minor fixes

models/fm4m.py

@@ -308,7 +308,8 @@ def single_modal(model,dataset, downstream_model,params):
                                 verbose=False)
             n_samples = np.minimum(1000, len(x_batch))
             features_umap = reducer.fit_transform(x_batch[:n_samples])
-            x = y_batch.values[:n_samples]
+            try:x = y_batch.values[:n_samples]
+            except: x = y_batch[:n_samples]
             index_0 = [index for index in range(len(x)) if x[index] == 0]
             index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -340,7 +341,8 @@ def single_modal(model,dataset, downstream_model,params):
             reducer = umap.UMAP(metric='euclidean', n_neighbors=  10, n_components=2, low_memory=True, min_dist=0.1, verbose=False)
             n_samples = np.minimum(1000,len(x_batch))
             features_umap = reducer.fit_transform(x_batch[:n_samples])
-            x = y_batch.values[:n_samples]
+            try:x = y_batch.values[:n_samples]
+            except:x = y_batch[:n_samples]
             index_0 = [index for index in range(len(x)) if x[index] == 0]
             index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -371,7 +373,8 @@ def single_modal(model,dataset, downstream_model,params):
                             verbose=False)
         n_samples = np.minimum(1000, len(x_batch))
         features_umap = reducer.fit_transform(x_batch[:n_samples])
-        x = y_batch.values[:n_samples]
+        try:x = y_batch.values[:n_samples]
+        except:x = y_batch[:n_samples]
         #index_0 = [index for index in range(len(x)) if x[index] == 0]
         #index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -398,7 +401,8 @@ def single_modal(model,dataset, downstream_model,params):
                             verbose=False)
         n_samples = np.minimum(1000, len(x_batch))
         features_umap = reducer.fit_transform(x_batch[:n_samples])
-        x = y_batch.values[:n_samples]
+        try:x = y_batch.values[:n_samples]
+        except:x = y_batch[:n_samples]
         # index_0 = [index for index in range(len(x)) if x[index] == 0]
         # index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -426,7 +430,8 @@ def single_modal(model,dataset, downstream_model,params):
                             verbose=False)
         n_samples = np.minimum(1000, len(x_batch))
         features_umap = reducer.fit_transform(x_batch[:n_samples])
-        x = y_batch.values[:n_samples]
+        try:x = y_batch.values[:n_samples]
+        except:x = y_batch[:n_samples]
         # index_0 = [index for index in range(len(x)) if x[index] == 0]
         # index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -454,7 +459,8 @@ def single_modal(model,dataset, downstream_model,params):
                             verbose=False)
         n_samples = np.minimum(1000, len(x_batch))
         features_umap = reducer.fit_transform(x_batch[:n_samples])
-        x = y_batch.values[:n_samples]
+        try:x = y_batch.values[:n_samples]
+        except:x = y_batch[:n_samples]
         # index_0 = [index for index in range(len(x)) if x[index] == 0]
         # index_1 = [index for index in range(len(x)) if x[index] == 1]
 
@@ -546,7 +552,8 @@ def multi_modal(model_list,dataset, downstream_model,params):
         features_umap = reducer.fit_transform(x_batch[:n_samples])
 
         if "Classifier" in downstream_model:
-            x = y_batch.values[:n_samples]
+            try:x = y_batch.values[:n_samples]
+            except:x = y_batch[:n_samples]
             index_0 = [index for index in range(len(x)) if x[index] == 0]
             index_1 = [index for index in range(len(x)) if x[index] == 1]