marcoabreu commented on a change in pull request #9777: [MX-9588] Add micro
averaging strategy for F1 metric
URL: https://github.com/apache/incubator-mxnet/pull/9777#discussion_r168025671
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File path: tests/python/unittest/test_metric.py
##########
@@ -56,18 +55,51 @@ def test_acc():
assert acc == expected_acc
def test_f1():
- pred = mx.nd.array([[0.3, 0.7], [1., 0], [0.4, 0.6], [0.6, 0.4], [0.9,
0.1]])
- label = mx.nd.array([0, 1, 1, 1, 1])
- positives = np.argmax(pred, axis=1).sum().asscalar()
- true_positives = (np.argmax(pred, axis=1) == label).sum().asscalar()
- precision = true_positives / positives
- overall_positives = label.sum().asscalar()
- recall = true_positives / overall_positives
- f1_expected = 2 * (precision * recall) / (precision + recall)
- metric = mx.metric.create('f1')
- metric.update([label], [pred])
- _, f1 = metric.get()
- assert f1 == f1_expected
+ microF1 = mx.metric.create("f1", average="micro")
+ macroF1 = mx.metric.F1(average="macro")
+
+ assert np.isnan(macroF1.get()[1])
+ assert np.isnan(microF1.get()[1])
+
+ # check divide by zero
+ pred = mx.nd.array([[0.9, 0.1],
+ [0.8, 0.2]])
+ label = mx.nd.array([0, 0])
+ macroF1.update([label], [pred])
+ microF1.update([label], [pred])
+ assert macroF1.get()[1] == 0.0
+ assert microF1.get()[1] == 0.0
+ macroF1.reset()
+ microF1.reset()
+
+ pred11 = mx.nd.array([[0.1, 0.9],
+ [0.5, 0.5]])
+ label11 = mx.nd.array([1, 0])
+ pred12 = mx.nd.array([[0.85, 0.15],
+ [1.0, 0.0]])
+ label12 = mx.nd.array([1, 0])
+ pred21 = mx.nd.array([[0.6, 0.4]])
+ label21 = mx.nd.array([0])
+ pred22 = mx.nd.array([[0.2, 0.8]])
+ label22 = mx.nd.array([1])
+
+ microF1.update([label11, label12], [pred11, pred12])
+ macroF1.update([label11, label12], [pred11, pred12])
+ assert microF1.num_inst == 4
+ assert macroF1.num_inst == 1
+ # f1 = 2 * tp / (2 * tp + fp + fn)
+ fscore1 = 2. * (1) / (2 * 1 + 1 + 0)
+ np.testing.assert_almost_equal(microF1.get()[1], fscore1)
+ np.testing.assert_almost_equal(macroF1.get()[1], fscore1)
+
+ microF1.update([label21, label22], [pred21, pred22])
+ macroF1.update([label21, label22], [pred21, pred22])
+ assert microF1.num_inst == 6
+ assert macroF1.num_inst == 2
+ fscore2 = 2. * (1) / (2 * 1 + 0 + 0)
+ fscore_total = 2. * (1 + 1) / (2 * (1 + 1) + (1 + 0) + (0 + 0))
+ np.testing.assert_almost_equal(microF1.get()[1], fscore_total)
+ np.testing.assert_almost_equal(macroF1.get()[1], (fscore1 + fscore2) / 2.)
Review comment:
Since we're lacking dependency support on Windows slaves yet, we can't use
this if scikit is not present as a dependency yet - I can't check right now.
I'd propose to just try it out and see whether it works or not.
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