pattern-drift 0.1.1

Automatic concept drift detection for streaming datasets

pattern-drift

Automatic concept drift detection for streaming datasets.

pattern-drift is a Python library for data scientists and ML engineers working with time-sensitive models. It continuously monitors incoming data distributions, detects statistical drift, and recommends optimal retraining windows — keeping models accurate without manual monitoring.

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Installation

pip install pattern-drift

Optional extras:

pip install pattern-drift[viz]      # adds matplotlib for drift timeline visualisation
pip install pattern-drift[alerts]   # adds requests for Slack and webhook callbacks
pip install pattern-drift[all]      # everything

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Quick Start

from pattern_drift import DriftMonitor

monitor = DriftMonitor(method="ADWIN", sensitivity=0.002)

for record in stream:                    # dict, pandas Series, or single-row DataFrame
    result = monitor.update(record)
    if result.drift_detected:
        print(f"Drift! type={result.drift_type}")
        print(f"Features: {result.drifted_features}")
        print(f"Score: {result.drift_score:.4f}")
        if result.retraining_window:
            rw = result.retraining_window
            print(f"Retrain on records {rw.start}–{rw.end} (confidence {rw.confidence:.2%})")

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Detection Algorithms

Algorithm	Mechanism	Best For
ADWIN (default)	Variable-length window split testing on mean differences	Gradual drift — adapts window size dynamically
PageHinkley	Cumulative sum of deviations from the running mean	Sudden drift — extremely fast and memory-efficient
KSWIN	Kolmogorov-Smirnov test comparing recent vs. reference window	Distribution shape changes beyond just mean shifts
DDM	Monitors prediction error rate vs. historical minimum	Classifier performance monitoring post-deployment

Switch algorithms with a single parameter — no other code changes required:

monitor = DriftMonitor(method="PageHinkley")
monitor = DriftMonitor(method="KSWIN")
monitor = DriftMonitor(method="DDM")

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API Reference

DriftMonitor

DriftMonitor(
    method="ADWIN",        # Detection algorithm
    sensitivity=0.002,     # Drift threshold — lower = more sensitive
    min_window=30,         # Minimum history before drift can be reported
    max_window=10_000,     # Maximum records retained in memory
    features=None,         # List of columns to monitor (None = auto-detect all numeric)
    callbacks=None,        # List of callables fired on drift
)

Methods

Method	Description
monitor.update(data)	Feed a single row (dict/Series) or micro-batch (DataFrame). Returns DriftResult.
monitor.reset()	Reset all internal detector state and history.
monitor.plot_drift_timeline()	Render an interactive drift score timeline chart.
monitor.export_report(path)	Export full drift history to JSON or CSV.
monitor.set_reference(data)	Manually set the reference distribution for comparison.
DriftMonitor.from_config(path)	Class method — instantiate from a YAML config file.

DriftResult Fields

Field	Type	Description
drift_detected	bool	True if drift was found in any monitored feature
drift_type	str | None	sudden · gradual · incremental · recurring
drifted_features	list[str]	Names of all features where drift was detected
drift_score	float	Maximum drift score across all features (0.0–1.0+)
retraining_window	RetrainingWindowResult | None	Suggested retraining window with start, end, n_samples, confidence
timestamp	datetime	UTC datetime when the drift event was recorded

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Alerts & Callbacks

from pattern_drift import DriftMonitor
from pattern_drift.dispatcher import AlertDispatcher

monitor = DriftMonitor(
    callbacks=[
        AlertDispatcher.slack_callback("https://hooks.slack.com/..."),
        AlertDispatcher.webhook_callback("https://my-service/drift"),
        AlertDispatcher.log_callback(level="warning"),
        lambda result: print(result),          # custom inline callback
    ]
)

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YAML Configuration

# drift_config.yaml
method: ADWIN
sensitivity: 0.002
min_window: 30
max_window: 10000
features:
  - age
  - income
  - session_duration

monitor = DriftMonitor.from_config("drift_config.yaml")

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scikit-learn Pipeline Integration

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from pattern_drift.sklearn_wrapper import DriftDetector

pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("drift",  DriftDetector(method="ADWIN", sensitivity=0.002)),
])

pipe.fit(X_train)

for batch in stream:
    X_out = pipe.transform(batch)   # data passes through unchanged

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Visualisation

monitor.plot_drift_timeline()           # interactive chart (requires matplotlib)
monitor.export_report("report.json")    # or "report.csv"

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Architecture

Each incoming record flows through five sequential stages:

1. Feature Extractor — splits each row into per-column numeric signals
2. Detector Pool — maintains one statistical detector per feature; computes drift score on every update
3. Drift Classifier — labels drift as sudden / gradual / incremental / recurring based on signal shape
4. Retraining Window Engine — scans history to find the last stable data window; returns a confidence-scored recommendation
5. Alert Dispatcher — fires registered callbacks (Slack, webhook, log, email, or custom)

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License

MIT