CSIT-1110: Integrate anomaly detection into PAL

+ Keep the original detection,
+ add the new one as subdirectory
  (both in source and in rendered tree).
- The new detection is not rebased over "Add dpdk mrr tests to trending".

New detection features:
+ Do not remove (nor detect) outliers.
+ Trend line shows the constant average within a group.
+ Anomaly circles are placed at the changed average.
+ Small bias against too similar averages.
+ Should be ready for moving the detection library out to pip.

Change-Id: I7ab1a92b79eeeed53ba65a071b1305e927816a89
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 63 insertions, 0 deletions

diff --git a/resources/tools/presentation/new/jumpavg/BitCountingClassifier.py b/resources/tools/presentation/new/jumpavg/BitCountingClassifier.py
new file mode 100644
index 0000000000..69b1d65bb2
--- /dev/null
+++ b/resources/tools/presentation/new/jumpavg/BitCountingClassifier.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2018 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from BitCountingGroup import BitCountingGroup
+from BitCountingGroupList import BitCountingGroupList
+from BitCountingMetadataFactory import BitCountingMetadataFactory
+from ClassifiedMetadataFactory import ClassifiedMetadataFactory
+
+
+class BitCountingClassifier(object):
+
+    @staticmethod
+    def classify(values):
+        """Return the values in groups of optimal bit count.
+
+        TODO: Could we return BitCountingGroupList and let caller process it?
+
+        :param values: Sequence of runs to classify.
+        :type values: Iterable of float or of AvgStdevMetadata
+        :returns: Classified group list.
+        :rtype: list of BitCountingGroup
+        """
+        max_value = BitCountingMetadataFactory.find_max_value(values)
+        factory = BitCountingMetadataFactory(max_value)
+        opened_at = []
+        closed_before = [BitCountingGroupList()]
+        for index, value in enumerate(values):
+            singleton = BitCountingGroup(factory, [value])
+            newly_opened = closed_before[index].with_group_appended(singleton)
+            opened_at.append(newly_opened)
+            record_group_list = newly_opened
+            for previous in range(index):
+                previous_opened_list = opened_at[previous]
+                still_opened = (
+                    previous_opened_list.with_value_added_to_last_group(value))
+                opened_at[previous] = still_opened
+                if still_opened.bits < record_group_list.bits:
+                    record_group_list = still_opened
+            closed_before.append(record_group_list)
+        partition = closed_before[-1]
+        previous_average = partition[0].metadata.avg
+        for group in partition:
+            if group.metadata.avg == previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "normal")
+            elif group.metadata.avg < previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "regression")
+            elif group.metadata.avg > previous_average:
+                group.metadata = ClassifiedMetadataFactory.with_classification(
+                    group.metadata, "progression")
+            previous_average = group.metadata.avg
+        return partition.group_list