Trending: switch back to 3*stdev

Reporting many fake pro/regressions is worse
then missing few of the real ones.

Change-Id: I2b23ae14ac4462b993dff8d1b15fb1e472caf490
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py
index d72be3d..e27a521 100644 (file)
--- a/resources/tools/presentation/generator_CPTA.py
+++ b/resources/tools/presentation/generator_CPTA.py
@@ -176,9 +176,9 @@ def _evaluate_results(trimmed_data, window=10):
                     or np.isnan(tmm[build_nr])
                     or np.isnan(tmstd[build_nr])):
                 results.append(0.0)
-            elif value < (tmm[build_nr] - 2 * tmstd[build_nr]):
+            elif value < (tmm[build_nr] - 3 * tmstd[build_nr]):
                 results.append(0.33)
-            elif value > (tmm[build_nr] + 2 * tmstd[build_nr]):
+            elif value > (tmm[build_nr] + 3 * tmstd[build_nr]):
                 results.append(1.0)
             else:
                 results.append(0.66)
@@ -187,10 +187,10 @@ def _evaluate_results(trimmed_data, window=10):
         try:
             tmm = np.median(trimmed_data)
             tmstd = np.std(trimmed_data)
-            if trimmed_data.values[-1] < (tmm - 2 * tmstd):
+            if trimmed_data.values[-1] < (tmm - 3 * tmstd):
                 results.append(0.33)
-            elif (tmm - 2 * tmstd) <= trimmed_data.values[-1] <= (
-                    tmm + 2 * tmstd):
+            elif (tmm - 3 * tmstd) <= trimmed_data.values[-1] <= (
+                    tmm + 3 * tmstd):
                 results.append(0.66)
             else:
                 results.append(1.0)

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 46aa71c..4ffa081 100644 (file)
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -808,9 +808,9 @@ 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] - 2 * stdev_t[build_nr]):
+                elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
                     classification_lst.append("regression")
-                elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
+                elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
                     classification_lst.append("progression")
                 else:
                     classification_lst.append("normal")