X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=5246952e20457e87bcf917451369ef6576573e00;hp=0c189426c68735bc6814c23909e237f6c1023c21;hb=6f5de201aadfbb31419c05dfae6495107a745899;hpb=0f662ea0defa9b30fa7a7d9256857fce92d20a6e diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 0c189426c6..5246952e20 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: @@ -21,8 +21,8 @@ import prettytable import pandas as pd from string import replace -from math import isnan -from numpy import nan +from collections import OrderedDict +from numpy import nan, isnan from xml.etree import ElementTree as ET from errors import PresentationError @@ -62,6 +62,8 @@ 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 @@ -128,10 +130,14 @@ 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) @@ -225,6 +231,8 @@ 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 @@ -356,16 +364,26 @@ 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 try: - header = ["Test case", - "{0} Throughput [Mpps]".format(table["reference"]["title"]), - "{0} stdev [Mpps]".format(table["reference"]["title"]), - "{0} Throughput [Mpps]".format(table["compare"]["title"]), - "{0} stdev [Mpps]".format(table["compare"]["title"]), - "Change [%]"] + header = ["Test case", ] + + history = table.get("history", None) + if history: + for item in history: + header.extend( + ["{0} Throughput [Mpps]".format(item["title"]), + "{0} Stdev [Mpps]".format(item["title"])]) + header.extend( + ["{0} Throughput [Mpps]".format(table["reference"]["title"]), + "{0} Stdev [Mpps]".format(table["reference"]["title"]), + "{0} Throughput [Mpps]".format(table["compare"]["title"]), + "{0} Stdev [Mpps]".format(table["compare"]["title"]), + "Change [%]"]) header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". @@ -400,10 +418,43 @@ def table_performance_comparison(table, input_data): pass except TypeError: tbl_dict.pop(tst_name, None) + if history: + for item in history: + for job, builds in item["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + if tbl_dict.get(tst_name, None) is None: + continue + if tbl_dict[tst_name].get("history", None) is None: + tbl_dict[tst_name]["history"] = OrderedDict() + if tbl_dict[tst_name]["history"].get(item["title"], + None) is None: + tbl_dict[tst_name]["history"][item["title"]] = \ + list() + try: + tbl_dict[tst_name]["history"][item["title"]].\ + append(tst_data["throughput"]["value"]) + except (TypeError, KeyError): + pass tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] + if history: + 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"]) + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + 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"]) @@ -426,9 +477,9 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) - if item[1] is not None and item[3] is not None: - item.append(int(relative_change(float(item[1]), float(item[3])))) - if len(item) == 6: + 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) # Sort the table according to the relative change @@ -553,6 +604,8 @@ 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 @@ -685,6 +738,8 @@ 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 @@ -703,12 +758,14 @@ 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"] @@ -720,21 +777,20 @@ def table_performance_trending_dashboard(table, input_data): 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) - + 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() stdev_t = data_t.rolling(window=win_size, min_periods=2).std() - median_first_idx = pd_data.size - long_win_size + median_first_idx = median_t.size - long_win_size try: - max_median = max([x for x in median_t.values[median_first_idx:] - if not isnan(x)]) + 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: @@ -746,23 +802,10 @@ def table_performance_trending_dashboard(table, input_data): 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]) \ + for build_nr, value in data_t.iteritems(): + if isnan(median_t[build_nr]) \ or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") @@ -777,19 +820,16 @@ def table_performance_trending_dashboard(table, input_data): rel_change_last = nan else: rel_change_last = round( - (last_median_t - median_t_14) / median_t_14, 2) + ((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, 2) - - logging.info("rel_change_last : {}".format(rel_change_last)) - logging.info("rel_change_long : {}".format(rel_change_long)) + ((last_median_t - max_median) / max_median) * 100, 2) 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, @@ -806,7 +846,8 @@ 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"]) @@ -869,8 +910,20 @@ 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: