Merge pull request #6618 from janezd/roc-output

ROC: Output a model with a new operating threshold

Author: VesnaT

Orange/widgets/evaluate/owcalibrationplot.py

@@ -16,7 +16,8 @@
 from Orange.widgets import widget, gui, settings
 from Orange.widgets.evaluate.contexthandlers import \
     EvaluationResultsContextHandler
-from Orange.widgets.evaluate.utils import results_for_preview
+from Orange.widgets.evaluate.utils import results_for_preview, \
+    check_can_calibrate
 from Orange.widgets.utils import colorpalettes
 from Orange.widgets.utils.widgetpreview import WidgetPreview
 from Orange.widgets.visualize.utils.customizableplot import \
@@ -486,23 +487,11 @@ def commit(self):
         wrapped = None
         results = self.results
         if results is not None:
-            problems = [
-                msg for condition, msg in (
-                    (results.folds is not None and len(results.folds) > 1,
-                     "each training data sample produces a different model"),
-                    (results.models is None,
-                     "test results do not contain stored models - try testing "
-                     "on separate data or on training data"),
-                    (len(self.selected_classifiers) != 1,
-                     "select a single model - the widget can output only one"),
-                    (self.score != 0 and len(results.domain.class_var.values) != 2,
-                     "cannot calibrate non-binary classes"))
-                if condition]
-            if len(problems) == 1:
-                self.Information.no_output(problems[0])
-            elif problems:
-                self.Information.no_output(
-                    "".join(f"\n - {problem}" for problem in problems))
+            problems = check_can_calibrate(
+                self.results, self.selected_classifiers,
+                require_binary=self.score != 0)
+            if problems:
+                self.Information.no_output(problems)
             else:
                 clsf_idx = self.selected_classifiers[0]
                 model = results.models[0, clsf_idx]

Orange/widgets/evaluate/owliftcurve.py

@@ -20,7 +20,8 @@
 from Orange.widgets import widget, gui, settings
 from Orange.widgets.evaluate.contexthandlers import \
     EvaluationResultsContextHandler
-from Orange.widgets.evaluate.utils import check_results_adequacy
+from Orange.widgets.evaluate.utils import check_results_adequacy, \
+    check_can_calibrate
 from Orange.widgets.utils import colorpalettes
 from Orange.widgets.utils.widgetpreview import WidgetPreview
 from Orange.widgets.visualize.utils.customizableplot import Updater, \
@@ -267,7 +268,7 @@ def _initialize(self, results):
             item = self.classifiers_list_box.item(i)
             item.setIcon(colorpalettes.ColorIcon(color))
 
-        class_values = results.data.domain.class_var.values
+        class_values = results.domain.class_var.values
         self.target_cb.addItems(class_values)
         if class_values:
             self.target_index = 0
@@ -493,23 +494,10 @@ def commit(self):
         wrapped = None
         results = self.results
         if results is not None:
-            problems = [
-                msg for condition, msg in (
-                    (results.folds is not None and len(results.folds) > 1,
-                     "each training data sample produces a different model"),
-                    (results.models is None,
-                     "test results do not contain stored models - try testing "
-                     "on separate data or on training data"),
-                    (len(self.selected_classifiers) != 1,
-                     "select a single model - the widget can output only one"),
-                    (len(results.domain.class_var.values) != 2,
-                     "cannot calibrate non-binary classes"))
-                if condition]
-            if len(problems) == 1:
-                self.Information.no_output(problems[0])
-            elif problems:
-                self.Information.no_output(
-                    "".join(f"\n - {problem}" for problem in problems))
+            problems = check_can_calibrate(
+                self.results, self.selected_classifiers)
+            if problems:
+                self.Information.no_output(problems)
             else:
                 clsf_idx = self.selected_classifiers[0]
                 model = results.models[0, clsf_idx]

Orange/widgets/evaluate/owrocanalysis.py

@@ -13,18 +13,21 @@
 import pyqtgraph as pg
 
 import Orange
+from Orange.base import Model
+from Orange.classification import ThresholdClassifier
+from Orange.evaluation.testing import Results
 from Orange.widgets import widget, gui, settings
 from Orange.widgets.evaluate.contexthandlers import \
     EvaluationResultsContextHandler
-from Orange.widgets.evaluate.utils import check_results_adequacy
+from Orange.widgets.evaluate.utils import check_results_adequacy, \
+    check_can_calibrate
 from Orange.widgets.utils import colorpalettes
 from Orange.widgets.utils.widgetpreview import WidgetPreview
 from Orange.widgets.visualize.utils.plotutils import GraphicsView, PlotItem
-from Orange.widgets.widget import Input
+from Orange.widgets.widget import Input, Output, Msg
 from Orange.widgets import report
 
 from Orange.widgets.evaluate.utils import results_for_preview
-from Orange.evaluation.testing import Results
 
 
 #: Points on a ROC curve
@@ -305,6 +308,12 @@ class OWROCAnalysis(widget.OWWidget):
     class Inputs:
         evaluation_results = Input("Evaluation Results", Orange.evaluation.Results)
 
+    class Outputs:
+        calibrated_model = Output("Calibrated Model", Model)
+
+    class Information(widget.OWWidget.Information):
+        no_output = Msg("Can't output a model: {}")
+
     buttons_area_orientation = None
     settingsHandler = EvaluationResultsContextHandler()
     target_index = settings.ContextSetting(0)
@@ -466,7 +475,7 @@ def _initialize(self, results):
             listitem = self.classifiers_list_box.item(i)
             listitem.setIcon(colorpalettes.ColorIcon(self.colors[i]))
 
-        class_var = results.data.domain.class_var
+        class_var = results.domain.class_var
         self.target_cb.addItems(class_var.values)
         self.target_index = 0
         self._set_target_prior()
@@ -620,8 +629,7 @@ def no_averaging():
         pen.setCosmetic(True)
         self.plot.plot([0, 1], [0, 1], pen=pen, antialias=True)
 
-        if self.roc_averaging == OWROCAnalysis.Merge:
-            self._update_perf_line()
+        self._update_perf_line()
 
         self._update_axes_ticks()
 
@@ -730,8 +738,7 @@ def _on_target_prior_changed(self):
         self._on_display_perf_line_changed()
 
     def _on_display_perf_line_changed(self):
-        if self.roc_averaging == OWROCAnalysis.Merge:
-            self._update_perf_line()
+        self._update_perf_line()
 
         if self.perf_line is not None:
             self.perf_line.setVisible(self.display_perf_line)
@@ -745,9 +752,12 @@ def _replot(self):
             self._setup_plot()
 
     def _update_perf_line(self):
-        if self._perf_line is None:
+
+        if self._perf_line is None or self.roc_averaging != OWROCAnalysis.Merge:
+            self._update_output(None)
             return
 
+        ind = None
         self._perf_line.setVisible(self.display_perf_line)
         if self.display_perf_line:
             m = roc_iso_performance_slope(
@@ -762,6 +772,26 @@ def _update_perf_line(self):
             else:
                 self._perf_line.setVisible(False)
 
+        self._update_output(None if ind is None else hull.thresholds[ind[0]])
+
+    def _update_output(self, threshold):
+        self.Information.no_output.clear()
+
+        if threshold is None:
+            self.Outputs.calibrated_model.send(None)
+            return
+
+        problems = check_can_calibrate(self.results, self.selected_classifiers)
+        if problems:
+            self.Information.no_output(problems)
+            self.Outputs.calibrated_model.send(None)
+            return
+
+        model = ThresholdClassifier(
+            self.results.models[0][self.selected_classifiers[0]],
+            threshold)
+        self.Outputs.calibrated_model.send(model)
+
     def onDeleteWidget(self):
         self.clear()
 

Orange/widgets/evaluate/tests/base.py

@@ -1,9 +1,59 @@
+from unittest.mock import Mock
+
+import numpy as np
+
 from Orange import classification, evaluation
-from Orange.data import Table
+from Orange.data import Table, Domain, DiscreteVariable
+from Orange.evaluation import Results
+from Orange.evaluation.performance_curves import Curves
+from Orange.tests import test_filename
+
+from Orange.widgets.tests.base import WidgetTest
+
 
+class EvaluateTest(WidgetTest):
+    def setUp(self):
+        super().setUp()
+
+        n, p = (0, 1)
+        actual, probs = np.array([
+            (p, .8), (n, .7), (p, .6), (p, .55), (p, .54), (n, .53), (n, .52),
+            (p, .51), (n, .505), (p, .4), (n, .39), (p, .38), (n, .37),
+            (n, .36), (n, .35), (p, .34), (n, .33), (p, .30), (n, .1)]).T
+        self.curves = Curves(actual, probs)
+        probs2 = (probs + 1) / 2
+        self.curves2 = Curves(actual, probs2)
+        pred = probs > 0.5
+        pred2 = probs2 > 0.5
+        probs = np.vstack((1 - probs, probs)).T
+        probs2 = np.vstack((1 - probs2, probs2)).T
+        domain = Domain([], DiscreteVariable("y", values=("a", "b")))
+        self.results = Results(
+            domain=domain,
+            actual=actual,
+            folds=np.array([Ellipsis]),
+            models=np.array([[Mock(), Mock()]]),
+            row_indices=np.arange(19),
+            predicted=np.array((pred, pred2)),
+            probabilities=np.array([probs, probs2]))
+
+        self.lenses = data = Table(test_filename("datasets/lenses.tab"))
+        majority = classification.MajorityLearner()
+        majority.name = "majority"
+        knn3 = classification.KNNLearner(n_neighbors=3)
+        knn3.name = "knn-3"
+        knn1 = classification.KNNLearner(n_neighbors=1)
+        knn1.name = "knn-1"
+        self.lenses_results = evaluation.TestOnTestData(
+            store_data=True, store_models=True)(
+                data=data[::2], test_data=data[1::2],
+                learners=[majority, knn3, knn1])
+        self.lenses_results.learner_names = ["majority", "knn-3", "knn-1"]
 
-class EvaluateTest:
     def test_many_evaluation_results(self):
+        if not hasattr(self, "widget"):
+            return
+
         data = Table("iris")
         learners = [
             classification.MajorityLearner(),

Orange/widgets/evaluate/tests/test_owcalibrationplot.py

@@ -11,56 +11,15 @@
 
 from orangewidget.utils.combobox import qcombobox_emit_activated
 
-from Orange.data import Table, DiscreteVariable, Domain, ContinuousVariable
-import Orange.evaluation
-import Orange.classification
-from Orange.evaluation import Results
+from Orange.data import Domain, ContinuousVariable
 from Orange.evaluation.performance_curves import Curves
 from Orange.widgets.evaluate.tests.base import EvaluateTest
 from Orange.widgets.evaluate.owcalibrationplot import OWCalibrationPlot
-from Orange.widgets.tests.base import WidgetTest
-from Orange.tests import test_filename
 
 
-class TestOWCalibrationPlot(WidgetTest, EvaluateTest):
+class TestOWCalibrationPlot(EvaluateTest):
     def setUp(self):
         super().setUp()
-
-        n, p = (0, 1)
-        actual, probs = np.array([
-            (p, .8), (n, .7), (p, .6), (p, .55), (p, .54), (n, .53), (n, .52),
-            (p, .51), (n, .505), (p, .4), (n, .39), (p, .38), (n, .37),
-            (n, .36), (n, .35), (p, .34), (n, .33), (p, .30), (n, .1)]).T
-        self.curves = Curves(actual, probs)
-        probs2 = (probs + 0.5) / 2 + 1
-        self.curves2 = Curves(actual, probs2)
-        pred = probs > 0.5
-        pred2 = probs2 > 0.5
-        probs = np.vstack((1 - probs, probs)).T
-        probs2 = np.vstack((1 - probs2, probs2)).T
-        domain = Domain([], DiscreteVariable("y", values=("a", "b")))
-        self.results = Results(
-            domain=domain,
-            actual=actual,
-            folds=np.array([Ellipsis]),
-            models=np.array([[Mock(), Mock()]]),
-            row_indices=np.arange(19),
-            predicted=np.array((pred, pred2)),
-            probabilities=np.array([probs, probs2]))
-
-        self.lenses = data = Table(test_filename("datasets/lenses.tab"))
-        majority = Orange.classification.MajorityLearner()
-        majority.name = "majority"
-        knn3 = Orange.classification.KNNLearner(n_neighbors=3)
-        knn3.name = "knn-3"
-        knn1 = Orange.classification.KNNLearner(n_neighbors=1)
-        knn1.name = "knn-1"
-        self.lenses_results = Orange.evaluation.TestOnTestData(
-            store_data=True, store_models=True)(
-                data=data[::2], test_data=data[1::2],
-                learners=[majority, knn3, knn1])
-        self.lenses_results.learner_names = ["majority", "knn-3", "knn-1"]
-
         self.widget = self.create_widget(OWCalibrationPlot)  # type: OWCalibrationPlot
         warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
 
@@ -382,6 +341,8 @@ def test_threshold_flips_on_two_classes(self):
     @patch("Orange.widgets.evaluate.owcalibrationplot.CalibratedLearner")
     def test_apply_no_output(self, *_):
         """Test no output warnings"""
+        # Similar to test_owcalibrationplot, but just a little different, hence
+        # pylint: disable=duplicate-code
         widget = self.widget
         model_list = widget.controls.selected_classifiers
 
@@ -395,7 +356,7 @@ def test_apply_no_output(self, *_):
             multiple_selected:
                 "select a single model - the widget can output only one",
             non_binary_class:
-                "cannot calibrate non-binary classes"}
+                "cannot calibrate non-binary models"}
 
         def test_shown(shown):
             widget_msg = widget.Information.no_output

Orange/widgets/evaluate/tests/test_owliftcurve.py

@@ -31,7 +31,7 @@
 SKIP_REASON = "Only test precision-recall with scikit-learn>=1.1.1"
 
 
-class TestOWLiftCurve(WidgetTest, EvaluateTest):
+class TestOWLiftCurve(EvaluateTest):
     @classmethod
     def setUpClass(cls):
         super().setUpClass()