Compatibility with Scikit-Learn

scikit-learn is the golden standard for general puprose machine learning. As a rule of thumb, we follow scikit-learn functional definitions.

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scikit-learn is a library for statistical learning, or machine-learning.

scikit-mine, on its side, is a library for (yet statistical) pattern mining.

So what does this change ? scikit-mine gives more attention to discrete values, because it looks for co-occuring symbols in the data. To this purpose, we sometimes need to extend scikit-learn capabilities to tightly integrate the notion of symbols in our learning processes.

Using data mining methods as feature extraction blocks for Machine Learning

The Feature Extraction module implements a set of Transformers/Encoders to get you from raw data to a more advanced, structured kind of data : the kind of data that is easily manageable and prone to give you the best performance.

Sometimes scikit-learn provides us the tools we exactly need, sometimes not. Scikit-mine addresses data ingestion by implementing its own blocks, which are compatible wtih scikit-learn.

Pipelines

scikit-mine models are designed for possible integration in scikit-learn pipelines. This makes possible to build “symbolic classifiers”, using scikit-mine pattern encoding schemes to serve predictions, or just use scikit-mine as the first part of a scikit-learn pipeline, as mentioned in the previous section.

Other implementation details

We use joblib as default to parallelise our code. We also set the prefer parameter when instantiating joblib.Parallel, so users don’t have to manually choose between threads and processes for optimal execution.