X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fnew%2Fgenerator_tables.py;h=735fd2185f95a9dd0de3f85922cd5bee34a867c6;hp=12f160145b4aa9f33ba5bd2eace5d9b8a94874b1;hb=0e8d8a59fd6b8477b17a9222a5cfb0d94d24ff22;hpb=beeb2acb9ac153eaa54983bea46a76d596168965 diff --git a/resources/tools/presentation/new/generator_tables.py b/resources/tools/presentation/new/generator_tables.py index 12f160145b..735fd2185f 100644 --- a/resources/tools/presentation/new/generator_tables.py +++ b/resources/tools/presentation/new/generator_tables.py @@ -450,24 +450,16 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) - if tbl_dict[tst_name]["ref-data"]: - data_t = tbl_dict[tst_name]["ref-data"] - # 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]) + data_t = tbl_dict[tst_name]["ref-data"] + if data_t: + 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 = tbl_dict[tst_name]["cmp-data"] - # 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]) + data_t = tbl_dict[tst_name]["cmp-data"] + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if item[-4] is not None and item[-2] is not None and item[-4] != 0: @@ -647,24 +639,16 @@ def table_performance_comparison_mrr(table, input_data): 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 = tbl_dict[tst_name]["ref-data"] - # 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]) + data_t = tbl_dict[tst_name]["ref-data"] + if data_t: + 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 = tbl_dict[tst_name]["cmp-data"] - # 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]) + data_t = tbl_dict[tst_name]["cmp-data"] + if data_t: + 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: @@ -716,7 +700,8 @@ def table_performance_comparison_mrr(table, input_data): def table_performance_trending_dashboard(table, input_data): - """Generate the table(s) with algorithm: table_performance_comparison + """Generate the table(s) with algorithm: + table_performance_trending_dashboard specified in the specification file. :param table: Table to generate. @@ -739,8 +724,7 @@ def table_performance_trending_dashboard(table, input_data): "Short-Term Change [%]", "Long-Term Change [%]", "Regressions [#]", - "Progressions [#]", - "Outliers [#]" + "Progressions [#]" ] header_str = ",".join(header) + "\n" @@ -765,59 +749,47 @@ def table_performance_trending_dashboard(table, input_data): tbl_lst = list() for tst_name in tbl_dict.keys(): - if len(tbl_dict[tst_name]["data"]) < 3: + if len(tbl_dict[tst_name]["data"]) < 2: continue - 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"]) - median_t = pd_data.rolling(window=win_size, min_periods=2).median() - median_first_idx = median_t.size - long_win_size + data_t = pd.Series(tbl_dict[tst_name]["data"]) + + classification_lst, avgs = classify_anomalies(data_t) + + win_size = min(data_t.size, table["window"]) + long_win_size = min(data_t.size, table["long-trend-window"]) try: - max_median = max( - [x for x in median_t.values[median_first_idx:-win_size] + max_long_avg = max( + [x for x in avgs[-long_win_size:-win_size] 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 + max_long_avg = nan + last_avg = avgs[-1] + avg_week_ago = avgs[max(-win_size, -len(avgs))] - if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: + if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0: rel_change_last = nan else: rel_change_last = round( - ((last_median_t - median_t_14) / median_t_14) * 100, 2) + ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2) - if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: + if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0: rel_change_long = nan else: rel_change_long = round( - ((last_median_t - max_median) / max_median) * 100, 2) - - # Classification list: - classification_lst, _ = classify_anomalies(pd_data) + ((last_avg - max_long_avg) / max_long_avg) * 100, 2) if classification_lst: if isnan(rel_change_last) and isnan(rel_change_long): continue tbl_lst.append( [tbl_dict[tst_name]["name"], - '-' if isnan(last_median_t) else - round(last_median_t / 1000000, 2), + '-' if isnan(last_avg) else + round(last_avg / 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")]) + classification_lst[-win_size:].count("regression"), + classification_lst[-win_size:].count("progression")]) tbl_lst.sort(key=lambda rel: rel[0]) @@ -825,15 +797,13 @@ def table_performance_trending_dashboard(table, input_data): 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[6] == nro] - tbl_out.sort(key=lambda rel: rel[2]) - tbl_sorted.extend(tbl_out) + tbl_out = [item for item in tbl_reg if item[5] == nrp] + tbl_out.sort(key=lambda rel: rel[2]) + tbl_sorted.extend(tbl_out) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) - logging.info(" Writing file: '{0}'".format(file_name)) + 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_sorted: @@ -841,7 +811,7 @@ def table_performance_trending_dashboard(table, input_data): txt_file_name = "{0}.txt".format(table["output-file"]) txt_table = None - logging.info(" Writing file: '{0}'".format(txt_file_name)) + logging.info(" Writing file: '{0}'".format(txt_file_name)) with open(file_name, 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') for row in csv_content: @@ -893,15 +863,12 @@ def table_performance_trending_dashboard_html(table, input_data): # Rows: colors = {"regression": ("#ffcccc", "#ff9999"), "progression": ("#c6ecc6", "#9fdf9f"), - "outlier": ("#e6e6e6", "#cccccc"), "normal": ("#e9f1fb", "#d4e4f7")} for r_idx, row in enumerate(csv_lst[1:]): if int(row[4]): color = "regression" elif int(row[5]): color = "progression" - elif int(row[6]): - color = "outlier" else: color = "normal" background = colors[color][r_idx % 2] @@ -917,7 +884,13 @@ def table_performance_trending_dashboard_html(table, input_data): anchor = "#" feature = "" if c_idx == 0: - if "memif" in item: + if "lbdpdk" in item or "lbvpp" in item: + file_name = "link_bonding.html" + + elif "testpmd" in item or "l3fwd" in item: + file_name = "dpdk.html" + + elif "memif" in item: file_name = "container_memif.html" elif "srv6" in item: @@ -980,13 +953,12 @@ def table_performance_trending_dashboard_html(table, input_data): ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item - if c_idx > 0: + else: td.text = item try: with open(table["output-file"], 'w') as html_file: - logging.info(" Writing file: '{0}'". - format(table["output-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")