type check with is instead of ==

neuro_py/ensemble/decoding/pipeline.py

@@ -262,19 +262,19 @@ def preprocess_data(hyperparams, ohe, nsv_train, nsv_val, nsv_test, bv_train, bv
             num_workers=hyperparams['num_workers'], modeltype=hyperparams['model'])
         hyperparams['model_args']['in_dim'] = X_train.shape[-1]
     else:
-        if type(bv_train[0]) == pd.DataFrame:
+        if type(bv_train[0]) is pd.DataFrame:
             y_train = [y.values[:, hyperparams['behaviors']] for y in bv_train]
         else:
             y_train = [y[:, hyperparams['behaviors']] for y in bv_train]
         nbins_per_tseg = [len(y) for y in y_train]  # number of time bins in each trial
         tseg_bounds_train = np.cumsum([0] + nbins_per_tseg)
-        if type(bv_val[0]) == pd.DataFrame:
+        if type(bv_val[0]) is pd.DataFrame:
             y_val = [y.values[:, hyperparams['behaviors']] for y in bv_val]
         else:
             y_val = [y[:, hyperparams['behaviors']] for y in bv_val]
         nbins_per_tseg = [len(y) for y in y_val]
         tseg_bounds_val = np.cumsum([0] + nbins_per_tseg)
-        if type(bv_test[0]) == pd.DataFrame:
+        if type(bv_test[0]) is pd.DataFrame:
             y_test = [y.values[:, hyperparams['behaviors']] for y in bv_test]
         else:
             y_test = [y[:, hyperparams['behaviors']] for y in bv_test]