`bucky.util.spline_smooth`¶

Method of smoothing data w/ splines. Based of a GAM from mgcv with a cr() basis.

Module Contents¶

`identity_link`	class for idenity link functions
`log_link`	class for log link functions

`PIRLS`(x, y, alp, pen, tol=1e-07, dist='g', max_it=10000, w=None, gamma=1.0, tqdm_label='PIRLS')	Penalized iterativly reweighted least squares
`_absorb_constraints`(design_matrix, constraints, pen=None)	Apply constraints to the design matrix.
`_compute_base_functions`(x, knots)	Return base functions for the spline basis.
`_cr`(x, df, center=True)	Python version of the R lib mgcv function cr().
`_find_knots_lower_bounds`(x, knots)	Find the lower bound for the knots.
`_get_free_crs_dmatrix`(x, knots)	Builds an unconstrained cubic regression spline design matrix.
`_get_natural_f`(knots)	Returns mapping of natural cubic spline values to 2nd derivatives.
`fit`(y, x=None, df=10, alp=0.6, dist='g', pirls=False, standardize=True, w=None, gamma=1.0, tol=1e-07, label='fit')	Perform fit of natural cubic splines to the vector y, return the smoothed y.
`nunique`(arr, axis=-1)	Return the number of uniq values along a given axis.
`opt_lam`(x, y, alp=0.6, w=None, pen=None, min_lam=0.1, step_size=None, tol=0.001, max_it=100, gamma=1.0)	Calculate the exact soln to the ridge regression of the weights for basis x that fit data y.
`ridge`(x, y, alp=0.0)	Calculate the exact soln to the ridge regression of the weights for basis x that fit data y.

class bucky.util.spline_smooth.identity_link[source]¶

class for idenity link functions

class bucky.util.spline_smooth.log_link[source]¶

class for log link functions

bucky.util.spline_smooth.PIRLS(x, y, alp, pen, tol=1e-07, dist='g', max_it=10000, w=None, gamma=1.0, tqdm_label='PIRLS')[source]¶: Penalized iterativly reweighted least squares

bucky.util.spline_smooth._absorb_constraints(design_matrix, constraints, pen=None)[source]¶: Apply constraints to the design matrix.

bucky.util.spline_smooth._compute_base_functions(x, knots)[source]¶: Return base functions for the spline basis.

bucky.util.spline_smooth._cr(x, df, center=True)[source]¶: Python version of the R lib mgcv function cr().

bucky.util.spline_smooth._find_knots_lower_bounds(x, knots)[source]¶: Find the lower bound for the knots.

bucky.util.spline_smooth._get_free_crs_dmatrix(x, knots)[source]¶: Builds an unconstrained cubic regression spline design matrix.

bucky.util.spline_smooth._get_natural_f(knots)[source]¶: Returns mapping of natural cubic spline values to 2nd derivatives.

bucky.util.spline_smooth.fit(y, x=None, df=10, alp=0.6, dist='g', pirls=False, standardize=True, w=None, gamma=1.0, tol=1e-07, label='fit')[source]¶: Perform fit of natural cubic splines to the vector y, return the smoothed y.

bucky.util.spline_smooth.nunique(arr, axis=- 1)[source]¶: Return the number of uniq values along a given axis.

bucky.util.spline_smooth.opt_lam(x, y, alp=0.6, w=None, pen=None, min_lam=0.1, step_size=None, tol=0.001, max_it=100, gamma=1.0)[source]¶: Calculate the exact soln to the ridge regression of the weights for basis x that fit data y.

bucky.util.spline_smooth.ridge(x, y, alp=0.0)[source]¶: Calculate the exact soln to the ridge regression of the weights for basis x that fit data y.

bucky.util.scoring bucky.util.update_data_repos