adenine 0.1.3b

A Data ExploratioN pIpeliNE

ADENINE is a machine learning and data mining Python pipeline that helps you to answer this tedious question: are my data relevant with the problem I’m dealing with?

The main structure of adenine can be summarized in the following 4 steps.

1. Imputing: Does your dataset have missing entries? In the first step you can fill the missing values choosing between different strategies: feature-wise median, mean and most frequent value or a more stable k-NN imputing.

2. Preprocessing: Have you ever wondered what would have changed if only your data have been preprocessed in a different way? Or is it data preprocessing a good idea after all? ADENINE offers several preprocessing procedures, such as: data recentering, Min-Max scaling, standardization or normalization and allows you to compare the results of the analysis made with different preprocessing step as starting point.

3. Dimensionality Reduction: In the context of data exploration, this phase becomes particularly helpful for high dimensional data. This step includes some manifold learning (such as isomap, multidimensional scaling, etc) and unsupervised dimensionality reduction (principal component analysis, kernel PCA) techniques.

4. Clustering: This step aims at grouping data into clusters in an unsupervised manner. Several techniques such as k-means, spectral or hierarchical clustering are offered.

The final output of adenine is a compact and textual representation of the results obtained from the pipelines made with each possible combination of the algorithms implemented at each step.