projections: Remove 'Preprocessor' output

Author: VesnaT

Orange/preprocess/preprocess.py

@@ -536,18 +536,6 @@ def transform(var):
         return data.transform(domain)
 
 
-class ApplyDomain(Preprocess):
-    def __init__(self, domain, name):
-        self._domain = domain
-        self._name = name
-
-    def __call__(self, data):
-        return data.transform(self._domain)
-
-    def __str__(self):
-        return self._name
-
-
 class PreprocessorList(Preprocess):
     """
     Store a list of preprocessors and on call apply them to the dataset.

Orange/widgets/tests/base.py

@@ -31,7 +31,7 @@
 )
 from Orange.modelling import Fitter
 from Orange.preprocess import RemoveNaNColumns, Randomize, Continuize
-from Orange.preprocess.preprocess import PreprocessorList, Preprocess
+from Orange.preprocess.preprocess import PreprocessorList
 from Orange.regression.base_regression import (
     LearnerRegression, ModelRegression
 )
@@ -1074,18 +1074,6 @@ def test_manual_move(self):
         self.assertEqual(len(self.widget.graph.scatterplot_item.data), nvalid)
         np.testing.assert_equal(self.widget.graph.selection, selection)
 
-    def test_output_preprocessor(self):
-        self.send_signal(self.widget.Inputs.data, self.data)
-        pp = self.get_output(self.widget.Outputs.preprocessor)
-        self.assertIsInstance(pp, Preprocess)
-        transformed = pp(self.data[::10])
-        self.assertIsInstance(transformed, Table)
-        self.assertEqual(transformed.X.shape, (len(self.data) / 10, 2))
-        output = self.get_output(self.widget.Outputs.annotated_data)
-        np.testing.assert_array_equal(transformed.X, output.metas[::10, :2])
-        self.assertEqual([a.name for a in transformed.domain.attributes],
-                         [m.name for m in output.domain.metas[:2]])
-
 
 class datasets:
     @staticmethod

Orange/widgets/unsupervised/owpca.py

@@ -40,7 +40,6 @@ class Outputs:
         transformed_data = Output("Transformed data", Table)
         components = Output("Components", Table)
         pca = Output("PCA", PCA, dynamic=False)
-        preprocessor = Output("Preprocessor", preprocess.Preprocess)
 
     settingsHandler = settings.DomainContextHandler()
 
@@ -265,7 +264,6 @@ def clear_outputs(self):
         self.Outputs.transformed_data.send(None)
         self.Outputs.components.send(None)
         self.Outputs.pca.send(self._pca_projector)
-        self.Outputs.preprocessor.send(None)
 
     def get_model(self):
         if self.rpca is None:
@@ -426,7 +424,7 @@ def _update_axis(self):
         axis.setTicks([[(i, str(i+1)) for i in range(0, p, d)]])
 
     def commit(self):
-        transformed = components = pp = None
+        transformed = components = None
         if self._pca is not None:
             if self._transformed is None:
                 # Compute the full transform (MAX_COMPONENTS components) only once.
@@ -450,13 +448,10 @@ def commit(self):
                                metas=metas)
             components.name = 'components'
 
-            pp = preprocess.ApplyDomain(domain, "PCA")
-
         self._pca_projector.component = self.ncomponents
         self.Outputs.transformed_data.send(transformed)
         self.Outputs.components.send(components)
         self.Outputs.pca.send(self._pca_projector)
-        self.Outputs.preprocessor.send(pp)
 
     def send_report(self):
         if self.data is None:

Orange/widgets/unsupervised/owtsne.py

@@ -14,7 +14,7 @@
 from Orange.widgets.utils.widgetpreview import WidgetPreview
 from Orange.widgets.visualize.owscatterplotgraph import OWScatterPlotBase
 from Orange.widgets.visualize.utils.widget import OWDataProjectionWidget
-from Orange.widgets.widget import Msg, Output
+from Orange.widgets.widget import Msg
 
 
 class TSNERunner:
@@ -86,9 +86,6 @@ class OWtSNE(OWDataProjectionWidget):
     #: Runtime state
     Running, Finished, Waiting, Paused = 1, 2, 3, 4
 
-    class Outputs(OWDataProjectionWidget.Outputs):
-        preprocessor = Output("Preprocessor", preprocess.Preprocess)
-
     class Error(OWDataProjectionWidget.Error):
         not_enough_rows = Msg("Input data needs at least 2 rows")
         constant_data = Msg("Input data is constant")
@@ -370,10 +367,6 @@ def setup_plot(self):
         super().setup_plot()
         self.start()
 
-    def commit(self):
-        super().commit()
-        self.send_preprocessor()
-
     def _get_projection_data(self):
         if self.data is None:
             return None
@@ -389,12 +382,6 @@ def _get_projection_data(self):
                 self.data.domain.metas + self.projection.domain.attributes)
         return data
 
-    def send_preprocessor(self):
-        prep = None
-        if self.data is not None and self.projection is not None:
-            prep = preprocess.ApplyDomain(self.projection.domain, self.projection.name)
-        self.Outputs.preprocessor.send(prep)
-
     def clear(self):
         super().clear()
         self.__state = OWtSNE.Waiting

Orange/widgets/unsupervised/tests/test_owpca.py

@@ -6,7 +6,7 @@
 
 from Orange.data import Table, Domain, ContinuousVariable, TimeVariable
 from Orange.preprocess import preprocess
-from Orange.preprocess.preprocess import Preprocess, Normalize
+from Orange.preprocess.preprocess import Normalize
 from Orange.widgets.tests.base import WidgetTest
 from Orange.widgets.tests.utils import table_dense_sparse
 from Orange.widgets.unsupervised.owpca import OWPCA
@@ -202,15 +202,3 @@ def test_do_not_mask_features(self):
         self.widget.set_data(data)
         ndata = Table("iris.tab")
         self.assertEqual(data.domain[0], ndata.domain[0])
-
-    def test_output_preprocessor(self):
-        self.send_signal(self.widget.Inputs.data, self.iris)
-        pp = self.get_output(self.widget.Outputs.preprocessor)
-        self.assertIsInstance(pp, Preprocess)
-        transformed_data = pp(self.iris[::10])
-        self.assertIsInstance(transformed_data, Table)
-        self.assertEqual(transformed_data.X.shape, (15, 2))
-        output = self.get_output(self.widget.Outputs.transformed_data)
-        np.testing.assert_array_equal(transformed_data.X, output.X[::10])
-        self.assertEqual([a.name for a in transformed_data.domain.attributes],
-                         [m.name for m in output.domain.attributes])

Orange/widgets/unsupervised/tests/test_owtsne.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from Orange.data import DiscreteVariable, ContinuousVariable, Domain, Table
-from Orange.preprocess import Preprocess, Normalize
+from Orange.preprocess import Normalize
 from Orange.projection.manifold import TSNE
 from Orange.widgets.tests.base import (
     WidgetTest, WidgetOutputsTestMixin, ProjectionWidgetTestMixin
@@ -111,28 +111,6 @@ def test_attr_models(self):
                 self.assertNotIn(var, controls.attr_size.model())
                 self.assertIn(var, controls.attr_shape.model())
 
-    def test_output_preprocessor(self):
-        # To test the validity of the preprocessor, we'll have to actually
-        # compute the projections
-        self.restore_mocked_functions()
-
-        self.send_signal(self.widget.Inputs.data, self.data)
-        self.wait_until_stop_blocking(wait=20000)
-        output_data = self.get_output(self.widget.Outputs.annotated_data)
-
-        # We send the same data to the widget, we expect the point locations to
-        # be fairly close to their original ones
-        pp = self.get_output(self.widget.Outputs.preprocessor)
-        self.assertIsInstance(pp, Preprocess)
-
-        transformed_data = pp(self.data)
-        self.assertIsInstance(transformed_data, Table)
-        self.assertEqual(transformed_data.X.shape, (len(self.data), 2))
-        np.testing.assert_allclose(transformed_data.X, output_data.metas[:, :2],
-                                   rtol=1, atol=3)
-        self.assertEqual([a.name for a in transformed_data.domain.attributes],
-                         [m.name for m in output_data.domain.metas[:2]])
-
     def test_multiscale_changed(self):
         self.assertFalse(self.widget.controls.multiscale.isChecked())
         self.assertTrue(self.widget.perplexity_spin.isEnabled())

Orange/widgets/visualize/utils/widget.py

@@ -10,7 +10,6 @@
 )
 from Orange.data.util import get_unique_names, array_equal
 from Orange.data.sql.table import SqlTable
-from Orange.preprocess.preprocess import Preprocess, ApplyDomain
 from Orange.statistics.util import bincount
 
 from Orange.widgets import gui, report
@@ -624,7 +623,6 @@ class OWAnchorProjectionWidget(OWDataProjectionWidget):
 
     class Outputs(OWDataProjectionWidget.Outputs):
         components = Output("Components", Table)
-        preprocessor = Output("Preprocessor", Preprocess)
 
     class Error(OWDataProjectionWidget.Error):
         sparse_data = Msg("Sparse data is not supported")
@@ -702,7 +700,6 @@ def _get_projection_data(self):
     def commit(self):
         super().commit()
         self.send_components()
-        self.send_preprocessor()
 
     def send_components(self):
         components = None
@@ -721,12 +718,6 @@ def _send_components_metas(self):
         variable_names = [a.name for a in self.projection.domain.attributes]
         return np.array(variable_names, dtype=object)[:, None]
 
-    def send_preprocessor(self):
-        prep = None
-        if self.data is not None and self.projection is not None:
-            prep = ApplyDomain(self.projection.domain, self.projection.name)
-        self.Outputs.preprocessor.send(prep)
-
     def clear(self):
         super().clear()
         self.projector = self.projection = None