Source code for Orange.widgets.tests.base

# 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.QtGui import QFont
from AnyQt.QtCore import QRectF, QPointF
from AnyQt.QtTest import QSignalSpy
from AnyQt.QtWidgets import (
    QComboBox, QSpinBox, QDoubleSpinBox, QSlider
)

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


[docs]class WidgetTest(WidgetTestBase): def assert_table_equal(self, table1, table2): if table1 is None or table2 is None: self.assertIs(table1, table2) return self.assert_domain_equal(table1.domain, table2.domain) np.testing.assert_array_equal(table1.X, table2.X) np.testing.assert_array_equal(table1.Y, table2.Y) np.testing.assert_array_equal(table1.metas, table2.metas)
[docs] def assert_domain_equal(self, domain1, domain2): """ Test domains for equality. Unlike in domain1 == domain2 uses `Variable.__eq__`, which in case of DiscreteVariable ignores `values`, this method also checks that both domain have equal `values`. """ for var1, var2 in zip(domain1.variables + domain1.metas, domain2.variables + domain2.metas): self.assertEqual(type(var1), type(var2)) self.assertEqual(var1.name, var2.name) if var1.is_discrete: self.assertEqual(var1.values, var2.values)
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 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("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("Data", self.data) self.assertEqual(self.widget.data, self.data) self.widget.apply_button.button.click() self.wait_until_stop_blocking() self.send_signal("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("Data", inadequate) self.widget.apply_button.button.click() self.wait_until_stop_blocking() self.assertTrue(self.widget.Error.data_error.is_shown()) for valid in self.valid_datasets: self.send_signal("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("Preprocessor", randomize) self.assertEqual( randomize, self.widget.preprocessors, 'Preprocessor not added to widget preprocessors') self.widget.apply_button.button.click() 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("Preprocessor", pp_list) self.widget.apply_button.button.click() 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("Preprocessor", randomize) self.widget.apply_button.button.click() self.wait_until_stop_blocking() self.assertEqual(randomize, self.widget.preprocessors) self.send_signal("Preprocessor", None) self.widget.apply_button.button.click() 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("Learner") self.assertIsNotNone(initial, "Does not initialize the learner output") self.widget.apply_button.button.click() newlearner = self.get_output("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.widget.apply_button.button.click() self.assertIsNone(self.get_output(self.widget.Outputs.model)) self.send_signal('Data', self.data) self.widget.apply_button.button.click() 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.widget.apply_button.button.click() self.assertEqual(self.widget.learner.name, self.widget.name_line_edit.text() or self.widget.name_line_edit.placeholderText()) self.widget.name_line_edit.setText(new_name) self.widget.apply_button.button.click() self.wait_until_stop_blocking() self.assertEqual(self.get_output("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("Data", self.data) self.widget.apply_button.button.click() 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("Data", self.data) self.widget.apply_button.button.click() self.wait_until_stop_blocking() model = self.get_output(self.widget.Outputs.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("Data", dataset) self.widget.apply_button.button.click() 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("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.widget.apply_button.button.click() 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("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("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")) 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): self.data = Table("iris") self.same_input_output_domain = True self.same_table_attributes = same_table_attributes def test_outputs(self, timeout=DEFAULT_TIMEOUT): self.send_signal(self.signal_name, self.signal_data) self.wait_until_finished(timeout=timeout) # check selected data output self.assertIsNone(self.get_output("Selected Data")) # 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("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("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 = self.widget.unconditional_commit = Mock() self.send_signal(self.widget.Inputs.data, table) self.widget.setup_plot.assert_called_once() self.widget.commit.assert_called_once() self.wait_until_finished(timeout=timeout) self.widget.setup_plot.assert_called_once() self.widget.commit.assert_called_once() self.widget.commit.reset_mock() self.send_signal(self.widget.Inputs.data_subset, table[::10]) self.widget.setup_plot.assert_called_once() self.widget.commit.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 = self.widget.graph.tiptexts[0] self.send_signal(self.widget.Inputs.data, Table("heart_disease")) self.assertEqual(self.widget.graph.tip_textitem.toPlainText(), text) self.widget.graph.controls.jitter_size.setValue(1) self.assertGreater(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) def test_in_out_summary(self, timeout=DEFAULT_TIMEOUT): info = self.widget.info self.assertEqual(info._StateInfo__input_summary.brief, "") self.assertEqual(info._StateInfo__output_summary.brief, "") self.assertIn(info._StateInfo__input_summary.details, ["", "No data on input"]) self.assertIn(info._StateInfo__output_summary.details, ["", "No data on output"]) 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" ) ind = self._select_data() self.assertEqual(info._StateInfo__input_summary.brief, str(len(self.data))) self.assertEqual(info._StateInfo__output_summary.brief, str(len(ind))) self.assertGreater(info._StateInfo__input_summary.details, "") self.assertGreater(info._StateInfo__output_summary.details, "") self.assertNotIn(info._StateInfo__input_summary.details, ["", "No data on input"]) self.assertNotIn(info._StateInfo__output_summary.details, ["", "No data on output"]) self.send_signal(self.widget.Inputs.data, None) self.assertEqual(info._StateInfo__input_summary.brief, "") self.assertEqual(info._StateInfo__output_summary.brief, "") self.assertIn(info._StateInfo__input_summary.details, ["", "No data on input"]) self.assertIn(info._StateInfo__output_summary.details, ["", "No data on output"]) 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") 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") 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() 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" ))) 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