X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=4bbee51ae5dff7985e7ea0d6ee8cb8631f032364;hp=65bf6d562e7fbfa91723e9b63962059e81079d0d;hb=1265b8792b8edd44407c8073aeba2ca24dc0ad82;hpb=482bb432e9607bce6cb92d41bf9e299c0e2fc288 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 65bf6d562e..4bbee51ae5 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -22,6 +22,7 @@ import pandas as pd from string import replace 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 @@ -354,7 +355,7 @@ 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: @@ -529,6 +530,130 @@ def table_performance_comparison(table, input_data): out_file.write(line) +def table_performance_comparison_mrr(table, input_data): + """Generate the table(s) with algorithm: table_performance_comparison_mrr + specified in the specification file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + # Transform the data + 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_str = ",".join(header) + "\n" + except (AttributeError, KeyError) as err: + logging.error("The model is invalid, missing parameter: {0}". + format(err)) + return + + # Prepare data to the table: + tbl_dict = dict() + for job, builds in table["reference"]["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: + name = "{0}-{1}".format(tst_data["parent"].split("-")[0], + "-".join(tst_data["name"]. + split("-")[1:])) + tbl_dict[tst_name] = {"name": name, + "ref-data": list(), + "cmp-data": list()} + try: + tbl_dict[tst_name]["ref-data"].\ + append(tst_data["result"]["throughput"]) + except TypeError: + pass # No data in output.xml for this test + + for job, builds in table["compare"]["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + try: + tbl_dict[tst_name]["cmp-data"].\ + append(tst_data["result"]["throughput"]) + except KeyError: + pass + except TypeError: + tbl_dict.pop(tst_name, None) + + tbl_lst = list() + for tst_name in tbl_dict.keys(): + 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)) + 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)) + else: + item.extend([None, 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) + + # Sort the table according to the relative change + tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) + + # Generate tables: + # All tests in csv: + tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-2t2c-full{1}".format(table["output-file"], + table["output-file-ext"]), + "{0}-4t4c-full{1}".format(table["output-file"], + table["output-file-ext"]) + ] + for file_name in tbl_names: + logging.info(" Writing file: '{0}'".format(file_name)) + with open(file_name, "w") as file_handler: + file_handler.write(header_str) + for test in tbl_lst: + if file_name.split("-")[-2] in test[0]: # cores + test[0] = "-".join(test[0].split("-")[:-1]) + file_handler.write(",".join([str(item) for item in test]) + + "\n") + + # All tests in txt: + tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]), + "{0}-2t2c-full.txt".format(table["output-file"]), + "{0}-4t4c-full.txt".format(table["output-file"]) + ] + + for i, txt_name in enumerate(tbl_names_txt): + txt_table = None + logging.info(" Writing file: '{0}'".format(txt_name)) + with open(tbl_names[i], 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + for row in csv_content: + if txt_table is None: + txt_table = prettytable.PrettyTable(row) + else: + txt_table.add_row(row) + txt_table.align["Test case"] = "l" + with open(txt_name, "w") as txt_file: + txt_file.write(str(txt_table)) + + def table_performance_trending_dashboard(table, input_data): """Generate the table(s) with algorithm: table_performance_comparison specified in the specification file. @@ -543,14 +668,16 @@ 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]", - "Change [Mpps]", + header = ["Test Case", + "Throughput Trend [Mpps]", + "Trend Compliance", + "Top Anomaly [Mpps]", "Change [%]", - "Anomaly"] + "Outliers [Number]" + ] header_str = ",".join(header) + "\n" # Prepare data to the table: @@ -563,59 +690,161 @@ 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: + pd_data = pd.Series(tbl_dict[tst_name]["data"]) win_size = pd_data.size \ if pd_data.size < table["window"] else table["window"] # Test name: name = tbl_dict[tst_name]["name"] - # Throughput trend: - trend = list(pd_data.rolling(window=win_size, min_periods=2). - median())[-2] - # Anomaly: - t_data, _ = find_outliers(pd_data) - last = list(t_data)[-1] - t_stdev = list(t_data.rolling(window=win_size, min_periods=2). - std())[-2] - if isnan(last): - anomaly = "outlier" - elif last < (trend - 3 * t_stdev): - anomaly = "regression" - elif last > (trend + 3 * t_stdev): - anomaly = "progression" - else: - anomaly = "normal" - if not isnan(last) and not isnan(trend): - # Change: - change = round(float(last - trend) / 1000000, 2) - # Relative change: - rel_change = int(relative_change(float(trend), float(last))) - - tbl_lst.append([name, - round(float(last) / 1000000, 2), - change, - rel_change, - anomaly]) - - # Sort the table according to the relative change - tbl_lst.sort(key=lambda rel: rel[-2], reverse=True) + 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() + + rel_change_lst = [None, ] + classification_lst = [None, ] + median_lst = [None, ] + sample_lst = [None, ] + first = True + 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]) \ + or isnan(stdev_t[build_nr]) \ + or isnan(value): + classification_lst.append("outlier") + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): + classification_lst.append("regression") + elif value > (median[build_nr] + 3 * 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" + else: + classification = "normal" - file_name = "{0}.{1}".format(table["output-file"], table["output-file-ext"]) + if classification == "normal": + index = len(classification_lst) - 1 + 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 relative_change[idx] > relative_change[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 + 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) + + file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) - for test in tbl_lst: + for test in tbl_sorted: file_handler.write(",".join([str(item) for item in test]) + '\n') txt_file_name = "{0}.txt".format(table["output-file"]) @@ -631,3 +860,69 @@ def table_performance_trending_dashboard(table, input_data): txt_table.align["Test case"] = "l" with open(txt_file_name, "w") as txt_file: txt_file.write(str(txt_table)) + + +def table_performance_trending_dashboard_html(table, input_data): + """Generate the table(s) with algorithm: + table_performance_trending_dashboard_html specified in the specification + file. + + :param table: Table to generate. + :param input_data: Data to process. + :type table: pandas.Series + :type input_data: InputData + """ + + logging.info(" Generating the table {0} ...". + format(table.get("title", ""))) + + try: + with open(table["input-file"], 'rb') as csv_file: + csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + csv_lst = [item for item in csv_content] + except KeyError: + logging.warning("The input file is not defined.") + return + except csv.Error as err: + logging.warning("Not possible to process the file '{0}'.\n{1}". + format(table["input-file"], err)) + return + + # Table: + dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) + + # Table header: + tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff")) + for idx, item in enumerate(csv_lst[0]): + alignment = "left" if idx == 0 else "center" + th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) + th.text = item + + # Rows: + for r_idx, row in enumerate(csv_lst[1:]): + background = "#D4E4F7" if r_idx % 2 else "white" + tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) + + # Columns: + 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: + if item == "regression": + td.set("bgcolor", "#eca1a6") + elif item == "failure": + td.set("bgcolor", "#d6cbd3") + elif item == "progression": + td.set("bgcolor", "#bdcebe") + td.text = item + + try: + with open(table["output-file"], 'w') as html_file: + logging.info(" Writing file: '{0}'". + format(table["output-file"])) + html_file.write(".. raw:: html\n\n\t") + html_file.write(ET.tostring(dashboard)) + html_file.write("\n\t



\n") + except KeyError: + logging.warning("The output file is not defined.") + return