diff options
Diffstat (limited to 'resources/tools/presentation')
| -rw-r--r-- | resources/tools/presentation/generator_CPTA.py | 51 | ||||
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 105 |
2 files changed, 88 insertions, 68 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 9ec196c0d9..72aef537cf 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -164,26 +164,26 @@ def _evaluate_results(in_data, trimmed_data, window=10): if len(in_data) > 2: win_size = in_data.size if in_data.size < window else window - results = [0.0, ] + results = [0.66, ] median = in_data.rolling(window=win_size, min_periods=2).median() stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std() - m_vals = median.values - s_vals = stdev_t.values - d_vals = in_data.values - t_vals = trimmed_data.values - for day in range(1, in_data.size): - if np.isnan(t_vals[day]) \ - or np.isnan(m_vals[day]) \ - or np.isnan(s_vals[day]) \ - or np.isnan(d_vals[day]): + + first = True + for build_nr, value in in_data.iteritems(): + if first: + first = False + continue + if np.isnan(trimmed_data[build_nr]) \ + or np.isnan(median[build_nr]) \ + or np.isnan(stdev_t[build_nr]) \ + or np.isnan(value): results.append(0.0) - elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]): + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): results.append(0.33) - elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \ - (m_vals[day] + 3 * s_vals[day]): - results.append(0.66) - else: + elif value > (median[build_nr] + 3 * stdev_t[build_nr]): results.append(1.0) + else: + results.append(0.66) else: results = [0.0, ] try: @@ -236,30 +236,23 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, in_data = _select_data(in_data, period, fill_missing=fill_missing, use_first=use_first) - # try: - # data_x = ["{0}/{1}".format(key, build_info[str(key)][1].split("~")[-1]) - # for key in in_data.keys()] - # except KeyError: - # data_x = [key for key in in_data.keys()] - hover_text = ["vpp-build: {0}".format(x[1].split("~")[-1]) - for x in build_info.values()] - data_x = [key for key in in_data.keys()] + data_x = [key for key in in_data.keys()] data_y = [val for val in in_data.values()] + + hover_text = list() + for idx in data_x: + hover_text.append("vpp-build: {0}". + format(build_info[str(idx)][1].split("~")[-1])) + data_pd = pd.Series(data_y, index=data_x) t_data, outliers = find_outliers(data_pd, outlier_const=1.5) - results = _evaluate_results(data_pd, t_data, window=moving_win_size) anomalies = pd.Series() anomalies_res = list() for idx, item in enumerate(in_data.items()): - # item_pd = pd.Series([item[1], ], - # index=["{0}/{1}". - # format(item[0], - # build_info[str(item[0])][1].split("~")[-1]), - # ]) item_pd = pd.Series([item[1], ], index=[item[0], ]) if item[0] in outliers.keys(): anomalies = anomalies.append(item_pd) diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 29e1006950..29e29d0468 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -355,7 +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: @@ -544,7 +544,7 @@ def table_performance_comparison_mrr(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: @@ -668,14 +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]", - "Anomaly [Mpps]", + "Throughput Trend [Mpps]", + "Trend Compliance", + "Anomaly Value [Mpps]", "Change [%]", - "Classification"] + "#Outliers" + ] header_str = ",".join(header) + "\n" # Prepare data to the table: @@ -688,55 +690,62 @@ 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: - sample_lst = tbl_dict[tst_name]["data"] - pd_data = pd.Series(sample_lst) + + 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"] - # Trend list: - trend_lst = list(pd_data.rolling(window=win_size, min_periods=2). - median()) - # Stdevs list: - t_data, _ = find_outliers(pd_data) - t_data_lst = list(t_data) - stdev_lst = list(t_data.rolling(window=win_size, min_periods=2). - std()) + 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, ] - for idx in range(1, len(trend_lst)): + 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(sample_lst[idx]) \ - and not isnan(trend_lst[idx])\ - and trend_lst[idx] != 0: + if not isnan(value) \ + and not isnan(median[build_nr]) \ + and median[build_nr] != 0: rel_change_lst.append( - int(relative_change(float(trend_lst[idx]), - float(sample_lst[idx])))) + int(relative_change(float(median[build_nr]), + float(value)))) else: rel_change_lst.append(None) + # Classification list: - if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]): + 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 sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]): + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): classification_lst.append("regression") - elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]): + 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(sample_lst) - 1 + last_idx = len(classification_lst) - 1 first_idx = last_idx - int(table["evaluated-window"]) if first_idx < 0: first_idx = 0 @@ -752,28 +761,46 @@ def table_performance_trending_dashboard(table, input_data): 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 - trend = round(float(trend_lst[-2]) / 1000000, 2) \ - if not isnan(trend_lst[-2]) else '' + 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[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, - sample, - rel_change, - classification]) + 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 ("regression", "progression", "outlier", "normal"): - tbl_tmp = [item for item in tbl_lst if item[4] == classification] + 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) @@ -832,7 +859,7 @@ def table_performance_trending_dashboard_html(table, input_data): # 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" + alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item @@ -845,10 +872,10 @@ 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 == 4: + if c_idx == 2: if item == "regression": td.set("bgcolor", "#eca1a6") - elif item == "outlier": + elif item == "failure": td.set("bgcolor", "#d6cbd3") elif item == "progression": td.set("bgcolor", "#bdcebe") |