Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill anomaly-detectorAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Anomaly Detector
Audience: Data engineers and analysts detecting outliers in datasets.
Goal: Provide production-ready anomaly detection functions for various data types.
Scripts
Execute detection functions from scripts/anomaly_detection.py:
python
from scripts.anomaly_detection import (
detect_anomalies_zscore,
detect_anomalies_iqr,
detect_anomalies_modified_zscore,
detect_anomalies_isolation_forest,
detect_anomalies_lof,
detect_anomalies_rolling,
detect_anomalies_stl,
detect_anomalies_ensemble
)Method Selection
| Method | Best For | Limitations |
|---|---|---|
| Z-Score | Normal distributions | Sensitive to outliers |
| IQR | Skewed distributions | Less sensitive overall |
| Modified Z-Score | Robust detection | Slower computation |
| Isolation Forest | High-dimensional data | Requires tuning |
| LOF | Local density anomalies | Computationally expensive |
| Rolling | Time-series with trends | Window size sensitive |
| STL | Seasonal time-series | Requires known period |
Usage Examples
Single Column Detection
python
import pandas as pd
from scripts.anomaly_detection import detect_anomalies_zscore, detect_anomalies_iqr
df = pd.read_csv('data.csv')
# Z-score method (good for normal distributions)
anomalies_z = detect_anomalies_zscore(df['value'], threshold=3.0)
# IQR method (robust to skewed data)
anomalies_iqr = detect_anomalies_iqr(df['value'], multiplier=1.5)
print(f"Z-score found {anomalies_z.sum()} anomalies")
print(f"IQR found {anomalies_iqr.sum()} anomalies")Multi-Column with Isolation Forest
python
from scripts.anomaly_detection import detect_anomalies_isolation_forest
numeric_cols = ['revenue', 'quantity', 'price']
anomalies = detect_anomalies_isolation_forest(df, numeric_cols, contamination=0.01)
df_anomalies = df[anomalies]Ensemble Approach (Recommended)
python
from scripts.anomaly_detection import detect_anomalies_ensemble
results = detect_anomalies_ensemble(
df,
columns=['revenue', 'quantity'],
methods=['zscore', 'iqr', 'isolation_forest'],
min_agreement=2 # Flag if 2+ methods agree
)
confirmed_anomalies = df[results['is_anomaly']]Time-Series Anomalies
python
from scripts.anomaly_detection import detect_anomalies_rolling, detect_anomalies_stl
# Rolling window (for trending data)
anomalies = detect_anomalies_rolling(df['daily_sales'], window=7, n_std=2.0)
# STL decomposition (for seasonal data)
anomalies = detect_anomalies_stl(df['monthly_revenue'], period=12, threshold=3.0)Dependencies
pandas
numpy
scikit-learn # For Isolation Forest, LOF
statsmodels # For STL decomposition
