X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=db79396857734478613dfa5dbfc4aff842507bcd;hp=985c787d2c81f2156e7896f14817c012fbdea260;hb=d5d53957f4686398727469e0f5b1774a5b6560fe;hpb=e3554783146e2c4f2b6b5084c8afc707787d6922 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 985c787d2c..db79396857 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -22,10 +22,12 @@ import pandas as pd from string import replace from math import isnan +from collections import OrderedDict +from numpy import nan from xml.etree import ElementTree as ET from errors import PresentationError -from utils import mean, stdev, relative_change, remove_outliers, find_outliers +from utils import mean, stdev, relative_change, remove_outliers, split_outliers def generate_tables(spec, data): @@ -355,16 +357,24 @@ def table_performance_comparison(table, input_data): format(table.get("title", ""))) # Transform the data - data = input_data.filter_data(table) + 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}". @@ -399,27 +409,66 @@ 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: + for hist_list in tbl_dict[tst_name]["history"].values(): + for hist_data in hist_list: + 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]) if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], - table["outlier-const"]) - item.append(round(mean(data_t) / 1000000, 2)) - item.append(round(stdev(data_t) / 1000000, 2)) + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + 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]) if tbl_dict[tst_name]["cmp-data"]: data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], - table["outlier-const"]) - item.append(round(mean(data_t) / 1000000, 2)) - item.append(round(stdev(data_t) / 1000000, 2)) + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + 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]) - 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[-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 len(item) == len(header): tbl_lst.append(item) # Sort the table according to the relative change @@ -544,7 +593,7 @@ def table_performance_comparison_mrr(table, input_data): format(table.get("title", ""))) # Transform the data - data = input_data.filter_data(table) + data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables try: @@ -594,19 +643,27 @@ def table_performance_comparison_mrr(table, input_data): item = [tbl_dict[tst_name]["name"], ] if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], - table["outlier-const"]) - item.append(round(mean(data_t) / 1000000, 2)) - item.append(round(stdev(data_t) / 1000000, 2)) + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + 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]) if tbl_dict[tst_name]["cmp-data"]: data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], - table["outlier-const"]) - item.append(round(mean(data_t) / 1000000, 2)) - item.append(round(stdev(data_t) / 1000000, 2)) + outlier_const=table["outlier-const"]) + # TODO: Specify window size. + 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]) - if item[1] is not None and item[3] is not None: + if item[1] is not None and item[3] is not None and item[1] != 0: item.append(int(relative_change(float(item[1]), float(item[3])))) if len(item) == 6: tbl_lst.append(item) @@ -668,14 +725,17 @@ def table_performance_trending_dashboard(table, input_data): format(table.get("title", ""))) # Transform the data - data = input_data.filter_data(table) + data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables - header = ["Test case", - "Thput trend [Mpps]", - "Anomaly [Mpps]", - "Change [%]", - "Classification"] + header = ["Test Case", + "Trend [Mpps]", + "Short-Term Change [%]", + "Long-Term Change [%]", + "Regressions [#]", + "Progressions [#]", + "Outliers [#]" + ] header_str = ",".join(header) + "\n" # Prepare data to the table: @@ -688,92 +748,106 @@ def table_performance_trending_dashboard(table, input_data): "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, - "data": list()} + "data": dict()} try: - tbl_dict[tst_name]["data"]. \ - append(tst_data["result"]["throughput"]) + tbl_dict[tst_name]["data"][str(build)] = \ + tst_data["result"]["throughput"] except (TypeError, KeyError): pass # No data in output.xml for this test tbl_lst = list() for tst_name in tbl_dict.keys(): if len(tbl_dict[tst_name]["data"]) > 2: - sample_lst = tbl_dict[tst_name]["data"] - pd_data = pd.Series(sample_lst) - win_size = pd_data.size \ - if pd_data.size < table["window"] else table["window"] + + 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"] - # Trend list: - trend_lst = list(pd_data.rolling(window=win_size, min_periods=2). - median()) - # Stdevs list: - t_data, _ = find_outliers(pd_data) - t_data_lst = list(t_data) - stdev_lst = list(t_data.rolling(window=win_size, min_periods=2). - std()) - - rel_change_lst = [None, ] - classification_lst = [None, ] - for idx in range(1, len(trend_lst)): - # Relative changes list: - if not isnan(sample_lst[idx]) \ - and not isnan(trend_lst[idx])\ - and trend_lst[idx] != 0: - rel_change_lst.append( - int(relative_change(float(trend_lst[idx]), - float(sample_lst[idx])))) - else: - rel_change_lst.append(None) - # Classification list: - if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]): + 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 sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]): + elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]): classification_lst.append("regression") - elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]): + elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal") - last_idx = len(sample_lst) - 1 - first_idx = last_idx - int(table["evaluated-window"]) - if first_idx < 0: - first_idx = 0 - - if "regression" in classification_lst[first_idx:]: - classification = "regression" - elif "outlier" in classification_lst[first_idx:]: - classification = "outlier" - elif "progression" in classification_lst[first_idx:]: - classification = "progression" + 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: - classification = "normal" - - idx = len(classification_lst) - 1 - while idx: - if classification_lst[idx] == classification: - break - idx -= 1 - - trend = round(float(trend_lst[-2]) / 1000000, 2) \ - if not isnan(trend_lst[-2]) else '' - sample = round(float(sample_lst[idx]) / 1000000, 2) \ - if not isnan(sample_lst[idx]) else '' - rel_change = rel_change_lst[idx] \ - if rel_change_lst[idx] is not None else '' - tbl_lst.append([name, - trend, - sample, - rel_change, - classification]) - - # Sort the table according to the classification + 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)) + + tbl_lst.append( + [name, + '-' if isnan(last_median_t) else + round(last_median_t / 1000000, 2), + '-' if isnan(rel_change_last) else rel_change_last, + '-' if isnan(rel_change_long) else rel_change_long, + classification_lst[win_first_idx:].count("regression"), + classification_lst[win_first_idx:].count("progression"), + classification_lst[win_first_idx:].count("outlier")]) + + tbl_lst.sort(key=lambda rel: rel[0]) + tbl_sorted = list() - for classification in ("regression", "outlier", "progression", "normal"): - tbl_tmp = [item for item in tbl_lst if item[4] == classification] - tbl_tmp.sort(key=lambda rel: rel[0]) - tbl_sorted.extend(tbl_tmp) + for nrr in range(table["window"], -1, -1): + tbl_reg = [item for item in tbl_lst if item[4] == nrr] + 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_sorted.extend(tbl_out) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) @@ -828,9 +902,9 @@ def table_performance_trending_dashboard_html(table, input_data): dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) # Table header: - tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff")) + tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) for idx, item in enumerate(csv_lst[0]): - alignment = "left" if idx == 0 else "right" + alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item @@ -843,14 +917,74 @@ def table_performance_trending_dashboard_html(table, input_data): for c_idx, item in enumerate(row): alignment = "left" if c_idx == 0 else "center" td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) - if c_idx == 4: - if item == "regression": - td.set("bgcolor", "#eca1a6") - elif item == "outlier": - td.set("bgcolor", "#d6cbd3") - elif item == "progression": - td.set("bgcolor", "#bdcebe") - td.text = item + # Name: + url = "../trending/" + file_name = "" + anchor = "#" + feature = "" + if c_idx == 0: + if "memif" in item: + file_name = "container_memif.html" + + elif "vhost" in item: + if "l2xcbase" in item or "l2bdbasemaclrn" in item: + file_name = "vm_vhost_l2.html" + elif "ip4base" in item: + file_name = "vm_vhost_ip4.html" + + elif "ipsec" in item: + file_name = "ipsec.html" + + elif "ethip4lispip" in item or "ethip4vxlan" in item: + file_name = "ip4_tunnels.html" + + elif "ip4base" in item or "ip4scale" in item: + file_name = "ip4.html" + if "iacl" in item or "snat" in item or "cop" in item: + feature = "-features" + + elif "ip6base" in item or "ip6scale" in item: + file_name = "ip6.html" + + elif "l2xcbase" in item or "l2xcscale" in item \ + or "l2bdbasemaclrn" in item or "l2bdscale" in item \ + or "l2dbbasemaclrn" in item or "l2dbscale" in item: + file_name = "l2.html" + if "iacl" in item: + feature = "-features" + + if "x520" in item: + anchor += "x520-" + elif "x710" in item: + anchor += "x710-" + elif "xl710" in item: + anchor += "xl710-" + + if "64b" in item: + anchor += "64b-" + elif "78b" in item: + anchor += "78b" + elif "imix" in item: + anchor += "imix-" + elif "9000b" in item: + anchor += "9000b-" + elif "1518" in item: + anchor += "1518b-" + + if "1t1c" in item: + anchor += "1t1c" + elif "2t2c" in item: + anchor += "2t2c" + elif "4t4c" in item: + anchor += "4t4c" + + url = url + file_name + anchor + feature + + ref = ET.SubElement(td, "a", attrib=dict(href=url)) + ref.text = item + + if c_idx > 0: + td.text = item try: with open(table["output-file"], 'w') as html_file: