Pca Versions Save

• Font color inherits the arrow color (default).
• Font colors can be adjusted in the plots.
• Sizes of the scatter can be adjusted with parameter s.
• Colors of the scatter ben be adjusted with parameter c.
• fig can be given as an input parameter to make iterative changes to the plot.

Examples can be found here.

• sklearn changed into scikit-learn in both requirements and setup file.

• Fix for overlapping annotations in biplot issue #33
• Updated docstrings.
• Set the same order for input parameters for the plotting functions.

Fix for big arrowheads.

• Added boolean to choose whether to update outlier parameters or not.
• Example can be found here.

• Detect and plot outliers for new unseen transformed samples

• added parameter method to change between pca, sparse_pca and trunc_svd.
• Fix for trunc_svd.

Examples:

model = pca(method='sparse_pca')
model = pca(method='trunc_svd')
model = pca(method='pca')

• Fix for not showing scatter in case of biplot using cmap=None

• Improved speed in plotting in case of having thousands of samples by setting parameter label=None

model.biplot(label=None)

• Density coloring implemented with the gradient parameter.

In this example, the cmap=Set1 will be used to color the class labels. The coloring will have a continuous scale towards the borders.

pca.scatter(cmap='Set1', gradient='#ffffff')