This is the python3 change, the python2 is still used for testing bisect.
+ New version is 0.2.0 due to amount o API changes.
+ Jumpavg is now part of CSIT resource package tree.
+ Perpatch migrated to new jumpavg.
- PAL NOT updated (the update moved to a different Change).
Change-Id: I7d7a8bf8a411196c20c2a40a8c64478d6709bc07
Signed-off-by: Vratko Polak <vrpolak@cisco.com>
--- /dev/null
+../../resources/libraries/python/jumpavg
\ No newline at end of file
+++ /dev/null
-# 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.
-
-"""Module holding AbstractGroupClassifier class."""
-
-from abc import ABCMeta, abstractmethod
-
-
-class AbstractGroupClassifier(object):
- """Abstract class defining API for classifier.
-
- The classifier is an object with classify() method
- which divides data into groups containing metadata.
- """
-
- __metaclass__ = ABCMeta
-
- @abstractmethod
- def classify(self, values):
- """Divide values into consecutive groups with metadata.
-
- The metadata does not need to follow any specific rules,
- although progression/regression/outlier description would be fine.
-
- :param values: Sequence of runs to classify.
- :type values: Iterable of float or of AvgStdevMetadata
- :returns: Classified groups
- :rtype: Iterable of RunGroup
- """
- pass
+++ /dev/null
-# 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.
-
-"""Module holding AbstractGroupMetadata class."""
-
-from abc import ABCMeta, abstractmethod
-
-
-class AbstractGroupMetadata(object):
- """Abstract classdefining API for metadata.
-
- At this level, only __str__() and __repr() methods are required."""
-
- __metaclass__ = ABCMeta
-
- @abstractmethod
- def __str__(self):
- """Return string with human readable description of the group.
-
- :returns: Readable description.
- :rtype: str
- """
- pass
-
- @abstractmethod
- def __repr__(self):
- """Return string executable as Python constructor call.
-
- :returns: Executable constructor call.
- :rtype: str
- """
- pass
+++ /dev/null
-# 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.
-
-"""Module for holding AvgStdevMetadata class."""
-
-from AbstractGroupMetadata import AbstractGroupMetadata
-
-
-class AvgStdevMetadata(AbstractGroupMetadata):
- """Class for metadata specifying the average and standard deviation."""
-
- def __init__(self, size=0, avg=0.0, stdev=0.0):
- """Construct the metadata by setting the values needed.
-
- The values are sanitized, so faulty callers to not cause math errors.
-
- :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.
- :type size: int
- :type avg: float
- :type stdev: float
- """
- self.size = size if size >= 0 else 0
- self.avg = avg if size >= 1 else 0.0
- self.stdev = stdev if size >= 2 else 0.0
-
- def __str__(self):
- """Return string with human readable description of the group.
-
- :returns: Readable description.
- :rtype: str
- """
- return "size={size} avg={avg} stdev={stdev}".format(
- size=self.size, avg=self.avg, stdev=self.stdev)
-
- def __repr__(self):
- """Return string executable as Python constructor call.
-
- :returns: Executable constructor call.
- :rtype: str
- """
- return "AvgStdevMetadata(size={size},avg={avg},stdev={stdev})".format(
- size=self.size, avg=self.avg, stdev=self.stdev)
+++ /dev/null
-# 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.
-
-"""Module holding AvgStdevMetadataFactory class."""
-
-import math
-
-from AvgStdevMetadata import AvgStdevMetadata
-
-
-class AvgStdevMetadataFactory(object):
- """Class factory which creates avg,stdev metadata from data."""
-
- @staticmethod
- def from_data(values):
- """Return new metadata object fitting the values.
-
- :param values: Run values to be processed.
- :type values: Iterable of float or of AvgStdevMetadata
- :returns: The metadata matching the values.
- :rtype: AvgStdevMetadata
- """
- # Using Welford method to be more resistant to rounding errors.
- # Adapted from code for sample standard deviation at:
- # https://www.johndcook.com/blog/standard_deviation/
- # The logic of plus operator is taken from
- # https://www.johndcook.com/blog/skewness_kurtosis/
- size = 0
- avg = 0.0
- moment_2 = 0.0
- for value in values:
- if not isinstance(value, AvgStdevMetadata):
- value = AvgStdevMetadata(size=1, avg=value)
- old_size = size
- delta = value.avg - avg
- size += value.size
- avg += delta * value.size / size
- moment_2 += value.stdev * value.stdev * value.size
- moment_2 += delta * delta * old_size * value.size / size
- if size < 1:
- return AvgStdevMetadata()
- stdev = math.sqrt(moment_2 / size)
- ret_obj = AvgStdevMetadata(size=size, avg=avg, stdev=stdev)
- return ret_obj
+++ /dev/null
-# 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.
-
-"""Module holding BitCountingClassifier class.
-
-This is the main class to be used by callers."""
-
-from AbstractGroupClassifier import AbstractGroupClassifier
-from BitCountingGroup import BitCountingGroup
-from BitCountingGroupList import BitCountingGroupList
-from BitCountingMetadataFactory import BitCountingMetadataFactory
-from ClassifiedMetadataFactory import ClassifiedMetadataFactory
-
-
-class BitCountingClassifier(AbstractGroupClassifier):
- """Classifier using Minimal Description Length principle."""
-
- def classify(self, values):
- """Return the values in groups of optimal bit count.
-
- The current implementation could be a static method,
- but we might support options in later versions,
- for example for chosing encodings.
-
- :param values: Sequence of runs to classify.
- :type values: Iterable of float or of AvgStdevMetadata
- :returns: Classified group list.
- :rtype: BitCountingGroupList
- """
- 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
+++ /dev/null
-# 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.
-
-"""Module holding BitCountingGroup class."""
-
-from RunGroup import RunGroup
-
-
-class BitCountingGroup(RunGroup):
- """RunGroup with BitCountingMetadata.
-
- Support with_run_added() method to simplify extending the group.
- As bit content has to be re-counted, metadata factory is stored.
- """
-
- def __init__(self, metadata_factory, values=[]):
- """Create the group from metadata factory and values.
-
- :param metadata_factory: Factory object to create metadata with.
- :param values: The runs belonging to this group.
- :type metadata_factory: BitCountingMetadataFactory
- :type values: Iterable of float or of AvgStdevMetadata
- """
- self.metadata_factory = metadata_factory
- metadata = metadata_factory.from_data(values)
- super(BitCountingGroup, self).__init__(metadata, values)
-
- def with_run_added(self, value):
- """Create and return a new group with one more run that self.
-
- :param value: The run value to add to the group.
- :type value: float or od AvgStdevMetadata
- :returns: New group with the run added.
- :rtype: BitCountingGroup
- """
- values = list(self.values)
- values.append(value)
- return BitCountingGroup(self.metadata_factory, values)
- # TODO: Is there a good way to save some computation
- # by copy&updating the metadata incrementally?
+++ /dev/null
-# 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.
-
-"""Module holding BitCountingGroupList class."""
-
-from BitCountingGroup import BitCountingGroup
-from BitCountingMetadataFactory import BitCountingMetadataFactory
-
-
-class BitCountingGroupList(list):
- """List of BitCountingGroup which tracks overall bit count.
-
- This is useful, as bit count of a subsequent group
- depends on average of the previous group.
- Having the logic encapsulated here spares the caller
- the effort to pass averages around.
-
- Method with_value_added_to_last_group() delegates to BitCountingGroup,
- with_group_appended() adds new group with recalculated bits.
-
- TODO: last_group.metadata_factory.max_value in with_group_appended()
- is ugly, find a more natural class design.
- """
-
- def __init__(self, group_list=[], bits=None):
- """Create a group list from given list of groups.
-
- :param group_list: List of groups to compose this group.
- :param bits: Bit count if known, else None.
- :type group_list: list of BitCountingGroup
- :type bits: float or None
- """
- super(BitCountingGroupList, self).__init__(group_list)
- if bits is not None:
- self.bits = bits
- return
- bits = 0.0
- for group in group_list:
- bits += group.metadata.bits
- self.bits = bits
-
- def with_group_appended(self, group):
- """Create and return new group list with given group more than self.
-
- The group argument object is updated with derivative metadata.
-
- :param group: Next group to be appended to the group list.
- :type group: BitCountingGroup
- :returns: New group list with added group.
- :rtype: BitCountingGroupList
- """
- group_list = list(self)
- if group_list:
- last_group = group_list[-1]
- factory = BitCountingMetadataFactory(
- last_group.metadata_factory.max_value, last_group.metadata.avg)
- group.metadata_factory = factory
- group.metadata = factory.from_data(group.values)
- group_list.append(group)
- bits = self.bits + group.metadata.bits
- return BitCountingGroupList(group_list, bits)
-
- def with_value_added_to_last_group(self, value):
- """Create and return new group list with value added to last group.
-
- :param value: The run value to add to the last group.
- :type value: float or od AvgStdevMetadata
- :returns: New group list with the last group updated.
- :rtype: BitCountingGroupList
- """
- group_list = list(self)
- last_group = group_list[-1]
- bits_before = last_group.metadata.bits
- last_group = last_group.with_run_added(value)
- group_list[-1] = last_group
- bits = self.bits - bits_before + last_group.metadata.bits
- return BitCountingGroupList(group_list, bits)
+++ /dev/null
-# 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.
-
-"""Module holding BitCountingMetadata class."""
-
-import math
-
-from AvgStdevMetadata import AvgStdevMetadata
-
-
-class BitCountingMetadata(AvgStdevMetadata):
- """Class for metadata which includes information content of a group.
-
- The information content is based on an assumption
- that the data consists of independent random values
- from a normal distribution.
- """
-
- 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)
+++ /dev/null
-# 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.
-
-"""Module holding BitCountingMetadataFactory class."""
-
-import math
-
-from AvgStdevMetadata import AvgStdevMetadata
-from AvgStdevMetadataFactory import AvgStdevMetadataFactory
-from BitCountingMetadata import BitCountingMetadata
-
-
-class BitCountingMetadataFactory(object):
- """Class for factory which creates bit counting metadata from data.
-
- TODO: Summarize the methods?
- """
-
- @staticmethod
- def find_max_value(values):
- """Return the max value.
-
- This is a separate helper method,
- because the whole set of values is usually larger than in from_data().
-
- :param values: Run values to be processed.
- :type values: Iterable of float
- :returns: 0.0 or the biggest value found.
- :rtype: float
- """
- max_value = 0.0
- for value in values:
- if isinstance(value, AvgStdevMetadata):
- value = value.avg
- if value > max_value:
- max_value = value
- return max_value
-
- def __init__(self, max_value, prev_avg=None):
- """Construct the factory instance with given arguments.
-
- :param max_value: Maximal expected value.
- :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 prev_avg: float or None
- """
- self.max_value = max_value
- self.prev_avg = prev_avg
-
- def from_avg_stdev_metadata(self, metadata):
- """Return new metadata object by adding bits to existing metadata.
-
- :param metadata: Metadata to count bits for.
- :type metadata: AvgStdevMetadata
- :returns: The metadata with bits counted.
- :rtype: BitCountingMetadata
- """
- return BitCountingMetadata(
- max_value=self.max_value, size=metadata.size,
- avg=metadata.avg, stdev=metadata.stdev, prev_avg=self.prev_avg)
-
- def from_data(self, values):
- """Return new metadata object fitting the values.
-
- :param values: Run values to be processed.
- :type values: Iterable of float or of AvgStdevMetadata
- :returns: The metadata matching the values.
- :rtype: BitCountingMetadata
- """
- metadata = AvgStdevMetadataFactory.from_data(values)
- return self.from_avg_stdev_metadata(metadata)
+++ /dev/null
-# 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.
-
-"""Module holding ClassifiedBitCountingMetadata class."""
-
-from BitCountingMetadata import BitCountingMetadata
-
-
-class ClassifiedBitCountingMetadata(BitCountingMetadata):
- """Class for metadata which includes classification.
-
- TODO: Can we create ClassifiedMetadata and inherit (also) from that?
- """
-
- def __init__(
- self, max_value, size=0, avg=0.0, stdev=0.0, prev_avg=None,
- classification=None):
- """Delegate to ancestor constructors and set classification.
-
- :param max_value: Maximal expected value.
- :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).
- :param classification: Arbitrary object classifying this group.
- :type max_value: float
- :type size: int
- :type avg: float
- :type stdev: float
- :type prev_avg: float
- :type classification: object
- """
- super(ClassifiedBitCountingMetadata, self).__init__(
- max_value, size, avg, stdev, prev_avg)
- self.classification = classification
-
- def __str__(self):
- """Return string with human readable description of the group.
-
- :returns: Readable description.
- :rtype: str
- """
- # str(super(...)) describes the proxy, not the proxied object.
- super_str = super(ClassifiedBitCountingMetadata, self).__str__()
- return super_str + " classification={classification}".format(
- classification=self.classification)
-
- def __repr__(self):
- """Return string executable as Python constructor call.
-
- :returns: Executable constructor call.
- :rtype: str
- """
- return ("ClassifiedBitCountingMetadata(max_value={max_value}," +
- "size={size},avg={avg},stdev={stdev},prev_avg={prev_avg}," +
- "classification={cls})").format(
- max_value=self.max_value, size=self.size, avg=self.avg,
- stdev=self.stdev, prev_avg=self.prev_avg,
- cls=self.classification)
+++ /dev/null
-# 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.
-
-"""Module holding ClassifiedBitCountingMetadata class."""
-
-from ClassifiedBitCountingMetadata import ClassifiedBitCountingMetadata
-
-
-class ClassifiedMetadataFactory(object):
- """Class for factory which adds classification to bit counting metadata."""
-
- @staticmethod
- def with_classification(metadata, classification):
- """Return new metadata object with added classification.
-
- TODO: Is there a way to add classification to any metadata,
- without messing up constructors and __repr__()?
-
- FIXME: Factories take raw resources. Find a name for the thing
- which takes semi-finished products. Transformer?
-
- :param metadata: Existing metadata without classification.
- :param classification: Arbitrary object classifying this group.
- :type metadata: BitCountingMetadata
- :type classification: object
- :returns: The metadata with added classification.
- :rtype: ClassifiedBitCountingMetadata
- """
- return ClassifiedBitCountingMetadata(
- max_value=metadata.max_value, size=metadata.size, avg=metadata.avg,
- stdev=metadata.stdev, prev_avg=metadata.prev_avg,
- classification=classification)
+++ /dev/null
-# 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.
-
-"""Module holding RunGroup class."""
-
-
-class RunGroup(object):
- """Effectively a named touple of data and metadata.
-
- TODO: This feels like an abstract class.
- Most uses assume restrictions on metadata type.
- Can this be defined similarly to C++ templates?
- """
-
- def __init__(self, metadata, values):
- """Create the group from metadata and values.
-
- :param metadata: Metadata object to associate with the group.
- :param values: The runs belonging to this group.
- :type metadata: AbstractGroupMetadata
- :type values: Iterable of float or od AvgStdevMetadata
- """
- self.metadata = metadata
- self.values = values
+#!/usr/bin/env python3
+
"""A setup module for setuptools.
See:
https://packaging.python.org/en/latest/distributing.html
"""
-from setuptools import setup, find_packages
+from setuptools import (setup, find_packages)
from os import path
from io import open
here = path.abspath(path.dirname(__file__))
-with open(path.join(here, "README.rst"), encoding="utf-8") as f:
+with open(path.join(here, u"README.rst"), encoding=u"utf-8") as f:
long_description = f.read()
setup(
- name="jumpavg",
- version="0.1.4", # This is currently the only place listing the version.
- description="Library for finding changes in time series by grouping results.",
+ name=u"jumpavg",
+ version=u"0.2.0", # This is currently the only place listing the version.
+ description=(
+ u"Library for locating changes in time series by grouping results."
+ ),
long_description=long_description,
- long_description_content_type="text/x-rst",
+ long_description_content_type=u"text/x-rst",
# TODO: Create a separate webpage for jumpavg library.
- url="https://gerrit.fd.io/r/gitweb?p=csit.git;a=tree;f=PyPI/jumpavg;hb=refs/heads/master",
- author="Cisco Systems Inc. and/or its affiliates",
- author_email="csit-dev@lists.fd.io",
+ url=(
+ u"https://gerrit.fd.io/r/gitweb?p=csit.git;a=tree;f=PyPI/jumpavg"
+ u";hb=refs/heads/master"
+ ),
+ author=u"Cisco Systems Inc. and/or its affiliates",
+ author_email=u"csit-dev@lists.fd.io",
classifiers=[
- "Development Status :: 3 - Alpha",
- "Intended Audience :: Science/Research",
+ u"Development Status :: 3 - Alpha",
+ u"Intended Audience :: Science/Research",
# Pick your license as you wish
- "License :: OSI Approved :: Apache Software License",
- "Natural Language :: English",
+ u"License :: OSI Approved :: Apache Software License",
+ u"Natural Language :: English",
# TODO: Test which Python versions is the code compatible with.
- "Programming Language :: Python :: 2.7",
- "Topic :: Scientific/Engineering :: Information Analysis"
+ u"Programming Language :: Python :: 2.7",
+ u"Topic :: Scientific/Engineering :: Information Analysis"
],
- keywords="progression regression anomaly detection",
+ keywords=u"progression regression anomaly detection statistics bits",
packages=find_packages(exclude=[]),
- # TODO: python_requires="~=2.7"
+ python_requires="~=3.6"
install_requires=[],
# TODO: Include simulator and tests.
extras_require={
package_data={
},
entry_points={
- "console_scripts": [
+ u"console_scripts": [
],
},
project_urls={
- "Bug Reports": "https://jira.fd.io/projects/CSIT/issues",
- "Source": "https://gerrit.fd.io/r/gitweb?p=csit.git;a=tree;f=PyPI/jumpavg;hb=refs/heads/master",
+ u"Bug Reports": u"https://jira.fd.io/projects/CSIT/issues",
+ u"Source": (
+ u"https://gerrit.fd.io/r/gitweb?p=csit.git;a=tree;f=PyPI/jumpavg"
+ u";hb=refs/heads/master"
+ ),
},
)
set -exuo pipefail
cd "${VPP_DIR}" || die "Change directory operation failed."
- # Reusing CSIT main virtualenv.
+ # Ply is installed as system level package, but not seen for some reason.
pip3 install -r "${PYTHON_SCRIPTS_DIR}/perpatch_requirements.txt" || {
- die "Perpatch Python requirements installation failed."
+ die "Compare script Python requirements installation failed."
}
- python3 "${PYTHON_SCRIPTS_DIR}/compare_perpatch.py"
+ # Reusing CSIT main virtualenv.
+ python3 "${TOOLS_DIR}/integrated/compare_perpatch.py"
# The exit code determines the vote result.
}
--- /dev/null
+# Copyright (c) 2019 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.
+
+"""Module holding AvgStdevStats class."""
+
+import math
+
+
+class AvgStdevStats:
+ """Class for statistics which include average and stdev of a group.
+
+ Contrary to other stats types, adding values to the group
+ is computationally light without any caching.
+
+ Instances are only statistics, the data itself is stored elsewhere.
+ """
+
+ def __init__(self, size=0, avg=0.0, stdev=0.0):
+ """Construct the stats object by storing the values needed.
+
+ Each value has to be numeric.
+ The values are not sanitized depending on size, wrong initialization
+ can cause delayed math errors.
+
+ :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.
+ :type size: int
+ :type avg: float
+ :type stdev: float
+ """
+ self.size = size
+ self.avg = avg
+ self.stdev = stdev
+
+ def __str__(self):
+ """Return string with human readable description of the group.
+
+ :returns: Readable description.
+ :rtype: str
+ """
+ return f"size={self.size} avg={self.avg} stdev={self.stdev}"
+
+ def __repr__(self):
+ """Return string executable as Python constructor call.
+
+ :returns: Executable constructor call.
+ :rtype: str
+ """
+ return (
+ f"AvgStdevStats(size={self.size!r},avg={self.avg!r}"
+ f",stdev={self.stdev!r})"
+ )
+
+ @classmethod
+ def for_runs(cls, runs):
+ """Return new stats instance describing the sequence of runs.
+
+ If you want to append data to existing stats object,
+ you can simply use the stats object as the first run.
+
+ Instead of a verb, "for" is used to start this method name,
+ to signify the result contains less information than the input data.
+
+ Here, Run is a hypothetical abstract class, an union of float and cls.
+ Defining that as a real abstract class in Python 2 is too much hassle.
+
+ :param runs: Sequence of data to describe by the new metadata.
+ :type runs: Iterable[Union[float, cls]]
+ :returns: The new stats instance.
+ :rtype: cls
+ """
+ # Using Welford method to be more resistant to rounding errors.
+ # Adapted from code for sample standard deviation at:
+ # https://www.johndcook.com/blog/standard_deviation/
+ # The logic of plus operator is taken from
+ # https://www.johndcook.com/blog/skewness_kurtosis/
+ total_size = 0
+ total_avg = 0.0
+ moment_2 = 0.0
+ for run in runs:
+ if isinstance(run, (float, int)):
+ run_size = 1
+ run_avg = run
+ run_stdev = 0.0
+ else:
+ run_size = run.size
+ run_avg = run.avg
+ run_stdev = run.stdev
+ old_total_size = total_size
+ delta = run_avg - total_avg
+ total_size += run_size
+ total_avg += delta * run_size / total_size
+ moment_2 += run_stdev * run_stdev * run_size
+ moment_2 += delta * delta * old_total_size * run_size / total_size
+ if total_size < 1:
+ # Avoid division by zero.
+ return cls(size=0)
+ # TODO: Is it worth tracking moment_2 instead, and compute and cache
+ # stdev on demand, just to possibly save some sqrt calls?
+ total_stdev = math.sqrt(moment_2 / total_size)
+ ret_obj = cls(size=total_size, avg=total_avg, stdev=total_stdev)
+ return ret_obj
--- /dev/null
+# Copyright (c) 2019 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.
+
+"""Module holding BitCountingGroup class."""
+
+import copy
+
+from .AvgStdevStats import AvgStdevStats
+from .BitCountingStats import BitCountingStats
+
+
+class BitCountingGroup:
+ # TODO: Inherit from collections.abc.Sequence in Python 3.
+ """Group of runs which tracks bit count in an efficient manner.
+
+ This class contains methods that mutate the internal state,
+ use copy() method to save the previous state.
+
+ The Sequence-like access is related to the list of runs,
+ for example group[0] returns the first run in the list.
+ Writable list-like methods are not implemented.
+
+ As the group bit count depends on previous average
+ and overall maximal value, those values are assumed
+ to be known beforehand (and immutable).
+
+ As the caller is allowed to divide runs into groups in any way,
+ a method to add a single run in an efficient manner is provided.
+ """
+
+ def __init__(self, run_list=None, stats=None, bits=None,
+ max_value=None, prev_avg=None, comment="unknown"):
+ """Set the internal state and partially the stats.
+
+ A "group" stands for an Iterable of runs, where "run" is either
+ a float value, or a stats-like object (only size, avg and stdev
+ are accessed). Run is a hypothetical abstract class,
+ defining it in Python 2 is too much hassle.
+
+ Only a copy of the run list argument value is stored in the instance,
+ so it is not a problem if the value object is mutated afterwards.
+
+ It is not verified whether the user provided values are valid,
+ e.g. whether the stats and bits values reflect the runs.
+
+ :param run_list: List of run to compose into this group. Default: empty.
+ :param stats: Stats object used for computing bits.
+ :param bits: Cached value of information content.
+ :param max_value: Maximal sample value to be used for computing.
+ :param prev_avg: Average of the previous group, affects bits.
+ :param comment: Any string giving more info, e.g. "regression".
+ :type run_list: Iterable[Run]
+ :type stats: Optional[AvgStdevStats]
+ :type bits: Optional[float]
+ :type max_value: float
+ :type prev_avg: Optional[float]
+ :type comment: str
+ """
+ self.run_list = copy.deepcopy(run_list) if run_list else list()
+ self.stats = stats
+ self.cached_bits = bits
+ self.max_value = max_value
+ self.prev_avg = prev_avg
+ self.comment = comment
+ if self.stats is None:
+ self.stats = AvgStdevStats.for_runs(self.run_list)
+
+ def __str__(self):
+ """Return string with human readable description of the group.
+
+ :returns: Readable description.
+ :rtype: str
+ """
+ return f"stats={self.stats} bits={self.cached_bits}"
+
+ def __repr__(self):
+ """Return string executable as Python constructor call.
+
+ :returns: Executable constructor call.
+ :rtype: str
+ """
+ return (
+ f"BitCountingGroup(run_list={self.run_list!r},stats={self.stats!r}"
+ f",bits={self.cached_bits!r},max_value={self.max_value!r}"
+ f",prev_avg={self.prev_avg!r},comment={self.comment!r})"
+ )
+
+ def __getitem__(self, index):
+ """Return the run at the index.
+
+ :param index: Index of the run to return.
+ :type index: int
+ :returns: The run at the index.
+ :rtype: Run
+ """
+ return self.run_list[index]
+
+ def __len__(self):
+ """Return the number of runs in the group.
+
+ :returns: The Length of run_list.
+ :rtype: int
+ """
+ return len(self.run_list)
+
+ def copy(self):
+ """Return a new instance with copied internal state.
+
+ :returns: The copied instance.
+ :rtype: BitCountingGroup
+ """
+ stats = AvgStdevStats.for_runs([self.stats])
+ return self.__class__(
+ run_list=self.run_list, stats=stats, bits=self.cached_bits,
+ max_value=self.max_value, prev_avg=self.prev_avg,
+ comment=self.comment)
+
+ @property
+ def bits(self):
+ """Return overall bit content of the group list.
+
+ If not cached, compute from stats and cache.
+
+ :returns: The overall information content in bits.
+ :rtype: float
+ """
+ if self.cached_bits is None:
+ self.cached_bits = BitCountingStats.for_runs(
+ [self.stats], self.max_value, self.prev_avg).bits
+ return self.cached_bits
+
+ def append(self, run):
+ """Mutate to add the new run, return self.
+
+ Stats are updated, but old bits value is deleted from cache.
+
+ :param run: The run value to add to the group.
+ :type value: Run
+ :returns: The updated self.
+ :rtype: BitCountingGroup
+ """
+ self.run_list.append(run)
+ self.stats = AvgStdevStats.for_runs([self.stats, run])
+ self.cached_bits = None
+ return self
+
+ def extend(self, runs):
+ """Mutate to add the new runs, return self.
+
+ This is saves small amount of computation
+ compared to adding runs one by one in a loop.
+
+ Stats are updated, but old bits value is deleted from cache.
+
+ :param runs: The runs to add to the group.
+ :type value: Iterable[Run]
+ :returns: The updated self.
+ :rtype: BitCountingGroup
+ """
+ self.run_list.extend(runs)
+ self.stats = AvgStdevStats.for_runs([self.stats] + runs)
+ self.cached_bits = None
+ return self
--- /dev/null
+# Copyright (c) 2019 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.
+
+"""Module holding BitCountingGroupList class."""
+
+import copy
+
+from .BitCountingGroup import BitCountingGroup
+
+
+class BitCountingGroupList:
+ # TODO: Inherit from collections.abc.Sequence in Python 3.
+ """List of data groups which tracks overall bit count.
+
+ The Sequence-like access is related to the list of groups,
+ for example group_list[0] returns the first group in the list.
+ Writable list-like methods are not implemented.
+
+ The overall bit count is the sum of bit counts of each group.
+ Group is a sequence of data samples accompanied by their stats.
+ Different partitioning of data samples into the groups
+ results in different overall bit count.
+ This can be used to group samples in various contexts.
+
+ As the group bit count depends on previous average
+ and overall maximal value, order of groups is important.
+ Having the logic encapsulated here spares the caller
+ the effort to pass averages around.
+
+ The data can be only added, and there is some logic to skip
+ recalculations if the bit count is not needed.
+ """
+
+ def __init__(self, group_list=None, bits_except_last=0.0, max_value=None):
+ """Set the internal state without any calculations.
+
+ The group list argument is copied deeply, so it is not a problem
+ if the value object is mutated afterwards.
+
+ A "group" stands for an Iterable of runs, where "run" is either
+ a float value, or a stats-like object (only size, avg and stdev
+ are accessed). Run is a hypothetical abstract class,
+ defining it in Python 2 is too much hassle.
+
+ It is not verified whether the user provided values are valid,
+ e.g. whether the cached bits values make sense.
+
+ The max_value is required and immutable,
+ it is recommended the callers find their maximum beforehand.
+
+ :param group_list: List of groups to compose this group list (or empty).
+ :param bits_except_last: Partial sum of all but one group bits.
+ :param max_value: Maximal sample value to base bits computation on.
+ :type group_list: Iterable[BitCountingGroup]
+ :type bits_except_last: float
+ :type max_value: float
+ """
+ self.group_list = copy.deepcopy(group_list) if group_list else list()
+ self.bits_except_last = bits_except_last
+ self.max_value = max_value
+
+ def __str__(self):
+ """Return string with human readable description of the group list.
+
+ :returns: Readable description.
+ :rtype: str
+ """
+ return u"group_list={self.group_list} bits={self.bits}"
+
+ def __repr__(self):
+ """Return string executable as Python constructor call.
+
+ :returns: Executable constructor call.
+ :rtype: str
+ """
+ return (
+ f"BitCountingGroupList(group_list={self.group_list!r}"
+ f",bits_except_last={self.bits_except_last!r}"
+ f",max_value={self.max_value!r})"
+ )
+
+ def __getitem__(self, index):
+ """Return the group at the index.
+
+ :param index: Index of the group to return.
+ :type index: int
+ :returns: The group at the index.
+ :rtype: BitCountingGroup
+ """
+ return self.group_list[index]
+
+ def __len__(self):
+ """Return the length of the group list.
+
+ :returns: The Length of group_list.
+ :rtype: int
+ """
+ return len(self.group_list)
+
+ def copy(self):
+ """Return a new instance with copied internal state.
+
+ :returns: The copied instance.
+ :rtype: BitCountingGroupList
+ """
+ return self.__class__(
+ group_list=self.group_list, bits_except_last=self.bits_except_last,
+ max_value=self.max_value
+ )
+
+ @property
+ def bits(self):
+ """Return overall bit content of the group list.
+
+ :returns: The overall information content in bits.
+ :rtype: float
+ """
+ if not self.group_list:
+ return 0.0
+ # TODO: Is it worth to cache the overall result?
+ return self.bits_except_last + self.group_list[-1].bits
+
+ def append_group_of_runs(self, runs):
+ """Mutate to add a new group based on the runs, return self.
+
+ The argument is copied before adding to the group list,
+ so further edits do not affect the grup list.
+ The argument can also be a group, only runs from it are used.
+
+ :param runs: Runs to form the next group to be appended to self.
+ :type runs: Union[Iterable[Run], BitCountingGroup]
+ :returns: The updated self.
+ :rtype: BitCountingGroupList
+ """
+ prev_avg = self.group_list[-1].stats.avg if self.group_list else None
+ if isinstance(runs, BitCountingGroup):
+ # It is faster to avoid stats recalculation.
+ new_group = runs.copy()
+ new_group.max_value = self.max_value
+ new_group.prev_avg = prev_avg
+ new_group.cached_bits = None
+ else:
+ new_group = BitCountingGroup(
+ run_list=runs, max_value=self.max_value, prev_avg=prev_avg)
+ self.bits_except_last = self.bits
+ self.group_list.append(new_group)
+ return self
+
+ def append_run_to_to_last_group(self, run):
+ """Mutate to add new run at the end of the last group.
+
+ Basically a one-liner, only returning group list instead of last group.
+
+ :param run: The run value to add to the last group.
+ :type run: Run
+ :returns: The updated self.
+ :rtype: BitCountingGroupList
+ :raises IndexError: If group list is empty, no last group to add to.
+ """
+ self.group_list[-1].append(run)
+ return self
+
+ def extend_runs_to_last_group(self, runs):
+ """Mutate to add new runs to the end of the last group.
+
+ A faster alternative to appending runs one by one in a loop.
+
+ :param runs: The runs to add to the last group.
+ :type runs: Iterable[Run]
+ :returns: The updated self
+ :rtype: BitCountingGroupList
+ :raises IndexError: If group list is empty, no last group to add to.
+ """
+ self.group_list[-1].extend(runs)
+ return self
--- /dev/null
+# Copyright (c) 2019 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.
+
+"""Module holding BitCountingStats class."""
+
+import math
+
+from .AvgStdevStats import AvgStdevStats
+
+
+class BitCountingStats(AvgStdevStats):
+ """Class for statistics which include information content of a group.
+
+ The information content is based on an assumption that the data
+ consists of independent random values from a normal distribution.
+
+ Instances are only statistics, the data itself is stored elsewhere.
+
+ The coding needs to know the previous average, and a maximal value
+ so both values are required as inputs.
+
+ This is a subclass of AvgStdevStats, even though all methods are overriden.
+ Only for_runs method calls the parent implementation, without using super().
+ """
+
+ def __init__(
+ self, size=0, avg=None, stdev=0.0, max_value=None, prev_avg=None):
+ """Construct the stats object by computing from the values needed.
+
+ The values are not sanitized, faulty callers can cause math errors.
+
+ The None values are allowed for stats for zero size data,
+ but such stats can report arbitrary avg and max_value.
+ Stats for nonzero size data cannot contain None,
+ else ValueError is raised.
+
+ The max_value needs to be numeric for nonzero size,
+ but its relations to avg and prev_avg are not examined.
+
+ 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 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 max_value: Maximal expected value.
+ TODO: This might be more optimal,
+ but max-invariant algorithm will be nicer.
+ :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 avg: float
+ :type size: int
+ :type stdev: float
+ :type max_value: Union[float, NoneType]
+ :type prev_avg: Union[float, NoneType]
+ """
+ self.avg = avg
+ self.size = size
+ self.stdev = stdev
+ self.max_value = max_value
+ self.prev_avg = prev_avg
+ # Zero size should in principle have non-zero bits (coding zero size),
+ # but zero allows users to add empty groups without affecting bits.
+ self.bits = 0.0
+ if self.size < 1:
+ return
+ if avg is None:
+ raise ValueError(f"Avg is None: {self!r}")
+ if max_value is None or max_value <= 0.0:
+ raise ValueError(f"Invalid max value: {self!r}")
+ # 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)
+ # This is compatible with zero size leading to zero bits.
+ 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 (
+ f"size={self.size} avg={self.avg} stdev={self.stdev}"
+ f" bits={self.bits}"
+ )
+
+ def __repr__(self):
+ """Return string executable as Python constructor call.
+
+ :returns: Executable constructor call.
+ :rtype: str
+ """
+ return (
+ f"BitCountingStats(size={self.size!r},avg={self.avg!r}"
+ f",stdev={self.stdev!r},max_value={self.max_value!r}"
+ f",prev_avg={self.prev_avg!r})"
+ )
+
+ @classmethod
+ def for_runs(cls, runs, max_value=None, prev_avg=None):
+ """Return new stats instance describing the sequence of runs.
+
+ If you want to append data to existing stats object,
+ you can simply use the stats object as the first run.
+
+ Instead of a verb, "for" is used to start this method name,
+ to signify the result contains less information than the input data.
+
+ The two optional values can come from outside of the runs provided.
+
+ The max_value cannot be None for non-zero size data.
+ The implementation does not check if no datapoint exceeds max_value.
+
+ TODO: Document the behavior for zero size result.
+
+ :param runs: Sequence of data to describe by the new metadata.
+ :param max_value: Maximal expected value.
+ :param prev_avg: Population average of the previous group, if any.
+ :type runs: Iterable[Union[float, AvgStdevStats]]
+ :type max_value: Union[float, NoneType]
+ :type prev_avg: Union[float, NoneType]
+ :returns: The new stats instance.
+ :rtype: cls
+ """
+ asd = AvgStdevStats.for_runs(runs)
+ ret_obj = cls(size=asd.size, avg=asd.avg, stdev=asd.stdev,
+ max_value=max_value, prev_avg=prev_avg)
+ return ret_obj
-# Copyright (c) 2018 Cisco and/or its affiliates.
+# Copyright (c) 2019 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:
"""
__init__ file for "jumpavg" Python package.
"""
+
+from .AvgStdevStats import AvgStdevStats
+from .BitCountingStats import BitCountingStats
+from .BitCountingGroup import BitCountingGroup
+from .BitCountingGroupList import BitCountingGroupList
+from .classify import classify
--- /dev/null
+# Copyright (c) 2019 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.
+
+"""Module holding the classify function
+
+Classification os one of primary purposes of this package.
+
+Minimal message length principle is used
+for grouping results into the list of groups,
+assuming each group is a population of different Gaussian distribution.
+"""
+
+from .AvgStdevStats import AvgStdevStats
+from .BitCountingGroupList import BitCountingGroupList
+
+
+def classify(values):
+ """Return the values in groups of optimal bit count.
+
+ Here, a value is either a float, or an iterable of floats.
+ Such iterables represent an undivisible sequence of floats.
+
+ Internally, such sequence is replaced by AvgStdevStats
+ after maximal value is found.
+
+ :param values: Sequence of runs to classify.
+ :type values: Iterable[Union[float, Iterable[float]]]
+ :returns: Classified group list.
+ :rtype: BitCountingGroupList
+ """
+ processed_values = list()
+ max_value = 0.0
+ for value in values:
+ if isinstance(value, (float, int)):
+ if value > max_value:
+ max_value = value
+ processed_values.append(value)
+ else:
+ for subvalue in value:
+ if subvalue > max_value:
+ max_value = subvalue
+ processed_values.append(AvgStdevStats.for_runs(value))
+ open_at = list()
+ closed_before = [BitCountingGroupList(max_value=max_value)]
+ for index, value in enumerate(processed_values):
+ newly_open = closed_before[index].copy()
+ newly_open.append_group_of_runs([value])
+ open_at.append(newly_open)
+ record_group_list = newly_open
+ for previous_index, old_open in enumerate(open_at[:index]):
+ new_open = old_open.copy().append_run_to_to_last_group(value)
+ open_at[previous_index] = new_open
+ if new_open.bits < record_group_list.bits:
+ record_group_list = new_open
+ closed_before.append(record_group_list)
+ partition = closed_before[-1]
+ previous_average = partition[0].stats.avg
+ for group in partition:
+ if group.stats.avg == previous_average:
+ group.comment = u"normal"
+ elif group.stats.avg < previous_average:
+ group.comment = u"regression"
+ elif group.stats.avg > previous_average:
+ group.comment = u"progression"
+ previous_average = group.stats.avg
+ return partition
_build/**
**/*tar.gz
+/tmp
import json
import sys
-from jumpavg.BitCountingMetadataFactory import BitCountingMetadataFactory
-from jumpavg.BitCountingClassifier import BitCountingClassifier
+from resources.libraries.python import jumpavg
def hack(value_list):
sys.exit(1)
parent_iterations.append(parent_lines)
current_iterations.append(current_lines)
-classifier = BitCountingClassifier()
exit_code = 0
for test_index in range(num_tests):
- val_max = 1.0
parent_values = list()
current_values = list()
for iteration_index in range(len(parent_iterations)):
print(f"Time-ordered MRR values for current build: {current_values}")
parent_values = hack(parent_values)
current_values = hack(current_values)
- parent_max = BitCountingMetadataFactory.find_max_value(parent_values)
- current_max = BitCountingMetadataFactory.find_max_value(current_values)
- val_max = max(val_max, parent_max, current_max)
- factory = BitCountingMetadataFactory(val_max)
- parent_stats = factory.from_data(parent_values)
- current_factory = BitCountingMetadataFactory(val_max, parent_stats.avg)
- current_stats = current_factory.from_data(current_values)
- both_stats = factory.from_data(parent_values + current_values)
+ max_value = max([1.0] + parent_values + current_values)
+ parent_stats = jumpavg.AvgStdevStats.for_runs(parent_values)
+ current_stats = jumpavg.AvgStdevStats.for_runs(current_values)
+ parent_group_list = jumpavg.BitCountingGroupList(
+ max_value=max_value).append_group_of_runs([parent_stats])
+ combined_group_list = parent_group_list.copy().extend_runs_to_last_group(
+ [current_stats])
+ separated_group_list = parent_group_list.append_group_of_runs(
+ [current_stats])
print(f"Value-ordered MRR values for parent build: {parent_values}")
print(f"Value-ordered MRR values for current build: {current_values}")
- difference = (current_stats.avg - parent_stats.avg) / parent_stats.avg
- print(f"Difference of averages relative to parent: {100 * difference}%")
+ avg_diff = (current_stats.avg - parent_stats.avg) / parent_stats.avg
+ print(f"Difference of averages relative to parent: {100 * avg_diff}%")
print(f"Jumpavg representation of parent group: {parent_stats}")
print(f"Jumpavg representation of current group: {current_stats}")
- print(f"Jumpavg representation of both as one group: {both_stats}")
- bits = parent_stats.bits + current_stats.bits - both_stats.bits
- compared = u"longer" if bits >= 0 else u"shorter"
print(
- f"Separate groups are {compared} than single group by {abs(bits)} bits"
+ f"Jumpavg representation of both as one group:"
+ f" {combined_group_list[0].stats}"
)
- classified_list = classifier.classify([parent_stats, current_stats])
+ bits_diff = separated_group_list.bits - combined_group_list.bits
+ compared = u"longer" if bits_diff >= 0 else u"shorter"
+ print(
+ f"Separate groups are {compared} than single group"
+ f" by {abs(bits_diff)} bits"
+ )
+ # TODO: Version of classify that takes max_value and list of stats?
+ # That matters if only stats (not list of floats) are given.
+ classified_list = jumpavg.classify([parent_values, current_values])
if len(classified_list) < 2:
print(f"Test test_index {test_index}: normal (no anomaly)")
continue
- anomaly = classified_list[1].metadata.classification
+ anomaly = classified_list[1].comment
if anomaly == u"regression":
print(f"Test test_index {test_index}: anomaly regression")
- exit_code = 1
+ exit_code = 3 # 1 or 2 can be caused by other errors
continue
print(f"Test test_index {test_index}: anomaly {anomaly}")
-print(f"Exit code {exit_code}")
+print(f"Exit code: {exit_code}")
sys.exit(exit_code)
-# Copyright (c) 2018 Cisco and/or its affiliates.
+# Copyright (c) 2019 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:
# See the License for the specific language governing permissions and
# limitations under the License.
-# TODO: Convert to use the code from cloned CSIT git, not from pip.
-jumpavg==0.1.3
+# TODO: Ply should be installed as system package, fix bootstrap to see it.
ply==3.11
Code Review / csit.git / commitdiff