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=f73357db304422d1efa20d6e84923f0bcdc9c35a;hb=d5d53957f4686398727469e0f5b1774a5b6560fe;hpb=bb81ef05b86154d000128ef15bd3ecffe997ef9a diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index f73357db30..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): @@ -359,12 +361,20 @@ def table_performance_comparison(table, input_data): # 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 @@ -594,16 +643,24 @@ 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 and item[1] != 0: @@ -672,11 +729,12 @@ def table_performance_trending_dashboard(table, input_data): # Prepare the header of the tables header = ["Test Case", - "Throughput Trend [Mpps]", - "Trend Compliance", - "Top Anomaly [Mpps]", - "Change [%]", - "Outliers [Number]" + "Trend [Mpps]", + "Short-Term Change [%]", + "Long-Term Change [%]", + "Regressions [#]", + "Progressions [#]", + "Outliers [#]" ] header_str = ",".join(header) + "\n" @@ -702,142 +760,94 @@ 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"]) - win_size = pd_data.size \ - if pd_data.size < table["window"] else table["window"] + 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"] - median = pd_data.rolling(window=win_size, min_periods=2).median() - trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5) - stdev_t = pd_data.rolling(window=win_size, min_periods=2).std() + 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)) - rel_change_lst = [None, ] - classification_lst = [None, ] - median_lst = [None, ] - sample_lst = [None, ] - first = True + # Classification list: + classification_lst = list() for build_nr, value in pd_data.iteritems(): - if first: - first = False - continue - # Relative changes list: - if not isnan(value) \ - and not isnan(median[build_nr]) \ - and median[build_nr] != 0: - rel_change_lst.append(round( - relative_change(float(median[build_nr]), float(value)), - 2)) - else: - rel_change_lst.append(None) - # Classification list: - if isnan(trimmed_data[build_nr]) \ - or isnan(median[build_nr]) \ + 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[build_nr] - 3 * stdev_t[build_nr]): + elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]): classification_lst.append("regression") - elif value > (median[build_nr] + 3 * stdev_t[build_nr]): + elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal") - sample_lst.append(value) - median_lst.append(median[build_nr]) - - last_idx = len(classification_lst) - 1 - first_idx = last_idx - int(table["evaluated-window"]) - if first_idx < 0: - first_idx = 0 - - nr_outliers = 0 - consecutive_outliers = 0 - failure = False - for item in classification_lst[first_idx:]: - if item == "outlier": - nr_outliers += 1 - consecutive_outliers += 1 - if consecutive_outliers == 3: - failure = True - else: - consecutive_outliers = 0 - - if failure: - classification = "failure" - elif "regression" in classification_lst[first_idx:]: - classification = "regression" - 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: - classification = "normal" + rel_change_last = round( + ((last_median_t - median_t_14) / median_t_14) * 100, 2) - if classification == "normal": - index = len(classification_lst) - 1 + if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: + rel_change_long = nan else: - tmp_classification = "outlier" if classification == "failure" \ - else classification - for idx in range(first_idx, len(classification_lst)): - if classification_lst[idx] == tmp_classification: - index = idx - break - for idx in range(index+1, len(classification_lst)): - if classification_lst[idx] == tmp_classification: - if rel_change_lst[idx] > rel_change_lst[index]: - index = idx - - # 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" - # elif "normal" in classification_lst[first_idx:]: - # classification = "normal" - # else: - # classification = None - # - # nr_outliers = 0 - # consecutive_outliers = 0 - # failure = False - # for item in classification_lst[first_idx:]: - # if item == "outlier": - # nr_outliers += 1 - # consecutive_outliers += 1 - # if consecutive_outliers == 3: - # failure = True - # else: - # consecutive_outliers = 0 - # - # idx = len(classification_lst) - 1 - # while idx: - # if classification_lst[idx] == classification: - # break - # idx -= 1 - # - # if failure: - # classification = "failure" - # elif classification == "outlier": - # classification = "normal" - - trend = round(float(median_lst[-1]) / 1000000, 2) \ - if not isnan(median_lst[-1]) else '' - sample = round(float(sample_lst[index]) / 1000000, 2) \ - if not isnan(sample_lst[index]) else '' - rel_change = rel_change_lst[index] \ - if rel_change_lst[index] is not None else '' - tbl_lst.append([name, - trend, - classification, - '-' if classification == "normal" else sample, - '-' if classification == "normal" else rel_change, - nr_outliers]) - - # 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 ("failure", "regression", "progression", "normal"): - tbl_tmp = [item for item in tbl_lst if item[2] == 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"]) @@ -937,7 +947,8 @@ def table_performance_trending_dashboard_html(table, input_data): file_name = "ip6.html" elif "l2xcbase" in item or "l2xcscale" in item \ - or "l2bdbasemaclrn" in item or "l2bdscale" 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" @@ -972,13 +983,6 @@ def table_performance_trending_dashboard_html(table, input_data): ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item - if c_idx == 2: - if item == "regression": - td.set("bgcolor", "#eca1a6") - elif item == "failure": - td.set("bgcolor", "#d6cbd3") - elif item == "progression": - td.set("bgcolor", "#bdcebe") if c_idx > 0: td.text = item