Removed a few 'final' arguments that are no longer used

atomsci/ddm/pipeline/perf_plots.py

@@ -138,7 +138,7 @@ def plot_pred_vs_actual(model, epoch_label='best', threshold=None, error_bars=Fa
                 subs_pred_df=pred_df[pred_df.subset==subset]
 
                 perf_data = wrapper.get_perf_data(subset, epoch_label)
-                pred_results = perf_data.get_prediction_results('final')
+                pred_results = perf_data.get_prediction_results()
                 # pred_df=pfm.predict_from_pipe(model)
                 std=len([x for x in pred_df.columns if 'std' in x]) > 0
                 if perf_data.num_tasks > 1:
@@ -164,7 +164,7 @@ def plot_pred_vs_actual(model, epoch_label='best', threshold=None, error_bars=Fa
         # % binding / inhibition data, with one row per subset.
         for s, subset in enumerate(subsets):
             perf_data = wrapper.get_perf_data(subset, epoch_label)
-            pred_results = perf_data.get_prediction_results('final')
+            pred_results = perf_data.get_prediction_results()
             y_actual = perf_data.get_real_values('final')
             ids, y_pred, y_std = perf_data.get_pred_values('final')
             r2 = pred_results['r2_score']

atomsci/ddm/test/integrative/hybrid/test_hybrid.py

@@ -52,7 +52,7 @@ def test():
 
     print("Check the model performance on validation data")
     pred_data = pl.model_wrapper.get_perf_data(subset="valid", epoch_label="best")
-    pred_results = pred_data.get_prediction_results('final')
+    pred_results = pred_data.get_prediction_results()
     print(pred_results)
 
     pred_score = pred_results['r2_score']

atomsci/ddm/utils/hyperparam_search_wrapper.py

@@ -1564,7 +1564,7 @@ def lossfn(p):
             for subset in subsets:
                 if not model_failed:
                     perf_data = pl.model_wrapper.get_perf_data(subset=subset, epoch_label="best")
-                    sub_pred_results = perf_data.get_prediction_results('final')
+                    sub_pred_results = perf_data.get_prediction_results()
                 else:
                     if tparam.prediction_type == "regression":
                         sub_pred_results = {"r2_score": 0, "rms_score": 100}