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import unittest
from unittest.mock import patch, Mock
import numpy as np
from Orange.data import Table, Domain, \
StringVariable, DiscreteVariable, ContinuousVariable
from Orange.widgets.evaluate.owfeatureaspredictor import OWFeatureAsPredictor
from Orange.widgets.tests.base import WidgetTest
class OWFeatureAsPredictorTest(WidgetTest):
def setUp(self):
self.widget = self.create_widget(OWFeatureAsPredictor)
self.disc_a = DiscreteVariable("a", values=("a", "b", "c"))
self.disc_b = DiscreteVariable("b", values=("c", "a", "b"))
self.disc_c = DiscreteVariable("c", values=("c", "a", "b", "d"))
self.disc_d = DiscreteVariable("d", values=("c", "b"))
self.disc_de = DiscreteVariable("de", values=("b", "c"))
self.cont_e = ContinuousVariable("e")
self.cont_f = ContinuousVariable("f")
self.cont_g = ContinuousVariable("g")
attrs = [self.disc_b, self.disc_c, self.disc_d,
self.cont_e]
meta_attrs = [self.cont_f, StringVariable("s"), self.disc_de]
x = np.array([[0, 1, 0, 0.1],
[1, 1, 1, 0.6],
[2, 0, np.nan, 0.2],
[0, np.nan, 1, np.nan],
[np.nan, 0, 6, 0.8]])
y = np.array([0, 1, 0, 1, 0])
metas = np.array([[-0.1, "a", 0],
[0.3, "b", 1],
[np.nan, "c", 1],
[0.9, "d", 1],
[0.24, "e", 0]])
self.class_data = Table.from_numpy(
Domain(attrs, self.disc_a, meta_attrs),
x, y, metas
)
self.bin_data = Table.from_numpy(
Domain(attrs, self.disc_de, meta_attrs[:-1]),
x, y, metas[:, :-1]
)
self.regr_data = Table.from_numpy(
Domain(attrs[1:], self.cont_g, meta_attrs),
x[:, 1:], y, metas
)
def set_column(self, var):
combo = self.widget.column_combo
index = combo.model().indexOf(var)
self.widget.column_combo.setCurrentIndex(index)
self.widget.column_combo.activated.emit(index)
def test_model(self):
w = self.widget
model = w.column_combo.model()
self.send_signal(self.class_data)
self.assertEqual(list(model), [self.disc_b, self.disc_d, self.disc_de])
self.assertIsNotNone(self.get_output(w.Outputs.model))
self.assertIsNotNone(self.get_output(w.Outputs.learner))
self.send_signal(None)
self.assertIsNone(self.get_output(w.Outputs.model))
self.assertIsNone(self.get_output(w.Outputs.learner))
self.assertEqual(list(model), [])
self.send_signal(self.bin_data)
self.assertEqual(list(model), [self.disc_d, self.cont_e, self.cont_f])
self.assertIsNotNone(self.get_output(w.Outputs.model))
self.assertIsNotNone(self.get_output(w.Outputs.learner))
self.send_signal(self.regr_data)
self.assertEqual(list(model), [self.cont_e, self.cont_f])
self.assertIsNotNone(self.get_output(w.Outputs.model))
self.assertIsNotNone(self.get_output(w.Outputs.learner))
self.send_signal(
self.bin_data.transform(Domain(self.bin_data.domain.attributes)))
self.assertIsNone(self.get_output(w.Outputs.model))
self.assertIsNone(self.get_output(w.Outputs.learner))
self.assertTrue(w.Error.no_class.is_shown())
self.assertFalse(w.Error.no_variables.is_shown())
self.send_signal(self.regr_data)
self.assertIsNotNone(self.get_output(w.Outputs.model))
self.assertIsNotNone(self.get_output(w.Outputs.learner))
self.assertFalse(w.Error.no_class.is_shown())
self.assertFalse(w.Error.no_variables.is_shown())
self.send_signal(
self.regr_data.transform(
Domain([self.disc_b, self.disc_c, self.disc_d], self.cont_g)))
self.assertIsNone(self.get_output(w.Outputs.model))
self.assertIsNone(self.get_output(w.Outputs.learner))
self.assertFalse(w.Error.no_class.is_shown())
self.assertTrue(w.Error.no_variables.is_shown())
def test_combo_hint(self):
self.send_signal(self.bin_data)
self.assertEqual(self.widget.column, self.disc_d)
self.send_signal(self.regr_data)
self.assertEqual(self.widget.column, self.cont_e)
self.set_column(self.cont_f)
# Keep f, because it exists
self.send_signal(self.bin_data)
self.assertEqual(self.widget.column, self.cont_f)
self.set_column(self.disc_d)
# Can't keep
self.send_signal(self.regr_data)
self.assertEqual(self.widget.column, self.cont_e)
# Keep hint when there is no data
self.set_column(self.cont_f)
self.send_signal(None)
self.send_signal(self.regr_data)
self.assertEqual(self.widget.column, self.cont_f)
def set_checked(self, checked):
cb = self.widget.cb_transformation
if cb.isChecked() != checked:
cb.click()
def test_update_transform_checkbox(self):
check = self.widget.cb_transformation
# No data: button is enabled
self.assertTrue(check.isEnabled())
# Check the checkbox so that we see it behaves properly
# when disabled, unchecked and re-enabled
self.set_checked(True)
with self.subTest("Multinomial target"):
self.send_signal(self.class_data)
# Discrete target: button is disabled and unchecked
# transformation not applied
self.assertFalse(check.isEnabled())
self.assertFalse(check.isChecked())
self.assertFalse(self.widget.apply_transformation)
# No data: re-enabled and re-checked
self.send_signal(None)
self.assertTrue(check.isEnabled())
self.assertTrue(check.isChecked())
self.send_signal(self.bin_data)
# Binary target, discrete column: button is disabled and unchecked
# transformation not applied
self.assertIs(self.widget.column, self.disc_d)
self.assertFalse(check.isEnabled())
self.assertFalse(check.isChecked())
self.assertFalse(self.widget.apply_transformation)
with self.subTest("Binary target, numeric column within range"):
# Binary target, numeric column within range:
# enabled, checked (because of setting)
self.set_column(self.cont_e)
self.assertTrue(check.isEnabled())
self.assertTrue(check.isChecked())
self.assertTrue(self.widget.apply_transformation)
# Binary target, numeric column outside range:
# disabled, unchecked (because of setting)
self.set_column(self.cont_e)
self.assertTrue(check.isEnabled())
self.assertTrue(check.isChecked())
self.assertTrue(self.widget.apply_transformation)
# Go back to numeric withing range to verify that it is re-checked
self.set_column(self.cont_e)
self.assertTrue(check.isEnabled())
self.assertTrue(check.isChecked())
self.assertTrue(self.widget.apply_transformation)
self.send_signal(None)
self.set_checked(False)
self.send_signal(self.bin_data)
self.set_column(self.cont_e)
self.assertTrue(check.isEnabled())
self.assertFalse(check.isChecked())
self.assertFalse(self.widget.apply_transformation)
with self.subTest("Regression target"):
self.send_signal(self.regr_data)
# Regression target: button is enabled and checked
self.assertTrue(check.isEnabled())
self.assertFalse(check.isChecked())
self.assertFalse(self.widget.apply_transformation)
self.set_checked(True)
self.assertTrue(self.widget.apply_transformation)
self.send_signal(self.class_data)
assert not check.isEnabled()
assert not check.isChecked()
self.send_signal(self.regr_data)
self.assertTrue(check.isEnabled())
self.assertTrue(check.isChecked())
self.assertTrue(self.widget.apply_transformation)
# Column that would be out of range for discrete,
# but regression doesn't mind
self.set_checked(False)
self.set_column(self.cont_f)
self.assertTrue(check.isEnabled())
self.assertFalse(check.isChecked())
self.assertFalse(self.widget.apply_transformation)
def test_checkbox_text(self):
check = self.widget.cb_transformation
self.send_signal(self.bin_data)
self.assertIn("logistic", check.text())
self.assertIn("logistic", check.toolTip())
self.send_signal(self.regr_data)
self.assertIn("linear", check.text())
self.assertIn("linear", check.toolTip())
self.send_signal(self.class_data)
self.assertIn("logistic", check.text())
self.assertIn("logistic", check.toolTip())
@patch("Orange.widgets.evaluate.owfeatureaspredictor.ColumnLearner")
def test_commit(self, learner):
model = self.widget.column_combo.model()
extract = self.regr_data.domain.class_var
self.assertIsNone(self.get_output(self.widget.Outputs.model))
self.assertIsNone(self.get_output(self.widget.Outputs.learner))
self.set_checked(False)
learner.reset_mock()
self.send_signal(self.regr_data)
learner.assert_called_once()
assert not self.widget.apply_transformation
self.assertEqual(learner.call_args, ((extract, model[0], False),))
self.assertIs(self.get_output(
self.widget.Outputs.learner), learner.return_value)
self.assertIs(self.get_output(
self.widget.Outputs.model), learner.return_value.return_value)
learner.reset_mock()
self.set_column(model[1])
assert not self.widget.apply_transformation
learner.assert_called_once()
self.assertEqual(learner.call_args, ((extract, model[1], False),))
self.assertIs(self.get_output(
self.widget.Outputs.learner), learner.return_value)
self.assertIs(self.get_output(
self.widget.Outputs.model), learner.return_value.return_value)
learner.reset_mock()
self.set_checked(True)
learner.assert_called_once()
self.assertEqual(learner.call_args, ((extract, model[1], True),))
self.assertIs(self.get_output(
self.widget.Outputs.learner), learner.return_value)
self.assertIs(self.get_output(
self.widget.Outputs.model), learner.return_value.return_value)
@patch("Orange.modelling.column.ColumnModel")
def test_report_data(self, model):
def assert_items(*expected):
self.assertEqual(tuple(i[1] for i in items.call_args[0][0][1:]),
expected)
items = self.widget.report_items = Mock()
model.return_value.intercept = 1
model.return_value.coefficient = 2
self.widget.send_report()
items.assert_not_called()
self.send_signal(self.class_data)
self.set_column(self.disc_d)
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "d")
assert_items(False, False, False)
self.set_column(self.disc_b)
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "b")
assert_items(False, False, False)
self.send_signal(self.regr_data)
self.set_checked(False)
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "e")
assert_items(False, False, False)
self.set_checked(True)
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "e")
assert_items("linear", 1, 2)
self.send_signal(self.bin_data)
self.set_column(self.disc_d)
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "d")
assert_items(False, False, False)
self.set_column(self.cont_e)
assert self.widget.apply_transformation
self.widget.send_report()
self.assertEqual(items.call_args[0][0][0][1], "e")
assert_items("logistic", 1, 2)
if __name__ == "__main__":
unittest.main()
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