OWManifold: Add absolute distances to tSNE

Author: pavlin-policar

Orange/widgets/unsupervised/owmanifoldlearning.py

@@ -3,17 +3,27 @@
 from AnyQt.QtWidgets import QWidget, QVBoxLayout
 
 from Orange.data import Table, Domain, ContinuousVariable
-from Orange.distance import (
-    Euclidean, Manhattan, Jaccard, Cosine, SpearmanR, PearsonR, Distance,
-    MahalanobisDistance,
-)
+from Orange import distance
 from Orange.projection import (MDS, Isomap, LocallyLinearEmbedding,
                                SpectralEmbedding, TSNE)
 from Orange.widgets import gui
 from Orange.widgets.settings import Setting, SettingProvider
 from Orange.widgets.widget import OWWidget, Msg, Input, Output
 
 
+METRICS = [
+    distance.Euclidean,
+    distance.Manhattan,
+    distance.MahalanobisDistance(),
+    distance.Jaccard,
+    distance.Cosine,
+    distance.SpearmanR,
+    distance.SpearmanRAbsolute,
+    distance.PearsonR,
+    distance.PearsonRAbsolute,
+]
+
+
 class ManifoldParametersEditor(QWidget, gui.OWComponent):
     def __init__(self, parent):
         QWidget.__init__(self, parent)
@@ -90,12 +100,8 @@ def __radio_parameter_update(self, name):
 
 
 class TSNEParametersEditor(ManifoldParametersEditor):
-    metrics = [
-        Euclidean, Manhattan, Jaccard, MahalanobisDistance(), Cosine,
-        SpearmanR, PearsonR,
-    ]
     metric_index = Setting(0)
-    metric_values = [(metric, metric.name) for metric in metrics]
+    metric_values = [(metric, metric.name) for metric in METRICS]
 
     perplexity = Setting(30)
     early_exaggeration = Setting(4)
@@ -107,6 +113,7 @@ class TSNEParametersEditor(ManifoldParametersEditor):
 
     def __init__(self, parent):
         super().__init__(parent)
+
         self.metric_combo = self._create_combo_parameter(
             "metric", "Metric:")
         self.perplexity_spin = self._create_spin_parameter(
@@ -208,9 +215,8 @@ class Error(OWWidget.Error):
         manifold_error = Msg("{}")
         sparse_methods = Msg('Only t-SNE method supported on sparse data')
         sparse_metric = Msg(
-            ", ".join(m.name for m in TSNEParametersEditor.metrics[:-1]
-                      if not m.supports_sparse)
-            + " and " + TSNEParametersEditor.metrics[-1].name +
+            ", ".join(m.name for m in METRICS[:-1] if not m.supports_sparse) +
+            " and " + METRICS[-1].name +
             " distances not supported with sparse data."
         )
 
@@ -286,7 +292,7 @@ def apply(self):
         metric = params.get('metric')
 
         # Make sure that the metric supports sparse data
-        if data.is_sparse() and isinstance(metric, Distance) \
+        if data.is_sparse() and isinstance(metric, distance.Distance) \
                 and not metric.supports_sparse:
             self.Error.sparse_metric()
 
@@ -297,7 +303,7 @@ def apply(self):
                             data.domain.metas)
             try:
                 # Mahalanobis distance must first be fit to the data
-                if isinstance(metric, MahalanobisDistance):
+                if isinstance(metric, distance.MahalanobisDistance):
                     metric.fit(data)
 
                 projector = method(**params)

Orange/widgets/unsupervised/tests/test_owmanifoldlearning.py

@@ -101,7 +101,8 @@ def test_singular_matrices(self):
                 [0, 1, 2],
                 [0, 1, 1]))
         )
-        metrics = {m.name: i for i, m in enumerate(self.widget.tsne_editor.metrics)}
+        metrics = {m.name: i for i, (m, _)
+                   in enumerate(self.widget.tsne_editor.metric_values)}
 
         self.send_signal(self.widget.Inputs.data, table)
         self.widget.manifold_methods_combo.activated.emit(0)  # t-SNE