def test_curves_from_results(self, init):
res = Results()
ytrue, probs = self.data.T
res.actual = ytrue.astype(float)
res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)
Curves.from_results(res)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue)
np.testing.assert_equal(cprobs, probs)
Curves.from_results(res, target_class=0)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, 1 - ytrue)
np.testing.assert_equal(cprobs, 1 - probs)
res.actual = ytrue.astype(float)
res.probabilities = np.random.random((2, 19, 2))
res.probabilities[1] = np.vstack((1 - probs, probs)).T
Curves.from_results(res, model_index=1)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue)
np.testing.assert_equal(cprobs, probs)
self.assertRaises(ValueError, Curves.from_results, res)
ytrue[ytrue == 0] = 2 * (np.arange(10) % 2)
res.actual = ytrue.astype(float)
res.probabilities = np.random.random((2, 19, 3))
res.probabilities[1] = np.vstack(
((1 - probs) / 3, probs, (1 - probs) * 2 / 3)).T
Curves.from_results(res, model_index=1, target_class=1)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue == 1)
np.testing.assert_equal(cprobs, probs)
Curves.from_results(res, model_index=1, target_class=0)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue == 0)
np.testing.assert_equal(cprobs, (1 - probs) / 3)
Curves.from_results(res, model_index=1, target_class=2)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue == 2)
np.testing.assert_equal(cprobs, (1 - probs) * 2 / 3)
self.assertRaises(ValueError, Curves.from_results, res, model_index=1)
def test_empty_results(self):
"""Test on empty results."""
res = Results(data=self.iris[:0], store_data=True)
res.row_indices = np.array([], dtype=int)
res.actual = np.array([])
res.predicted = np.array([[]])
res.probabilities = np.zeros((1, 0, 3))
self.send_signal(self.widget.Inputs.evaluation_results, res)
self.widget.select_correct()
self.widget.select_wrong()
def test_empty_results(self):
"""Test on empty results."""
res = Results(data=self.iris[:0], store_data=True)
res.row_indices = np.array([], dtype=int)
res.actual = np.array([])
res.predicted = np.array([[]])
res.probabilities = np.zeros((1, 0, 3))
self.send_signal(self.widget.Inputs.evaluation_results, res)
self.widget.select_correct()
self.widget.select_wrong()
def test_nan_results(self):
"""Test on results with nan values in actual/predicted"""
res = Results(data=self.iris, nmethods=2, store_data=True)
res.row_indices = np.array([0, 50, 100], dtype=int)
res.actual = np.array([0., np.nan, 2.])
res.predicted = np.array([[np.nan, 1, 2], [np.nan, np.nan, np.nan]])
res.probabilities = np.zeros((1, 3, 3))
self.send_signal(self.widget.Inputs.evaluation_results, res)
self.assertTrue(self.widget.Error.invalid_values.is_shown())
self.send_signal(self.widget.Inputs.evaluation_results, None)
self.assertFalse(self.widget.Error.invalid_values.is_shown())
def test_curves_from_results_nans(self, init):
res = Results()
ytrue, probs = self.data.T
ytrue[0] = np.nan
probs[-1] = np.nan
res.actual = ytrue.astype(float)
res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)
Curves.from_results(res)
cytrue, cprobs = init.call_args[0]
np.testing.assert_equal(cytrue, ytrue[1:-1])
np.testing.assert_equal(cprobs, probs[1:-1])
def test_nan_results(self):
"""Test on results with nan values in actual/predicted"""
res = Results(data=self.iris, nmethods=2, store_data=True)
res.row_indices = np.array([0, 50, 100], dtype=int)
res.actual = np.array([0., np.nan, 2.])
res.predicted = np.array([[np.nan, 1, 2],
[np.nan, np.nan, np.nan]])
res.probabilities = np.zeros((1, 3, 3))
self.send_signal(self.widget.Inputs.evaluation_results, res)
self.assertTrue(self.widget.Error.invalid_values.is_shown())
self.send_signal(self.widget.Inputs.evaluation_results, None)
self.assertFalse(self.widget.Error.invalid_values.is_shown())
def get_fold(self, fold):
results = Results()
results.data = self.data
if self.folds is None:
raise ValueError("This 'Results' instance does not have folds.")
if self.models is not None:
results.models = self.models[fold]
results.row_indices = self.row_indices
results.actual = self.actual
results.predicted = self.predicted[:, fold, :]
results.domain = self.domain
return results
