Word Enrichment: Added ConcurrentWidgetMixin to compute in another th…

…read

orangecontrib/text/widgets/owwordenrichment.py

@@ -1,20 +1,48 @@
+from types import SimpleNamespace
+from typing import List, Optional, Any
+
 import numpy as np
-from AnyQt.QtWidgets import QTreeWidget, QTreeView, QTreeWidgetItem, \
-    QApplication
+from AnyQt.QtWidgets import QTreeWidget, QTreeView, QTreeWidgetItem
 
 from Orange.data import Table, Domain
 from Orange.widgets import gui
 from Orange.widgets.settings import Setting
+from Orange.widgets.utils.concurrent import ConcurrentWidgetMixin, TaskState
 from Orange.widgets.widget import OWWidget, Msg, Input
 from Orange.statistics.util import FDR
 from PyQt5.QtCore import QSize
 from orangecontrib.text import Corpus
 from orangecontrib.text.util import np_sp_sum
 from orangecontrib.text.stats import hypergeom_p_values
-from orangecontrib.text.vectorization import BowVectorizer
 
 
-class OWWordEnrichment(OWWidget):
+class Result(SimpleNamespace):
+    words: Optional[List[str]] = None
+    p_values: Optional[List[float]] = None
+    fdr_values: Optional[List[float]] = None
+
+
+class Runner:
+    @staticmethod
+    def run(
+            selected_data_transformed: Table,
+            data: Table,
+            result: Result,
+            state: TaskState
+    ) -> None:
+        state.set_status("Listing words")
+        result.words = [
+            i.name for i in selected_data_transformed.domain.attributes]
+        state.set_status("Computing p-values")
+        result.p_values = hypergeom_p_values(
+            data.X, selected_data_transformed.X,
+            callback=state.set_progress_value
+        )
+        state.set_status("Computing FDR values")
+        result.fdr_values = FDR(result.p_values)
+
+
+class OWWordEnrichment(OWWidget, ConcurrentWidgetMixin):
     # Basic widget info
     name = "Word Enrichment"
     description = "Word enrichment analysis for selected documents."
@@ -35,28 +63,22 @@ class Error(OWWidget.Error):
         all_selected = Msg('All examples can not be selected!')
 
     # Settings
-    filter_by_p = Setting(False)
-    filter_p_value = Setting(0.01)
-    filter_by_fdr = Setting(True)
-    filter_fdr_value = Setting(0.2)
+    filter_by_p: bool = Setting(False)
+    filter_p_value: float = Setting(0.01)
+    filter_by_fdr: bool = Setting(True)
+    filter_fdr_value: float = Setting(0.2)
 
     def __init__(self):
-        super().__init__()
+        OWWidget.__init__(self)
+        ConcurrentWidgetMixin.__init__(self)
 
         # Init data
         self.data = None
         self.selected_data = None
-        self.selected_data_transformed = None   # used for transforming the 'selected data' into the 'data' domain
-
-        self.words = []
-        self.p_values = []
-        self.fdr_values = []
+        # used for transforming the 'selected data' into the 'data' domain
+        self.selected_data_transformed = None
 
-        # Info section
-        fbox = gui.widgetBox(self.controlArea, "Info")
-        self.info_all = gui.label(fbox, self, 'Cluster words:')
-        self.info_sel = gui.label(fbox, self, 'Selected words:')
-        self.info_fil = gui.label(fbox, self, 'After filtering:')
+        self.results = Result()
 
         # Filtering settings
         fbox = gui.widgetBox(self.controlArea, "Filter")
@@ -68,7 +90,6 @@ def __init__(self):
         self.spin_p = gui.doubleSpin(hbox, self, 'filter_p_value',
                                      1e-4, 1, step=1e-4, labelWidth=15,
                                      callback=self.filter_and_display,
-                                     callbackOnReturn=True,
                                      tooltip="Max p-value for word")
         self.spin_p.setEnabled(self.filter_by_p)
 
@@ -79,7 +100,6 @@ def __init__(self):
         self.spin_fdr = gui.doubleSpin(hbox, self, 'filter_fdr_value',
                                        1e-4, 1, step=1e-4, labelWidth=15,
                                        callback=self.filter_and_display,
-                                       callbackOnReturn=True,
                                        tooltip="Max p-value for word")
         self.spin_fdr.setEnabled(self.filter_by_fdr)
         gui.rubber(self.controlArea)
@@ -90,7 +110,7 @@ def __init__(self):
         self.sig_words.setColumnCount(len(self.cols))
         self.sig_words.setHeaderLabels(self.cols)
         self.sig_words.setSortingEnabled(True)
-        self.sig_words.setSelectionMode(QTreeView.ExtendedSelection)
+        self.sig_words.setSelectionMode(QTreeView.NoSelection)
         self.sig_words.sortByColumn(2, 0)   # 0 is ascending order
         for i in range(len(self.cols)):
             self.sig_words.resizeColumnToContents(i)
@@ -102,6 +122,8 @@ def sizeHint(self):
     @Inputs.data
     def set_data(self, data=None):
         self.data = data
+        # selected data transformed depends on data domain
+        self.selected_data_transformed = None
 
     @Inputs.selected_data
     def set_data_selected(self, data=None):
@@ -135,7 +157,8 @@ def check_data(self):
                 self.clear()
                 return
             self.data = Corpus.from_table(bow_domain, self.data)
-            self.selected_data_transformed = Corpus.from_table(bow_domain, self.selected_data)
+            self.selected_data_transformed = Corpus.from_table(
+                bow_domain, self.selected_data)
 
             if np_sp_sum(self.selected_data_transformed.X) == 0:
                 self.Error.no_words_overlap()
@@ -144,15 +167,15 @@ def check_data(self):
                 self.Error.all_selected()
                 self.clear()
             else:
+                self.set_input_info()
                 self.apply()
         else:
             self.clear()
 
     def clear(self):
         self.sig_words.clear()
-        self.info_all.setText('Cluster words:')
-        self.info_sel.setText('Selected words:')
-        self.info_fil.setText('After filtering:')
+        self.info.set_input_summary(self.info.NoInput)
+        self.info.set_output_summary(self.info.NoOutput)
 
     def filter_enabled(self, b):
         self.chb_p.setEnabled(b)
@@ -168,43 +191,58 @@ def filter_and_display(self):
         if self.selected_data_transformed is None:  # do nothing when no Data
             return
 
-        count = 0
-        if self.words:
-            for word, pval, fval in zip(self.words, self.p_values, self.fdr_values):
-                if (not self.filter_by_p or pval <= self.filter_p_value) and \
-                        (not self.filter_by_fdr or fval <= self.filter_fdr_value):
-                    it = EATreeWidgetItem(word, pval, fval, self.sig_words)
-                    self.sig_words.addTopLevelItem(it)
-                    count += 1
+        if self.results.words:
+            count = self.build_tree()
+        else:
+            count = 0
 
         for i in range(len(self.cols)):
             self.sig_words.resizeColumnToContents(i)
+        self.set_output_info(count)
 
-        self.info_all.setText('Cluster words: {}'.format(len(self.selected_data_transformed.domain.attributes)))
-        self.info_sel.setText('Selected words: {}'.format(np.count_nonzero(np_sp_sum(self.selected_data_transformed.X, axis=0))))
-        if not self.filter_by_p and not self.filter_by_fdr:
-            self.info_fil.setText('After filtering:')
-            self.info_fil.setEnabled(False)
-        else:
-            self.info_fil.setEnabled(True)
-            self.info_fil.setText('After filtering: {}'.format(count))
-
-    def progress(self, p):
-        self.progressBarSet(p)
+    def build_tree(self) -> int:
+        count = 0
+        for word, pval, fval in zip(
+                self.results.words,
+                self.results.p_values,
+                self.results.fdr_values
+        ):
+            if ((not self.filter_by_p or pval <= self.filter_p_value) and
+                    (not self.filter_by_fdr or fval <= self.filter_fdr_value)):
+                it = EATreeWidgetItem(word, pval, fval, self.sig_words)
+                self.sig_words.addTopLevelItem(it)
+                count += 1
+        return count
+
+    def set_input_info(self) -> None:
+        cluster_words = len(self.selected_data_transformed.domain.attributes)
+        selected_words = np.count_nonzero(np_sp_sum(
+            self.selected_data_transformed.X, axis=0))
+
+        self.info.set_input_summary(
+            f"{cluster_words}|{selected_words}",
+            f"Total words: {cluster_words}\n"
+            f"Words in subset: {selected_words}")
+
+    def set_output_info(self, count: int) -> None:
+        self.info.set_output_summary(str(count), f"{count} words displayed")
 
     def apply(self):
-        self.clear()
-        self.progressBarInit()
+        self.sig_words.clear()
         self.filter_enabled(False)
-
-        self.words = [i.name for i in self.selected_data_transformed.domain.attributes]
-        self.p_values = hypergeom_p_values(self.data.X,
-                                           self.selected_data_transformed.X,
-                                           callback=self.progress)
-        self.fdr_values = FDR(self.p_values)
+        self.start(
+            Runner.run,
+            self.selected_data_transformed,
+            self.data,
+            self.results
+        )
+
+    def on_done(self, result: Result) -> None:
         self.filter_and_display()
         self.filter_enabled(True)
-        self.progressBarFinished()
+
+    def on_exception(self, ex: Exception) -> None:
+        self.filter_enabled(True)
 
     def tree_to_table(self):
         view = [self.cols]
@@ -214,12 +252,13 @@ def tree_to_table(self):
             for j in range(3):
                 line.append(self.sig_words.topLevelItem(i).text(j))
             view.append(line)
-        return(view)
+        return view
 
     def send_report(self):
-        if self.words:
+        if self.results.words:
             self.report_table("Enriched words", self.tree_to_table())
 
+
 fp = lambda score: "%0.5f" % score if score > 10e-3 else "%0.1e" % score
 fpt = lambda score: "%0.9f" % score if score > 10e-3 else "%0.5e" % score
 
@@ -238,19 +277,13 @@ def __lt__(self, other):
         col = self.treeWidget().sortColumn()
         return self.data[col] < other.data[col]
 
-def main():
+
+if __name__ == '__main__':
+    from orangewidget.utils.widgetpreview import WidgetPreview
+    from orangecontrib.text.vectorization import BowVectorizer
 
     corpus = Corpus.from_file('book-excerpts')
     vect = BowVectorizer()
     corpus_vect = vect.transform(corpus)
-    app = QApplication([])
-    widget = OWWordEnrichment()
-    widget.set_data(corpus_vect)
-    subset_corpus = corpus_vect[:10]
-    widget.set_data_selected(subset_corpus)
-    widget.handleNewSignals()
-    widget.show()
-    app.exec()
-
-if __name__ == '__main__':
-    main()
+    WidgetPreview(OWWordEnrichment).run(
+        set_data_selected=corpus_vect[:10], set_data=corpus_vect)