def main()

in prediction_generation/old-code/cpdbench_mozilla_rep_debug.py [0:0]

• 75 lines of code
• 24 McCabe index (conditional complexity)

def main():
    logger = logging.getLogger(__name__)
    args = parse_args()
    data, mat = load_dataset(args.input)
    raw_data = data.copy()
    start_time = time.time()
    with open(args.signatures_attributes, 'r') as file:
        signatures_attributes = json.load(file)
    signature_id = os.path.splitext(os.path.basename(args.input))[0]
    signature_attributes = signatures_attributes[signature_id]
    Signature = namedtuple('Signature', signature_attributes.keys())
    signature = Signature(**signature_attributes)
    # print(len(data['time']['raw']))
    # print(len(data['series'][0]['raw']))
    raw_args = copy.deepcopy(args)
    #try:
    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



            # This is where we create the alert aka append its index in the locations list
            locations += [str(i) + "/t_value/" + str(cur.t) + "/pct_value/" + str(alert_properties.pct_change) + "/prev_value/" + str(prev_value) + "/new_value/" + str(new_value) for i, ts in enumerate(unique_push_timestamp) if ts == cur.push_timestamp]
            # PerformanceAlert.objects.update_or_create(
            #     summary=summary,
            #     series_signature=signature,
            #     defaults={
            #         "noise_profile": noise_profile,
            #         "is_regression": alert_properties.is_regression,
            #         "amount_pct": alert_properties.pct_change,
            #         "amount_abs": alert_properties.delta,
            #         "prev_value": prev_value,
            #         "new_value": new_value,
            #         "t_value": t_value,
            #     },
            # )

    stop_time = time.time()
    runtime = stop_time - start_time
    exit_success(raw_data, raw_args, vars(args), locations, runtime, __file__)