X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=29e1006950a5fdc7e3054b994d7ff2547f291dcc;hp=9f0096557e6892a6b675f2c5909c4f96c09507fa;hb=2387340f7050112311cd231f7d30d06731da4836;hpb=295a68551716af397597bc721d6a3115572009f9 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 9f0096557e..29e1006950 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 @@ -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) + + # 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. @@ -548,9 +673,9 @@ def table_performance_trending_dashboard(table, input_data): # Prepare the header of the tables header = ["Test case", "Thput trend [Mpps]", - "Change [Mpps]", + "Anomaly [Mpps]", "Change [%]", - "Anomaly"] + "Classification"] header_str = ",".join(header) + "\n" # Prepare data to the table: @@ -567,52 +692,97 @@ def table_performance_trending_dashboard(table, input_data): try: tbl_dict[tst_name]["data"]. \ append(tst_data["result"]["throughput"]) - except TypeError: + 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"]) + 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"] # Test name: name = tbl_dict[tst_name]["name"] - # Throughput trend: - trend = list(pd_data.rolling(window=win_size).median())[-2] - # Anomaly: + + # Trend list: + trend_lst = list(pd_data.rolling(window=win_size, min_periods=2). + median()) + # Stdevs list: 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" + 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]): + classification_lst.append("outlier") + elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]): + classification_lst.append("regression") + elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]): + 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" + elif "normal" in classification_lst[first_idx:]: + classification = "normal" else: - anomaly = "normal" - # Change: - change = round(float(last - trend) / 1000000, 2) - # Relative change: - rel_change = int(relative_change(float(trend), float(last))) - + classification = None + + 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, - round(float(last) / 1000000, 2), - change, + trend, + sample, rel_change, - anomaly]) + classification]) - # Sort the table according to the relative change - tbl_lst.sort(key=lambda rel: rel[-2], reverse=True) + # Sort the table according to the classification + tbl_sorted = list() + for classification in ("regression", "progression", "outlier", "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) - file_name = "{0}.{1}".format(table["output-file"], table["output-file-ext"]) + 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"]) @@ -628,3 +798,69 @@ 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 "right" + 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 == 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 + + 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