change for PR

src_py/apiServer/stats.py

@@ -146,78 +146,6 @@ def expend_labels_df(self, df):
         return df
 
 
-    def survey(results, category_names):
-        """
-        Parameters
-        ----------
-        results : dict
-            A mapping from question labels to a list of answers per category.
-            It is assumed all lists contain the same number of entries and that
-            it matches the length of *category_names*.
-        category_names : list of str
-            The category labels.
-        """
-        labels = list(results.keys())
-        data = np.array(list(results.values()))
-        data_cum = data.cumsum(axis=1)
-        category_colors = plt.colormaps['RdYlGn'](np.linspace(0.15, 0.85, data.shape[1]))
-        fig, ax = plt.subplots(figsize=(9.2, 5))
-        ax.invert_yaxis()
-        ax.xaxis.set_visible(False)
-        ax.set_xlim(0, np.sum(data, axis=1).max())
-
-        for i, (colname, color) in enumerate(zip(category_names, category_colors)):
-            widths = data[:, i]
-            starts = data_cum[:, i] - widths
-            rects = ax.barh(labels, widths, left=starts, height=0.5,label=colname, color=color)
-
-            r, g, b, _ = color
-            text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
-            ax.bar_label(rects, label_type='center', color=text_color)
-            ax.legend(ncols=len(category_names), bbox_to_anchor=(0, 1),loc='lower left', fontsize='small')
-
-        return fig, ax
-
-    def get_distributed_train_labels(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
-        assert self.phase == PHASE_TRAINING_STR, "This function is only available for train phase"   
-        sources_pieces_list = self.experiment_phase.get_sources_pieces()
-        workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
-        dict_worker = {}
-        for worker in workers_model_db_list:
-            dict_worker[worker.get_worker_name()] = []
-        labels_d = []
-        for source_piece_inst in sources_pieces_list:
-            sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet()
-            df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
-            print(df_actual_labels,"df_actual_labels")
-            num_of_labels = df_actual_labels.shape[1]
-            header_list = range(num_of_labels) 
-            df_actual_labels.columns = header_list
-            labels_d = header_list
-            source_name = source_piece_inst.get_source_name()
-            # build confusion matrix for each worker
-            target_workers = source_piece_inst.get_target_workers()
-            batch_size = source_piece_inst.get_batch_size()
-            for worker_db in workers_model_db_list:
-                worker_name = worker_db.get_worker_name()
-                if worker_name not in target_workers:
-                    continue
-                if(dict_worker[worker_name] == []):
-                    dict_worker[worker_name] = [0] * num_of_labels
-                df_worker_labels = df_actual_labels.copy()
-                total_batches_per_source = worker_db.get_total_batches_per_source(source_name)
-                for batch_id in range(total_batches_per_source):
-                    batch_db = worker_db.get_batch(source_name, str(batch_id))
-                    if not batch_db: # if batch is missing
-                        if not self.missed_batches_warning_msg:
-                            LOG_WARNING(f"missed batches")
-                            self.missed_batches_warning_msg = True
-                        dict_worker[worker_name][sum(df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size])] +=1 # set the actual label to None for the predict labels in the df 
-
-        self.survey(dict_worker, labels_d)
-        plt.show()
-
-
     def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
         assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   

src_py/apiServer/statsTiles.py

@@ -21,6 +21,12 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
         confusion_matrix_source_dict, confusion_matrix_distributed_dict = self.get_confusion_matrices_tiles(normalize ,plot ,saveToFile )
         return confusion_matrix_source_dict, confusion_matrix_distributed_dict
 
+
+    """
+        This function is used to calculate the confusion matrix for each cluster and each class in the distributed tokens
+        attention! get_confusion_matrices_tiles take the sum of the labels and from that do majority vote and
+        then decide 0 or 1 for each class
+    """
     def get_confusion_matrices_tiles(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
         assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
@@ -168,7 +174,12 @@ def argmax_axis_1(self,list_of_lists):
         result = [sublist.index(max(sublist)) for sublist in list_of_lists]
         return result
 
+    """
+        This function is used to calculate the confusion matrix for each cluster and each class in the distributed tokens
+        attention! get_confusion_matrices_tiles_new take the sum of the probabilities and from that do majority vote and
+        then decide 0 or 1 for each class
 
+    """
     def get_confusion_matrices_tiles_new(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
         assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"