def get_winner_assessment_for_canarybg(
experiment_resource: ExperimentResource):
"""
Get winner assessment using experiment resource for Canary or BlueGreen experiments
"""
was = WinnerAssessmentAnalysis()
versions = [experiment_resource.spec.versionInfo.baseline]
versions += experiment_resource.spec.versionInfo.candidates
feasible_versions = get_feasible_versions(experiment_resource, versions)
# names of feasible versions
fvn = list(map(lambda version: version.name, feasible_versions))
if versions[1].name in fvn:
was.data = WinnerAssessmentData(winnerFound = True, winner = versions[1].name, \
bestVersions = [versions[1].name])
was.message = Message.join_messages([Message(MessageLevel.INFO, \
"candidate satisfies all objectives")])
elif versions[0].name in fvn:
was.data = WinnerAssessmentData(winnerFound = True, winner = versions[0].name, \
bestVersions = [versions[0].name])
was.message = Message.join_messages([Message(MessageLevel.INFO, \
"baseline satisfies all objectives; candidate does not")])
return was
def get_exploitation_weights():
"""Create exploitation weights; in fraction
if there are three versions:
if there are no best versions:
exploitation_weights = [1.0, 0, 0], i.e., baseline gets to be exploited
if there is a single best version, say, the 2nd version:
exploitation_weights = [0, 1.0, 0], i.e., the best version gets exploited
if there are two best versions, say, the 2nd and 3rd versions:
exploitation_weights = [0, 0.5, 0.5], i.e., best versions get exploited evenly
"""
exploitation_weights = np.full((len(versions), ), 0.0)
try:
bvs = experiment_resource.status.analysis.winner_assessment.data.bestVersions
assert len(bvs) > 0
messages.append(Message(MessageLevel.INFO,
"found best version(s)"))
for i, version in enumerate(versions):
if version.name in bvs:
exploitation_weights[i] = 1 / len(bvs)
except (KeyError, AssertionError):
exploitation_weights = np.full((len(versions), ), 0.0)
exploitation_weights[0] = 1.0
messages.append(
Message(MessageLevel.INFO, "no best version(s) found"))
return exploitation_weights
def get_winner_assessment_for_conformance(experiment_resource: ExperimentResource):
"""
Get winner assessment using experiment resource for Conformance
"""
was = WinnerAssessmentAnalysis()
versions = [experiment_resource.spec.versionInfo.baseline]
# no version assessments data ...
# this is because there are no objectives in the experiment to satisfy ...
# declare all versions to be feasible
if experiment_resource.status.analysis.version_assessments.data is None or \
len(experiment_resource.status.analysis.version_assessments.data) == 0:
feasible_versions = versions
else:
# there is version assessment data
# filter out feasible versions
feasible_versions = list(filter(lambda version: \
all(experiment_resource.status.analysis.version_assessments.data[version.name]), versions))
# extract names of feasible versions
fvn = list(map(lambda version: version.name, feasible_versions))
if versions[0].name in fvn:
was.data = WinnerAssessmentData(winnerFound = True, winner = versions[0].name, \
bestVersions = [versions[0].name])
was.message = Message.join_messages([Message(MessageLevel.INFO, \
"baseline satisfies all objectives")])
return was
def get_aggregated_metrics(expr: ExperimentResource):
"""
Get aggregated metrics using experiment resource and metric resources.
"""
versions = [expr.spec.versionInfo.baseline]
if expr.spec.versionInfo.candidates is not None:
versions += expr.spec.versionInfo.candidates
# messages not working as intended...
messages = []
# initialize aggregated metrics object
iam = get_builtin_metrics(expr)
# check if start time is greater than now
# this is problematic.... start time is set by etc3 but checked by analytics.
# clocks are not synced, so this is not right...
if expr.status.startTime > (datetime.now(timezone.utc)):
messages.append(
Message(MessageLevel.ERROR,
"Invalid startTime: greater than current time"))
iam.message = Message.join_messages(messages)
return iam
# there are no metrics to be fetched
if expr.status.metrics is None:
iam.message = Message.join_messages(messages)
return iam
for metric_info in expr.status.metrics:
# only custom metrics is handled below... not builtin metrics
if metric_info.metricObj.spec.provider is None or \
metric_info.metricObj.spec.provider != "iter8":
iam.data[metric_info.name] = AggregatedMetric(data={})
# fetch the metric value for each version...
for version in versions:
# initialize metric object for this version...
iam.data[metric_info.name].data[version.name] = VersionMetric()
val, err = get_metric_value(metric_info.metricObj, version, \
expr.status.startTime)
if err is None and val is not None:
iam.data[metric_info.name].data[version.name].value = val
else:
try:
val = float(expr.status.analysis.aggregated_metrics.data\
[metric_info.name].data[version.name].value)
except AttributeError:
val = None
iam.data[metric_info.name].data[version.name].value = val
if err is not None:
messages.append(Message(MessageLevel.ERROR, \
f"Error from metrics backend for metric: {metric_info.name} \
and version: {version.name}" ))
iam.message = Message.join_messages(messages)
logger.debug("Analysis object after metrics collection")
logger.debug(pprint.PrettyPrinter().pformat(iam))
return iam
def get_version_assessments(experiment_resource: ExperimentResource):
"""
Get version assessments using experiment resource.
"""
versions = [experiment_resource.spec.versionInfo.baseline]
if experiment_resource.spec.versionInfo.candidates is not None:
versions += experiment_resource.spec.versionInfo.candidates
messages = []
def check_limits(obj: Objective, value: float) -> bool:
if (obj.upper_limit is not None) and (value > float(obj.upper_limit)):
return False
if (obj.lower_limit is not None) and (value < float(obj.lower_limit)):
return False
return True
aggregated_metric_data = experiment_resource.status.analysis.aggregated_metrics.data
version_assessments = VersionAssessmentsAnalysis(data={})
if experiment_resource.spec.criteria is None or \
experiment_resource.spec.criteria.objectives is None:
return version_assessments
# objectives are available
for version in versions:
version_assessments.data[version.name] = [False] * \
len(experiment_resource.spec.criteria.objectives)
for ind, obj in enumerate(experiment_resource.spec.criteria.objectives):
if obj.metric in aggregated_metric_data:
versions_metric_data = aggregated_metric_data[obj.metric].data
for version in versions:
if version.name in versions_metric_data:
if versions_metric_data[version.name].value is not None:
version_assessments.data[version.name][ind] = \
check_limits(obj, float(versions_metric_data[version.name].value))
else:
messages.append(Message(MessageLevel.WARNING, \
f"Value for {obj.metric} metric and {version.name} version is None."))
else:
messages.append(Message(MessageLevel.WARNING, \
f"Value for {obj.metric} metric and {version.name} version is unavailable."))
else:
messages.append(Message(MessageLevel.WARNING, \
f"Aggregated metric object for {obj.metric} metric is unavailable."))
version_assessments.message = Message.join_messages(messages)
logger.debug("version assessments: %s",
pprint.PrettyPrinter().pformat(version_assessments))
return version_assessments
def get_aggregated_metrics(expr: ExperimentResource):
"""
Get aggregated metrics from experiment resource and metric resources.
"""
versions = [expr.spec.versionInfo.baseline]
if expr.spec.versionInfo.candidates is not None:
versions += expr.spec.versionInfo.candidates
# messages not working as intended...
messages = []
# initialize aggregated metrics object
iam = AggregatedMetricsAnalysis(data={})
#check if start time is greater than now
if expr.status.startTime > (datetime.now(timezone.utc)):
messages.append(
Message(MessageLevel.ERROR,
"Invalid startTime: greater than current time"))
iam.message = Message.join_messages(messages)
return iam
# if there are metrics to be fetched...
if expr.status.metrics is not None:
for metric_resource in expr.status.metrics:
iam.data[metric_resource.name] = AggregatedMetric(data={})
# fetch the metric value for each version...
for version in versions:
# initialize metric object for this version...
iam.data[metric_resource.name].data[
version.name] = VersionMetric()
val, err = get_metric_value(metric_resource.metricObj, version, \
expr.status.startTime)
if err is None:
iam.data[metric_resource.name].data[
version.name].value = val
else:
messages.append(Message(MessageLevel.ERROR, \
f"Error from metrics backend for metric: {metric_resource.name} \
and version: {version.name}" ))
iam.message = Message.join_messages(messages)
logger.debug("Analysis object after metrics collection")
logger.debug(pprint.PrettyPrinter().pformat(iam))
return iam
def get_winner_assessment_for_conformance(
experiment_resource: ExperimentResource):
"""
Get winner assessment using experiment resource for Conformance
"""
was = WinnerAssessmentAnalysis()
versions = [experiment_resource.spec.versionInfo.baseline]
feasible_versions = get_feasible_versions(experiment_resource, versions)
# extract names of feasible versions
fvn = list(map(lambda version: version.name, feasible_versions))
if versions[0].name in fvn:
was.data = WinnerAssessmentData(winnerFound = True, winner = versions[0].name, \
bestVersions = [versions[0].name])
was.message = Message.join_messages([Message(MessageLevel.INFO, \
"baseline satisfies all objectives")])
return was
def get_weights(experiment_resource: ExperimentResource):
"""
Get weights using experiment resource. All weight values in the output will be integers.
"""
if experiment_resource.spec.strategy.testingPattern == TestingPattern.CONFORMANCE:
return WeightsAnalysis(data = [], \
message = "weight computation is not applicable to a conformance experiment")
versions = [experiment_resource.spec.versionInfo.baseline]
versions += experiment_resource.spec.versionInfo.candidates
messages = []
# create exploration weights; in fraction
# if there are three versions:
# exploration_weights = [1/3, 1/3, 1/3]
exploration_weights = np.full((len(versions), ), 1.0 / len(versions))
def get_exploitation_weights():
"""Create exploitation weights; in fraction
if there are three versions:
if there are no best versions:
exploitation_weights = [1.0, 0, 0], i.e., baseline gets to be exploited
if there is a single best version, say, the 2nd version:
exploitation_weights = [0, 1.0, 0], i.e., the best version gets exploited
if there are two best versions, say, the 2nd and 3rd versions:
exploitation_weights = [0, 0.5, 0.5], i.e., best versions get exploited evenly
"""
exploitation_weights = np.full((len(versions), ), 0.0)
try:
bvs = experiment_resource.status.analysis.winner_assessment.data.bestVersions
assert len(bvs) > 0
messages.append(Message(MessageLevel.INFO,
"found best version(s)"))
for i, version in enumerate(versions):
if version.name in bvs:
exploitation_weights[i] = 1 / len(bvs)
except (KeyError, AssertionError):
exploitation_weights = np.full((len(versions), ), 0.0)
exploitation_weights[0] = 1.0
messages.append(
Message(MessageLevel.INFO, "no best version(s) found"))
return exploitation_weights
exploitation_weights = get_exploitation_weights()
def get_constrained_weights(input_weights):
"""
Take input weights in percentage.
Apply weight constraints and return modified weights.
Example illustrating the inner workings of this function:
old_weights = [20, 40, 40]
input_weights = [20, 30, 50]
maxCandidateWeightIncrement = 10
maxCandidateWeight = 40
after i = 0, constrained_weights = [20, 30, 50]
during i = 1
increase = -10
excess = max(0, -10 - 10, 30 - 40) = max(0, -20, -10) = 0
after i = 1, constrained_weights = [20, 30, 50]
during i = 2
increase = 10
excess = max(0, 10 - 10, 50 - 40) = 10
after i = 2, constrained_weights = [30, 30, 40]
"""
# Suppose there are 3 versions. old_weights initialized to [100, 0, 0]
old_weights = [100] + ([0] * (len(versions) - 1))
# and then, old_weights are updated to currentWeightDistribution, e.g., [5, 25, 70]
if experiment_resource.status.currentWeightDistribution is not None:
old_weights = list(map(lambda x: x.value, \
experiment_resource.status.currentWeightDistribution))
logger.debug("Old weights: %s", old_weights)
logger.debug("Input weights: %s", input_weights)
constrained_weights = input_weights.copy()
if experiment_resource.spec.strategy.weights is not None:
for i in range(len(versions)):
if i == 0:
continue
# for each candidate, compute excess
increase = input_weights[i] - old_weights[i]
excess = max(0, \
increase - \
experiment_resource.spec.strategy.weights.maxCandidateWeightIncrement, \
input_weights[i] - experiment_resource.spec.strategy.weights.maxCandidateWeight)
# cap candidate weight and add the excess to baseline
constrained_weights[i] -= excess
constrained_weights[0] += excess
logger.debug("Constrained weights: %s", constrained_weights)
return constrained_weights
# create mix-weight: in fraction
ewf = AdvancedParameters.exploration_traffic_percentage / 100.0
# Suppose, ewf = 0.1 (i.e., exploration_traffic_percentage = 10%)
# Let exploration_weights = [1/3, 1/3, 1/3]
# Let exploitation_weights = [0, 0.5, 0.5]
# Then, mix_weights = 0.1 * exploration_weights + 0.9 * exploitation_weights
# = 0.1 * [1/3, 1/3, 1/3] + 0.9 * [0, 0.5, 0.5]
# = [0.033333, 0.033333, 0.033333] + [0.0, 0.45, 0.45]
# = [0.033333, 0.483333, 0.483333]
mix_weights = (exploration_weights * ewf) + (exploitation_weights *
(1 - ewf))
# create mix-weight: in percent
# in the above example, we have mix_weights (in percent) = [3.3333, 48.3333, 48.3333]
mix_weights *= 100.0
# apply weight constraints
constrained_weights = get_constrained_weights(mix_weights)
# perform rounding of weights, so that they sum up to 100
integral_weights = gen_round(constrained_weights, 100)
data = []
for version in versions:
data.append(
VersionWeight(name=version.name, value=next(integral_weights)))
_weights = WeightsAnalysis(data=data)
_weights.message = Message.join_messages(
[Message(MessageLevel.INFO, "all ok")])
logger.debug("weights: %s", pprint.PrettyPrinter().pformat(_weights))
return _weights
def get_winner_assessment_for_abn(experiment_resource: ExperimentResource):
"""
Get winner assessment using experiment resource for ab or abn experiments
"""
was = WinnerAssessmentAnalysis()
versions = [experiment_resource.spec.versionInfo.baseline]
versions += experiment_resource.spec.versionInfo.candidates
logger.info("Versions: %s", versions)
feasible_versions = get_feasible_versions(experiment_resource, versions)
logger.info("Feasible versions: %s", feasible_versions)
# names of feasible versions
fvn = list(map(lambda version: version.name, feasible_versions))
def get_inf_reward(reward: Reward):
if reward.preferredDirection == PreferredDirection.HIGH:
return -math.inf
else:
return math.inf
def first_better_than_second(first: float, second: float, \
preferred_direction: PreferredDirection):
"""
Return True if first is better than second, else return False
"""
if preferred_direction is None:
err = "Metrics cannot be compared without preferred direction"
logger.error(err)
return False, err
if preferred_direction is PreferredDirection.HIGH:
return (first > second), None
return (first < second), None
aggregated_metric_data = experiment_resource.status.analysis.aggregated_metrics.data
if experiment_resource.spec.criteria.rewards is not None:
reward_metric = experiment_resource.spec.criteria.rewards[0].metric
if reward_metric in aggregated_metric_data:
reward_metric_data = aggregated_metric_data[reward_metric].data
(top_reward, best_versions) = (get_inf_reward(\
experiment_resource.spec.criteria.rewards[0]), [])
messages = []
if not fvn:
messages.append(
Message(MessageLevel.INFO,
"no version satisfies all objectives"))
for fver in fvn: # for each feasible version
if fver in reward_metric_data:
if reward_metric_data[fver].value is not None:
if reward_metric_data[fver].value == top_reward:
best_versions.append(fver)
else: # this reward not equal to top reward
is_better, err = first_better_than_second(\
float(reward_metric_data[fver].value), float(top_reward), \
experiment_resource.spec.criteria.rewards[0].preferredDirection)
if err is None:
if is_better:
(top_reward, best_versions) = \
(reward_metric_data[fver].value, [fver])
else: # there is an error in comparison
was.message = Message.join_messages(Message(MessageLevel.ERROR, \
str(err)))
return was
else: # found a feasible version without reward value
messages.append(Message(MessageLevel.WARNING, \
f"reward value for feasible version {fver} is not available"))
else: # found a feasible version without reward value
messages.append(Message(MessageLevel.WARNING, \
f"reward value for feasible version {fver} is not available"))
was.data.bestVersions = best_versions
if len(best_versions) == 1:
was.data.winnerFound = True
was.data.winner = best_versions[0]
messages.append(
Message(MessageLevel.INFO, "found unique winner"))
elif len(best_versions) > 1:
messages.append(Message(MessageLevel.INFO, \
"no unique winner; two or more feasible versions with same reward value"))
was.message = Message.join_messages(messages)
else: # reward metric values are not available
was.message = Message.join_messages([Message(MessageLevel.WARNING, \
"reward metric values are not available")])
else: # ab or abn experiment without reward metric
was.message = Message.join_messages([Message(MessageLevel.WARNING, \
"No reward metric in experiment. Winner assessment cannot be computed for ab or abn experiments without reward metric.")])
return was
