AverageDrift

Description

'Average Drift' verses configure Mona to find segments, in which a metric's average differs significantly between a target data set and a benchmark data set.

In the standard approach we check if the difference between the average metric value in the target set to the average metric value in the benchmark set is larger than a given multiplier * the standard deviation of the metric value distribution in the united benchmark and target sets.

Advanced section:
No-STD-mode: This approach can be activated above a certain size, because the STD-aware approach biases against large segments.

In the no-STD-mode, we measure the difference between target and benchmark as absolute percentage of, regardless of the metric's variance (because we assume that large segments are stable). Since values may have different reference points other than 0 other values are also allowed to be considered as a reference points to measure both target and benchmark metric averages difference and evaluate the percent change of the target diff from the benchmark's diff. If more than one reference is over the threshold of min percentage, the value that accounts for the largest change is chosen. The anomaly_level is the change in fraction of the benchmark's diff of the chosen reference value. For example, if the reference values are 0 and 1, and we get average metrics in benchmark of 0.4 and in target 0.1, the min anomaly level required to create a signal is 0.5 (because the diffs from 1 are 0.6 and 0.9 respectively, which is 50% change). If only 0 is supplied as reference, the required min anomaly level will be 0.75.

{
  "stanzas": {
    "stanza_name": {
      "verses": [
        {
          "type": "AverageDrift",
          "segment_by": [
            "company_id",
            "country"
          ],
          "metrics": [
            "confidence_score",
            "failed_classification"
          ],
          "min_segment_size_fraction": 0.05,
          "min_anomaly_level": 0.5,
          "trend_directions": [
            "desc"
          ],
          "time_resolution": "1w",
          "target_set_period": "7d",
          "benchmark_set_period": "28d"
        }
      ]
    }
  }
}

In this example we see an AverageDrift verse which is configured to search for statistically significant decreases (not increases due to the "trend_directions" param) in the average of "confidence_score" and "failed_classification" in any specific "company_id" or "country" (or any intersection of country and company), between a "target" dataset from the last 7 days, and "benchmark" dataset from the 28 days prior to that.

We use "min_anomaly_level" to define that a drift occurs when the change in averages between the benchmark and target sets is at least 0.5 standard deviations.
The "min_segment_size" param will filter out segments that are smaller than 5% of the data.
Lastly, "time_resolution" will configure the resolution of the time series chart in the insight's page for insights created by this configuration.

Basic Params

see more
benchmark_set_period

Name

Description

Type

Default

benchmark_set_period

Time period for benchmark set. By default means the period just before the target set period. Expected format is "" where can be any positive integer, and options currently include: "d" (days), or "w" (weeks). e.g, "1d" means 1 day period.

TimePeriod

6w

{ "benchmark_set_period": "50d" }
benchmark_set_period_type

Name

Description

Type

Default

benchmark_set_period_type

Sets the end time of the benchmark set period. Supports 'previous_to_target' (benchmark ends when target starts) and "latest" (both ends on the same date).

BenchmarkSetPeriodType

previous_to_target

{ "benchmark_set_period_type": "latest" }
metrics

Name

Description

Type

Default

metrics

Relevant metrics to search anomalies for in the verse, relevant only for types who search for anomalies in metrics behavior.

MetricsListType

[]

{ "metrics": [ "top_score", "delta_top_to_second_score" ] }
min_anomaly_level

Name

Description

Type

Default

min_anomaly_level

This parameter sets the threshold for the minimal anomaly level for which an insight will be generated. In standard mode - anomaly level is the diff between target and benchmark averages, normalized by overall STD. In no-STD-mode the anomaly threshold will be defined by "no_std_mode_min_diff_percent".

PositiveFloat

0.3

{ "min_anomaly_level": 0.6 }
min_segment_size

Name

Description

Type

Default

min_segment_size

Minimal segment size for the united benchmark+target segments.

PositiveInt

100

{ "min_segment_size": 100 }
min_segment_size_fraction

Name

Description

Type

Default

min_segment_size_fraction

Minimal segment size in fraction from baseline segment, which a segment must have in order to be considered in the search.

InclusiveFraction

0

{ "min_segment_size_fraction": 0.05 }
name

Name

Description

Type

Default

name

The name of the verse. If missing, this field defaults to "<stanza's name><verse's type>". Please note, a verse's name must be different from other verses in the same stanza. Therefore, in cases where there is more than one verse of the same type, using the default name ("<stanza's name><verse's type>") is not supported and custom names have to be provided for these verses.

None

None

{ "name": "confidence_outliers" }
segment_by

Name

Description

Type

Default

segment_by

The dimensions to use to segment the data in order to search for anomalies. This list must be a sublist of all arc class' dimensions. Avoiding insights generation on a specific value of a segmentation field can be done by using the "avoid_values" key in the segmentation json object, as seen in the example.

SegmentationsListType

[]

{ "segment_by": [ "city", "bot_id", {"name": "provider-code", "avoid_values": ["zoom"]}, {"name": "selected-language", "avoid_values": ["eng", "spa"]} ] }
target_set_period

Name

Description

Type

Default

target_set_period

Time period for the target set, ending on the day of the latest available data. Format detailed in common/util.py's get_time_period_for_string.

TimePeriod

2w

{ "target_set_period": "1w" }
time_resolution

Name

Description

Type

Default

time_resolution

The time resolution of time series charts for insights of this verse. NOTE: this parameter doesn't affect the behavior of the insights finding mechanism, but only the data representation in the output.

VisualTimeResolution

1d

{ "time_resolution": "1w" }
trend_directions

Name

Description

Type

Default

trend_directions

A list of allowed anomalies trends directions - either 'asc' for ascending (anomalies in which the found value is LARGER THAN the relevant benchmark), or 'desc' for descending (anomalies in which the found value is SMALLER THAN the relevant benchmark).

TrendDirectionsType

['desc', 'asc']

{ "trend_directions": [ "asc" ] }

Advanced Misc Params

see more
avoid_same_field_for_segment_and_metric

Name

Description

Type

Default

avoid_same_field_for_segment_and_metric

If True, insights would not be created for segments based on the same field as the given metric.

bool

True

{ "avoid_same_field_for_segment_and_metric": false }
cadence

Name

Description

Type

Default

cadence

The cadence for evaluation of this verse. Only the following cadences are valid: Minutes: 1m, 5m, 10m, 15m, 20m, 30m. Hours: 1h, 2h, 3h, 4h, 6h, 8h, 12h. Days: 1d, 2d, 3d, 4d, 5d, 6d. Weeks: 1w, 2w, 3w, 4w, 5w.

CadenceType

1d

{ "cadence": "6h" }
create_extra_adjacent_signals

Name

Description

Type

Default

create_extra_adjacent_signals

If set to true (default), will cause Mona to create new signals from existing signals with adjacent numeric segments. So if there are two signals defined on 1 <= x < 2 and 2 <= x < 3 - Mona will automatically create a new signal with 1 <= x < 3. This will allow the later clustering algorithm to create an insight with the most relevant segment for its main signal.

bool

True

{ "create_extra_adjacent_signals": false }
disabled

Name

Description

Type

Default

disabled

If set to True - this verse won't be used when searching for new insights.

bool

False

{ "disabled": true }
expire_after

Name

Description

Type

Default

expire_after

Insights detected by this verse will continue to be considered active for at least this amount of time after the last time they were detected.

TimePeriod

3d

{ "expire_after": "2d" }

Advanced Score Calculation Params

see more
no_std_mode_min_segment_size

Name

Description

Type

Default

no_std_mode_min_segment_size

Minimum segment size to start using no-STD mode. -1 means don't use this method at all. 0 means only use no_std_mode. If this is positive, smaller segments will use the std-aware mode.

NonNegativeOrMinusOneInt

-1

{ "no_std_mode_min_segment_size": 0 }
no_std_mode_reference_values

Name

Description

Type

Default

no_std_mode_reference_values

In no-STD mode, use these values as reference to compare both benchmark and target averages.

frozenset

[0]

{ "no_std_mode_reference_values": [ 0, 1 ] }
score_anomaly_level_exponent

Name

Description

Type

Default

score_anomaly_level_exponent

An exponent to put on the anomaly level in the score after multiplying it by the given multiplier.

float

1.0

{ "score_anomaly_level_exponent": 0.5 }
score_anomaly_level_multiplier

Name

Description

Type

Default

score_anomaly_level_multiplier

Multiplier for an anomaly level to use before using the exponent.

float

1.0

{ "score_anomaly_level_multiplier": 1.2 }
score_segment_size_exponent

Name

Description

Type

Default

score_segment_size_exponent

An exponent to put on the segment's size (or relative size) in the combined score. If score_segment_size_log_base is not 0, the exponent will be applied before the logarithm will.

float

0.5

{ "score_segment_size_exponent": 1.5 }
score_segment_size_log_base

Name

Description

Type

Default

score_segment_size_log_base

Changes the log base used for the segment's size (or relative size) in the combined score, or remove the log altogether by setting 0 here. Unless its 0 this value must be larger than 1.

float

0.0

{ "score_segment_size_log_base": 5 }
score_use_segment_absolute_size

Name

Description

Type

Default

score_use_segment_absolute_size

If true, use the segment absolute size in the combined score, otherwise use the segment's size relative to its baseline (fraction).

bool

True

{ "score_use_segment_absolute_size": false }

Anomaly Thresholds Params

see more
epsilon

Name

Description

Type

Default

epsilon

Minimal absolute difference between benchmark and value.

NonNegativeFloat

0.01

{ "epsilon": 0.5 }
min_anomaly_level

Name

Description

Type

Default

min_anomaly_level

This parameter sets the threshold for the minimal anomaly level for which an insight will be generated. In standard mode - anomaly level is the diff between target and benchmark averages, normalized by overall STD. In no-STD-mode the anomaly threshold will be defined by "no_std_mode_min_diff_percent".

PositiveFloat

0.3

{ "min_anomaly_level": 0.6 }
min_score

Name

Description

Type

Default

min_score

The minimal score for a signal to be considered as an anomaly.

float

0.0

{ "min_score": 4 }
no_std_mode_min_diff_percent

Name

Description

Type

Default

no_std_mode_min_diff_percent

In no-STD mode, acts as the anomaly threshold and is the minimal difference required in percents, calculated according to the proximity to the closest reference point, as configured in the param no_std_mode_reference_values.

PositiveFloat

25.0

{ "no_std_mode_min_diff_percent": 30.0 }

Data Filtering Params

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avoid_segmenting_on_missing

Name

Description

Type

Default

avoid_segmenting_on_missing

When true, insights will not be generated for segments which are (partially or fully) defined by a missing field.

bool

False

{ "avoid_segmenting_on_missing": true }
baseline_segment

Name

Description

Type

Default

baseline_segment

The baseline segment of this verse. This segment defines "the world" as far as this verse is concerned. Only data from this segment will be considered when finding insights.

Segment

{}

{ "baseline_segment": { "model_version": [ { "value": "V1" } ] } }
benchmark_baseline_segment

Name

Description

Type

Default

benchmark_baseline_segment

Benchmark baseline segment. This segment is intersected with any data we search for in the benchmark segments.

Segment

{}

{ "benchmark_baseline_segment": { "model_version": [ { "value": "V2" } ] } }
enhance_exclude_segments

Name

Description

Type

Default

enhance_exclude_segments

If True, when exclude segments are added to any level of configuration (either in the verse, the stanza or the stanzas_global_defaults) they are ADDED to the excluded segments of higher level defaults, if exists any. For example, if we have in stanzas_global_default a single excluded segment of {dimensionA: MISSING}, and the stanza (or verse) has a single excluded segment of {dimensionB: 0}, then if enhance_exclude_segments is True, the excluded segments will include both {dimensionA: MISSING} and {dimensionB: 0} and will filter either one. Otherwise (if enhance_exclude_segments is False) it will be overridden to just the one segment in the verse {dimensionB: 0}.

bool

False

{ "enhance_exclude_segments": true }
exclude_segments

Name

Description

Type

Default

exclude_segments

Segments to exclude in the baseline of this verse. Each data we search for will not include these segments - both tested segments as well as any benchmarks used to find the anomalies. Notice that whether or not this param will override definitions for exclude_segments in other levels is decided by enhance_exclude_segments.

SegmentsListType

[]

{ "exclude_segments": [ { "text_length": [ { "min_value": 0, "max_value": 100 } ] } ] }
target_baseline_segment

Name

Description

Type

Default

target_baseline_segment

Target baseline segment. This segment is intersected with any data we search for in the tested segments.

Segment

{}

{ "target_baseline_segment": { "model_version": [ { "value": "V1" } ] } }

Related Anomalies Params

see more
avoid_related_anomalies_for

Name

Description

Type

Default

avoid_related_anomalies_for

A list of fields to avoid checking for correlated anomalies to the main anomaly in a generated insight. See "find_related_anomalies_for" for further details.

MetricsListType

[]

{ "avoid_related_anomalies_for": ["delta_top_to_second_score"] }
find_related_anomalies_for

Name

Description

Type

Default

find_related_anomalies_for

A list of fields to check for correlated anomalies to the main anomaly in a generated insight. These correlated anomalies might help with understanding the possible cause of an insight. Leave empty to search in all fields.

MetricsListType

[]

{ "find_related_anomalies_for": ["sentiment_score", "confidence_interval"] }
related_anomalies_min_correlation

Name

Description

Type

Default

related_anomalies_min_correlation

Minimal Pearson correlation between the metric on which an anomaly was found and another metric with an anomaly on the same segment, below which Mona will not use the other metric as a related anomaly.

NonNegativeFloat

0.3

{ "related_anomalies_min_correlation": 0.5 }

Segmentation Params

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always_segment_baseline_by

Name

Description

Type

Default

always_segment_baseline_by

A list of dimensions to always segment the baseline segment by. This is useful when separating the world to completely unrelated parts - e.g., in a case where you have a different model developed for each customer and there's no need to look for insights across different customers. Avoiding insights generation on a specific value of a segmentation field can be done by using the "avoid_values" key in the segmentation json object, as seen in the example.

SegmentationsListType

[]

{ "always_segment_baseline_by": [ "country", {"name": "city", "avoid_values": ["Tel Aviv"]}, ] }
avoid_segmenting_on_missing

Name

Description

Type

Default

avoid_segmenting_on_missing

When true, insights will not be generated for segments which are (partially or fully) defined by a missing field.

bool

False

{ "avoid_segmenting_on_missing": true }
max_segment_baseline_by_depth

Name

Description

Type

Default

max_segment_baseline_by_depth

The maximum number of fields Mona should combine for segmenting the baseline (if "segment_baseline_by" fields given).

PositiveInt

2

{ "max_segment_baseline_by_depth": 3 }
max_segment_by_depth

Name

Description

Type

Default

max_segment_by_depth

The maximum number of fields Mona should combine to create sub-segments to search in. Baseline segment fields and parent fields are "free", and are not counted for depth. Notice this parameter has a exponential effect on the performance and should be kept within SLAs.

PositiveInt

2

{ "max_segment_by_depth": 3 }
min_segment_baseline_by_depth

Name

Description

Type

Default

min_segment_baseline_by_depth

The minimum number of fields Mona should combine for segmenting the baseline (if "segment_baseline_by" fields given).

NonNegativeInt

0

{ "min_segment_baseline_by_depth": 1 }
min_segment_by_depth

Name

Description

Type

Default

min_segment_by_depth

The minimum number of fields Mona should combine to create sub-segments to search in.

NonNegativeInt

0

{ "min_segment_by_depth": 1 }
segment_baseline_by

Name

Description

Type

Default

segment_baseline_by

A list of dimensions to potentially segment the baseline segment by. Avoiding insights generation on a specific value of a segmentation field can be done by using the "avoid_values" key in the segmentation json object.

SegmentationsListType

[]

{ "segment_baseline_by": [ "model_version" ] }
segment_by

Name

Description

Type

Default

segment_by

The dimensions to use to segment the data in order to search for anomalies. This list must be a sublist of all arc class' dimensions. Avoiding insights generation on a specific value of a segmentation field can be done by using the "avoid_values" key in the segmentation json object, as seen in the example.

SegmentationsListType

[]

{ "segment_by": [ "city", "bot_id", {"name": "provider-code", "avoid_values": ["zoom"]}, {"name": "selected-language", "avoid_values": ["eng", "spa"]} ] }

Size Thresholds Params

see more
baseline_min_segment_size

Name

Description

Type

Default

baseline_min_segment_size

Minimal segment size for the baseline segment.

PositiveFloat

1

{ "baseline_min_segment_size": 100 }
benchmark_baseline_min_segment_size

Name

Description

Type

Default

benchmark_baseline_min_segment_size

Minimal segment size for the benchmark baseline segment.

PositiveFloat

1

{ "benchmark_baseline_min_segment_size": 100 }
benchmark_max_segment_size

Name

Description

Type

Default

benchmark_max_segment_size

Maximal benchmark segment size in number of records. Leave empty to not have such a threshold.

PositiveIntOrNone

None

{ "benchmark_max_segment_size": 1000 } benchmark_max_segment_size_fraction (NonInclusiveFractionOrNoneType) Maximal
benchmark_max_segment_size_fraction

Name

Description

Type

Default

benchmark_max_segment_size_fraction

Maximal benchmark segment size in fraction from baseline segment. Leave empty to not have such a threshold.

NonInclusiveFractionOrNone

None

{ "benchmark_max_segment_size_fraction": 0.2 }
benchmark_min_segment_size

Name

Description

Type

Default

benchmark_min_segment_size

Minimal benchmark segment size in number of records.

PositiveInt

100

{ "benchmark_min_segment_size": 50 }
benchmark_min_segment_size_fraction

Name

Description

Type

Default

benchmark_min_segment_size_fraction

Minimal benchmark segment size in fraction from baseline segment.

InclusiveFraction

0

{ "benchmark_min_segment_size_fraction": 0.05 }
max_segment_size

Name

Description

Type

Default

max_segment_size

Maximal segment size which a segment must have (bigger segments won't be considered in the search). Leave empty to not have such a threshold.

PositiveIntOrNone

None

{ "max_segment_size": 10000 }
max_segment_size_fraction

Name

Description

Type

Default

max_segment_size_fraction

Maximal segment size in fraction from baseline segment, which a segment must have. Leave empty to not have such a threshold.

NonInclusiveFractionOrNone

None

{ "max_segment_size_fraction": 0.2 }
min_segment_size

Name

Description

Type

Default

min_segment_size

Minimal segment size for the united benchmark+target segments.

PositiveInt

100

{ "min_segment_size": 100 }
min_segment_size_fraction

Name

Description

Type

Default

min_segment_size_fraction

Minimal segment size in fraction from baseline segment, which a segment must have in order to be considered in the search.

InclusiveFraction

0

{ "min_segment_size_fraction": 0.05 }
min_target_benchmark_size_ratio

Name

Description

Type

Default

min_target_benchmark_size_ratio

The minimum required ratio of segment size (after time range normalization) between target and benchmark periods.

InclusiveFraction

0.01

{ "min_target_benchmark_size_ratio": 0.05 }
target_baseline_min_segment_size

Name

Description

Type

Default

target_baseline_min_segment_size

Minimal segment size for the target baseline segment.

PositiveFloat

1

{ "target_baseline_min_segment_size": 0.8 }
target_max_segment_size

Name

Description

Type

Default

target_max_segment_size

Maximal target segment size in number of records. Leave empty to not have such a threshold.

PositiveIntOrNone

None

{ "target_max_segment_size": 10000 } target_max_segment_size_fraction (NonInclusiveFractionOrNoneType) Maximal target
target_max_segment_size_fraction

Name

Description

Type

Default

target_max_segment_size_fraction

Maximal target segment size in fraction from baseline segment. Leave empty to not have such a threshold.

NonInclusiveFractionOrNone

None

{ "target_max_segment_size_fraction": 0.2 }
target_min_segment_size

Name

Description

Type

Default

target_min_segment_size

Minimal target segment size in number of records.

PositiveInt

100

{ "target_min_segment_size": 50 } target_min_segment_size_fraction (InclusiveFractionType) Minimal target segment
target_min_segment_size_fraction

Name

Description

Type

Default

target_min_segment_size_fraction

Minimal target segment size in fraction from baseline segment.

InclusiveFraction

0

{ "target_min_segment_size_fraction": 0.05 }

Time Related Params

see more
benchmark_set_period

Name

Description

Type

Default

benchmark_set_period

Time period for benchmark set. By default means the period just before the target set period. Expected format is "" where can be any positive integer, and options currently include: "d" (days), or "w" (weeks). e.g, "1d" means 1 day period.

TimePeriod

6w

{ "benchmark_set_period": "50d" }
cadence

Name

Description

Type

Default

cadence

The cadence for evaluation of this verse. Only the following cadences are valid: Minutes: 1m, 5m, 10m, 15m, 20m, 30m. Hours: 1h, 2h, 3h, 4h, 6h, 8h, 12h. Days: 1d, 2d, 3d, 4d, 5d, 6d. Weeks: 1w, 2w, 3w, 4w, 5w.

CadenceType

1d

{ "cadence": "6h" }
expire_after

Name

Description

Type

Default

expire_after

Insights detected by this verse will continue to be considered active for at least this amount of time after the last time they were detected.

TimePeriod

3d

{ "expire_after": "2d" }
target_set_period

Name

Description

Type

Default

target_set_period

Time period for the target set, ending on the day of the latest available data. Format detailed in common/util.py's get_time_period_for_string.

TimePeriod

2w

{ "target_set_period": "1w" }
time_resolution

Name

Description

Type

Default

time_resolution

The time resolution of time series charts for insights of this verse. NOTE: this parameter doesn't affect the behavior of the insights finding mechanism, but only the data representation in the output.

VisualTimeResolution

1d

{ "time_resolution": "1w" }

Visuals and Enrichments Params

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field_vectors

Name

Description

Type

Default

field_vectors

This attribute lists metric vectors for the FE to show on an insight card of this verse. A value in this field can either be a string (in which case the string should correspond to a kapi_vector name in the config) or an array (in which case the array should be treated as an ad-hoc kapi vector defined specifically for this verse).

FieldVectorsList

[]

{ "field_vectors": [ "field_vector_group_1", "field_vector_group_2", "field_vector_group_3" ] }
investigate_no_drill

Name

Description

Type

Default

investigate_no_drill

Dictates the link to the investigations page to add to the found insights. If True, the link will point to investigations page with a drilldown to the segment that was found. If it's false the link will point to the investigations page without drilldown, but with the found segment selected so it can be compared with a benchmark of a higher level.

bool

False

{ "investigate_no_drill": true }
time_resolution

Name

Description

Type

Default

time_resolution

The time resolution of time series charts for insights of this verse. NOTE: this parameter doesn't affect the behavior of the insights finding mechanism, but only the data representation in the output.

VisualTimeResolution

1d

{ "time_resolution": "1w" }