From 8743631b2e63393d2d8b86bbf9dbf2bdaf9f7b8e Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Fri, 3 Nov 2017 10:48:37 -0400
Subject: [PATCH] Full tests for metrics scores
---
.travis.yml | 4 +-
.../Metrics/f1_score.py | 2 +-
Code/Tests/test_ExecClassif.py | 42 +++++++++++++++++++
3 files changed, 45 insertions(+), 3 deletions(-)
diff --git a/.travis.yml b/.travis.yml
index 897b933b..ce8e60a1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,8 +10,8 @@ addons:
- gfortran
install:
- pip install -U pip pip-tools
-- pip install numpy scipy scikit-learn matplotlib logging joblib argparse h5py
-- git clone https://github.com/aldro61/pyscm.git /tmp/pyscm && cd /tmp/pyscm/ && python setup.py install && cd ~/babau1/multiview-machine-learning-omis
+- pip install numpy scipy scikit-learn matplotlib joblib argparse h5py
+- cd .. && git clone https://github.com/aldro61/pyscm.git && cd pyscm/ && python setup.py install && cd ../multiview-machine-learning-omis
script:
- python -m unittest discover
diff --git a/Code/MonoMultiViewClassifiers/Metrics/f1_score.py b/Code/MonoMultiViewClassifiers/Metrics/f1_score.py
index ab33833e..467420b4 100644
--- a/Code/MonoMultiViewClassifiers/Metrics/f1_score.py
+++ b/Code/MonoMultiViewClassifiers/Metrics/f1_score.py
@@ -27,7 +27,7 @@ def score(y_true, y_pred, **kwargs):
try:
average = kwargs["3"]
except:
- average = "binary"
+ average = "micro"
score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels, pos_label=pos_label, average=average)
return score
diff --git a/Code/Tests/test_ExecClassif.py b/Code/Tests/test_ExecClassif.py
index 7ee3811f..8930a29d 100644
--- a/Code/Tests/test_ExecClassif.py
+++ b/Code/Tests/test_ExecClassif.py
@@ -205,6 +205,7 @@ class Test_genMetricsScores(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])
@@ -215,6 +216,47 @@ class Test_genMetricsScores(unittest.TestCase):
multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
+ def test_multiple_clf(cls):
+ cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
+ "cheese_is_no_disease": {"labels": cls.wrong_labels}},
+ ]
+ multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
+ cls.assertEqual(0, multiclassResults[0]["cheese_is_no_disease"]["metricsScores"]["accuracy_score"])
+ cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
+
+ def test_multiple_metrics(cls):
+ from sklearn.metrics import f1_score
+ cls.score_to_get_f1 = f1_score(cls.true_labels, cls.multiclass_labels, average="micro")
+ cls.metrics = [["accuracy_score"], ["f1_score"]]
+ multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
+ cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
+ cls.assertEqual(cls.score_to_get_f1, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["f1_score"])
+
+ def test_multiple_iterations(cls):
+ cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels}},
+ {"chicken_is_heaven": {"labels": cls.wrong_labels}},
+ ]
+ multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
+ cls.assertEqual(0, multiclassResults[1]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
+ cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
+
+ def test_all(cls):
+ cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
+ "cheese_is_no_disease": {"labels": cls.wrong_labels}},
+ {"chicken_is_heaven": {"labels": cls.wrong_labels},
+ "cheese_is_no_disease": {"labels": cls.multiclass_labels}},
+ ]
+ cls.metrics = [["accuracy_score"], ["f1_score"]]
+ from sklearn.metrics import f1_score
+ cls.score_to_get_f1 = f1_score(cls.true_labels, cls.multiclass_labels, average="micro")
+ multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
+ 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}},
+# {}}}
--
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