# pylint: disable=protected-access,unused-import
from contextlib import contextmanager
import os
import pickle
from unittest.mock import Mock, patch
import numpy as np
import scipy.sparse as sp
from AnyQt.QtCore import Qt, QRectF, QPointF
from AnyQt.QtGui import QFont, QTextDocumentFragment
from AnyQt.QtTest import QSignalSpy
from AnyQt.QtWidgets import (
QComboBox, QSpinBox, QDoubleSpinBox, QSlider
)
from orangewidget.widget import StateInfo
from orangewidget.tests.base import (
GuiTest, WidgetTest as WidgetTestBase, DummySignalManager, DEFAULT_TIMEOUT
)
from Orange.base import SklModel, Model
from Orange.classification.base_classification import (
LearnerClassification, ModelClassification
)
from Orange.data import (
Table, Domain, DiscreteVariable, ContinuousVariable, Variable
)
from Orange.modelling import Fitter
from Orange.preprocess import RemoveNaNColumns, Randomize, Continuize
from Orange.preprocess.preprocess import PreprocessorList
from Orange.regression.base_regression import (
LearnerRegression, ModelRegression
)
from Orange.widgets.tests.utils import simulate
from Orange.widgets.utils.annotated_data import (
ANNOTATED_DATA_FEATURE_NAME, ANNOTATED_DATA_SIGNAL_NAME
)
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.visualize.utils.plotutils import AnchorItem
from Orange.widgets.widget import OWWidget
class TestWidgetTest(WidgetTest):
"""Meta tests for widget test helpers"""
def test_process_events_handles_timeouts(self):
with self.assertRaises(TimeoutError):
self.process_events(until=lambda: False, timeout=0)
def test_minimum_size(self):
return # skip this test
class BaseParameterMapping:
"""Base class for mapping between gui components and learner's parameters
when testing learner widgets.
Parameters
----------
name : str
Name of learner's parameter.
gui_element : QWidget
Gui component who's corresponding parameter is to be tested.
values: list
List of values to be tested.
getter: function
It gets component's value.
setter: function
It sets component's value.
"""
def __init__(self, name, gui_element, values, getter, setter,
problem_type="both"):
self.name = name
self.gui_element = gui_element
self.values = values
self.get_value = getter
self.set_value = setter
self.problem_type = problem_type
def __str__(self):
if self.problem_type == "both":
return self.name
else:
return "%s (%s)" % (self.name, self.problem_type)
class DefaultParameterMapping(BaseParameterMapping):
"""Class for mapping between gui components and learner's parameters
when testing unchecked properties and therefore default parameters
should be used.
Parameters
----------
name : str
Name of learner's parameter.
default_value: str, int,
Value that should be used by default.
"""
def __init__(self, name, default_value):
super().__init__(name, None, [default_value],
lambda: default_value, lambda x: None)
class ParameterMapping(BaseParameterMapping):
"""Class for mapping between gui components and learner parameters
when testing learner widgets
Parameters
----------
name : str
Name of learner's parameter.
gui_element : QWidget
Gui component who's corresponding parameter is to be tested.
values: list, mandatory for ComboBox, optional otherwise
List of values to be tested. When None, it is set according to
component's type.
getter: function, optional
It gets component's value. When None, it is set according to
component's type.
setter: function, optional
It sets component's value. When None, it is set according to
component's type.
"""
def __init__(self, name, gui_element, values=None,
getter=None, setter=None, **kwargs):
super().__init__(
name, gui_element,
values or self._default_values(gui_element),
getter or self._default_get_value(gui_element, values),
setter or self._default_set_value(gui_element, values),
**kwargs)
@staticmethod
def get_gui_element(widget, attribute):
return widget.controlled_attributes[attribute][0].control
@classmethod
def from_attribute(cls, widget, attribute, parameter=None):
return cls(parameter or attribute, cls.get_gui_element(widget, attribute))
@staticmethod
def _default_values(gui_element):
if isinstance(gui_element, (QSpinBox, QDoubleSpinBox, QSlider)):
return [gui_element.minimum(), gui_element.maximum()]
else:
raise TypeError("{} is not supported".format(gui_element))
@staticmethod
def _default_get_value(gui_element, values):
if isinstance(gui_element, (QSpinBox, QDoubleSpinBox, QSlider)):
return gui_element.value
elif isinstance(gui_element, QComboBox):
return lambda: values[gui_element.currentIndex()]
else:
raise TypeError("{} is not supported".format(gui_element))
@staticmethod
def _default_set_value(gui_element, values):
if isinstance(gui_element, (QSpinBox, QDoubleSpinBox, QSlider)):
return lambda val: gui_element.setValue(val)
elif isinstance(gui_element, QComboBox):
def fun(val):
value = values.index(val)
gui_element.activated.emit(value)
gui_element.setCurrentIndex(value)
return fun
else:
raise TypeError("{} is not supported".format(gui_element))
class WidgetLearnerTestMixin:
"""Base class for widget learner tests.
Contains init method to set up testing parameters and test methods.
All widget learner tests should extend it (beside extending WidgetTest
class as well). Learners with extra parameters, which can be set on the
widget, should override self.parameters list in the setUp method. The
list should contain mapping: learner parameter - gui component.
"""
widget = None # type: OWBaseLearner
def init(self):
cls_ds = Table(datasets.path("testing_dataset_cls"))
reg_ds = Table(datasets.path("testing_dataset_reg"))
if issubclass(self.widget.LEARNER, Fitter):
self.data = cls_ds
self.valid_datasets = (cls_ds, reg_ds)
self.inadequate_dataset = ()
self.learner_class = Fitter
self.model_class = Model
self.model_name = 'Model'
elif issubclass(self.widget.LEARNER, LearnerClassification):
self.data = cls_ds
self.valid_datasets = (cls_ds,)
self.inadequate_dataset = (reg_ds,)
self.learner_class = LearnerClassification
self.model_class = ModelClassification
self.model_name = 'Classifier'
else:
self.data = reg_ds
self.valid_datasets = (reg_ds,)
self.inadequate_dataset = (cls_ds,)
self.learner_class = LearnerRegression
self.model_class = ModelRegression
self.model_name = 'Predictor'
self.parameters = []
def click_apply(self):
self.widget.apply_button.button.clicked.emit()
def test_has_unconditional_apply(self):
self.assertTrue(hasattr(self.widget, "unconditional_apply"))
def test_input_data(self):
"""Check widget's data with data on the input"""
self.assertEqual(self.widget.data, None)
self.send_signal(self.widget.Inputs.data, self.data)
self.assertEqual(self.widget.data, self.data)
self.wait_until_stop_blocking()
def test_input_data_disconnect(self):
"""Check widget's data and model after disconnecting data from input"""
self.send_signal(self.widget.Inputs.data, self.data)
self.assertEqual(self.widget.data, self.data)
self.click_apply()
self.wait_until_stop_blocking()
self.send_signal(self.widget.Inputs.data, None)
self.wait_until_stop_blocking()
self.assertEqual(self.widget.data, None)
self.assertIsNone(self.get_output(self.widget.Outputs.model))
def test_input_data_learner_adequacy(self):
"""Check if error message is shown with inadequate data on input"""
for inadequate in self.inadequate_dataset:
self.send_signal(self.widget.Inputs.data, inadequate)
self.click_apply()
self.wait_until_stop_blocking()
self.assertTrue(self.widget.Error.data_error.is_shown())
for valid in self.valid_datasets:
self.send_signal(self.widget.Inputs.data, valid)
self.wait_until_stop_blocking()
self.assertFalse(self.widget.Error.data_error.is_shown())
def test_input_preprocessor(self):
"""Check learner's preprocessors with an extra pp on input"""
randomize = Randomize()
self.send_signal(self.widget.Inputs.preprocessor, randomize)
self.assertEqual(
randomize, self.widget.preprocessors,
'Preprocessor not added to widget preprocessors')
self.click_apply()
self.wait_until_stop_blocking()
self.assertEqual(
(randomize,), self.widget.learner.preprocessors,
'Preprocessors were not passed to the learner')
def test_input_preprocessors(self):
"""Check multiple preprocessors on input"""
pp_list = PreprocessorList([Randomize(), RemoveNaNColumns()])
self.send_signal(self.widget.Inputs.preprocessor, pp_list)
self.click_apply()
self.wait_until_stop_blocking()
self.assertEqual(
(pp_list,), self.widget.learner.preprocessors,
'`PreprocessorList` was not added to preprocessors')
def test_input_preprocessor_disconnect(self):
"""Check learner's preprocessors after disconnecting pp from input"""
randomize = Randomize()
self.send_signal(self.widget.Inputs.preprocessor, randomize)
self.click_apply()
self.wait_until_stop_blocking()
self.assertEqual(randomize, self.widget.preprocessors)
self.send_signal(self.widget.Inputs.preprocessor, None)
self.click_apply()
self.wait_until_stop_blocking()
self.assertIsNone(self.widget.preprocessors,
'Preprocessors not removed on disconnect.')
def test_output_learner(self):
"""Check if learner is on output after apply"""
initial = self.get_output(self.widget.Outputs.learner)
self.assertIsNotNone(initial, "Does not initialize the learner output")
self.click_apply()
newlearner = self.get_output(self.widget.Outputs.learner)
self.assertIsNot(initial, newlearner,
"Does not send a new learner instance on `Apply`.")
self.assertIsNotNone(newlearner)
self.assertIsInstance(newlearner, self.widget.LEARNER)
def test_output_model(self):
"""Check if model is on output after sending data and apply"""
self.assertIsNone(self.get_output(self.widget.Outputs.model))
self.click_apply()
self.assertIsNone(self.get_output(self.widget.Outputs.model))
self.send_signal(self.widget.Inputs.data, self.data)
self.click_apply()
self.wait_until_stop_blocking()
model = self.get_output(self.widget.Outputs.model)
self.assertIsNotNone(model)
self.assertIsInstance(model, self.widget.LEARNER.__returns__)
self.assertIsInstance(model, self.model_class)
def test_output_learner_name(self):
"""Check if learner's name properly changes"""
new_name = "Learner Name"
self.click_apply()
self.assertEqual(self.widget.learner.name,
self.widget.effective_learner_name())
self.assertEqual(self.widget.effective_learner_name(),
self.widget.name_line_edit.placeholderText())
self.widget.name_line_edit.setText(new_name)
self.click_apply()
self.wait_until_stop_blocking()
self.assertEqual(self.get_output(self.widget.Outputs.learner).name,
new_name)
def test_output_model_name(self):
"""Check if model's name properly changes"""
new_name = "Model Name"
self.widget.name_line_edit.setText(new_name)
self.send_signal(self.widget.Inputs.data, self.data)
self.click_apply()
self.wait_until_stop_blocking()
self.assertEqual(self.get_output(self.widget.Outputs.model).name, new_name)
def test_output_model_picklable(self):
"""Check if model can be pickled"""
self.send_signal(self.widget.Inputs.data, self.data)
self.click_apply()
self.wait_until_stop_blocking()
model = self.get_output(self.widget.Outputs.model)
self.assertIsNotNone(model)
pickle.dumps(model)
@staticmethod
def _get_param_value(learner, param):
if isinstance(learner, Fitter):
# Both is just a was to indicate to the tests, fitters don't
# actually support this
if param.problem_type == "both":
problem_type = learner.CLASSIFICATION
else:
problem_type = param.problem_type
return learner.get_params(problem_type).get(param.name)
else:
return learner.params.get(param.name)
def test_parameters_default(self):
"""Check if learner's parameters are set to default (widget's) values
"""
for dataset in self.valid_datasets:
self.send_signal(self.widget.Inputs.data, dataset)
self.click_apply()
self.wait_until_stop_blocking()
for parameter in self.parameters:
# Skip if the param isn't used for the given data type
if self._should_check_parameter(parameter, dataset):
self.assertEqual(
self._get_param_value(self.widget.learner, parameter),
parameter.get_value())
def test_parameters(self):
"""Check learner and model for various values of all parameters"""
# Test params on every valid dataset, since some attributes may apply
# to only certain problem types
for dataset in self.valid_datasets:
self.send_signal(self.widget.Inputs.data, dataset)
self.wait_until_stop_blocking()
for parameter in self.parameters:
# Skip if the param isn't used for the given data type
if not self._should_check_parameter(parameter, dataset):
continue
assert isinstance(parameter, BaseParameterMapping)
for value in parameter.values:
parameter.set_value(value)
self.click_apply()
self.wait_until_stop_blocking()
param = self._get_param_value(self.widget.learner, parameter)
self.assertEqual(
param, parameter.get_value(),
"Mismatching setting for parameter '%s'" % parameter)
self.assertEqual(
param, value,
"Mismatching setting for parameter '%s'" % parameter)
param = self._get_param_value(
self.get_output(self.widget.Outputs.learner),
parameter)
self.assertEqual(
param, value,
"Mismatching setting for parameter '%s'" % parameter)
if issubclass(self.widget.LEARNER, SklModel):
model = self.get_output(self.widget.Outputs.model)
if model is not None:
self.assertEqual(self._get_param_value(model, parameter), value)
self.assertFalse(self.widget.Error.active)
else:
self.assertTrue(self.widget.Error.active)
def test_params_trigger_settings_changed(self):
"""Check that the learner gets updated whenever a param is changed."""
for dataset in self.valid_datasets:
self.send_signal(self.widget.Inputs.data, dataset)
self.wait_until_stop_blocking()
for parameter in self.parameters:
# Skip if the param isn't used for the given data type
if not self._should_check_parameter(parameter, dataset):
continue
assert isinstance(parameter, BaseParameterMapping)
# Set the mock here so we can include the param name in the
# error message, so if any test fails, we see where
# We mock `apply` and not `settings_changed` since that's
# sometimes connected with Qt signals, which are not directly
# called
self.widget.apply = Mock(name="apply(%s)" % parameter)
# Since the settings only get updated when the value actually
# changes, find a value that isn't the same as the current
# value and try with that
new_value = [x for x in parameter.values
if x != parameter.get_value()][0]
parameter.set_value(new_value)
# wait for asynchronous calls
self.process_events(lambda: self.widget.apply.call_args is not None)
self.widget.apply.assert_called_once()
@staticmethod
def _should_check_parameter(parameter, data):
"""Should the param be passed into the learner given the data"""
return ((parameter.problem_type == "classification" and
data.domain.has_discrete_class) or
(parameter.problem_type == "regression" and
data.domain.has_continuous_class) or
(parameter.problem_type == "both"))
def test_send_report(self, timeout=DEFAULT_TIMEOUT):
"""Test report"""
self.send_signal(self.widget.Inputs.data, self.data)
self.widget.report_button.click()
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data, None)
self.widget.report_button.click()
class WidgetOutputsTestMixin:
"""Class for widget's outputs testing.
Contains init method to set up testing parameters and a test method, which
checks Selected Data and (Annotated) Data outputs.
Since widgets have different ways of selecting data instances, _select_data
method should be implemented when subclassed. The method should assign
value to selected_indices parameter.
If output's expected domain differs from input's domain, parameter
same_input_output_domain should be set to False.
If Selected Data and Data domains differ, override method
_compare_selected_annotated_domains.
"""
def init(self, same_table_attributes=True, output_all_on_no_selection=False):
self.data = Table("iris")
self.same_input_output_domain = True
self.same_table_attributes = same_table_attributes
self.output_all_on_no_selection = output_all_on_no_selection
def test_outputs(self, timeout=DEFAULT_TIMEOUT):
self.widget.linkage = 1
self.send_signal(self.signal_name, self.signal_data)
self.wait_until_finished(timeout=timeout)
# check selected data output
output = self.get_output(self.widget.Outputs.selected_data)
if self.output_all_on_no_selection:
self.assertEqual(output, self.signal_data)
else:
self.assertIsNone(output)
# check annotated data output
feature_name = ANNOTATED_DATA_FEATURE_NAME
annotated = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
self.assertEqual(0, np.sum([i[feature_name] for i in annotated]))
# select data instances
selected_indices = self._select_data()
# check selected data output
selected = self.get_output(self.widget.Outputs.selected_data)
n_sel, n_attr = len(selected), len(self.data.domain.attributes)
self.assertGreater(n_sel, 0)
self.assertEqual(selected.domain == self.data.domain,
self.same_input_output_domain)
np.testing.assert_array_equal(selected.X[:, :n_attr],
self.data.X[selected_indices])
if self.same_table_attributes:
self.assertEqual(selected.attributes, self.data.attributes)
# check annotated data output
annotated = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
self.assertEqual(n_sel, np.sum([i[feature_name] for i in annotated]))
if self.same_table_attributes:
self.assertEqual(annotated.attributes, self.data.attributes)
# compare selected and annotated data domains
self._compare_selected_annotated_domains(selected, annotated)
# check output when data is removed
self.send_signal(self.signal_name, None)
self.assertIsNone(self.get_output(self.widget.Outputs.selected_data))
self.assertIsNone(self.get_output(ANNOTATED_DATA_SIGNAL_NAME))
def _select_data(self):
raise NotImplementedError("Subclasses should implement select_data")
def _compare_selected_annotated_domains(self, selected, annotated):
selected_vars = selected.domain.variables + selected.domain.metas
annotated_vars = annotated.domain.variables + annotated.domain.metas
self.assertLess(set(selected_vars), set(annotated_vars))
class ProjectionWidgetTestMixin:
"""Class for projection widget testing"""
def init(self):
self.data = Table("iris")
def _select_data(self):
rect = QRectF(QPointF(-20, -20), QPointF(20, 20))
self.widget.graph.select_by_rectangle(rect)
return self.widget.graph.get_selection()
def _compare_selected_annotated_domains(self, selected, annotated):
selected_vars = selected.domain.variables
annotated_vars = annotated.domain.variables
self.assertLessEqual(set(selected_vars), set(annotated_vars))
def test_setup_graph(self, timeout=DEFAULT_TIMEOUT):
"""Plot should exist after data has been sent in order to be
properly set/updated"""
self.send_signal(self.widget.Inputs.data, self.data)
self.assertTrue(
self.signal_manager.wait_for_finished(self.widget, timeout),
f"Did not finish in the specified {timeout}ms timeout"
)
self.assertIsNotNone(self.widget.graph.scatterplot_item)
def test_default_attrs(self, timeout=DEFAULT_TIMEOUT):
"""Check default values for 'Color', 'Shape', 'Size' and 'Label'"""
self.send_signal(self.widget.Inputs.data, self.data)
self.assertIs(self.widget.attr_color, self.data.domain.class_var)
self.assertIsNone(self.widget.attr_label)
self.assertIsNone(self.widget.attr_shape)
self.assertIsNone(self.widget.attr_size)
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data, None)
self.assertIsNone(self.widget.attr_color)
def test_attr_models(self):
"""Check possible values for 'Color', 'Shape', 'Size' and 'Label'"""
self.send_signal(self.widget.Inputs.data, self.data)
controls = self.widget.controls
self.assertEqual(len(controls.attr_color.model()), 8)
self.assertEqual(len(controls.attr_shape.model()), 3)
self.assertTrue(5 < len(controls.attr_size.model()) < 8)
self.assertEqual(len(controls.attr_label.model()), 8)
# color and label should contain all variables
# size should contain only continuous variables
# shape should contain only discrete variables
for var in self.data.domain.variables + self.data.domain.metas:
self.assertIn(var, controls.attr_color.model())
self.assertIn(var, controls.attr_label.model())
if var.is_continuous:
self.assertIn(var, controls.attr_size.model())
self.assertNotIn(var, controls.attr_shape.model())
if var.is_discrete:
self.assertNotIn(var, controls.attr_size.model())
self.assertIn(var, controls.attr_shape.model())
def test_attr_label_metas(self, timeout=DEFAULT_TIMEOUT):
"""Set 'Label' from string meta attribute"""
cont = Continuize(multinomial_treatment=Continuize.AsOrdinal)
data = cont(Table("zoo"))
self.send_signal(self.widget.Inputs.data, data)
self.wait_until_finished(timeout=timeout)
simulate.combobox_activate_item(self.widget.controls.attr_label,
data.domain[-1].name)
def test_handle_primitive_metas(self):
"""Set 'Color' from continuous meta attribute"""
d, attrs = self.data.domain, self.data.domain.attributes
data = self.data.transform(Domain(attrs[:2], d.class_vars, attrs[2:]))
self.send_signal(self.widget.Inputs.data, data)
simulate.combobox_activate_item(self.widget.controls.attr_color,
data.domain.metas[0].name)
def test_datasets(self, timeout=DEFAULT_TIMEOUT):
"""Test widget for datasets with missing values and constant features"""
for ds in datasets.datasets():
self.send_signal(self.widget.Inputs.data, ds)
self.wait_until_finished(timeout=timeout)
def test_none_data(self):
"""Test widget for empty dataset"""
self.send_signal(self.widget.Inputs.data, self.data[:0])
def test_plot_once(self, timeout=DEFAULT_TIMEOUT):
"""Test if data is plotted only once but committed on every input change"""
table = Table("heart_disease")
self.widget.setup_plot = Mock()
self.widget.commit.now = self.widget.commit.deferred = Mock()
self.send_signal(self.widget.Inputs.data, table)
self.widget.setup_plot.assert_called_once()
self.widget.commit.now.assert_called_once()
self.wait_until_finished(timeout=timeout)
self.widget.setup_plot.assert_called_once()
self.widget.commit.now.assert_called_once()
self.widget.commit.now.reset_mock()
self.send_signal(self.widget.Inputs.data_subset, table[::10])
self.widget.setup_plot.assert_called_once()
self.widget.commit.now.assert_called_once()
def test_subset_data_color(self, timeout=DEFAULT_TIMEOUT):
self.send_signal(self.widget.Inputs.data, self.data)
self.assertTrue(
self.signal_manager.wait_for_finished(self.widget, timeout),
f"Did not finish in the specified {timeout}ms timeout"
)
self.send_signal(self.widget.Inputs.data_subset, self.data[:10])
subset = [brush.color().name() == "#46befa" for brush in
self.widget.graph.scatterplot_item.data['brush'][:10]]
other = [brush.color().name() == "#000000" for brush in
self.widget.graph.scatterplot_item.data['brush'][10:]]
self.assertTrue(all(subset))
self.assertTrue(all(other))
def test_class_density(self, timeout=DEFAULT_TIMEOUT):
"""Check class density update"""
self.send_signal(self.widget.Inputs.data, self.data)
self.widget.cb_class_density.click()
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data, None)
self.widget.cb_class_density.click()
def test_dragging_tooltip(self):
"""Dragging tooltip depends on data being jittered"""
text = QTextDocumentFragment.fromHtml(
self.widget.graph.tiptexts[Qt.NoModifier]).toPlainText()
self.send_signal(self.widget.Inputs.data, Table("heart_disease"))
self.assertEqual(self.widget.graph.tip_textitem.toPlainText(), text)
def test_sparse_data(self, timeout=DEFAULT_TIMEOUT):
"""Test widget for sparse data"""
table = Table("iris").to_sparse()
self.assertTrue(sp.issparse(table.X))
self.send_signal(self.widget.Inputs.data, table)
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data_subset, table[::30])
self.assertEqual(len(self.widget.subset_data), 5)
def test_invalidated_embedding(self, timeout=DEFAULT_TIMEOUT):
"""Check if graph has been replotted when sending same data"""
self.widget.graph.update_coordinates = Mock()
self.widget.graph.update_point_props = Mock()
self.send_signal(self.widget.Inputs.data, self.data)
self.wait_until_finished(timeout=timeout)
self.widget.graph.update_coordinates.assert_called()
self.widget.graph.update_point_props.assert_called()
self.widget.graph.update_coordinates.reset_mock()
self.widget.graph.update_point_props.reset_mock()
self.send_signal(self.widget.Inputs.data, self.data)
self.wait_until_finished(timeout=timeout)
self.widget.graph.update_coordinates.assert_not_called()
self.widget.graph.update_point_props.assert_called_once()
def test_saved_selection(self, timeout=DEFAULT_TIMEOUT):
self.send_signal(self.widget.Inputs.data, self.data)
self.assertTrue(
self.signal_manager.wait_for_finished(self.widget, timeout),
f"Did not finish in the specified {timeout}ms timeout"
)
self.widget.graph.select_by_indices(list(range(0, len(self.data), 10)))
settings = self.widget.settingsHandler.pack_data(self.widget)
w = self.create_widget(self.widget.__class__, stored_settings=settings)
self.send_signal(self.widget.Inputs.data, self.data, widget=w)
self.assertTrue(
self.signal_manager.wait_for_finished(w, timeout),
f"Did not finish in the specified {timeout}ms timeout"
)
self.assertEqual(np.sum(w.graph.selection), 15)
np.testing.assert_equal(self.widget.graph.selection, w.graph.selection)
def test_send_report(self, timeout=DEFAULT_TIMEOUT):
"""Test report """
self.send_signal(self.widget.Inputs.data, self.data)
self.widget.report_button.click()
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data, None)
self.widget.report_button.click()
def test_hidden_effective_variables(self, timeout=DEFAULT_TIMEOUT):
hidden_var1 = ContinuousVariable("c1")
hidden_var1.attributes["hidden"] = True
hidden_var2 = ContinuousVariable("c2")
hidden_var2.attributes["hidden"] = True
class_vars = [DiscreteVariable("cls", values=("a", "b"))]
table = Table(Domain([hidden_var1, hidden_var2], class_vars),
np.array([[0., 1.], [2., 3.]]),
np.array([[0.], [1.]]))
self.send_signal(self.widget.Inputs.data, table)
self.wait_until_finished(timeout=timeout)
self.send_signal(self.widget.Inputs.data, table)
@WidgetTest.skipNonEnglish
def test_visual_settings(self, timeout=DEFAULT_TIMEOUT):
graph = self.widget.graph
font = QFont()
font.setItalic(True)
font.setFamily("Helvetica")
self.send_signal(self.widget.Inputs.data, self.data)
self.wait_until_finished(timeout=timeout)
key, value = ("Fonts", "Font family", "Font family"), "Helvetica"
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Title", "Font size"), 20
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Title", "Italic"), True
self.widget.set_visual_settings(key, value)
font.setPointSize(20)
self.assertFontEqual(graph.parameter_setter.title_item.item.font(), font)
key, value = ("Fonts", "Label", "Font size"), 10
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Label", "Italic"), True
self.widget.set_visual_settings(key, value)
simulate.combobox_activate_item(self.widget.controls.attr_label,
self.data.domain[0].name)
font.setPointSize(10)
self.assertFontEqual(graph.labels[0].textItem.font(), font)
key, value = ("Fonts", "Categorical legend", "Font size"), 14
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Categorical legend", "Italic"), True
self.widget.set_visual_settings(key, value)
font.setPointSize(14)
legend_item = list(graph.parameter_setter.cat_legend_items)[0]
self.assertFontEqual(legend_item[1].item.font(), font)
key, value = ("Fonts", "Numerical legend", "Font size"), 12
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Numerical legend", "Italic"), True
self.widget.set_visual_settings(key, value)
simulate.combobox_activate_item(self.widget.controls.attr_color,
self.data.domain[0].name)
variables = self.data.domain.variables + self.data.domain.metas
discrete_var = next(
(x for x in variables if isinstance(x, DiscreteVariable)), None
)
if discrete_var:
# activate item only when discrete variable available
simulate.combobox_activate_item(
self.widget.controls.attr_shape, discrete_var.name
)
font.setPointSize(12)
self.assertFontEqual(graph.parameter_setter.num_legend.items[0][0].font, font)
key, value = ("Annotations", "Title", "Title"), "Foo"
self.widget.set_visual_settings(key, value)
self.assertEqual(graph.parameter_setter.title_item.item.toPlainText(), "Foo")
self.assertEqual(graph.parameter_setter.title_item.text, "Foo")
def assertFontEqual(self, font1, font2):
self.assertEqual(font1.family(), font2.family())
self.assertEqual(font1.pointSize(), font2.pointSize())
self.assertEqual(font1.italic(), font2.italic())
class AnchorProjectionWidgetTestMixin(ProjectionWidgetTestMixin):
def test_embedding_missing_values(self):
table = Table("heart_disease")
with table.unlocked():
table.X[0] = np.nan
self.send_signal(self.widget.Inputs.data, table)
self.assertFalse(np.all(self.widget.valid_data))
output = self.get_output(ANNOTATED_DATA_SIGNAL_NAME)
embedding_mask = np.all(np.isnan(output.metas[:, :2]), axis=1)
np.testing.assert_array_equal(~embedding_mask, self.widget.valid_data)
# reload
self.send_signal(self.widget.Inputs.data, table)
def test_sparse_data(self, timeout=DEFAULT_TIMEOUT):
table = Table("iris")
with table.unlocked():
table.X = sp.csr_matrix(table.X)
self.assertTrue(sp.issparse(table.X))
self.send_signal(self.widget.Inputs.data, table)
self.assertTrue(self.widget.Error.sparse_data.is_shown())
self.send_signal(self.widget.Inputs.data_subset, table[::30])
self.assertEqual(len(self.widget.subset_data), 5)
self.send_signal(self.widget.Inputs.data, None)
self.assertFalse(self.widget.Error.sparse_data.is_shown())
def test_manual_move(self):
data = self.data.copy()
with data.unlocked():
data[1, 0] = np.nan
nvalid, nsample = len(self.data) - 1, self.widget.SAMPLE_SIZE
self.send_signal(self.widget.Inputs.data, data)
self.widget.graph.select_by_indices(list(range(0, len(data), 10)))
# remember state
selection = self.widget.graph.selection.copy()
# simulate manual move
self.widget._manual_move_start()
self.widget._manual_move(0, 1, 1)
self.assertEqual(len(self.widget.graph.scatterplot_item.data), nsample)
self.widget._manual_move_finish(0, 1, 2)
# check new state
self.assertEqual(len(self.widget.graph.scatterplot_item.data), nvalid)
np.testing.assert_equal(self.widget.graph.selection, selection)
def test_visual_settings(self, timeout=DEFAULT_TIMEOUT):
super().test_visual_settings(timeout)
graph = self.widget.graph
font = QFont()
font.setItalic(True)
font.setFamily("Helvetica")
key, value = ("Fonts", "Anchor", "Font size"), 10
self.widget.set_visual_settings(key, value)
key, value = ("Fonts", "Anchor", "Italic"), True
self.widget.set_visual_settings(key, value)
font.setPointSize(10)
for item in graph.anchor_items:
if isinstance(item, AnchorItem):
item = item._label
self.assertFontEqual(item.textItem.font(), font)
class datasets:
@staticmethod
def path(filename):
dirname = os.path.join(os.path.dirname(__file__), "datasets")
return os.path.join(dirname, filename)
@classmethod
def missing_data_1(cls):
"""
Dataset with 3 continuous features (X{1,2,3}) where all the columns
and rows contain at least one NaN value.
One discrete class D with NaN values
Mixed continuous/discrete/string metas ({X,D,S}M)
Returns
-------
data : Orange.data.Table
"""
return Table(cls.path("missing_data_1.tab"))
@classmethod
def missing_data_2(cls):
"""
Dataset with 3 continuous features (X{1,2,3}) where all the columns
and rows contain at least one NaN value and X1, X2 are constant.
One discrete constant class D with NaN values.
Mixed continuous/discrete/string class metas ({X,D,S}M)
Returns
-------
data : Orange.data.Table
"""
return Table(cls.path("missing_data_2.tab"))
@classmethod
def missing_data_3(cls):
"""
Dataset with 3 discrete features D{1,2,3} where all the columns and
rows contain at least one NaN value
One discrete class D with NaN values
Mixes continuous/discrete/string metas ({X,D,S}M)
Returns
-------
data : Orange.data.Table
"""
return Table(cls.path("missing_data_3.tab"))
@classmethod
def data_one_column_vals(cls, value=np.nan):
"""
Dataset with two continuous features and one discrete. One continuous
columns has custom set values (default nan).
Returns
-------
data : Orange.data.Table
"""
table = Table.from_list(
Domain(
[ContinuousVariable("a"),
ContinuousVariable("b"),
DiscreteVariable("c", values=("y", "n"))]
),
list(zip(
[42.48, 16.84, 15.23, 23.8],
["", "", "", ""],
"ynyn"
)))
with table.unlocked():
table[:, 1] = value
return table
@classmethod
def data_one_column_nans(cls):
"""
Dataset with two continuous features and one discrete. One continuous
columns has missing values (NaN).
Returns
-------
data : Orange.data.Table
"""
return cls.data_one_column_vals(value=np.nan)
@classmethod
def data_one_column_infs(cls):
return cls.data_one_column_vals(value=np.inf)
@classmethod
def datasets(cls):
"""
Yields multiple datasets.
Returns
-------
data : Generator of Orange.data.Table
"""
ds_cls = Table(cls.path("testing_dataset_cls"))
ds_reg = Table(cls.path("testing_dataset_reg"))
for ds in (ds_cls, ds_reg):
d, a = ds.domain, ds.domain.attributes
for i in range(0, len(a), 2):
yield ds.transform(Domain(a[i: i + 2], d.class_vars, d.metas))
yield ds.transform(Domain(a[:2] + a[8: 10], d.class_vars, d.metas))
yield cls.missing_data_1()
yield cls.missing_data_2()
yield cls.missing_data_3()
yield cls.data_one_column_nans()
yield ds_cls
yield ds_reg
@contextmanager
def open_widget_classes():
with patch.object(OWWidget, "__init_subclass__"):
yield