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))