def test_nan_plot(self):
data = datasets.missing_data_1()
espy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
with excepthook_catch():
self.send_signal(self.widget.Inputs.data, data)
# ensure delayed replot request is processed
if not espy.events():
assert espy.wait(1000)
cb = self.widget.graph.controls
simulate.combobox_run_through_all(cb.attr_color)
simulate.combobox_run_through_all(cb.attr_size)
data = data.copy()
data.X[:, 0] = np.nan
data.Y[:] = np.nan
spy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
self.send_signal(self.widget.Inputs.data, data)
self.send_signal(self.widget.Inputs.data_subset, data[2:3])
if not spy.events():
assert spy.wait()
with excepthook_catch():
simulate.combobox_activate_item(cb.attr_color, "X1")
with excepthook_catch():
simulate.combobox_activate_item(cb.attr_size, "X1")
def test_nan_plot(self):
data = datasets.missing_data_1()
espy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
with excepthook_catch():
self.send_signal(self.widget.Inputs.data, data)
# ensure delayed replot request is processed
if not espy.events():
assert espy.wait(1000)
cb = self.widget.graph.controls
simulate.combobox_run_through_all(cb.attr_color)
simulate.combobox_run_through_all(cb.attr_size)
data = data.copy()
data.X[:, 0] = np.nan
data.Y[:] = np.nan
spy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
self.send_signal(self.widget.Inputs.data, data)
self.send_signal(self.widget.Inputs.data_subset, data[2:3])
if not spy.events():
assert spy.wait()
with excepthook_catch():
simulate.combobox_activate_item(cb.attr_color, "X1")
with excepthook_catch():
simulate.combobox_activate_item(cb.attr_size, "X1")
def test_changed_class_var(self):
def set_input(data, model):
self.send_signals([
(self.widget.Inputs.data, data),
(self.widget.Inputs.predictors, model)
])
iris = self.iris
learner = ConstantLearner()
heart_disease = Table("heart_disease")
# catch exceptions in item delegates etc. during switching inputs
with excepthook_catch():
set_input(iris[:5], learner(iris))
set_input(Table("housing"), None)
set_input(heart_disease[:5], learner(heart_disease))
def test_bad_data(self):
"""
Firstly it creates predictions with TreeLearner. Then sends predictions and
different data with different domain to Predictions widget. Those different
data and domain are similar to original data and domain but they have three
different target values instead of two.
GH-2129
"""
Variable._clear_all_caches()
filestr1 = """\
age\tsex\tsurvived
d\td\td
\t\tclass
adult\tmale\tyes
adult\tfemale\tno
child\tmale\tyes
child\tfemale\tyes
"""
file1 = io.StringIO(filestr1)
table = TabReader(file1).read()
learner = TreeLearner()
tree = learner(table)
filestr2 = """\
age\tsex\tsurvived
d\td\td
\t\tclass
adult\tmale\tyes
adult\tfemale\tno
child\tmale\tyes
child\tfemale\tunknown
"""
file2 = io.StringIO(filestr2)
bad_table = TabReader(file2).read()
self.send_signal(self.widget.Inputs.predictors, tree, 1)
with excepthook_catch():
self.send_signal(self.widget.Inputs.data, bad_table)
Variable._clear_all_caches(
) # so that test excepting standard titanic work
def test_bad_data(self):
"""
Firstly it creates predictions with TreeLearner. Then sends predictions and
different data with different domain to Predictions widget. Those different
data and domain are similar to original data and domain but they have three
different target values instead of two.
GH-2129
"""
Variable._clear_all_caches()
filestr1 = """\
age\tsex\tsurvived
d\td\td
\t\tclass
adult\tmale\tyes
adult\tfemale\tno
child\tmale\tyes
child\tfemale\tyes
"""
file1 = io.StringIO(filestr1)
table = TabReader(file1).read()
learner = TreeLearner()
tree = learner(table)
filestr2 = """\
age\tsex\tsurvived
d\td\td
\t\tclass
adult\tmale\tyes
adult\tfemale\tno
child\tmale\tyes
child\tfemale\tunknown
"""
file2 = io.StringIO(filestr2)
bad_table = TabReader(file2).read()
self.send_signal(self.widget.Inputs.predictors, tree, 1)
with excepthook_catch():
self.send_signal(self.widget.Inputs.data, bad_table)
Variable._clear_all_caches() # so that test excepting standard titanic work
def test_scheme_examples(self):
"""
Test if Orange workflow examples can be opened. Examples in canvas
and also those placed "workflows" subfolder.
GH-2240
"""
reg = registry()
test_workflows = chain(
discover_workflows(dirname(workflows.__file__)),
discover_workflows(join(dirname(__file__), "workflows")))
for ows_file in test_workflows:
new_scheme = widgetsscheme.WidgetsScheme()
new_scheme.widget_manager.set_creation_policy(
new_scheme.widget_manager.Immediate)
with open(ows_file, "rb") as f:
try:
with excepthook_catch(raise_on_exit=True):
new_scheme.load_from(f, registry=reg)
except Exception as e:
self.fail(
"Old workflow '{}' could not be loaded\n'{}'".format(
ows_file, str(e)))
finally:
new_scheme.clear()
def test_nan_plot(self):
data = datasets.missing_data_1()
espy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
with excepthook_catch():
self.send_signal("Data", data)
# ensure delayed replot request is processed
if not espy.events():
assert espy.wait(1000)
cb_color = self.widget.controls.color_index
cb_size = self.widget.controls.size_index
cb_shape = self.widget.controls.shape_index
cb_jitter = self.widget.controls.jitter_value
simulate.combobox_run_through_all(cb_color)
simulate.combobox_run_through_all(cb_size)
simulate.combobox_run_through_all(cb_shape)
with excepthook_catch():
simulate.combobox_activate_index(cb_jitter, 1, delay=1)
data = data.copy()
data.X[:, 0] = np.nan
data.Y[:] = np.nan
spy = EventSpy(self.widget, OWLinearProjection.ReplotRequest)
self.send_signal("Data", data)
self.send_signal("Data Subset", data[2:3])
if not spy.events():
assert spy.wait()
with excepthook_catch():
simulate.combobox_activate_item(cb_color, "X1")
with excepthook_catch():
simulate.combobox_activate_item(cb_size, "X1")
with excepthook_catch():
simulate.combobox_activate_item(cb_shape, "D")
with excepthook_catch():
simulate.combobox_activate_index(cb_jitter, 2, delay=1)
