StreamNDR

Stream Novelty Detection for River (StreamNDR) is a Python library for online novelty detection. StreamNDR aims to enable novelty detection in data streams for Python. It is based on the River API and follows its implementation and format.

📋 Features

• ✅ Online novelty detection for data streams
• ✅ Based on the River API
• ✅ Implements MINAS algorithm
• ✅ Implements ECSMiner algorithm
• ✅ Implements ECSMiner-WF (without feedback) algorithm
• ✅ Built-in novelty detection metrics (MNew, FNew, ErrRate)
• ✅ Full documentation available
• ✅ Available on PyPI

🛠 Installation

StreamNDR requires Python 3.6 or above and the ClusOpt-Core package, which needs a C/C++ compiler (such as gcc) and the Boost.Thread library. On Debian systems:

sudo apt install libboost-thread-dev

Install the package with pip:

pip install streamndr

⚡️ Example Usage

As a quick example, we can train a MINAS model to classify a synthetic dataset created using RandomRBF. The model is trained on only two of four generated classes and will try to detect the remaining classes as novelty patterns in an online fashion.

from streamndr.model import Minas

clf = Minas(kini=100, cluster_algorithm='clustream', 
            window_size=600, threshold_strategy=1, threshold_factor=1.1, 
            min_short_mem_trigger=100, min_examples_cluster=20, verbose=1, random_state=42)

clf.learn_many(np.array(X_train), np.array(y_train))

The model can then be used in an online fashion, with unsupervised clusters automatically updated on each call to predict_one:

from streamndr.metrics import MNew, FNew, ErrRate

known_classes = [0, 1]
m_new = MNew(known_classes)
f_new = FNew(known_classes)
err_rate = ErrRate(known_classes)

for x, y_true in zip(X_test, y_test):
    y_pred = clf.predict_one(x)
    if y_pred is not None:
        m_new.update(y_true, y_pred[0])
        f_new.update(y_true, y_pred[0])
        err_rate.update(y_true, y_pred[0])

🏛️ License

BSD-3-Clause