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Predictions: Output annotated table #6718

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Feb 7, 2024
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Predictions: Output annotated table #6718

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Predictions: Output annotated table
janezd Jan 26, 2024
98b2049
Predictions: Output annotated table
janezd Feb 6, 2024
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25 changes: 17 additions & 8 deletions Orange/widgets/evaluate/owpredictions.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output, MultiInput
from Orange.widgets.utils.itemmodels import TableModel
from Orange.widgets.utils.annotated_data import lazy_annotated_table
from Orange.widgets.utils.sql import check_sql_input
from Orange.widgets.utils.state_summary import format_summary_details
from Orange.widgets.utils.colorpalettes import LimitedDiscretePalette
Expand Down Expand Up @@ -72,7 +73,9 @@ class Inputs:
predictors = MultiInput("Predictors", Model, filter_none=True)

class Outputs:
predictions = Output("Predictions", Orange.data.Table)
selected_predictions = Output("Selected Predictions", Orange.data.Table,
default=True, replaces=["Predictions"])
annotated = Output("Predictions", Orange.data.Table)
evaluation_results = Output("Evaluation Results", Results)

class Warning(OWWidget.Warning):
Expand Down Expand Up @@ -814,7 +817,8 @@ def _commit_evaluation_results(self):

def _commit_predictions(self):
if not self.data:
self.Outputs.predictions.send(None)
self.Outputs.selected_predictions.send(None)
self.Outputs.annotated.send(None)
return

newmetas = []
Expand Down Expand Up @@ -855,12 +859,17 @@ def _commit_predictions(self):
# Reorder rows as they are ordered in view
shown_rows = datamodel.mapFromSourceRows(rows)
rows = rows[numpy.argsort(shown_rows)]
predictions = predictions[rows]
elif datamodel.sortColumn() >= 0 \
or predmodel is not None and predmodel.sortColumn() > 0:
# No selection: output all, but in the shown order
predictions = predictions[datamodel.mapToSourceRows(...)]
self.Outputs.predictions.send(predictions)
selected = predictions[rows]
annotated_data = lazy_annotated_table(predictions, rows)
else:
if datamodel.sortColumn() >= 0 \
or predmodel is not None and predmodel.sortColumn() > 0:
predictions = predictions[datamodel.mapToSourceRows(...)]
selected = predictions
annotated_data = predictions
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Annotated data is missing the Selected attribute when no rows are selected.

self.Outputs.selected_predictions.send(selected)
self.Outputs.annotated.send(annotated_data)

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While at it, you can also remove the redundant new line :)


def _add_classification_out_columns(self, slot, newmetas, newcolumns, index):
pred = slot.predictor
Expand Down
94 changes: 70 additions & 24 deletions Orange/widgets/evaluate/tests/test_owpredictions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
from Orange.evaluation import Results
from Orange.widgets.tests.utils import excepthook_catch, \
possible_duplicate_table, simulate
from Orange.widgets.utils.annotated_data import ANNOTATED_DATA_FEATURE_NAME
from Orange.widgets.utils.colorpalettes import LimitedDiscretePalette


Expand All @@ -62,7 +63,7 @@ def test_nan_target_input(self):
yvec = data.get_column(data.domain.class_var)
self.send_signal(self.widget.Inputs.data, data)
self.send_signal(self.widget.Inputs.predictors, ConstantLearner()(data), 1)
pred = self.get_output(self.widget.Outputs.predictions)
pred = self.get_output(self.widget.Outputs.selected_predictions)
self.assertIsInstance(pred, Table)
np.testing.assert_array_equal(
yvec, pred.get_column(data.domain.class_var))
Expand Down Expand Up @@ -92,7 +93,7 @@ def test_no_values_target(self):
test = Table(domain, np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]]),
np.full((3, 1), np.nan))
self.send_signal(self.widget.Inputs.data, test)
pred = self.get_output(self.widget.Outputs.predictions)
pred = self.get_output(self.widget.Outputs.selected_predictions)
self.assertEqual(len(pred), len(test))

results = self.get_output(self.widget.Outputs.evaluation_results)
Expand Down Expand Up @@ -145,7 +146,7 @@ def test_no_class_on_test(self):
no_class = titanic.transform(Domain(titanic.domain.attributes, None))
self.send_signal(self.widget.Inputs.predictors, majority_titanic, 1)
self.send_signal(self.widget.Inputs.data, no_class)
out = self.get_output(self.widget.Outputs.predictions)
out = self.get_output(self.widget.Outputs.selected_predictions)
np.testing.assert_allclose(out.get_column("constant"), 0)

predmodel = self.widget.predictionsview.model()
Expand Down Expand Up @@ -500,7 +501,7 @@ def test_unique_output_domain(self):
self.send_signal(self.widget.Inputs.data, data)
self.send_signal(self.widget.Inputs.predictors, predictor)

output = self.get_output(self.widget.Outputs.predictions)
output = self.get_output(self.widget.Outputs.selected_predictions)
self.assertEqual(output.domain.metas[0].name, 'constant (1)')

def test_select(self):
Expand All @@ -515,6 +516,51 @@ def test_select(self):
for index in self.widget.dataview.selectionModel().selectedIndexes()}
self.assertEqual(sel, {(1, col) for col in range(5)})

def test_selection_output(self):
log_reg_iris = LogisticRegressionLearner()(self.iris)
self.send_signal(self.widget.Inputs.predictors, log_reg_iris)
self.send_signal(self.widget.Inputs.data, self.iris)

selmodel = self.widget.dataview.selectionModel()
pred_model = self.widget.predictionsview.model()

selmodel.select(self.widget.dataview.model().index(1, 0), QItemSelectionModel.Select)
selmodel.select(self.widget.dataview.model().index(3, 0), QItemSelectionModel.Select)
output = self.get_output(self.widget.Outputs.selected_predictions)
self.assertEqual(len(output), 2)
self.assertEqual(output[0], self.iris[1])
self.assertEqual(output[1], self.iris[3])
output = self.get_output(self.widget.Outputs.annotated)
self.assertEqual(len(output), len(self.iris))
col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
self.assertEqual(np.sum(col), 2)
self.assertEqual(col[1], 1)
self.assertEqual(col[3], 1)

pred_model.sort(0)
output = self.get_output(self.widget.Outputs.selected_predictions)
self.assertEqual(len(output), 2)
self.assertEqual(output[0], self.iris[1])
self.assertEqual(output[1], self.iris[3])
output = self.get_output(self.widget.Outputs.annotated)
self.assertEqual(len(output), len(self.iris))
col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
self.assertEqual(np.sum(col), 2)
self.assertEqual(col[1], 1)
self.assertEqual(col[3], 1)

pred_model.sort(0, Qt.DescendingOrder)
output = self.get_output(self.widget.Outputs.selected_predictions)
self.assertEqual(len(output), 2)
self.assertEqual(output[0], self.iris[3])
self.assertEqual(output[1], self.iris[1])
output = self.get_output(self.widget.Outputs.annotated)
self.assertEqual(len(output), len(self.iris))
col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
self.assertEqual(np.sum(col), 2)
self.assertEqual(col[1], 1)
self.assertEqual(col[3], 1)

def test_select_data_first(self):
log_reg_iris = LogisticRegressionLearner()(self.iris)
self.send_signal(self.widget.Inputs.data, self.iris)
Expand All @@ -537,7 +583,7 @@ def test_selection_in_setting(self):
for index in widget.dataview.selectionModel().selectedIndexes()}
self.assertEqual(sel, {(row, col)
for row in [1, 3, 4] for col in range(5)})
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
exp = self.iris[np.array([1, 3, 4])]
np.testing.assert_equal(out.X, exp.X)

Expand Down Expand Up @@ -883,32 +929,32 @@ def test_output_wrt_shown_probs_1(self):

widget.shown_probs = widget.NO_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 1, 2])

widget.shown_probs = widget.DATA_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 0, 110, 2, 210, 211])

widget.shown_probs = widget.MODEL_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 110, 111, 2, 210, 211, 212])

widget.shown_probs = widget.BOTH_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 110, 2, 210, 211])

widget.shown_probs = widget.BOTH_PROBS + 1
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 1, 0, 2, 210])

widget.shown_probs = widget.BOTH_PROBS + 2
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 11, 1, 110, 2, 211])

def test_output_wrt_shown_probs_2(self):
Expand All @@ -933,37 +979,37 @@ def test_output_wrt_shown_probs_2(self):

widget.shown_probs = widget.NO_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 1])

widget.shown_probs = widget.DATA_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 0, 1, 110, 111, 112])

widget.shown_probs = widget.MODEL_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 110, 111, 112])

widget.shown_probs = widget.BOTH_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 110, 111, 112])

widget.shown_probs = widget.BOTH_PROBS + 1
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 1, 110])

widget.shown_probs = widget.BOTH_PROBS + 2
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 11, 1, 111])

widget.shown_probs = widget.BOTH_PROBS + 3
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 0, 1, 112])

def test_output_regression(self):
Expand All @@ -973,7 +1019,7 @@ def test_output_regression(self):
LinearRegressionLearner()(self.housing), 0)
self.send_signal(widget.Inputs.predictors,
MeanLearner()(self.housing), 1)
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
np.testing.assert_equal(
out.metas[:, [0, 2]],
np.hstack([pred.results.predicted.T for pred in widget.predictors]))
Expand Down Expand Up @@ -1001,12 +1047,12 @@ def test_classless(self):

widget.shown_probs = widget.NO_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 1, 2])

widget.shown_probs = widget.MODEL_PROBS
widget._commit_predictions()
out = self.get_output(widget.Outputs.predictions)
out = self.get_output(widget.Outputs.selected_predictions)
self.assertEqual(list(out.metas[0]), [0, 10, 11, 1, 110, 111, 2, 210, 211, 212])

@patch("Orange.widgets.evaluate.owpredictions.usable_scorers",
Expand Down Expand Up @@ -1047,7 +1093,7 @@ def test_multi_target_input(self):

self.send_signal(widget.Inputs.data, data)
self.send_signal(widget.Inputs.predictors, mock_model, 1)
pred = self.get_output(widget.Outputs.predictions)
pred = self.get_output(widget.Outputs.selected_predictions)
self.assertIsInstance(pred, Table)

def test_error_controls_visibility(self):
Expand Down Expand Up @@ -1202,7 +1248,7 @@ def test_output_error_reg(self):
self.send_signal(self.widget.Inputs.predictors, lin_reg(data), 0)
self.send_signal(self.widget.Inputs.predictors,
LinearRegressionLearner(fit_intercept=False)(data), 1)
pred = self.get_output(self.widget.Outputs.predictions)
pred = self.get_output(self.widget.Outputs.selected_predictions)

names = ["", " (error)"]
names = [f"{n}{i}" for i in ("", " (1)") for n in names]
Expand All @@ -1220,7 +1266,7 @@ def test_output_error_cls(self):
with data.unlocked(data.Y):
data.Y[1] = np.nan
self.send_signal(self.widget.Inputs.data, data)
pred = self.get_output(self.widget.Outputs.predictions)
pred = self.get_output(self.widget.Outputs.selected_predictions)

names = [""] + [f" ({v})" for v in
list(data.domain.class_var.values) + ["error"]]
Expand Down
1 change: 1 addition & 0 deletions i18n/si/msgs.jaml
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -8940,6 +8940,7 @@ widgets/evaluate/owpredictions.py:
Data: Podatki
Predictors: Modeli
class `Outputs`:
Selected Predictions: Izbrane napovedi
Predictions: Napovedi
Evaluation Results: Rezultati vrednotenja
class `Warning`:
Expand Down
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