def testAccuracyScore(self):
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
score = accuracy_score(y_true, y_pred)
result = self.executor.execute_tileables([score])[0]
expected = sklearn_accuracy_score(y_true, y_pred)
self.assertAlmostEqual(result, expected)
score = accuracy_score(y_true, y_pred, normalize=False)
result = self.executor.execute_tileables([score])[0]
expected = sklearn_accuracy_score(y_true, y_pred, normalize=False)
self.assertAlmostEqual(result, expected)
y_pred = np.array([[0, 1], [1, 1]])
y_true = np.ones((2, 2))
score = accuracy_score(y_true, y_pred)
result = self.executor.execute_tileables([score])[0]
expected = sklearn_accuracy_score(y_true, y_pred)
self.assertAlmostEqual(result, expected)
sample_weight = [0.7, 0.3]
score = accuracy_score(y_true, y_pred, sample_weight=sample_weight)
result = self.executor.execute_tileables([score])[0]
expected = sklearn_accuracy_score(y_true, y_pred, sample_weight=sample_weight)
self.assertAlmostEqual(result, expected)
score = accuracy_score(mt.tensor(y_true), mt.tensor(y_pred),
sample_weight=mt.tensor(sample_weight), normalize=False)
result = self.executor.execute_tileables([score])[0]
expected = sklearn_accuracy_score(y_true, y_pred, sample_weight=sample_weight,
normalize=False)
self.assertAlmostEqual(result, expected)
def test_accuracy_score(setup):
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
score = accuracy_score(y_true, y_pred)
result = score.execute().fetch()
expected = sklearn_accuracy_score(y_true, y_pred)
assert pytest.approx(result) == expected
score = accuracy_score(y_true, y_pred, normalize=False)
result = score.execute().fetch()
expected = sklearn_accuracy_score(y_true, y_pred, normalize=False)
assert pytest.approx(result) == expected
y_pred = np.array([[0, 1], [1, 1]])
y_true = np.ones((2, 2))
score = accuracy_score(y_true, y_pred)
result = score.execute().fetch()
expected = sklearn_accuracy_score(y_true, y_pred)
assert pytest.approx(result) == expected
sample_weight = [0.7, 0.3]
score = accuracy_score(y_true, y_pred, sample_weight=sample_weight)
result = score.execute().fetch()
expected = sklearn_accuracy_score(y_true, y_pred, sample_weight=sample_weight)
assert pytest.approx(result) == expected
score = accuracy_score(mt.tensor(y_true), mt.tensor(y_pred),
sample_weight=mt.tensor(sample_weight), normalize=False)
result = score.execute().fetch()
expected = sklearn_accuracy_score(y_true, y_pred, sample_weight=sample_weight,
normalize=False)
assert pytest.approx(result) == expected
def test_dataframe_accuracy_score(setup):
rs = np.random.RandomState(0)
raw = pd.DataFrame({'a': rs.randint(0, 10, (10,)),
'b': rs.randint(0, 10, (10,))})
df = md.DataFrame(raw)
y = df['a'].to_tensor().astype('int')
pred = df['b'].astype('int')
score = accuracy_score(y, pred)
expect = sklearn_accuracy_score(raw['a'].to_numpy().astype('int'),
raw['b'].to_numpy().astype('int'))
assert pytest.approx(score.fetch()) == expect
def testAccuracyScore(self):
service_ep = 'http://127.0.0.1:' + self.web_port
timeout = 120 if 'CI' in os.environ else -1
with new_session(service_ep) as sess:
run_kwargs = {'timeout': timeout}
rs = np.random.RandomState(0)
raw = pd.DataFrame({
'a': rs.randint(0, 10, (10, )),
'b': rs.randint(0, 10, (10, ))
})
df = md.DataFrame(raw)
y = df['a'].to_tensor().astype('int')
pred = df['b'].astype('int')
score = accuracy_score(y,
pred,
session=sess,
run_kwargs=run_kwargs)
expect = sklearn_accuracy_score(raw['a'].to_numpy().astype('int'),
raw['b'].to_numpy().astype('int'))
self.assertAlmostEqual(score.fetch(session=sess), expect)