authored by bbauvin's avatar bbauvin
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Simple test for error on labels

parent 8743631b
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......@@ -259,8 +259,8 @@ def getErrorOnLabels(multiclassResults, multiclassLabels):
logging.debug("Start:\t Getting errors on each example for each classifier")
for iterIndex, iterResults in enumerate(multiclassResults):
for classifierName, classifierResults in iterResults.items():
errorOnExamples = np.where(classifierResults["labels"] == multiclassLabels)
multiclassResults[iterIndex][classifierName]["errorOnExample"] = errorOnExamples
errorOnExamples = classifierResults["labels"] == multiclassLabels
multiclassResults[iterIndex][classifierName]["errorOnExample"] = errorOnExamples.astype(int)
logging.debug("Done:\t Getting errors on each example for each classifier")
......
......@@ -253,13 +253,20 @@ class Test_genMetricsScores(unittest.TestCase):
cls.assertEqual(0, multiclassResults[1]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
cls.assertEqual(cls.score_to_get_f1, multiclassResults[1]["cheese_is_no_disease"]["metricsScores"]["f1_score"])
# {},
# {"cheese_is_no_disease": {"labels": cls.multiclass_labels}}}
# {{{"chicken_is_heaven": {"labels": cls.wrong_labels}},
# {}}}
class Test_getErrorOnLabels(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.multiclass_labels = np.array([0,1,2,3,4,5,2,1,3])
cls.wrong_labels = np.array([1,3,3,4,5,0,2,4,3])
cls.multiclassResults = [{"chicken_is_heaven":
{"labels": cls.multiclass_labels}}]
cls.true_labels = np.array([0,2,2,3,4,5,1,3,2])
def test_simple(cls):
multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
#
# class Essai(unittest.TestCase):
#
......
# Mono- and Multi-view classification benchmark
[![Build Status](https://travis-ci.com/babau1/multiview-machine-learning-omis.svg?token=pjoowx3poApRRtwqHTpd&branch=master)](https://travis-ci.com/babau1/multiview-machine-learning-omis)
This project aims to be an easy-to use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.
......
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