X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=84a6a411dc33f690c86914810076018061bfbc3f;hp=db79396857734478613dfa5dbfc4aff842507bcd;hb=f31dbcd6553ca6e7436736a5bc3aeec8fe18cad1;hpb=d5d53957f4686398727469e0f5b1774a5b6560fe diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index db79396857..84a6a411dc 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -1,4 +1,4 @@ -# Copyright (c) 2017 Cisco and/or its affiliates. +# Copyright (c) 2018 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -21,13 +21,13 @@ import prettytable import pandas as pd from string import replace -from math import isnan from collections import OrderedDict -from numpy import nan +from numpy import nan, isnan from xml.etree import ElementTree as ET from errors import PresentationError -from utils import mean, stdev, relative_change, remove_outliers, split_outliers +from utils import mean, stdev, relative_change, remove_outliers,\ + split_outliers, classify_anomalies def generate_tables(spec, data): @@ -63,6 +63,8 @@ def table_details(table, input_data): format(table.get("title", ""))) # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table) # Prepare the header of the tables @@ -129,10 +131,14 @@ def table_merged_details(table, input_data): format(table.get("title", ""))) # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table) data = input_data.merge_data(data) data.sort_index(inplace=True) + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) suites = input_data.filter_data(table, data_set="suites") suites = input_data.merge_data(suites) @@ -226,6 +232,8 @@ def table_performance_improvements(table, input_data): return None # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table) # Prepare the header of the tables @@ -357,6 +365,8 @@ def table_performance_comparison(table, input_data): format(table.get("title", ""))) # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables @@ -432,8 +442,8 @@ def table_performance_comparison(table, input_data): for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if history: - for hist_list in tbl_dict[tst_name]["history"].values(): - for hist_data in hist_list: + if tbl_dict[tst_name].get("history", None) is not None: + for hist_data in tbl_dict[tst_name]["history"].values(): if hist_data: data_t = remove_outliers( hist_data, outlier_const=table["outlier-const"]) @@ -444,6 +454,8 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) + else: + item.extend([None, None]) if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], outlier_const=table["outlier-const"]) @@ -466,8 +478,8 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) - if item[-5] is not None and item[-3] is not None and item[-5] != 0: - item.append(int(relative_change(float(item[-5]), float(item[-3])))) + if item[-4] is not None and item[-2] is not None and item[-4] != 0: + item.append(int(relative_change(float(item[-4]), float(item[-2])))) if len(item) == len(header): tbl_lst.append(item) @@ -593,6 +605,8 @@ def table_performance_comparison_mrr(table, input_data): format(table.get("title", ""))) # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables @@ -725,6 +739,8 @@ def table_performance_trending_dashboard(table, input_data): format(table.get("title", ""))) # Transform the data + logging.info(" Creating the data set for the {0} '{1}'.". + format(table.get("type", ""), table.get("title", ""))) data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables @@ -743,12 +759,14 @@ def table_performance_trending_dashboard(table, input_data): for job, builds in table["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): + if tst_name.lower() in table["ignore-list"]: + continue if tbl_dict.get(tst_name, None) is None: name = "{0}-{1}".format(tst_data["parent"].split("-")[0], "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, - "data": dict()} + "data": OrderedDict()} try: tbl_dict[tst_name]["data"][str(build)] = \ tst_data["result"]["throughput"] @@ -757,79 +775,52 @@ def table_performance_trending_dashboard(table, input_data): tbl_lst = list() for tst_name in tbl_dict.keys(): - if len(tbl_dict[tst_name]["data"]) > 2: - - pd_data = pd.Series(tbl_dict[tst_name]["data"]) - last_key = pd_data.keys()[-1] - win_size = min(pd_data.size, table["window"]) - win_first_idx = pd_data.size - win_size - key_14 = pd_data.keys()[win_first_idx] - long_win_size = min(pd_data.size, table["long-trend-window"]) - - data_t, _ = split_outliers(pd_data, outlier_const=1.5, - window=win_size) - - median_t = data_t.rolling(window=win_size, min_periods=2).median() - stdev_t = data_t.rolling(window=win_size, min_periods=2).std() - median_first_idx = pd_data.size - long_win_size - try: - max_median = max([x for x in median_t.values[median_first_idx:] - if not isnan(x)]) - except ValueError: - max_median = nan - try: - last_median_t = median_t[last_key] - except KeyError: - last_median_t = nan - try: - median_t_14 = median_t[key_14] - except KeyError: - median_t_14 = nan - - # Test name: - name = tbl_dict[tst_name]["name"] - - logging.info("{}".format(name)) - logging.info("pd_data : {}".format(pd_data)) - logging.info("data_t : {}".format(data_t)) - logging.info("median_t : {}".format(median_t)) - logging.info("last_median_t : {}".format(last_median_t)) - logging.info("median_t_14 : {}".format(median_t_14)) - logging.info("max_median : {}".format(max_median)) - - # Classification list: - classification_lst = list() - for build_nr, value in pd_data.iteritems(): - - if isnan(data_t[build_nr]) \ - or isnan(median_t[build_nr]) \ - or isnan(stdev_t[build_nr]) \ - or isnan(value): - classification_lst.append("outlier") - elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]): - classification_lst.append("regression") - elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]): - classification_lst.append("progression") - else: - classification_lst.append("normal") + if len(tbl_dict[tst_name]["data"]) < 3: + continue + + pd_data = pd.Series(tbl_dict[tst_name]["data"]) + data_t, _ = split_outliers(pd_data, outlier_const=1.5, + window=table["window"]) + last_key = data_t.keys()[-1] + win_size = min(data_t.size, table["window"]) + win_first_idx = data_t.size - win_size + key_14 = data_t.keys()[win_first_idx] + long_win_size = min(data_t.size, table["long-trend-window"]) + median_t = data_t.rolling(window=win_size, min_periods=2).median() + median_first_idx = median_t.size - long_win_size + try: + max_median = max( + [x for x in median_t.values[median_first_idx:-win_size] + if not isnan(x)]) + except ValueError: + max_median = nan + try: + last_median_t = median_t[last_key] + except KeyError: + last_median_t = nan + try: + median_t_14 = median_t[key_14] + except KeyError: + median_t_14 = nan - if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: - rel_change_last = nan - else: - rel_change_last = round( - ((last_median_t - median_t_14) / median_t_14) * 100, 2) + if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: + rel_change_last = nan + else: + rel_change_last = round( + ((last_median_t - median_t_14) / median_t_14) * 100, 2) - if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: - rel_change_long = nan - else: - rel_change_long = round( - ((last_median_t - max_median) / max_median) * 100, 2) + if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: + rel_change_long = nan + else: + rel_change_long = round( + ((last_median_t - max_median) / max_median) * 100, 2) - logging.info("rel_change_last : {}".format(rel_change_last)) - logging.info("rel_change_long : {}".format(rel_change_long)) + # Classification list: + classification_lst = classify_anomalies(data_t, window=14) + if classification_lst: tbl_lst.append( - [name, + [tbl_dict[tst_name]["name"], '-' if isnan(last_median_t) else round(last_median_t / 1000000, 2), '-' if isnan(rel_change_last) else rel_change_last, @@ -846,7 +837,8 @@ def table_performance_trending_dashboard(table, input_data): for nrp in range(table["window"], -1, -1): tbl_pro = [item for item in tbl_reg if item[5] == nrp] for nro in range(table["window"], -1, -1): - tbl_out = [item for item in tbl_pro if item[5] == nro] + tbl_out = [item for item in tbl_pro if item[6] == nro] + tbl_out.sort(key=lambda rel: rel[2]) tbl_sorted.extend(tbl_out) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) @@ -909,8 +901,20 @@ def table_performance_trending_dashboard_html(table, input_data): th.text = item # Rows: + colors = {"regression": ("#ffcccc", "#ff9999"), + "progression": ("#c6ecc6", "#9fdf9f"), + "outlier": ("#e6e6e6", "#cccccc"), + "normal": ("#e9f1fb", "#d4e4f7")} for r_idx, row in enumerate(csv_lst[1:]): - background = "#D4E4F7" if r_idx % 2 else "white" + if int(row[4]): + color = "regression" + elif int(row[5]): + color = "progression" + elif int(row[6]): + color = "outlier" + else: + color = "normal" + background = colors[color][r_idx % 2] tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) # Columns: @@ -963,7 +967,7 @@ def table_performance_trending_dashboard_html(table, input_data): if "64b" in item: anchor += "64b-" elif "78b" in item: - anchor += "78b" + anchor += "78b-" elif "imix" in item: anchor += "imix-" elif "9000b" in item: