Updated list of models to load; added a model description for those t…

…hat have an embedding layer that learned simultaneously

callbacks.py

@@ -151,44 +151,54 @@ def load_model_callback(model_name, dataset_name):
     # Based on the chosen dataset and model type, set a file path to the desired model
     if dataset_name == 'ALS':
         subdata_path = 'ALS/'
-        if model_name == 'Bidir LSTM, embedded, time aware':
+        if model_name == 'Bidir LSTM, time aware':
             # Specify the model file name and class
-            model_file_name = 'lstm_bidir_pre_embedded_delta_ts_90dayswindow_0.3481valloss_06_07_2020_04_15'
+            model_file_name = 'lstm_bidir_one_hot_encoded_delta_ts_90dayswindow_0.3784valloss_08_07_2020_04_14'
             model_class = Models.VanillaLSTM
             is_custom = False
-        elif model_name == 'Bidir LSTM, time aware':
+        elif model_name == 'Bidir LSTM, embedded':
             # Specify the model file name and class
-            model_file_name = 'lstm_bidir_one_hot_encoded_delta_ts_90dayswindow_0.3809valloss_06_07_2020_04_08'
+            model_file_name = 'lstm_bidir_pre_embedded_90dayswindow_0.2490valloss_06_07_2020_03_47'
             model_class = Models.VanillaLSTM
             is_custom = False
-        elif model_name == 'Bidir LSTM, embedded':
+        elif model_name == 'Bidir LSTM':
             # Specify the model file name and class
-            model_file_name = 'lstm_bidir_pre_embedded_90dayswindow_0.2490valloss_06_07_2020_03_47'
+            model_file_name = 'lstm_bidir_one_hot_encoded_90dayswindow_0.4497valloss_08_07_2020_04_31'
+            model_class = Models.VanillaLSTM
+            is_custom = False
+        elif model_name == 'Bidir LSTM, embedded, time aware':
+            # Specify the model file name and class
+            model_file_name = 'lstm_bidir_pre_embedded_delta_ts_90dayswindow_0.3705valloss_08_07_2020_04_04'
             model_class = Models.VanillaLSTM
             is_custom = False
         elif model_name == 'LSTM':
             # Specify the model file name and class
-            model_file_name = 'lstm_one_hot_encoded_90dayswindow_0.4363valloss_06_07_2020_03_28'
+            model_file_name = 'lstm_one_hot_encoded_90dayswindow_0.5125valloss_08_07_2020_04_41'
             model_class = Models.VanillaLSTM
             is_custom = False
         elif model_name == 'Bidir RNN, embedded, time aware':
             # Specify the model file name and class
-            model_file_name = 'rnn_bidir_pre_embedded_delta_ts_90dayswindow_0.3059valloss_06_07_2020_03_10'
+            model_file_name = 'rnn_bidir_pre_embedded_delta_ts_90dayswindow_0.3579valloss_08_07_2020_03_55'
             model_class = Models.VanillaRNN
             is_custom = False
-        elif model_name == 'RNN, embedded':
+        elif model_name == 'Bidir RNN':
             # Specify the model file name and class
-            model_file_name = 'rnn_with_embedding_90dayswindow_0.5569valloss_30_06_2020_17_04.pth'
+            model_file_name = 'rnn_bidir_one_hot_encoded_90dayswindow_0.3713valloss_08_07_2020_04_49'
+            model_class = Models.VanillaRNN
+            is_custom = False
+        elif model_name == 'RNN, time aware':
+            # Specify the model file name and class
+            model_file_name = 'rnn_one_hot_encoded_delta_ts_90dayswindow_0.5354valloss_21_08_2020_04_24.pth'
             model_class = Models.VanillaRNN
             is_custom = False
         elif model_name == 'RNN':
             # Specify the model file name and class
-            model_file_name = 'rnn_one_hot_encoded_90dayswindow_0.5497valloss_30_06_2020_18_25.pth'
+            model_file_name = 'rnn_one_hot_encoded_90dayswindow_0.5445valloss_21_08_2020_04_34.pth'
             model_class = Models.VanillaRNN
             is_custom = False
         elif model_name == 'MF1-LSTM':
             # Specify the model file name and class
-            model_file_name = 'mf1lstm_one_hot_encoded_90dayswindow_0.6009valloss_07_07_2020_03_46'
+            model_file_name = 'mf1lstm_pre_embedded_90dayswindow_0.6516valloss_07_07_2020_03_35'
             model_class = Models.MF1LSTM
             is_custom = True
     elif dataset_name == 'Toy Example':
@@ -276,7 +286,9 @@ def load_model_description(model_mod, model_file_name):
     else:
         description_list.append(dbc.ListGroupItem(descriptions['embedding']['one hot encoded']))
     # Add time variation description
-    if 'delta_ts' in model_file_name:
+    if 'mf1lstm' in model_file_name:
+        description_list.append(dbc.ListGroupItem(descriptions['delta_ts']['integrates_in_model']))
+    elif 'delta_ts' in model_file_name:
         description_list.append(dbc.ListGroupItem(descriptions['delta_ts']['uses delta_ts']))
     else:
         description_list.append(dbc.ListGroupItem(descriptions['delta_ts']['no delta_ts']))

layouts.py

@@ -75,12 +75,14 @@
             dcc.Dropdown(
                 id='model_dropdown',
                 options=[
-                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='Bidir LSTM, time aware', value='Bidir LSTM, time aware'),
                     dict(label='Bidir LSTM, embedded', value='Bidir LSTM, embedded'),
+                    dict(label='Bidir LSTM', value='Bidir LSTM'),
+                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='LSTM', value='LSTM'),
                     dict(label='Bidir RNN, embedded, time aware', value='Bidir RNN, embedded, time aware'),
-                    dict(label='RNN, embedded', value='RNN, embedded'),
+                    dict(label='Bidir RNN', value='Bidir RNN'),
+                    dict(label='RNN, time aware', value='RNN, time aware'),
                     dict(label='RNN', value='RNN'),
                     dict(label='MF1-LSTM', value='MF1-LSTM')
                 ],
@@ -305,12 +307,14 @@
             dcc.Dropdown(
                 id='model_dropdown',
                 options=[
-                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='Bidir LSTM, time aware', value='Bidir LSTM, time aware'),
                     dict(label='Bidir LSTM, embedded', value='Bidir LSTM, embedded'),
+                    dict(label='Bidir LSTM', value='Bidir LSTM'),
+                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='LSTM', value='LSTM'),
                     dict(label='Bidir RNN, embedded, time aware', value='Bidir RNN, embedded, time aware'),
-                    dict(label='RNN, embedded', value='RNN, embedded'),
+                    dict(label='Bidir RNN', value='Bidir RNN'),
+                    dict(label='RNN, time aware', value='RNN, time aware'),
                     dict(label='RNN', value='RNN'),
                     dict(label='MF1-LSTM', value='MF1-LSTM')
                 ],
@@ -502,12 +506,14 @@
         dcc.Dropdown(
             id='model_dropdown',
             options=[
-                dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                 dict(label='Bidir LSTM, time aware', value='Bidir LSTM, time aware'),
                 dict(label='Bidir LSTM, embedded', value='Bidir LSTM, embedded'),
+                dict(label='Bidir LSTM', value='Bidir LSTM'),
+                dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                 dict(label='LSTM', value='LSTM'),
                 dict(label='Bidir RNN, embedded, time aware', value='Bidir RNN, embedded, time aware'),
-                dict(label='RNN, embedded', value='RNN, embedded'),
+                dict(label='Bidir RNN', value='Bidir RNN'),
+                dict(label='RNN, time aware', value='RNN, time aware'),
                 dict(label='RNN', value='RNN'),
                 dict(label='MF1-LSTM', value='MF1-LSTM')
             ],
@@ -781,12 +787,14 @@
             dcc.Dropdown(
                 id='model_dropdown',
                 options=[
-                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='Bidir LSTM, time aware', value='Bidir LSTM, time aware'),
                     dict(label='Bidir LSTM, embedded', value='Bidir LSTM, embedded'),
+                    dict(label='Bidir LSTM', value='Bidir LSTM'),
+                    dict(label='Bidir LSTM, embedded, time aware', value='Bidir LSTM, embedded, time aware'),
                     dict(label='LSTM', value='LSTM'),
                     dict(label='Bidir RNN, embedded, time aware', value='Bidir RNN, embedded, time aware'),
-                    dict(label='RNN, embedded', value='RNN, embedded'),
+                    dict(label='Bidir RNN', value='Bidir RNN'),
+                    dict(label='RNN, time aware', value='RNN, time aware'),
                     dict(label='RNN', value='RNN'),
                     dict(label='MF1-LSTM', value='MF1-LSTM')
                 ],

models/model_descriptions.yml

@@ -20,5 +20,7 @@ embedding:
 delta_ts:
   uses delta_ts:
     Has time variation between samples in consideration, using that information as an additional feature.
+  integrates_in_model:
+    Incorporates time variation information inside the model architecture.
   no delta_ts:
-    Doesn't directly consider time variation between samples.
+    Doesn't directly consider time variation between samples.