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streamDM-C++: C++ Stream Data Mining

streamDM in C++ implements extremely fast streaming decision trees in C++ for big data streams. It is a project developed at Huawei Noah's Ark Lab. streamDM in C++ is licensed under Apache Software License v2.0.

The main advantages of streamDM in C++ over other C/C++ data stream libraries are the following:

  • Faster than VFML in C and MOA in Java.
  • Evaluation and learners are separated, not linked together.
  • It contains several methods for learning numeric attributes.
  • It is easy to extend and add new methods.
  • The adaptive decision tree is more accurate and does not need an expert user to choose optimal parameters to use.
  • It contains powerful ensemble methods.
  • It is much faster and uses less memory.

Getting Started

Getting Started

First download and build streamDM in C++:

git clone https://github.com/huawei-noah/streamDM-Cpp.git
cd streamDM-Cpp
make

Download a dataset:

wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip"
unzip covtypeNorm.arff.zip

Evaluate the dataset:

./streamdm-cpp "EvaluatePrequential -l (HoeffdingTree -l NBAdaptive) -r ArffReader -ds covtypeNorm.arff -e (BasicClassificationEvaluator -f 100000)"

Methods

streamDM in C++ executes tasks. Tasks can be evaluation tasks as "EvaluatePrequential" or "EvaluateHoldOut" and the parameters needed are a learner, a stream reader, and an evaluator.

The methods currently implemented are: Naive Bayes, Logistic Regression, Perceptron, Majority Class, Hoeffding Tree, Hoeffding Adaptive Tree, and Bagging.

The stream readers currently implemented support Arff, C45, and LibSVM formats.