Optimize MetisTuner

src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py

@@ -37,40 +37,40 @@ def _outlierDetection_threaded(inputs):
     sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
                         % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
     outlier = None
-    
+
     # Create a diagnostic regression model which removes the sample that we want to evaluate
-    diagnostic_regressor_gp = gp_create_model.createModel(\
+    diagnostic_regressor_gp = gp_create_model.create_model(\
                                     samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
                                     samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
     mu, sigma = gp_prediction.predict(samples_x[samples_idx], diagnostic_regressor_gp['model'])
-    
+
     # 2.33 is the z-score for 98% confidence level
     if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
         outlier = {"samples_idx": samples_idx,
                    "expected_mu": mu,
                    "expected_sigma": sigma,
                    "difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)}
     return outlier
-    
+
 def outlierDetection_threaded(samples_x, samples_y_aggregation):
-    ''' 
+    '''
     Use Multi-thread to detect the outlier
     '''
     outliers = []
-    
+
     threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
                             for samples_idx in range(0, len(samples_x))]
     threads_pool = ThreadPool(min(4, len(threads_inputs)))
     threads_results = threads_pool.map(_outlierDetection_threaded, threads_inputs)
     threads_pool.close()
     threads_pool.join()
-    
+
     for threads_result in threads_results:
         if threads_result is not None:
             outliers.append(threads_result)
         else:
             print("error here.")
-    
+
     outliers = None if len(outliers) == 0 else outliers
     return outliers
 
@@ -79,21 +79,19 @@ def outlierDetection(samples_x, samples_y_aggregation):
     '''
     outliers = []
     for samples_idx in range(0, len(samples_x)):
-        #sys.stderr.write("[%s] DEBUG: Evaluating %d of %d samples\n" 
+        #sys.stderr.write("[%s] DEBUG: Evaluating %d of %d samples\n"
         #  \ % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
-        diagnostic_regressor_gp = gp_create_model.createModel(\
+        diagnostic_regressor_gp = gp_create_model.create_model(\
                                         samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
                                         samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
         mu, sigma = gp_prediction.predict(samples_x[samples_idx],
-                                                     diagnostic_regressor_gp['model'])
+                                          diagnostic_regressor_gp['model'])
         # 2.33 is the z-score for 98% confidence level
         if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
             outliers.append({"samples_idx": samples_idx,
                              "expected_mu": mu,
                              "expected_sigma": sigma,
                              "difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)})
-    
+
     outliers = None if len(outliers) == 0 else outliers
     return outliers
-
-