-
Notifications
You must be signed in to change notification settings - Fork 59
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Adding Diganostics to Ecdf Plots in the spirit of TARP (#261)
* ecdf with random points * single axis * single axis * add comments * clean * clean * title fix * docstring * posterior 2d * posterior 2d fix * posterior 2d fix * posterior 2d fix * fix reference * clean up * add comment * add tests * pass kwargs * fix title * make more customizable * fix conflict --------- Co-authored-by: Jerry <[email protected]>
- Loading branch information
1 parent
0537f2a
commit 24e70aa
Showing
8 changed files
with
255 additions
and
39 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,2 @@ | ||
| from .simultaneous_ecdf_bands import simultaneous_ecdf_bands | ||
| from .ranks import fractional_ranks, distance_ranks |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| import numpy as np | ||
|
|
||
|
|
||
| def fractional_ranks(post_samples: np.ndarray, prior_samples: np.ndarray) -> np.ndarray: | ||
| """Compute fractional ranks (using broadcasting)""" | ||
| return np.mean(post_samples < prior_samples[:, np.newaxis, :], axis=1) | ||
|
|
||
|
|
||
| def _helper_distance_ranks( | ||
| post_samples: np.ndarray, | ||
| prior_samples: np.ndarray, | ||
| stacked: bool, | ||
| references: np.ndarray, | ||
| distance: callable, | ||
| p_norm: int, | ||
| ) -> np.ndarray: | ||
| """ | ||
| Helper function to compute ranks of true parameter wrt posterior samples | ||
| based on distances (defined on the p_norm) between samples and a given references. | ||
| """ | ||
| if distance is None: | ||
| # compute distances to references | ||
| dist_post = np.abs((references[:, np.newaxis, :] - post_samples)) | ||
| dist_prior = np.abs(references - prior_samples) | ||
|
|
||
| if stacked: | ||
| # compute ranks for all parameters jointly | ||
| samples_distances = np.sum(dist_post**p_norm, axis=-1) ** (1 / p_norm) | ||
| theta_distances = np.sum(dist_prior**p_norm, axis=-1) ** (1 / p_norm) | ||
|
|
||
| ranks = np.mean((samples_distances < theta_distances[:, np.newaxis]), axis=1)[:, np.newaxis] | ||
| else: | ||
| # compute marginal ranks for each parameter | ||
| ranks = np.mean((dist_post < dist_prior[:, np.newaxis]), axis=1) | ||
|
|
||
| else: | ||
| # compute distances using the given distance function | ||
| if stacked: | ||
| # compute distance over joint parameters | ||
| dist_post = np.array([distance(post_samples[i], references[i]) for i in range(references.shape[0])]) | ||
| dist_prior = np.array([distance(prior_samples[i], references[i]) for i in range(references.shape[0])]) | ||
| ranks = np.mean((dist_post < dist_prior[:, np.newaxis]), axis=1)[:, np.newaxis] | ||
| else: | ||
| # compute distances per parameter | ||
| dist_post = np.zeros_like(post_samples) | ||
| dist_prior = np.zeros_like(prior_samples) | ||
| for i in range(references.shape[0]): # Iterate over samples | ||
| for j in range(references.shape[1]): # Iterate over parameters | ||
| dist_post[i, :, j] = distance(post_samples[i, :, j], references[i, j]) | ||
| dist_prior[i, j] = distance(prior_samples[i, j], references[i, j]) | ||
|
|
||
| ranks = np.mean((dist_post < dist_prior[:, np.newaxis]), axis=1) | ||
| return ranks | ||
|
|
||
|
|
||
| def distance_ranks( | ||
| post_samples: np.ndarray, | ||
| prior_samples: np.ndarray, | ||
| stacked: bool, | ||
| references: np.ndarray = None, | ||
| distance: callable = None, | ||
| p_norm: int = 2, | ||
| ) -> np.ndarray: | ||
| """ | ||
| Compute ranks of true parameter wrt posterior samples based on distances between samples and optional references. | ||
| Parameters | ||
| ---------- | ||
| post_samples : np.ndarray | ||
| The posterior samples. | ||
| prior_samples : np.ndarray | ||
| The prior samples. | ||
| references : np.ndarray, optional | ||
| The references to compute the ranks. | ||
| stacked : bool | ||
| If True, compute ranks for all parameters jointly. Otherwise, compute marginal ranks. | ||
| distance : callable, optional | ||
| The distance function to compute the ranks. If None, the distance defined by the p_norm is used. Must be | ||
| a function that takes two arrays (if stacked, it gets the full parameter vectors, if not only the single | ||
| parameters) and returns an array with the distances. This could be based on the log-posterior, for example. | ||
| p_norm : int, optional | ||
| The norm to compute the distance if no distance is passed. Default is L2-norm. | ||
| """ | ||
| # Reference is the origin | ||
| if references is None: | ||
| references = np.zeros((prior_samples.shape[0], prior_samples.shape[1])) | ||
| else: | ||
| # Validate reference | ||
| if references.shape[0] != prior_samples.shape[0]: | ||
| raise ValueError("The number of references must match the number of prior samples.") | ||
| if references.shape[1] != prior_samples.shape[1]: | ||
| raise ValueError("The dimension of references must match the dimension of the parameters.") | ||
|
|
||
| ranks = _helper_distance_ranks( | ||
| post_samples=post_samples, | ||
| prior_samples=prior_samples, | ||
| stacked=stacked, | ||
| references=references, | ||
| distance=distance, | ||
| p_norm=p_norm, | ||
| ) | ||
| return ranks |
Oops, something went wrong.