def test_mean(self): for X in self.data: X_sparse = csr_matrix(X) np.testing.assert_array_equal(mean(X_sparse), np.mean(X)) with self.assertWarns(UserWarning): mean([1, np.nan, 0])
def test_mean(self): # This warning is not unexpected and it's correct warnings.filterwarnings("ignore", r".*mean\(\) resulted in nan.*") for X in self.data: X_sparse = csr_matrix(X) np.testing.assert_array_equal(mean(X_sparse), np.mean(X)) with self.assertWarns(UserWarning): mean([1, np.nan, 0])
def test_mean(self): for X in self.data: X_sparse = csr_matrix(X) np.testing.assert_array_equal( mean(X_sparse), np.mean(X)) with self.assertWarns(UserWarning): mean([1, np.nan, 0])
def test_mean(self): # This warning is not unexpected and it's correct warnings.filterwarnings("ignore", r".*mean\(\) resulted in nan.*") for X in self.data: X_sparse = csr_matrix(X) np.testing.assert_array_equal( mean(X_sparse), np.mean(X)) with self.assertWarns(UserWarning): mean([1, np.nan, 0])
def _init_feature_marker_values(self): self.feature_marker_values = [] cls_index = self.target_class_index instances = Table(self.domain, self.instances) \ if self.instances else None for i, attr in enumerate(self.domain.attributes): value, feature_val = 0, None if len(self.log_reg_coeffs): if attr.is_discrete: ind, n = unique(self.data.X[:, i], return_counts=True) feature_val = np.nan_to_num(ind[np.argmax(n)]) else: feature_val = mean(self.data.X[:, i]) inst_in_dom = instances and attr in instances.domain if inst_in_dom and not np.isnan(instances[0][attr]): feature_val = instances[0][attr] if feature_val is not None: value = self.points[i][cls_index][int(feature_val)] \ if attr.is_discrete else \ self.log_reg_coeffs_orig[i][cls_index][0] * feature_val self.feature_marker_values.append(value)
def test_mean(self): for X in self.data: X_sparse = csr_matrix(X) np.testing.assert_array_equal( mean(X_sparse), np.mean(X))