Add unlabel functionality

CHANGELOG.md

@@ -9,11 +9,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 * Index searching to data manager.
 * Search highlighting in data manager.
+* "Unlabel" option to remove previously labeled data from training set.
 
 ### Fixed
 * Data column sorting not working.
 * Needing to render the notebook's first model view cell twice for ipyvuetify
-    stylesheets to propagate.
+    stylesheets to propagate. (Use `icat.initialize()` now, instead of
+    `pn.extension()`)
 
 
 

icat/data.py

@@ -323,6 +323,7 @@ def _handle_ipv_search_cleared(self, widget, event, data):
         self.search_value = ""
         self.search_box.success = False
         self.search_box.error = False
+        self.search_box.label = "Search (use 'ID:X' to search index)"
 
     def _handle_row_selected(self, point_id):
         """Event handler from table row select."""

icat/model.py

@@ -73,7 +73,11 @@ def __init__(
         self.anchor_list.build_tfidf_features()
 
     def _on_data_label(self, index: int | list[int], new_label: int | list[int]):
-        """Event handler for datamanager."""
+        """Event handler for datamanager.
+
+        if a -1 is passed for (or in the list of) new_label, remove it from the
+        training data, if found.
+        """
 
         # expand a single value pass to list for consistent handling below
         if type(index) != list:
@@ -98,22 +102,12 @@ def _on_data_label(self, index: int | list[int], new_label: int | list[int]):
                         ]
                     )
 
-        # update if it's a row that's already in the training data
-        # elif (
-        #     index in self.training_data.index
-        #     and self.training_data.loc[index, self.data.text_col]
-        #     == self.data.active_data.loc[index, self.data.text_col]
-        # ):
-        #     self.training_data.at[index, self.data.label_col] = new_label
-
-        # else:
-        #     self.training_data = pd.concat(
-        #         # self.training_data, pd.DataFrame(self.data.active_data[index, :])
-        #         [
-        #             self.training_data,
-        #             pd.DataFrame([self.data.active_data.loc[index, :]]),
-        #         ]
-        #     )
+        # Remove any rows from the resulting table where the label_col is "-1", indicating an
+        # unlabelling operation
+        self.training_data = self.training_data.drop(
+            self.training_data[self.training_data[self.data.label_col] == -1].index
+        )
+
         # note that we don't call fit because we don't need to re-featurize, only a label has changed
         self._train_model()
         self.view.refresh_data()

icat/table.py

@@ -94,6 +94,10 @@ def vue_applyAbsoluteLabelInteresting(self, point_id):
         for callback in self._apply_label_callbacks:
             callback(point_id, 1)
 
+    def vue_applyAbsoluteLabelUnlabeled(self, point_id):
+        for callback in self._apply_label_callbacks:
+            callback(point_id, -1)
+
     def vue_addToExampleAnchor(self, point_id):
         for callback in self._add_example_callbacks:
             callback(point_id)
@@ -152,6 +156,9 @@ def _template(self):
                                 <span>Add this instance to the current sample set.</span>
                             </v-tooltip>
                             <div v-html="item.labeled" />
+                            <v-btn x-small v-if="item.labeled != ''" class="red darken-3" @click.stop="applyAbsoluteLabelUnlabeled(item.id)">
+                                unlabel
+                            </v-btn>
                         </td>
                     </tr>
                 </tbody>