add option to keep crossval folds constant for a session

src/dynamic_routing_analysis/decoding_utils.py

@@ -36,7 +36,8 @@ def dump_dict_to_zarr(group, data):
                 print(f'Could not save {key} of type {type(value)}')
 
 # 'linearSVC' or 'LDA' or 'RandomForest'
-def decoder_helper(input_data,labels,decoder_type='linearSVC',crossval='5_fold',crossval_index=None,labels_as_index=False):
+def decoder_helper(input_data,labels,decoder_type='linearSVC',crossval='5_fold',
+                   crossval_index=None,labels_as_index=False,train_test_split_input=None):
     #helper function to decode labels from input data using different decoder models
 
     if decoder_type=='linearSVC':
@@ -172,6 +173,11 @@ def decoder_helper(input_data,labels,decoder_type='linearSVC',crossval='5_fold',
         skf = StratifiedKFold(n_splits=5,shuffle=True)
         train_test_split = skf.split(input_data, labels)
 
+    elif crossval=='5_fold_constant':
+        if train_test_split_input is None:
+            raise ValueError('Must provide train_test_split_input')
+        train_test_split = train_test_split_input
+
     for train,test in train_test_split:
 
         clf.fit(X[train],y[train])
@@ -199,7 +205,7 @@ def decoder_helper(input_data,labels,decoder_type='linearSVC',crossval='5_fold',
         if decoder_type == 'LDA' or decoder_type == 'RandomForest' or decoder_type=='LogisticRegression':
             ypred_proba[test,:] = clf.predict_proba(X[test])
         else:
-            ypred_proba[test,:] = np.full((len(test),len(np.unique(labels))), fill_value=np.nan)
+            ypred_proba[test,:] = np.full((len(test),len(np.unique(labels))), fill_value=False)
 
         models.append(clf)
 
@@ -210,7 +216,7 @@ def decoder_helper(input_data,labels,decoder_type='linearSVC',crossval='5_fold',
     if decoder_type == 'LDA' or decoder_type == 'linearSVC':
         coefs = clf.coef_
     else:
-        coefs = np.full((X.shape[1]), fill_value=np.nan)
+        coefs = np.full((X.shape[1]), fill_value=False)
 
     output['cr']=cr_dict_test
 
@@ -878,6 +884,7 @@ def decode_context_with_linear_shift(session=None,params=None,trials=None,units=
     decoder_type=params['decoder_type']
     # use_coefs=params['use_coefs']
     # generate_labels=params['generate_labels']
+
     if 'only_use_all_units' in params:
         only_use_all_units=params['only_use_all_units']
     else:
@@ -887,6 +894,12 @@ def decode_context_with_linear_shift(session=None,params=None,trials=None,units=
         return_results=params['return_results']
     else:
         return_results=False
+
+    if crossval=='5_fold_constant':
+        skf = StratifiedKFold(n_splits=5,shuffle=True)
+        train_test_split = skf.split(input_data, labels)
+    else:
+        train_test_split=None
 
     if session is not None:
         session_info=npc_lims.get_session_info(session)
@@ -1122,7 +1135,8 @@ def decode_context_with_linear_shift(session=None,params=None,trials=None,units=
                         decoder_type=decoder_type,
                         crossval=crossval,
                         crossval_index=None,
-                        labels_as_index=labels_as_index)
+                        labels_as_index=labels_as_index,
+                        train_test_split_input=train_test_split)
 
                 if nunits=='all':
                     break
@@ -1414,11 +1428,11 @@ def compute_significant_decoding_by_area(all_decoder_results):
         diff_from_null_DR['n_expts_DR'].append(len(DR_linear_shift_df.query('area==@area')))
         for nu in n_units:
             diff_from_null_DR['diff_from_null_mean_DR'+nu].append((DR_linear_shift_df.query('area==@area')['true_accuracy'+nu]-
-                                                        DR_linear_shift_df.query('area==@area')['null_accuracy_mean'+nu]).mean())
+                                                        DR_linear_shift_df.query('area==@area')['null_accuracy_median'+nu]).mean())
             diff_from_null_DR['diff_from_null_median_DR'+nu].append((DR_linear_shift_df.query('area==@area')['true_accuracy'+nu]-
                                                             DR_linear_shift_df.query('area==@area')['null_accuracy_median'+nu]).median())
             diff_from_null_DR['diff_from_null_sem_DR'+nu].append((DR_linear_shift_df.query('area==@area')['true_accuracy'+nu]-
-                                                                  DR_linear_shift_df.query('area==@area')['null_accuracy_mean'+nu]).sem())
+                                                                  DR_linear_shift_df.query('area==@area')['null_accuracy_median'+nu]).sem())
             diff_from_null_DR['true_accuracy_DR'+nu].append(DR_linear_shift_df.query('area==@area')['true_accuracy'+nu].median())
             diff_from_null_DR['true_accuracy_sem_DR'+nu].append(DR_linear_shift_df.query('area==@area')['true_accuracy'+nu].sem())
             diff_from_null_DR['null_median_DR'+nu].append(DR_linear_shift_df.query('area==@area')['null_accuracy_median'+nu].median())