Trending: use 2*stdev

[csit.git] / resources / tools / presentation / generator_tables.py

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 0c18942..db79396 100644 (file)
--- 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 collections import OrderedDict
 from numpy import nan
 from xml.etree import ElementTree as ET
 
@@ -360,12 +361,20 @@ def table_performance_comparison(table, input_data):
 
     # 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 = ["Test case", ]
+
+        history = table.get("history", None)
+        if history:
+            for item in history:
+                header.extend(
+                    ["{0} Throughput [Mpps]".format(item["title"]),
+                     "{0} Stdev [Mpps]".format(item["title"])])
+        header.extend(
+            ["{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}".
@@ -400,10 +409,41 @@ def table_performance_comparison(table, input_data):
                     pass
                 except TypeError:
                     tbl_dict.pop(tst_name, None)
+    if history:
+        for item in history:
+            for job, builds in item["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:
+                            continue
+                        if tbl_dict[tst_name].get("history", None) is None:
+                            tbl_dict[tst_name]["history"] = OrderedDict()
+                        if tbl_dict[tst_name]["history"].get(item["title"],
+                                                             None) is None:
+                            tbl_dict[tst_name]["history"][item["title"]] = \
+                                list()
+                        try:
+                            tbl_dict[tst_name]["history"][item["title"]].\
+                                append(tst_data["throughput"]["value"])
+                        except (TypeError, KeyError):
+                            pass
 
     tbl_lst = list()
     for tst_name in tbl_dict.keys():
         item = [tbl_dict[tst_name]["name"], ]
+        if history:
+            for hist_list in tbl_dict[tst_name]["history"].values():
+                for hist_data in hist_list:
+                    if hist_data:
+                        data_t = remove_outliers(
+                            hist_data, outlier_const=table["outlier-const"])
+                        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]["ref-data"]:
             data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
                                      outlier_const=table["outlier-const"])
@@ -426,9 +466,9 @@ def table_performance_comparison(table, input_data):
                 item.extend([None, None])
         else:
             item.extend([None, None])
-        if item[1] is not None and item[3] is not None:
-            item.append(int(relative_change(float(item[1]), float(item[3]))))
-        if len(item) == 6:
+        if item[-5] is not None and item[-3] is not None and item[-5] != 0:
+            item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+        if len(item) == len(header):
             tbl_lst.append(item)
 
     # Sort the table according to the relative change
@@ -723,7 +763,7 @@ def table_performance_trending_dashboard(table, input_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]
+            key_14 = pd_data.keys()[win_first_idx]
             long_win_size = min(pd_data.size, table["long-trend-window"])
 
             data_t, _ = split_outliers(pd_data, outlier_const=1.5,
@@ -766,9 +806,9 @@ def table_performance_trending_dashboard(table, input_data):
                         or isnan(stdev_t[build_nr]) \
                         or isnan(value):
                     classification_lst.append("outlier")
-                elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
+                elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
                     classification_lst.append("regression")
-                elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
+                elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
                     classification_lst.append("progression")
                 else:
                     classification_lst.append("normal")
@@ -777,13 +817,13 @@ def table_performance_trending_dashboard(table, input_data):
                 rel_change_last = nan
             else:
                 rel_change_last = round(
-                    (last_median_t - median_t_14) / median_t_14, 2)
+                    ((last_median_t - median_t_14) / median_t_14) * 100, 2)
 
             if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
                 rel_change_long = nan
             else:
                 rel_change_long = round(
-                    (last_median_t - max_median) / max_median, 2)
+                    ((last_median_t - max_median) / max_median) * 100, 2)
 
             logging.info("rel_change_last : {}".format(rel_change_last))
             logging.info("rel_change_long : {}".format(rel_change_long))