From 272dec4e1f9bb0d04e0546e705aaecf314d7cd28 Mon Sep 17 00:00:00 2001 From: Tibor Frank Date: Mon, 24 Apr 2023 16:29:08 +0200 Subject: C-Dash: Fix anomaly detection for the news Signed-off-by: Tibor Frank Change-Id: I63e280e15e82583b65691be17a07208d0ab788ce --- csit.infra.dash/app/cdash/utils/utils.py | 54 +------------------------------- 1 file changed, 1 insertion(+), 53 deletions(-) (limited to 'csit.infra.dash/app/cdash/utils/utils.py') diff --git a/csit.infra.dash/app/cdash/utils/utils.py b/csit.infra.dash/app/cdash/utils/utils.py index d9347b1c13..29bee3d039 100644 --- a/csit.infra.dash/app/cdash/utils/utils.py +++ b/csit.infra.dash/app/cdash/utils/utils.py @@ -11,7 +11,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Function used by Dash applications. +"""Functions used by Dash applications. """ import pandas as pd @@ -22,65 +22,13 @@ import hdrh.histogram import hdrh.codec from math import sqrt -from numpy import isnan from dash import dcc from datetime import datetime -from ..jumpavg import classify from ..utils.constants import Constants as C from ..utils.url_processing import url_encode -def classify_anomalies(data): - """Process the data and return anomalies and trending values. - - Gather data into groups with average as trend value. - Decorate values within groups to be normal, - the first value of changed average as a regression, or a progression. - - :param data: Full data set with unavailable samples replaced by nan. - :type data: OrderedDict - :returns: Classification and trend values - :rtype: 3-tuple, list of strings, list of floats and list of floats - """ - # NaN means something went wrong. - # Use 0.0 to cause that being reported as a severe regression. - bare_data = [0.0 if isnan(sample) else sample for sample in data.values()] - # TODO: Make BitCountingGroupList a subclass of list again? - group_list = classify(bare_data).group_list - group_list.reverse() # Just to use .pop() for FIFO. - classification = list() - avgs = list() - stdevs = list() - active_group = None - values_left = 0 - avg = 0.0 - stdv = 0.0 - for sample in data.values(): - if isnan(sample): - classification.append("outlier") - avgs.append(sample) - stdevs.append(sample) - continue - if values_left < 1 or active_group is None: - values_left = 0 - while values_left < 1: # Ignore empty groups (should not happen). - active_group = group_list.pop() - values_left = len(active_group.run_list) - avg = active_group.stats.avg - stdv = active_group.stats.stdev - classification.append(active_group.comment) - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - continue - classification.append("normal") - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - return classification, avgs, stdevs - - def get_color(idx: int) -> str: """Returns a color from the list defined in Constants.PLOT_COLORS defined by its index. -- cgit 1.2.3-korg