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# pylint: disable=protected-access
# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import random
import unittest
from unittest.mock import Mock
import numpy as np
from orangewidget.settings import Context
import Orange.distance
from Orange.data import (
Table, Domain, ContinuousVariable, DiscreteVariable, StringVariable)
from Orange.misc import DistMatrix
from Orange.widgets.utils.annotated_data import ANNOTATED_DATA_SIGNAL_NAME
from Orange.widgets.visualize.owsilhouetteplot import OWSilhouettePlot
from Orange.widgets.tests.base import WidgetTest, WidgetOutputsTestMixin
from Orange.widgets.tests.utils import possible_duplicate_table
class TestOWSilhouettePlot(WidgetTest, WidgetOutputsTestMixin):
@classmethod
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
cls.same_input_output_domain = False
cls.signal_name = OWSilhouettePlot.Inputs.data
cls.signal_data = cls.data
cls.scorename = "Silhouette ({})".format(cls.data.domain.class_var.name)
def setUp(self):
self.widget = self.create_widget(OWSilhouettePlot,
stored_settings={"auto_commit": True})
self.widget = self.widget # type: OWSilhouettePlot
def test_no_data(self):
"""Check that the widget doesn't crash on empty data"""
self.send_signal(self.widget.Inputs.data, self.data[:0])
def test_outputs_add_scores(self):
# check output when appending scores
self.send_signal(self.widget.Inputs.data, self.data)
selected_indices = self._select_data()
selected = self.get_output(self.widget.Outputs.selected_data)
annotated = self.get_output(self.widget.Outputs.annotated_data)
self.assertEqual(self.scorename, selected.domain.metas[0].name)
self.assertEqual(self.scorename, annotated.domain.metas[0].name)
np.testing.assert_array_equal(selected.X, self.data.X[selected_indices])
def _select_data(self):
random.seed(42)
points = random.sample(range(0, len(self.data)), 20)
self.widget._silplot.setSelection(points)
return sorted(points)
def test_insufficient_clusters(self):
iris = self.data
data_one_cluster = iris[:3] # three instances Iris-setosa only
self.send_signal(self.widget.Inputs.data, data_one_cluster)
self.assertTrue(self.widget.Error.need_two_clusters.is_shown())
data_singletons = iris[[0, 50, 100]]
assert len(np.unique(data_singletons.Y)) == 3 # 3 instances 3 labels
self.send_signal(self.widget.Inputs.data, data_singletons)
self.assertTrue(self.widget.Error.singleton_clusters_all.is_shown())
def test_not_symmetric(self):
w = self.widget
self.send_signal(w.Inputs.data, DistMatrix([[1, 2, 3], [4, 5, 6]]))
self.assertTrue(w.Error.input_validation_error.is_shown())
self.send_signal(w.Inputs.data, None)
self.assertFalse(w.Error.input_validation_error.is_shown())
def test_unknowns_in_labels(self):
data = self.data[[0, 1, 2, 50, 51, 52, 100, 101, 102]]
with data.unlocked(data.Y):
data.Y[::3] = np.nan
valid = ~np.isnan(data.Y.flatten())
self.send_signal(self.widget.Inputs.data, data)
output = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
scores = output[:, self.scorename].metas.flatten()
self.assertTrue(np.all(np.isnan(scores[::3])))
self.assertTrue(np.all(np.isfinite(scores[valid])))
# Run again on subset with known labels
data_1 = data[np.flatnonzero(valid)]
self.send_signal(self.widget.Inputs.data, data_1)
output_1 = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
scores_1 = output_1[:, self.scorename].metas.flatten()
self.assertTrue(np.all(np.isfinite(scores_1)))
# the scores must match
np.testing.assert_almost_equal(scores_1, scores[valid], decimal=12)
def test_nan_distances(self):
self.widget.distance_idx = 2
self.assertEqual(self.widget.Distances[self.widget.distance_idx][0],
'Cosine')
data = self.data[[0, 1, 2, 50, 51, 52, 100, 101, 102]]
with data.unlocked(data.X):
data.X[::3] = 0
valid = np.any(data.X != 0, axis=1)
self.assertFalse(self.widget.Warning.nan_distances.is_shown())
self.send_signal(self.widget.Inputs.data, data)
self.assertTrue(np.isnan(self.widget._matrix).any())
self.assertTrue(self.widget.Warning.nan_distances.is_shown())
output = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
scores = output[:, self.scorename].metas.flatten()
self.assertTrue(np.all(np.isnan(scores[::3])))
self.assertTrue(np.all(np.isfinite(scores[valid])))
def test_ignore_categorical(self):
data = Table('heart_disease')
self.widget.distance_idx = 2
self.assertEqual(self.widget.Distances[self.widget.distance_idx][0],
'Cosine')
self.assertFalse(self.widget.Warning.ignoring_categorical.is_shown())
self.send_signal(self.widget.Inputs.data, data)
self.assertTrue(self.widget.Warning.ignoring_categorical.is_shown())
output = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
self.assertEqual(len(output.domain.variables), len(data.domain.variables))
self.widget.distance_idx = 0
self.widget._update()
self.assertFalse(self.widget.Warning.ignoring_categorical.is_shown())
def test_meta_object_dtype(self):
# gh-1875: Test on mixed string/discrete metas
data = self.data[::5]
domain = Domain(data.domain.attributes,
[],
[data.domain["iris"], StringVariable("S")])
data = data.from_table(domain, data)
self.send_signal(self.widget.Inputs.data, data)
def test_memory_error(self):
"""
Handling memory error.
GH-2336
Handling value error as well. This value error is in a relation with memory error.
GH-2521
"""
for i, side_effect in enumerate([MemoryError, ValueError]):
data = Table("iris")[::3]
self.send_signal(self.widget.Inputs.data, data)
self.assertFalse(self.widget.Error.memory_error.is_shown())
self.assertFalse(self.widget.Error.value_error.is_shown())
with unittest.mock.patch("numpy.asarray", side_effect=side_effect):
self.widget._matrix = None
self.widget.data = data
self.widget._effective_data = data
self.widget._update()
self.assertTrue(self.widget.Error.memory_error.is_shown() != i)
self.assertTrue(self.widget.Error.value_error.is_shown() == i)
def test_bad_data_range(self):
"""
Silhouette Plot now sets axis range properly.
GH-2377
"""
nan = np.nan
table = Table.from_list(
Domain(
[ContinuousVariable("a"), ContinuousVariable("b"), ContinuousVariable("c")],
[DiscreteVariable("d", values=("y", "n"))]),
list(zip([4, nan, nan],
[15, nan, nan],
[16, nan, nan],
"nyy"))
)
self.send_signal(self.widget.Inputs.data, table)
def test_saved_selection(self):
self.widget.settingsHandler.pack_data(self.widget)
# no assert here, just test is doesn't crash on empty
iris = Table("iris")
self.send_signal(self.widget.Inputs.data, iris)
random.seed(42)
points = random.sample(range(0, len(self.data)), 20)
self.widget._silplot.setSelection(points)
settings = self.widget.settingsHandler.pack_data(self.widget)
w = self.create_widget(OWSilhouettePlot, stored_settings=settings)
self.send_signal(w.Inputs.data, iris, widget=w)
self.assertEqual(len(self.get_output(w.Outputs.selected_data)), 20)
def test_distance_input(self):
widget = self.widget
data = Table("heart_disease")[::4]
matrix = Orange.distance.Euclidean(data)
self.send_signal(widget.Inputs.data, matrix, widget=widget)
self.assertIsNotNone(widget.distances)
self.assertIsNotNone(widget.data)
self.assertFalse(widget._distances_gui_box.isEnabled())
self.send_signal(widget.Inputs.data, data, widget=widget)
self.assertIsNone(widget.distances)
self.assertIsNotNone(widget.data)
self.assertTrue(widget._distances_gui_box.isEnabled())
def test_input_distance_no_data(self):
widget = self.widget
matrix = DistMatrix(
np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]),
row_items=None
)
self.send_signal(widget.Inputs.data, matrix, widget=widget)
self.assertTrue(widget.Error.input_validation_error.is_shown())
self.assertIsNone(widget.data)
self.assertIsNone(widget.distances)
self.send_signal(widget.Inputs.data, None, widget=widget)
self.assertFalse(widget.Error.input_validation_error.is_shown())
def test_no_group_var(self):
widget = self.widget
data = Table("iris")[::4]
data = data[:, data.domain.attributes]
matrix = Orange.distance.Euclidean(data)
self.send_signal(widget.Inputs.data, matrix, widget=widget)
self.assertTrue(widget.Error.input_validation_error.is_shown())
self.assertIsNone(widget.data)
self.assertIsNone(widget.distances)
self.send_signal(widget.Inputs.data, None, widget=widget)
self.assertFalse(widget.Error.input_validation_error.is_shown())
def test_unique_output_domain(self):
widget = self.widget
data = possible_duplicate_table('Silhouette (iris)')
matrix = Orange.distance.Euclidean(data)
self.send_signal(widget.Inputs.data, matrix, widget=widget)
output = self.get_output(self.widget.Outputs.annotated_data)
self.assertEqual(output.domain.metas[0].name, 'Silhouette (iris) (1)')
def test_report(self):
widget = self.widget
widget.report_plot = Mock()
widget.report_caption = Mock()
widget.send_report()
widget.report_plot.assert_not_called()
widget.report_caption.assert_not_called()
data = Table("zoo")
self.send_signal(widget.Inputs.data, data)
widget.annotation_var = None
widget.send_report()
widget.report_plot.assert_called()
widget.report_caption.assert_called()
text = widget.report_caption.call_args[0][0]
self.assertIn(data.domain.class_var.name, text)
self.assertNotIn("nnotated", text)
widget.annotation_var = data.domain.metas[0]
widget._silplot.rowNamesVisible = lambda: True
widget.send_report()
text = widget.report_caption.call_args[0][0]
self.assertIn(data.domain.class_var.name, text)
self.assertIn("nnotated", text)
self.assertIn(data.domain.metas[0].name, text)
def test_migration(self):
enc_domain = dict(
attributes=(('foo', 1), ('bar', 1), ('baz', 1), ('bax', 1),
('cfoo', 1), ('mbaz', 1)))
# No annotation
context = Context(
values=dict(cluster_var_idx=(0, -2), annotation_var_idx=(0, -2)),
**enc_domain
)
OWSilhouettePlot.migrate_context(context, 1)
values = context.values
self.assertNotIn("cluster_var_idx", values)
self.assertNotIn("annotation_var_idx", values)
self.assertEqual(values["cluster_var"], ("foo", 101))
self.assertEqual(values["annotation_var"], None)
# We have annotation
context = Context(
values=dict(cluster_var_idx=(2, -2), annotation_var_idx=(4, -2)),
**enc_domain
)
OWSilhouettePlot.migrate_context(context, 1)
self.assertNotIn("cluster_var_idx", values)
self.assertNotIn("annotation_var_idx", values)
self.assertEqual(context.values["cluster_var"], ("baz", 101))
self.assertEqual(context.values["annotation_var"], ("bax", 101))
# We thought was had annotation, but the index is wrong due to
# incorrect domain
context = Context(
values=dict(cluster_var_idx=(4, -2), annotation_var_idx=(7, -2)),
**enc_domain
)
OWSilhouettePlot.migrate_context(context, 1)
self.assertEqual(context.values["cluster_var"], ("cfoo", 101))
self.assertNotIn("annotation_var_idx", values)
if __name__ == "__main__":
unittest.main()
|
