detectk-detectors 0.2.0

Core detectors for DetectK: threshold, statistical (MAD, Z-score, IQR)

detectk-detectors

Core detectors for DetectK: threshold-based, statistical (MAD, Z-score, IQR).

Installation

pip install detectk-detectors

Detectors

ThresholdDetector

Simple but powerful detector that compares metric values against static thresholds.

Features:

• Multiple comparison operators
• Absolute and percentage-based thresholds
• Range checks (between/outside)
• Auto-registration in DetectorRegistry

Operators:

• greater_than: value > threshold
• greater_equal: value >= threshold
• less_than: value < threshold
• less_equal: value <= threshold
• equals: value == threshold (with tolerance)
• not_equals: value != threshold (with tolerance)
• between: threshold <= value <= upper_threshold (anomaly if INSIDE range)
• outside: value < threshold OR value > upper_threshold (anomaly if OUTSIDE range)

Usage Examples

Simple Threshold (absolute value)

# config.yaml
name: "sessions_10min"

collector:
  type: "clickhouse"
  params:
    query: |
      SELECT
        toStartOfInterval(toDateTime('{{ period_finish }}'), INTERVAL 10 MINUTE) as period_time,
        count() as value
      FROM sessions
      WHERE timestamp >= toDateTime('{{ period_start }}')
        AND timestamp < toDateTime('{{ period_finish }}')

detector:
  type: "threshold"
  params:
    threshold: 1000
    operator: "greater_than"  # Alert if > 1000

alerter:
  type: "mattermost"
  params:
    webhook_url: "${MATTERMOST_WEBHOOK}"

Percentage Change Detection

Detect anomalies based on percentage change from baseline:

detector:
  type: "threshold"
  params:
    threshold: 10.0  # 10% increase
    operator: "greater_than"
    percent: true
    baseline: 1000  # Baseline value

Example: If sessions jump from 1000 to 1150 (15% increase), anomaly is detected.

Range Check (outside bounds)

Alert if value falls outside expected range:

detector:
  type: "threshold"
  params:
    threshold: 900        # Lower bound
    upper_threshold: 1100 # Upper bound
    operator: "outside"   # Anomaly if < 900 OR > 1100

Range Check (inside bounds)

Alert if value is INSIDE a specific range (opposite of outside):

detector:
  type: "threshold"
  params:
    threshold: 900
    upper_threshold: 1100
    operator: "between"  # Anomaly if 900 <= value <= 1100

Use case: Detect when metric is in a "bad" range (e.g., error rate between 5-10%).

Decrease Detection

Alert on significant decreases:

detector:
  type: "threshold"
  params:
    threshold: 800
    operator: "less_than"  # Alert if < 800

Or with percentage:

detector:
  type: "threshold"
  params:
    threshold: -10.0  # 10% decrease
    operator: "less_than"
    percent: true
    baseline: 1000

Equals Detection (with tolerance)

Alert when value equals specific number (useful for zero-value detection):

detector:
  type: "threshold"
  params:
    threshold: 0
    operator: "equals"
    tolerance: 0.1  # Within ±0.1

Multiple Detectors (A/B Testing)

Compare different threshold strategies:

detectors:
  # Conservative (fewer false positives)
  - id: "threshold_high"
    type: "threshold"
    params:
      threshold: 1500
      operator: "greater_than"

  # Aggressive (catch more anomalies)
  - id: "threshold_medium"
    type: "threshold"
    params:
      threshold: 1200
      operator: "greater_than"

  # Percentage-based
  - id: "percent_20"
    type: "threshold"
    params:
      threshold: 20.0
      operator: "greater_than"
      percent: true
      baseline: 1000

Configuration Parameters

Required

• threshold (float): Threshold value (or lower bound for range operators)

Optional

• operator (str): Comparison operator (default: "greater_than")

  • Options: "greater_than", "greater_equal", "less_than", "less_equal", "equals", "not_equals", "between", "outside"

• upper_threshold (float): Upper bound for between/outside operators (required for these operators)

• percent (bool): If true, threshold is percentage change from baseline (default: false)

• baseline (float): Baseline value for percentage calculation (required if percent=true)

• tolerance (float): Tolerance for equals/not_equals operators (default: 0.001)

Detection Result

ThresholdDetector returns DetectionResult with:

• is_anomaly (bool): Whether value violates threshold
• score (float): Distance from threshold (higher = more anomalous)
• lower_bound / upper_bound (float | None): Expected bounds for visualization
• direction (str | None): "up", "down", or None
• percent_deviation (float | None): Percentage deviation from threshold (if anomaly)
• metadata (dict): Detector configuration and comparison details

Edge Cases

Zero Threshold

detector:
  type: "threshold"
  params:
    threshold: 0
    operator: "greater_than"

Works correctly: any positive value triggers anomaly.

Negative Values

detector:
  type: "threshold"
  params:
    threshold: -100
    operator: "less_than"

Works correctly: -150 < -100 → anomaly.

Zero Baseline (percentage mode)

❌ Not allowed - raises ConfigurationError:

detector:
  type: "threshold"
  params:
    threshold: 10
    percent: true
    baseline: 0  # ERROR: Division by zero

Testing

cd packages/detectors/core
pytest tests/ -v

All 23 tests passing ✅

License

MIT