prediction_generation/cpdbench_mozilla_rep.py [258:343]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - series = data['series'][0]['raw'] push_timestamp = data['time']['raw'] push_timestamp = [datetime.strptime(ts, "%Y-%m-%d %H:%M:%S") for ts in push_timestamp] #unique_push_timestamp = sorted(set(datetime.strptime(ts, "%Y-%m-%d %H:%M:%S") for ts in push_timestamp)) unique_push_timestamp = sorted(set(push_timestamp)) grouped_data = defaultdict(list) for ts, value in zip(push_timestamp, series): grouped_data[ts].append(value) data = [RevisionDatum(ts, ts, grouped_data[ts]) for ts in grouped_data] # data_sorted = sorted(data) # These values are the default taken from the Mozilla code, Note that min_back_window, max_back_window, and fore_window come from class Performancesignature, I did not find them on record in the signatures data we have o we will be using the defaults min_back_window=12 max_back_window=24 fore_window=12 alert_threshold=2 analyzed_series = detect_changes( data, min_back_window=min_back_window, max_back_window=max_back_window, fore_window=fore_window, ) locations = [] #with transaction.atomic(): for prev, cur in zip(analyzed_series, analyzed_series[1:]): if cur.change_detected: prev_value = cur.historical_stats["avg"] new_value = cur.forward_stats["avg"] alert_properties = get_alert_properties( prev_value, new_value, signature.lower_is_better ) noise_profile = "N/A" try: # Gather all data up to the current data point that # shows the regression and obtain a noise profile on it. # This helps us to ignore this alert and others in the # calculation that could influence the profile. noise_data = [] for point in analyzed_series: if point == cur: break noise_data.append(geomean(point.values)) noise_profile, _ = moz_measure_noise.deviance(noise_data) if not isinstance(noise_profile, str): raise Exception( f"Expecting a string as a noise profile, got: {type(noise_profile)}" ) except Exception: # Fail without breaking the alert computation newrelic.agent.notice_error() logger.error("Failed to obtain a noise profile.") # ignore regressions below the configured regression # threshold # ALERT_PCT, ALERT_ABS, and ALERT_CHANGE_TYPES come from the PerformanceSignature class in the Treeherder code ALERT_PCT = 0 ALERT_ABS = 1 ALERT_CHANGE_TYPES = ((ALERT_PCT, "percentage"), (ALERT_ABS, "absolute")) if ( ( signature.alert_change_type is None or signature.alert_change_type == ALERT_PCT ) and alert_properties.pct_change < alert_threshold ) or ( signature.alert_change_type == ALERT_ABS and abs(alert_properties.delta) < alert_threshold ): continue # summary, _ = PerformanceAlertSummary.objects.get_or_create( # repository=signature.repository, # framework=signature.framework, # push_id=cur.push_id, # prev_push_id=prev.push_id, # defaults={ # "manually_created": False, # "created": datetime.utcfromtimestamp(cur.push_timestamp), # }, # ) # django/mysql doesn't understand "inf", so just use some # arbitrarily high value for that case t_value = cur.t if t_value == float("inf"): t_value = 1000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - prediction_generation/old-code/cpdbench_mozilla_rep copy.py [258:343]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - series = data['series'][0]['raw'] push_timestamp = data['time']['raw'] push_timestamp = [datetime.strptime(ts, "%Y-%m-%d %H:%M:%S") for ts in push_timestamp] #unique_push_timestamp = sorted(set(datetime.strptime(ts, "%Y-%m-%d %H:%M:%S") for ts in push_timestamp)) unique_push_timestamp = sorted(set(push_timestamp)) grouped_data = defaultdict(list) for ts, value in zip(push_timestamp, series): grouped_data[ts].append(value) data = [RevisionDatum(ts, ts, grouped_data[ts]) for ts in grouped_data] # data_sorted = sorted(data) # These values are the default taken from the Mozilla code, Note that min_back_window, max_back_window, and fore_window come from class Performancesignature, I did not find them on record in the signatures data we have o we will be using the defaults min_back_window=12 max_back_window=24 fore_window=12 alert_threshold=2 analyzed_series = detect_changes( data, min_back_window=min_back_window, max_back_window=max_back_window, fore_window=fore_window, ) locations = [] #with transaction.atomic(): for prev, cur in zip(analyzed_series, analyzed_series[1:]): if cur.change_detected: prev_value = cur.historical_stats["avg"] new_value = cur.forward_stats["avg"] alert_properties = get_alert_properties( prev_value, new_value, signature.lower_is_better ) noise_profile = "N/A" try: # Gather all data up to the current data point that # shows the regression and obtain a noise profile on it. # This helps us to ignore this alert and others in the # calculation that could influence the profile. noise_data = [] for point in analyzed_series: if point == cur: break noise_data.append(geomean(point.values)) noise_profile, _ = moz_measure_noise.deviance(noise_data) if not isinstance(noise_profile, str): raise Exception( f"Expecting a string as a noise profile, got: {type(noise_profile)}" ) except Exception: # Fail without breaking the alert computation newrelic.agent.notice_error() logger.error("Failed to obtain a noise profile.") # ignore regressions below the configured regression # threshold # ALERT_PCT, ALERT_ABS, and ALERT_CHANGE_TYPES come from the PerformanceSignature class in the Treeherder code ALERT_PCT = 0 ALERT_ABS = 1 ALERT_CHANGE_TYPES = ((ALERT_PCT, "percentage"), (ALERT_ABS, "absolute")) if ( ( signature.alert_change_type is None or signature.alert_change_type == ALERT_PCT ) and alert_properties.pct_change < alert_threshold ) or ( signature.alert_change_type == ALERT_ABS and abs(alert_properties.delta) < alert_threshold ): continue # summary, _ = PerformanceAlertSummary.objects.get_or_create( # repository=signature.repository, # framework=signature.framework, # push_id=cur.push_id, # prev_push_id=prev.push_id, # defaults={ # "manually_created": False, # "created": datetime.utcfromtimestamp(cur.push_timestamp), # }, # ) # django/mysql doesn't understand "inf", so just use some # arbitrarily high value for that case t_value = cur.t if t_value == float("inf"): t_value = 1000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -