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, 102 insertions, 0 deletions

diff --git a/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py b/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
new file mode 100644
index 0000000000..67d111985f
--- /dev/null
+++ b/resources/tools/presentation/new/jumpavg/BitCountingMetadata.py
@@ -0,0 +1,102 @@
+# 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.
+
+import math
+
+from AvgStdevMetadata import AvgStdevMetadata
+
+
+class BitCountingMetadata(AvgStdevMetadata):
+    """Class for metadata which includes information content."""
+
+    def __init__(self, max_value, size=0, avg=0.0, stdev=0.0, prev_avg=None):
+        """Construct the metadata by computing from the values needed.
+
+        The bit count is not real, as that would depend on numeric precision
+        (number of significant bits in values).
+        The difference is assumed to be constant per value,
+        which is consistent with Gauss distribution
+        (but not with floating point mechanic).
+        The hope is the difference will have
+        no real impact on the classification procedure.
+
+        :param max_value: Maximal expected value.
+            TODO: This might be more optimal,
+            but max-invariant algorithm will be nicer.
+        :param size: Number of values participating in this group.
+        :param avg: Population average of the participating sample values.
+        :param stdev: Population standard deviation of the sample values.
+        :param prev_avg: Population average of the previous group.
+            If None, no previous average is taken into account.
+            If not None, the given previous average is used to discourage
+            consecutive groups with similar averages
+            (opposite triangle distribution is assumed).
+        :type max_value: float
+        :type size: int
+        :type avg: float
+        :type stdev: float
+        :type prev_avg: float or None
+        """
+        super(BitCountingMetadata, self).__init__(size, avg, stdev)
+        self.max_value = max_value
+        self.prev_avg = prev_avg
+        self.bits = 0.0
+        if self.size < 1:
+            return
+        # Length of the sequence must be also counted in bits,
+        # otherwise the message would not be decodable.
+        # Model: probability of k samples is 1/k - 1/(k+1)
+        # == 1/k/(k+1)
+        self.bits += math.log(size * (size + 1), 2)
+        if prev_avg is None:
+            # Avg is considered to be uniformly distributed
+            # from zero to max_value.
+            self.bits += math.log(max_value + 1.0, 2)
+        else:
+            # Opposite triangle distribution with minimum.
+            self.bits += math.log(
+                max_value * (max_value + 1) / (abs(avg - prev_avg) + 1), 2)
+        if self.size < 2:
+            return
+        # Stdev is considered to be uniformly distributed
+        # from zero to max_value. That is quite a bad expectation,
+        # but resilient to negative samples etc.
+        self.bits += math.log(max_value + 1.0, 2)
+        # Now we know the samples lie on sphere in size-1 dimensions.
+        # So it is (size-2)-sphere, with radius^2 == stdev^2 * size.
+        # https://en.wikipedia.org/wiki/N-sphere
+        sphere_area_ln = math.log(2) + math.log(math.pi) * ((size - 1) / 2.0)
+        sphere_area_ln -= math.lgamma((size - 1) / 2.0)
+        sphere_area_ln += math.log(stdev + 1.0) * (size - 2)
+        sphere_area_ln += math.log(size) * ((size - 2) / 2.0)
+        self.bits += sphere_area_ln / math.log(2)
+
+    def __str__(self):
+        """Return string with human readable description of the group.
+
+        :returns: Readable description.
+        :rtype: str
+        """
+        return "size={size} avg={avg} stdev={stdev} bits={bits}".format(
+            size=self.size, avg=self.avg, stdev=self.stdev, bits=self.bits)
+
+    def __repr__(self):
+        """Return string executable as Python constructor call.
+
+        :returns: Executable constructor call.
+        :rtype: str
+        """
+        return ("BitCountingMetadata(max_value={max_value},size={size}," +
+                "avg={avg},stdev={stdev},prev_avg={prev_avg})").format(
+                    max_value=self.max_value, size=self.size, avg=self.avg,
+                    stdev=self.stdev, prev_avg=self.prev_avg)