Merge pull request #47 from VesnaT/ice_other_models

ICE: Support all Orange models

orangecontrib/explain/inspection.py

@@ -1,13 +1,13 @@
 """ Permutation feature importance for models. """
-from typing import Callable, Tuple, Optional, Dict
+from typing import Callable, Dict
 
 import numpy as np
 import scipy.sparse as sp
 from sklearn.inspection import partial_dependence
 
-from Orange.base import Model, SklModel
+from Orange.base import Model
 from Orange.classification import Model as ClsModel
-from Orange.data import Table, Variable, DiscreteVariable
+from Orange.data import Table, Variable
 from Orange.evaluation import Results
 from Orange.evaluation.scoring import Score, TargetScore, RegressionScore, R2
 from Orange.regression import Model as RegModel
@@ -178,7 +178,7 @@ def _calculate_permutation_scores(
 
 
 def individual_condition_expectation(
-        model: SklModel,
+        model: Model,
         data: Table,
         feature: Variable,
         grid_resolution: int = 1000,
@@ -194,10 +194,18 @@ def individual_condition_expectation(
     assert feature.name in [a.name for a in data.domain.attributes]
     feature_index = data.domain.index(feature.name)
 
-    assert isinstance(model, SklModel), f"Model ({model}) is not supported."
+    # fake sklearn estimator
+    model.fit = None
+    model.fit_ = None
+    if model.domain.class_var.is_discrete:
+        model._estimator_type = "classifier"
+        model.classes_ = model.domain.class_var.values
+    else:
+        model._estimator_type = "regressor"
+
     progress_callback(0.1)
 
-    dep = partial_dependence(model.skl_model,
+    dep = partial_dependence(model,
                              data.X,
                              [feature_index],
                              grid_resolution=grid_resolution,

orangecontrib/explain/tests/test_inspection.py

@@ -350,35 +350,6 @@ def test_mixed_features(self):
         self.assertEqual(res["individual"].shape, (2, 303, 41))
         self.assertEqual(res["values"].shape, (41,))
 
-    def _test_sklearn(self):
-        from matplotlib import pyplot as plt
-        from sklearn.ensemble import RandomForestClassifier, \
-            RandomForestRegressor
-        from sklearn.inspection import PartialDependenceDisplay
-
-        X = self.housing.X
-        y = self.housing.Y
-        model = RandomForestRegressor(random_state=0)
-
-        # X = self.iris.X[:100]
-        # y = self.iris.Y[:100]
-        # y = np.abs(y - 1)
-        # model = RandomForestClassifier(random_state=0)
-        model.fit(X, y)
-        display = PartialDependenceDisplay.from_estimator(
-            model,
-            X,
-            [X.shape[1] - 1],
-            target=0,
-            kind="both",
-            centered=True,
-            subsample=1000,
-            # grid_resolution=100,
-            random_state=0,
-        )
-
-        plt.show()
-
 
 if __name__ == "__main__":
     unittest.main()

orangecontrib/explain/widgets/owice.py

@@ -15,9 +15,9 @@
 from orangecanvas.gui.utils import disconnected
 from orangewidget.utils.listview import ListViewSearch
 
-from Orange.base import Model, SklModel, RandomForestModel
+from Orange.base import Model
 from Orange.data import Table, ContinuousVariable, Variable, \
-    DiscreteVariable
+    DiscreteVariable, Domain
 from Orange.data.table import DomainTransformationError
 from Orange.widgets import gui
 from Orange.widgets.settings import ContextSetting, Setting, \
@@ -491,7 +491,7 @@ class OWICE(OWWidget, ConcurrentWidgetMixin):
     priority = 130
 
     class Inputs:
-        model = Input("Model", (SklModel, RandomForestModel))
+        model = Input("Model", Model)
         data = Input("Data", Table)
 
     class Outputs:
@@ -534,6 +534,7 @@ def __init__(self):
         self.__pending_selection = self.selection
         self.model: Optional[Model] = None
         self.data: Optional[Table] = None
+        self.domain: Optional[Domain] = None
         self.graph: ICEPlot = None
         self._target_combo: QComboBox = None
         self._features_view: ListViewSearch = None
@@ -624,13 +625,9 @@ def _add_buttons(self):
     @Inputs.data
     @check_sql_input
     def set_data(self, data: Optional[Table]):
-        self.closeContext()
         self.data = data
         self.__sampled_mask = None
         self._check_data()
-        self._setup_controls()
-        self.openContext(self.data.domain if self.data else None)
-        self.set_list_view_selection()
 
     @Inputs.model
     def set_model(self, model: Optional[Model]):
@@ -660,8 +657,29 @@ def _check_data(self):
             self.__sampled_mask[np.random.choice(len(self.data), **kws)] = True
             self.Information.data_sampled()
 
+    def handleNewSignals(self):
+        self.closeContext()
+        self.domain = None
+        if self.data and self.model:
+            model_domain = [a.name for a in self.model.domain]
+            attributes = [a for a in self.data.domain.attributes
+                          if a.is_continuous and a.name in model_domain
+                          or a.is_discrete]
+            class_var = self.model.domain.class_var
+            metas = [m for m in self.data.domain.metas if m.is_discrete]
+            self.domain = Domain(attributes, class_var, metas)
+        self._setup_controls()
+        self.openContext(self.domain)
+        self.set_list_view_selection()
+
+        self.__results_avgs = None
+        self._apply_feature_sorting()
+        self._run()
+        self.selection = None
+        self.commit.now()
+
     def _setup_controls(self):
-        domain = self.data.domain if self.data else None
+        domain = self.domain
 
         self._target_combo.clear()
         self._target_combo.setEnabled(True)
@@ -702,13 +720,6 @@ def _ensure_selection_visible(view):
         if len(selection) == 1:
             view.scrollTo(selection[0])
 
-    def handleNewSignals(self):
-        self.__results_avgs = None
-        self._apply_feature_sorting()
-        self._run()
-        self.selection = None
-        self.commit.now()
-
     def _apply_feature_sorting(self):
         if self.data is None or self.model is None:
             return

orangecontrib/explain/widgets/tests/test_owice.py

@@ -4,9 +4,14 @@
 
 from AnyQt.QtCore import Qt, QPointF
 
-from Orange.classification import RandomForestLearner
+from Orange.classification import RandomForestLearner, CalibratedLearner, \
+    ThresholdLearner
 from Orange.data import Table
-from Orange.regression import RandomForestRegressionLearner
+from Orange.regression import RandomForestRegressionLearner, \
+    SimpleRandomForestLearner
+from Orange.tests.test_classification import all_learners as all_cls_learners
+from Orange.tests.test_regression import all_learners as all_reg_learners, \
+    init_learner
 from Orange.widgets.tests.base import WidgetTest
 from orangecontrib.explain.widgets.owice import OWICE
 
@@ -51,6 +56,30 @@ def test_output(self):
         annotated = self.get_output(self.widget.Outputs.annotated_data)
         self.assertEqual(len(annotated), len(self.heart))
 
+    def test_all_reg_models(self):
+        data = self.housing[:10]
+        self.send_signal(self.widget.Inputs.data, data)
+        for learner in all_reg_learners():
+            if issubclass(learner, (SimpleRandomForestLearner,)):
+                continue
+            learner = init_learner(learner, data)
+            model = learner(data)
+            self.send_signal(self.widget.Inputs.model, model)
+            self.wait_until_finished()
+            self.assertFalse(self.widget.Error.unknown_err.is_shown())
+
+    def test_all_cls_models(self):
+        data = self.heart[:10]
+        self.send_signal(self.widget.Inputs.data, data)
+        for learner in all_cls_learners():
+            if issubclass(learner, (CalibratedLearner, ThresholdLearner)):
+                model = learner(RandomForestLearner())(data)
+            else:
+                model = learner()(data)
+            self.send_signal(self.widget.Inputs.model, model)
+            self.wait_until_finished()
+            self.assertFalse(self.widget.Error.unknown_err.is_shown())
+
     def test_discrete_features(self):
         self.send_signal(self.widget.Inputs.data, self.titanic)
         self.assertTrue(self.widget.Error.no_cont_features.is_shown())

setup.py

@@ -38,7 +38,7 @@
 ]
 
 INSTALL_REQUIRES = [
-    "Orange3 >= 3.28.0",
+    "Orange3 >= 3.33.0",
     "orange-widget-base",
     "AnyQt",
     "numpy",

tox.ini

@@ -26,11 +26,10 @@ setenv =
 deps =
     pyqt5==5.12.*
     pyqtwebengine==5.12.*
-    oldest: scikit-learn~=0.22.0
-    oldest: orange3==3.27.1
-    # Use newer canvas-core and widget-base to avoid segfaults on windows
-    oldest: orange-canvas-core==0.1.18
-    oldest: orange-widget-base==4.9.0
+    oldest: scikit-learn==1.0.1
+    oldest: orange3==3.33.0
+    oldest: orange-canvas-core==0.1.27
+    oldest: orange-widget-base==4.18.0
     latest: https://github.com/biolab/orange3/archive/refs/heads/master.zip#egg=orange3
     latest: https://github.com/biolab/orange-canvas-core/archive/refs/heads/master.zip#egg=orange-canvas-core
     latest: https://github.com/biolab/orange-widget-base/archive/refs/heads/master.zip#egg=orange-widget-base