Frouros is a Python library for drift detection in machine learning systems that provides a combination of classical and more recent algorithms for both concept and data drift detection.

"Everything changes and nothing stands still"

"You could not step twice into the same river"

⚡️ Quickstart

🔄 Concept drift

As a quick example, we can use the breast cancer dataset to which concept drift it is induced and show the use of a concept drift detector like DDM (Drift Detection Method). We can see how concept drift affects the performance in terms of accuracy.

```python import numpy as np from sklearn.datasets import loadbreastcancer from sklearn.linearmodel import LogisticRegression from sklearn.modelselection import traintestsplit from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler

from frouros.detectors.concept_drift import DDM, DDMConfig from frouros.metrics import PrequentialError

np.random.seed(seed=31)

Load breast cancer dataset

X, y = loadbreastcancer(returnXy=True)

Split train (70%) and test (30%)

( Xtrain, Xtest, ytrain, ytest, ) = traintestsplit(X, y, trainsize=0.7, randomstate=31)

Define and fit model

pipeline = Pipeline( [ ("scaler", StandardScaler()), ("model", LogisticRegression()), ] ) pipeline.fit(X=Xtrain, y=ytrain)

Detector configuration and instantiation

config = DDMConfig( warninglevel=2.0, driftlevel=3.0, minnuminstances=25, # minimum number of instances before checking for concept drift ) detector = DDM(config=config)

Metric to compute accuracy

metric = PrequentialError(alpha=1.0) # alpha=1.0 is equivalent to normal accuracy

def streamtest(Xtest, ytest, y, metric, detector): """Simulate data stream over Xtest and ytest. y is the true label.""" driftflag = False for i, (X, y) in enumerate(zip(Xtest, ytest)): ypred = pipeline.predict(X.reshape(1, -1)) error = 1 - (ypred.item() == y.item()) metricerror = metric(errorvalue=error) _ = detector.update(value=error) status = detector.status if status["drift"] and not driftflag: driftflag = True print(f"Concept drift detected at step {i}. Accuracy: {1 - metricerror:.4f}") if not driftflag: print("No concept drift detected") print(f"Final accuracy: {1 - metric_error:.4f}\n")

Simulate data stream (assuming test label available after each prediction)

No concept drift is expected to occur

streamtest( Xtest=Xtest, ytest=y_test, y=y, metric=metric, detector=detector, )

>> No concept drift detected

>> Final accuracy: 0.9766

IMPORTANT: Induce/simulate concept drift in the last part (20%)

of y_test by modifying some labels (50% approx). Therefore, changing P(y|X))

driftsize = int(ytest.shape[0] * 0.2) ytestdrift = ytest[-driftsize:] modifyidx = np.random.rand(*ytestdrift.shape) <= 0.5 ytestdrift[modifyidx] = (ytestdrift[modifyidx] + 1) % len(np.unique(ytest)) ytest[-driftsize:] = ytestdrift

Reset detector and metric

detector.reset() metric.reset()

Simulate data stream (assuming test label available after each prediction)

Concept drift is expected to occur because of the label modification

streamtest( Xtest=Xtest, ytest=y_test, y=y, metric=metric, detector=detector, )

>> Concept drift detected at step 142. Accuracy: 0.9510

>> Final accuracy: 0.8480

```

More concept drift examples can be found here.

📊 Data drift

As a quick example, we can use the iris dataset to which data drift is induced and show the use of a data drift detector like Kolmogorov-Smirnov test.

```python import numpy as np from sklearn.datasets import loadiris from sklearn.modelselection import traintestsplit from sklearn.tree import DecisionTreeClassifier

from frouros.detectors.data_drift import KSTest

np.random.seed(seed=31)

Load iris dataset

X, y = loadiris(returnX_y=True)

Split train (70%) and test (30%)

( Xtrain, Xtest, ytrain, ytest, ) = traintestsplit(X, y, trainsize=0.7, randomstate=31)

Set the feature index to which detector is applied

feature_idx = 0

IMPORTANT: Induce/simulate data drift in the selected feature of y_test by

applying some gaussian noise. Therefore, changing P(X))

Xtest[:, featureidx] += np.random.normal( loc=0.0, scale=3.0, size=X_test.shape[0], )

Define and fit model

model = DecisionTreeClassifier(randomstate=31) model.fit(X=Xtrain, y=y_train)

Set significance level for hypothesis testing

alpha = 0.001

Define and fit detector

detector = KSTest() _ = detector.fit(X=Xtrain[:, featureidx])

Apply detector to the selected feature of X_test

result, _ = detector.compare(X=Xtest[:, featureidx])

Check if drift is taking place

if result.pvalue <= alpha: print(f"Data drift detected at feature {featureidx}") else: print(f"No data drift detected at feature {feature_idx}")

>> Data drift detected at feature 0

Therefore, we can reject H0 (both samples come from the same distribution).

```

More data drift examples can be found here.

🛠 Installation

Frouros can be installed via pip:

bash pip install frouros

🕵🏻‍♂️️ Drift detection methods

The currently implemented detectors are listed in the following table.

❗ What is and what is not Frouros?

Unlike other libraries that in addition to provide drift detection algorithms, include other functionalities such as anomaly/outlier detection, adversarial detection, imbalance learning, among others, Frouros has and will ONLY have one purpose: drift detection.

We firmly believe that machine learning related libraries or frameworks should not follow Jack of all trades, master of none principle. Instead, they should be focused on a single task and do it well.

✅ Who is using Frouros?

Frouros is actively being used by the following projects to implement drift detection in machine learning pipelines:

• AI4EOSC.
• iMagine.

If you want your project listed here, do not hesitate to send us a pull request.

👍 Contributing

Check out the contribution section.

💬 Citation

If you want to cite Frouros you can use the SoftwareX publication.

bibtex @article{CESPEDESSISNIEGA2024101733, title = {Frouros: An open-source Python library for drift detection in machine learning systems}, journal = {SoftwareX}, volume = {26}, pages = {101733}, year = {2024}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2024.101733}, url = {https://www.sciencedirect.com/science/article/pii/S2352711024001043}, author = {Jaime {Céspedes Sisniega} and Álvaro {López García}}, keywords = {Machine learning, Drift detection, Concept drift, Data drift, Python}, abstract = {Frouros is an open-source Python library capable of detecting drift in machine learning systems. It provides a combination of classical and more recent algorithms for drift detection, covering both concept and data drift. We have designed it to be compatible with any machine learning framework and easily adaptable to real-world use cases. The library is developed following best development and continuous integration practices to ensure ease of maintenance and extensibility.} }

📝 License

Frouros is an open-source software licensed under the BSD-3-Clause license.

🙏 Acknowledgements

Frouros has received funding from the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017-0765.