remove debugging print statements

mlcommons · Oct 30, 2023 · 4df526b · 4df526b
1 parent e9d3c7d
commit 4df526b
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Showing 2 changed files with 8 additions and 17 deletions.
diff --git a/scoring/score_submission.py b/scoring/score_submission.py
@@ -21,7 +21,6 @@
 
 def main(_):
   df = scoring_utils.get_experiment_df(FLAGS.experiment_path)
-  print(df)
   results = {
       FLAGS.submission_tag: df,
   }
@@ -33,7 +32,7 @@ def main(_):
       reference_submission_tag=None,
       num_points=100,
       scale='linear',
-      )
+      verbosity=0)
   if not os.path.exists(FLAGS.output_dir):
     os.mkdir(FLAGS.output_dir)
   scoring.plot_performance_profiles(

diff --git a/scoring/scoring.py b/scoring/scoring.py
@@ -140,10 +140,7 @@ def get_index_that_reaches_target(workload_df,
     return -1, -1
   else:
     index_reached = target_reached.apply(np.argmax)
-    print(index_reached)
     trial = index_reached.idxmin()
-    print(trial)
-    print(index_reached)
     return trial, index_reached[trial]
 
 
@@ -168,7 +165,6 @@ def get_times_for_submission(submission,
 
   for workload, group in submission.groupby('workload'):
     workload_name = re.match(WORKLOAD_NAME_PATTERN, workload).group(1)
-    print(workload_name)
     framework = re.match(WORKLOAD_NAME_PATTERN, workload).group(2)
     workload_metadata = WORKLOADS[workload_name]
 
@@ -272,19 +268,18 @@ def compute_performance_profiles(results,
 
   if verbosity > 0:
     print(f'\n`{time_col}` to reach target normalized to best:')
-    # with pd.option_context('display.max_rows',
-    #                        None,
-    #                        'display.max_columns',
-    #                        None,
-    #                        'display.width',
-    #                        1000):
-      # print(df)
+    with pd.option_context('display.max_rows',
+                           None,
+                           'display.max_columns',
+                           None,
+                           'display.width',
+                           1000):
+      print(df)
 
   # If no max_tau is supplied, choose the value of tau that would plot all non
   # inf or nan data.
   if max_tau is None:
     max_tau = df.replace(float('inf'), -1).replace(np.nan, -1).values.max()
-    print(f"MAX TAU: {max_tau}")
 
   if scale == 'linear':
     points = np.linspace(min_tau, max_tau, num=num_points)
@@ -361,9 +356,6 @@ def plot_performance_profiles(perf_df,
   Returns:
     None. If a valid save_dir is provided, save both the plot and perf_df.
   """
-  print("PERF DF")
-  print(perf_df.columns)  
-  print(perf_df.T)
   fig = perf_df.T.plot(figsize=figsize)
   df_col_display = f'log10({df_col})' if scale == 'log' else df_col
   fig.set_xlabel(