X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=724519f2d115dba377a8fe00e79c0134a3dc293b;hp=29e29d04680f35cac0aab20c1dbf77202532cf60;hb=52f64f232293130904d54a62609eaffc1b145608;hpb=6942369b1102a8b9a3b705f9192f1ecb959382d1 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 29e29d0468..724519f2d1 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -25,7 +25,7 @@ from math import isnan 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): @@ -405,16 +405,24 @@ def table_performance_comparison(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: @@ -594,16 +602,24 @@ 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: @@ -671,12 +687,13 @@ def table_performance_trending_dashboard(table, input_data): data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables - header = ["Test case", + header = ["Test Case", "Throughput Trend [Mpps]", + "Long Trend Compliance", "Trend Compliance", - "Anomaly Value [Mpps]", + "Top Anomaly [Mpps]", "Change [%]", - "#Outliers" + "Outliers [Number]" ] header_str = ",".join(header) + "\n" @@ -702,13 +719,16 @@ 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"] + win_size = min(pd_data.size, table["window"]) # 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) + median_idx = pd_data.size - table["long-trend-window"] + median_idx = 0 if median_idx < 0 else median_idx + max_median = max(median.values[median_idx:]) + trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5, + window=win_size) stdev_t = pd_data.rolling(window=win_size, min_periods=2).std() rel_change_lst = [None, ] @@ -724,9 +744,9 @@ def table_performance_trending_dashboard(table, input_data): if not isnan(value) \ and not isnan(median[build_nr]) \ and median[build_nr] != 0: - rel_change_lst.append( - int(relative_change(float(median[build_nr]), - float(value)))) + rel_change_lst.append(round( + relative_change(float(median[build_nr]), float(value)), + 2)) else: rel_change_lst.append(None) @@ -750,17 +770,6 @@ def table_performance_trending_dashboard(table, input_data): 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" - elif "normal" in classification_lst[first_idx:]: - classification = "normal" - else: - classification = None - nr_outliers = 0 consecutive_outliers = 0 failure = False @@ -773,36 +782,82 @@ def table_performance_trending_dashboard(table, input_data): 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": + elif "regression" in classification_lst[first_idx:]: + classification = "regression" + elif "progression" in classification_lst[first_idx:]: + classification = "progression" + else: classification = "normal" - trend = round(float(median_lst[-1]) / 1000000, 2) \ - if not isnan(median_lst[-1]) 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, - classification, - '-' if classification == "normal" else sample, - '-' if classification == "normal" else rel_change, - nr_outliers]) + if classification == "normal": + index = len(classification_lst) - 1 + else: + tmp_classification = "outlier" if classification == "failure" \ + else classification + index = None + for idx in range(first_idx, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + if rel_change_lst[idx]: + index = idx + break + if index is None: + continue + for idx in range(index+1, len(classification_lst)): + if classification_lst[idx] == tmp_classification: + if rel_change_lst[idx]: + if (abs(rel_change_lst[idx]) > + abs(rel_change_lst[index])): + index = idx + + logging.info("{}".format(name)) + logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst)) + logging.info("median_lst: {} - {}".format(len(median_lst), median_lst)) + logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst)) + logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst)) + logging.info("index: {}".format(index)) + logging.info("classifica: {}".format(classification)) + + try: + 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 '-' + if not isnan(max_median): + if not isnan(sample_lst[index]): + long_trend_threshold = max_median * \ + (table["long-trend-threshold"] / 100) + if sample_lst[index] < long_trend_threshold: + long_trend_classification = "failure" + else: + long_trend_classification = '-' + else: + long_trend_classification = "failure" + else: + long_trend_classification = '-' + tbl_lst.append([name, + trend, + long_trend_classification, + classification, + '-' if classification == "normal" else sample, + '-' if classification == "normal" else rel_change, + nr_outliers]) + except IndexError as err: + logging.error("{}".format(err)) + continue # Sort the table according to the classification 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 long_trend_class in ("failure", '-'): + tbl_long = [item for item in tbl_lst if item[2] == long_trend_class] + for classification in \ + ("failure", "regression", "progression", "normal"): + tbl_tmp = [item for item in tbl_long if item[3] == classification] + tbl_tmp.sort(key=lambda rel: rel[0]) + tbl_sorted.extend(tbl_tmp) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) @@ -857,7 +912,7 @@ 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 "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) @@ -872,14 +927,81 @@ 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 == 2: + # 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 == 3: if item == "regression": td.set("bgcolor", "#eca1a6") elif item == "failure": td.set("bgcolor", "#d6cbd3") elif item == "progression": td.set("bgcolor", "#bdcebe") - td.text = item + if c_idx > 0: + td.text = item try: with open(table["output-file"], 'w') as html_file: