X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_CPTA.py;h=2c62e11a97aa5561e7f77c4ab90d72ff91ae32ef;hp=73d55affa2638145fa41b01b731058c9dc73cad3;hb=f31dbcd6553ca6e7436736a5bc3aeec8fe18cad1;hpb=6f5de201aadfbb31419c05dfae6495107a745899 diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 73d55affa2..2c62e11a97 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -22,13 +22,13 @@ import prettytable import plotly.offline as ploff import plotly.graph_objs as plgo import plotly.exceptions as plerr -import numpy as np import pandas as pd from collections import OrderedDict from datetime import datetime -from utils import split_outliers, archive_input_data, execute_command, Worker +from utils import split_outliers, archive_input_data, execute_command,\ + classify_anomalies, Worker # Command to build the html format of the report @@ -87,61 +87,6 @@ def generate_cpta(spec, data): return ret_code -def _evaluate_results(trimmed_data, window=10): - """Evaluates if the sample value is regress, normal or progress compared to - previous data within the window. - We use the intervals defined as: - - regress: less than trimmed moving median - 3 * stdev - - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev - - progress: more than trimmed moving median + 3 * stdev - where stdev is trimmed moving standard deviation. - - :param trimmed_data: Full data set with the outliers replaced by nan. - :param window: Window size used to calculate moving average and moving stdev. - :type trimmed_data: pandas.Series - :type window: int - :returns: Evaluated results. - :rtype: list - """ - - if len(trimmed_data) > 2: - win_size = trimmed_data.size if trimmed_data.size < window else window - results = [0.66, ] - tmm = trimmed_data.rolling(window=win_size, min_periods=2).median() - tmstd = trimmed_data.rolling(window=win_size, min_periods=2).std() - - first = True - for build_nr, value in trimmed_data.iteritems(): - if first: - first = False - continue - if (np.isnan(value) - or np.isnan(tmm[build_nr]) - or np.isnan(tmstd[build_nr])): - results.append(0.0) - elif value < (tmm[build_nr] - 3 * tmstd[build_nr]): - results.append(0.33) - elif value > (tmm[build_nr] + 3 * tmstd[build_nr]): - results.append(1.0) - else: - results.append(0.66) - else: - results = [0.0, ] - try: - tmm = np.median(trimmed_data) - tmstd = np.std(trimmed_data) - if trimmed_data.values[-1] < (tmm - 3 * tmstd): - results.append(0.33) - elif (tmm - 3 * tmstd) <= trimmed_data.values[-1] <= ( - tmm + 3 * tmstd): - results.append(0.66) - else: - results.append(1.0) - except TypeError: - results.append(None) - return results - - def _generate_trending_traces(in_data, build_info, moving_win_size=10, show_trend_line=True, name="", color=""): """Generate the trending traces: @@ -182,29 +127,27 @@ def _generate_trending_traces(in_data, build_info, moving_win_size=10, t_data, outliers = split_outliers(data_pd, outlier_const=1.5, window=moving_win_size) - results = _evaluate_results(t_data, window=moving_win_size) + anomaly_classification = classify_anomalies(t_data, window=moving_win_size) anomalies = pd.Series() - anomalies_res = list() - for idx, item in enumerate(data_pd.items()): - item_pd = pd.Series([item[1], ], index=[item[0], ]) - if item[0] in outliers.keys(): - anomalies = anomalies.append(item_pd) - anomalies_res.append(0.0) - elif results[idx] in (0.33, 1.0): - anomalies = anomalies.append(item_pd) - anomalies_res.append(results[idx]) - anomalies_res.extend([0.0, 0.33, 0.66, 1.0]) + anomalies_colors = list() + anomaly_color = { + "outlier": 0.0, + "regression": 0.33, + "normal": 0.66, + "progression": 1.0 + } + if anomaly_classification: + for idx, item in enumerate(data_pd.items()): + if anomaly_classification[idx] in \ + ("outlier", "regression", "progression"): + anomalies = anomalies.append(pd.Series([item[1], ], + index=[item[0], ])) + anomalies_colors.append( + anomaly_color[anomaly_classification[idx]]) + anomalies_colors.extend([0.0, 0.33, 0.66, 1.0]) # Create traces - color_scale = [[0.00, "grey"], - [0.25, "grey"], - [0.25, "red"], - [0.50, "red"], - [0.50, "white"], - [0.75, "white"], - [0.75, "green"], - [1.00, "green"]] trace_samples = plgo.Scatter( x=xaxis, @@ -236,8 +179,15 @@ def _generate_trending_traces(in_data, build_info, moving_win_size=10, marker={ "size": 15, "symbol": "circle-open", - "color": anomalies_res, - "colorscale": color_scale, + "color": anomalies_colors, + "colorscale": [[0.00, "grey"], + [0.25, "grey"], + [0.25, "red"], + [0.50, "red"], + [0.50, "white"], + [0.75, "white"], + [0.75, "green"], + [1.00, "green"]], "showscale": True, "line": { "width": 2 @@ -279,7 +229,7 @@ def _generate_trending_traces(in_data, build_info, moving_win_size=10, ) traces.append(trace_trend) - return traces, results[-1] + return traces, anomaly_classification[-1] def _generate_all_charts(spec, input_data): @@ -371,8 +321,6 @@ def _generate_all_charts(spec, input_data): except plerr.PlotlyEmptyDataError: logs.append(("WARNING", "No data for the plot. Skipped.")) - logging.info(" Done.") - data_out = { "csv_table": csv_tbl, "results": res, @@ -419,7 +367,7 @@ def _generate_all_charts(spec, input_data): work_queue.put((chart, )) work_queue.join() - results = list() + anomaly_classifications = list() # Create the header: csv_table = list() @@ -435,7 +383,7 @@ def _generate_all_charts(spec, input_data): while not data_queue.empty(): result = data_queue.get() - results.extend(result["results"]) + anomaly_classifications.extend(result["results"]) csv_table.extend(result["csv_table"]) for item in result["logs"]: @@ -487,17 +435,16 @@ def _generate_all_charts(spec, input_data): txt_file.write(str(txt_table)) # Evaluate result: - result = "PASS" - for item in results: - if item is None: - result = "FAIL" - break - if item == 0.66 and result == "PASS": - result = "PASS" - elif item == 0.33 or item == 0.0: - result = "FAIL" - - logging.info("Partial results: {0}".format(results)) + if anomaly_classifications: + result = "PASS" + for classification in anomaly_classifications: + if classification == "regression" or classification == "outlier": + result = "FAIL" + break + else: + result = "FAIL" + + logging.info("Partial results: {0}".format(anomaly_classifications)) logging.info("Result: {0}".format(result)) return result