dantis 0.0.1

DANTIS brings together **90+ anomaly detection algorithms** (from classical statistical methods and machine learning to deep learning) under a **unified scikit-learn–style API**. It consolidates models from diverse libraries and includes proprietary algorithms developed by our team.

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DANTIS brings together 50+ anomaly detection algorithms—from classical statistical methods and machine learning to deep learning—under a unified scikit-learn–style API. It consolidates models from diverse libraries and includes proprietary algorithms developed by our team.

Installation

pip install dantis              # From PyPI (recommended)

Quick start

from dantis.machine_learning import DecisionTreeClassifier
from dantis.deep_learning import ALAD
from sklearn.metrics import accuracy_score
import pandas as pd

# Load your time series data
df = pd.read_csv("anomaly_datasets/anomaly_dataset_1.csv")

X = df.drop(columns=["is_anomaly"])
y = df["is_anomaly"]

# Split data chronologically for time series (no shuffling)
split_idx = int(len(df) * 0.8)
X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]
y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]

decision_tree_model = DecisionTreeClassifier()
decision_tree_model.fit(X_train, y_train)



y_pred = decision_tree_model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"DecisionTree Accuracy: {acc:.3f}")

alad_model = ALAD()
alad_model.fit(X_train, y_train)

y_pred = alad_model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"ALAD Accuracy: {acc:.3f}")

Key features

• Wide algorithm coverage – 50+ detectors across deep learning, machine learning, and statistical methods.
• Consistent API – all detectors follow the scikit-learn fit/predict paradigm and expose decision_scores_, predict_proba, …
• Pipeline & preprocessing layer – compose detectors with built-in scalers, transforms, and windowing utilities.
• Comprehensive evaluation – metrics and visualisation helpers for rapid benchmarking.
• Extensibility – subclass AlgorithBase and plug your own model into the ecosystem.

GUI: Desktop interface for model training & evaluation

DANTIS includes a full-featured graphical interface to simplify the use of the library for users without programming experience. Inspired by tools like Weka and KNIME, this desktop application enables:

• Training and testing of anomaly detection models
• Preprocessing pipelines and parameter tuning
• Quantitative comparison of models using the StaTDS library

Developed in:

Documentación

Documentation is currently available on GitHub.

📚 A full documentation site with tutorials and API reference is being developed at https://dantis.readthedocs.io.

Funding & affiliations

DANTIS has been developed within the research activities of the DaSCI Institute (Andalusian Inter-University Institute in Data Science and Computational Intelligence), by members of the research groups KDISLAB (Knowledge Discovery and Intelligent Systems, University of Córdoba) and SCI2S (Soft Computing and Intelligent Information Systems, University of Granada).

This work has been supported by the following projects:

• TED2021-132702B-C22 - Mantenimiento Predictivo basado en Detección de Anomalías: Framework y Mantenimiento de Camiones de Alto Tonelaje (PREMAD-Truck)

We gratefully acknowledge this support.

        
    

Available algorithms:

Deep Learning

Name	Function
ALAD	anomaly_lib.deep_learning.alad.ALAD
AnoGAN	anomaly_lib.deep_learning.anogan.AnoGAN
AutoEncoder	anomaly_lib.deep_learning.auto_encoder.AutoEncoder
DeepSVDD	anomaly_lib.deep_learning.deep_svdd.DeepSVDD
MO_GAAL	anomaly_lib.deep_learning.mo_gaal.MO_GAAL
SO_GAAL	anomaly_lib.deep_learning.so_gaal.SO_GAAL
VAE	anomaly_lib.deep_learning.vae.VAE

Machine Learning

Name	Function
DecisionTreeClassifierClassOD	anomaly_lib.machine_learning.decision_tree_classifier.DecisionTreeClassifierClassOD
ECOD	anomaly_lib.machine_learning.ecod.ECOD
IForest	anomaly_lib.machine_learning.iforest.IForest
KNN	anomaly_lib.machine_learning.knn.KNN
LOF	anomaly_lib.machine_learning.lof.LOF
MLPClassOD	anomaly_lib.machine_learning.mlp_classifier.MLPClassOD
MultinomialNBClassOD	anomaly_lib.machine_learning.multinomial_nb.MultinomialNBClassOD
GaussianNBClassOD	anomaly_lib.machine_learning.naive_bayes.GaussianNBClassOD
OCSVM	anomaly_lib.machine_learning.ocsvm.OCSVM
RandomForestClassOD	anomaly_lib.machine_learning.random_forest_classifier.RandomForestClassOD
SVMClassOD	anomaly_lib.machine_learning.svm.SVMClassOD

Statistical

Name	Function
ABOD	anomaly_lib.statistical.abod.ABOD
CBLOF	anomaly_lib.statistical.cblof.CBLOF
CD	anomaly_lib.statistical.cd.CD
COF	anomaly_lib.statistical.cof.COF
LUNAR	anomaly_lib.statistical.lunar.LUNAR
COPOD	anomaly_lib.statistical.copod.COPOD
FeatureBagging	anomaly_lib.statistical.feature_bagging.FeatureBagging
GMM	anomaly_lib.statistical.gmm.GMM
HBOS	anomaly_lib.statistical.hbos.HBOS
INNE	anomaly_lib.statistical.inne.INNE
KDE	anomaly_lib.statistical.kde.KDE
KPCA	anomaly_lib.statistical.kpca.KPCA
LMDD	anomaly_lib.statistical.lmdd.LMDD
LOCI	anomaly_lib.statistical.loci.LOCI
LODA	anomaly_lib.statistical.loda.LODA
LSCP	anomaly_lib.statistical.lscp.LSCP
MAD	anomaly_lib.statistical.mad.MAD
MCD	anomaly_lib.statistical.mcd.MCD
PCA	anomaly_lib.statistical.pca.PCA
QMCD	anomaly_lib.statistical.qmcd.QMCD
RGraph	anomaly_lib.statistical.rgraph.RGraph
ROD	anomaly_lib.statistical.rod.ROD
Sampling	anomaly_lib.statistical.sampling.Sampling
SOD	anomaly_lib.statistical.sod.SOD
SOS	anomaly_lib.statistical.sos.SOS
XGBOD	anomaly_lib.statistical.xgbod.XGBOD
MatrixProfile	anomaly_lib.statistical.matrixprofile.MatrixProfile
ARIMA	anomaly_lib.statistical.arima.ARIMA
SARIMAX	anomaly_lib.statistical.sarimax.SARIMAX
VARMAX	anomaly_lib.statistical.varmax.VARMAX
HoltWinters	anomaly_lib.statistical.holt_winters.HoltWinters
SingleExponentialSmoothing	anomaly_lib.statistical.singleExponentialSmoothing.SingleExponentialSmoothing

License

DANTIS is distributed under the MIT License. See LICENSE for details.