From ffc1ee512e1bcfabc1a3965e7e9fd2b063053e15 Mon Sep 17 00:00:00 2001 From: Priya Kasimbeg Date: Tue, 2 Jul 2024 18:42:22 +0000 Subject: [PATCH] remove debugging statements --- scoring/performance_profile.py | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/scoring/performance_profile.py b/scoring/performance_profile.py index ae5984381..31106f057 100644 --- a/scoring/performance_profile.py +++ b/scoring/performance_profile.py @@ -236,8 +236,6 @@ def get_workloads_time_to_target(submission, df = pd.DataFrame.from_records(workloads) df = df.pivot(index='submission', columns='workload', values=time_col) - logging.info("HELLOOOOOOOOO") - print_dataframe(df) return df @@ -306,16 +304,10 @@ def compute_performance_profiles(submissions, strict)) df = pd.concat(dfs) - logging.info("TIME TO TARGET") - print_dataframe(df) - # Set score to inf if not within 4x of fastest submission best_scores = df.min(axis=0) df[df.apply(lambda x: x > 4 * best_scores, axis=1)] = np.inf - logging.info("4X of budget") - print_dataframe(df) - # For each held-out workload if variant target was not hit set submission to inf framework = None for workload in df.keys(): @@ -325,9 +317,6 @@ def compute_performance_profiles(submissions, df[base_workload] = df.apply( variant_criteria_filter(base_workload, workload), axis=1) - logging.info("HELDOUT_WORKLOAD FILTER") - print_dataframe(df) - df = df[BASE_WORKLOADS] if verbosity > 0: @@ -356,17 +345,11 @@ def compute_performance_profiles(submissions, 1000): logging.info(df) - logging.info('DIVIDE BY FASTEST') - print_dataframe(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() - logging.info('AFTER MAYBE SETTING MAX TAU') - print_dataframe(df) - if scale == 'linear': points = np.linspace(min_tau, max_tau, num=num_points) elif scale == 'log':