bucky.util.spline_smooth

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

Module Contents

Classes

identity_link	Class for idenity link functions.
log_link	Class for log link functions.

Functions

PIRLS(x, y, alp, pen, tol=1e-07, dist='g', max_it=10000, w=None, gamma=1.0, tqdm_label='PIRLS', fixed_lam=False, ret_beta=False, bootstrap=False)	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=2.0, dist='g', standardize=True, w=None, gamma=1.4, tol=1e-07, clip=(None, None), label='fit', bootstrap=False)	Perform fit of natural cubic splines to the vector y, return the smoothed y.
lin_reg(y, x=None, alp=0.0, quad=False, return_fit=True)	Calculate exact soln for batched linear regression and return either weights or fitted values.
logistic_fit(y, x_out, x=None, alp=0.6, t0_max=200, L=None)	WIP Fit a logistic function to batched y.
make_DP(x)	Add I to a batch of matrices (...,M,M) until all are positive-definite (and cholesky-able).
nunique(arr, axis=-1)	Return the number of uniq values along a given axis.
opt_lam(x, y, alp=0.6, pen=None, min_lam=0.1, step_size=None, tol=0.001, max_it=100, gamma=1.0, fixed_lam=False)	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 batched data y.

Attributes

dtype

bucky.util.spline_smooth.dtype

class bucky.util.spline_smooth.identity_link

Class for idenity link functions.

static g(mu)

Id link - $g$.

static g_prime(mu)

Id link - $g’$.

static mu(eta)

Id link - $mu$.

class bucky.util.spline_smooth.log_link

Class for log link functions.

static g(mu)

Log link - $g$.

static g_prime(mu)

Log link - $g’$.

static mu(eta)

Log link - $mu$.

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', fixed_lam=False, ret_beta=False, bootstrap=False)

Penalized iterativly reweighted least squares.

bucky.util.spline_smooth._absorb_constraints(design_matrix, constraints, pen=None)

Apply constraints to the design matrix.

bucky.util.spline_smooth._compute_base_functions(x, knots)

Return base functions for the spline basis.

bucky.util.spline_smooth._cr(x, df, center=True)

Python version of the R lib mgcv function cr().

bucky.util.spline_smooth._find_knots_lower_bounds(x, knots)

Find the lower bound for the knots.

bucky.util.spline_smooth._get_free_crs_dmatrix(x, knots)

Builds an unconstrained cubic regression spline design matrix.

bucky.util.spline_smooth._get_natural_f(knots)

Returns mapping of natural cubic spline values to 2nd derivatives.

bucky.util.spline_smooth.fit(y, x=None, df=10, alp=2.0, dist='g', standardize=True, w=None, gamma=1.4, tol=1e-07, clip=(None, None), label='fit', bootstrap=False)

Perform fit of natural cubic splines to the vector y, return the smoothed y.

bucky.util.spline_smooth.lin_reg(y, x=None, alp=0.0, quad=False, return_fit=True)

Calculate exact soln for batched linear regression and return either weights or fitted values.

bucky.util.spline_smooth.logistic_fit(y, x_out, x=None, alp=0.6, t0_max=200, L=None)

WIP Fit a logistic function to batched y.

bucky.util.spline_smooth.make_DP(x)

Add I to a batch of matrices (…,M,M) until all are positive-definite (and cholesky-able).

bucky.util.spline_smooth.nunique(arr, axis=- 1)

Return the number of uniq values along a given axis.

bucky.util.spline_smooth.opt_lam(x, y, alp=0.6, pen=None, min_lam=0.1, step_size=None, tol=0.001, max_it=100, gamma=1.0, fixed_lam=False)

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)

Calculate the exact soln to the ridge regression of the weights for basis x that fit batched data y.