"""Functions related to parameter sampling"""
import numpy as np
import pandas as pd
from typing import Tuple
from . import parameters
from .C import * # noqa: F403
__all__ = ['sample_from_prior', 'sample_parameter_startpoints']
[docs]def sample_from_prior(prior: Tuple[str, list, str, list],
n_starts: int) -> np.array:
"""Creates samples for one parameter based on prior
Arguments:
prior: A tuple as obtained from ``petab.parameter.get_priors_from_df``
n_starts: Number of samples
Returns:
Array with sampled values
"""
# unpack info
p_type, p_params, scaling, bounds = prior
# define a function to rescale the sampled points to parameter scale
def scale(x):
if scaling == LIN:
return x
if scaling == LOG:
return np.log(x)
if scaling == LOG10:
return np.log10(x)
raise NotImplementedError(
f"Parameter priors on the parameter scale {scaling} are "
"currently not implemented.")
def clip_to_bounds(x: np.array):
"""Clip values in array x to bounds"""
x = np.maximum(np.minimum(scale(bounds[1]), x), scale(bounds[0]))
return x
# define lambda functions for each parameter
if p_type == UNIFORM:
sp = scale((p_params[1] - p_params[0]) * np.random.random((
n_starts,)) + p_params[0])
elif p_type == PARAMETER_SCALE_UNIFORM:
sp = (p_params[1] - p_params[0]) * np.random.random((n_starts,
)) + p_params[0]
elif p_type == NORMAL:
sp = scale(np.random.normal(loc=p_params[0], scale=p_params[1],
size=(n_starts,)))
elif p_type == LOG_NORMAL:
sp = scale(np.exp(np.random.normal(
loc=p_params[0], scale=p_params[1], size=(n_starts,))))
elif p_type == PARAMETER_SCALE_NORMAL:
sp = np.random.normal(loc=p_params[0], scale=p_params[1],
size=(n_starts,))
elif p_type == LAPLACE:
sp = scale(np.random.laplace(
loc=p_params[0], scale=p_params[1], size=(n_starts,)))
elif p_type == LOG_LAPLACE:
sp = scale(np.exp(np.random.laplace(
loc=p_params[0], scale=p_params[1], size=(n_starts,))))
elif p_type == PARAMETER_SCALE_LAPLACE:
sp = np.random.laplace(loc=p_params[0], scale=p_params[1],
size=(n_starts,))
else:
raise NotImplementedError(
f"Parameter priors of type {prior[0]} are not implemented.")
return clip_to_bounds(sp)
[docs]def sample_parameter_startpoints(parameter_df: pd.DataFrame,
n_starts: int = 100,
seed: int = None) -> np.array:
"""Create numpy.array with starting points for an optimization
Arguments:
parameter_df: PEtab parameter DataFrame
n_starts: Number of points to be sampled
seed: Random number generator seed (see numpy.random.seed)
Returns:
Array of sampled starting points with dimensions
n_startpoints x n_optimization_parameters
"""
if seed is not None:
np.random.seed(seed)
# get types and parameters of priors from dataframe
prior_list = parameters.get_priors_from_df(
parameter_df, mode=INITIALIZATION)
startpoints = [sample_from_prior(prior, n_starts) for prior in prior_list]
return np.array(startpoints).T