Data processing

Functions to preprocess data.

bartz.prepcovars.quantilized_splits_from_matrix(X, max_bins)

Determine bins that make the distribution of each predictor uniform.

Parameters:

• X (array (p, n)) – A matrix with p predictors and n observations.

• max_bins (int) – The maximum number of bins to produce.

Returns:

• splits (array (p, m)) – A matrix containing, for each predictor, the boundaries between bins. m is min(max_bins, n) - 1, which is an upper bound on the number of splits. Each predictor may have a different number of splits; unused values at the end of each row are filled with the maximum value representable in the type of X.

• max_split (array (p,)) – The number of actually used values in each row of splits.

bartz.prepcovars.uniform_splits_from_matrix(X, num_bins)

Make an evenly spaced binning grid.

Parameters:

• X (array (p, n)) – A matrix with p predictors and n observations.

• num_bins (int) – The number of bins to produce.

Returns:

• splits (array (p, num_bins - 1)) – A matrix containing, for each predictor, the boundaries between bins. The excluded endpoints are the minimum and maximum value in each row of X.

• max_split (array (p,)) – The number of cutpoints in each row of splits, i.e., num_bins - 1.

bartz.prepcovars.bin_predictors(X, splits, **kw)

Bin the predictors according to the given splits.

A value x is mapped to bin i iff splits[i - 1] < x <= splits[i].

Parameters:

• X (array (p, n)) – A matrix with p predictors and n observations.

• splits (array (p, m)) – A matrix containing, for each predictor, the boundaries between bins. m is the maximum number of splits; each row may have shorter actual length, marked by padding unused locations at the end of the row with the maximum value allowed by the type.

• **kw (dict) – Additional arguments are passed to jax.numpy.searchsorted.

Returns:

X_binned (int array (p, n)) – A matrix with p predictors and n observations, where each predictor has been replaced by the index of the bin it falls into.