-
Notifications
You must be signed in to change notification settings - Fork 71
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
9 changed files
with
238 additions
and
17 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
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 |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| import yaml, sys | ||
| from flare.bffs.sgp.calculator import SGP_Calculator | ||
|
|
||
|
|
||
| def build_map(config): | ||
| flare_file = config.get("file") | ||
| lmp_name = config.get("lmp_name") | ||
| contributor = config.get("contributor") | ||
| sgp_calc, _ = SGP_Calculator.from_file(flare_file) | ||
| sgp_calc.gp_model.write_mapping_coefficients(lmp_name, contributor, 0) | ||
|
|
||
|
|
||
| def rebuild_gp(config): | ||
| flare_config = config["flare_calc"] | ||
|
|
||
| # get the SGP settings from file | ||
| flare_from_file, _ = SGP_Calculator.from_file(flare_config.get("file"), build=False) | ||
|
|
||
| flare_from_file | ||
|
|
||
|
|
||
| def main(): | ||
| with open(sys.argv[1], "r") as f: | ||
| config = yaml.safe_load(f) | ||
|
|
||
| mode = config.get("otf").get("mode", "fresh") | ||
| if mode == "fresh": | ||
| fresh_start_otf(config) | ||
| elif mode == "restart": | ||
| restart_otf(config) |
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 +1,182 @@ | ||
| # Downloaded from: https://github.com/mir-group/nequip/blob/main/nequip/utils/regressor.py | ||
| # nequip/nequip/utils/regressor.py | ||
| # Author: Lixin Sun | ||
|
|
||
| import logging | ||
| import numpy as np | ||
| from typing import Optional | ||
| from sklearn.gaussian_process import GaussianProcessRegressor | ||
| from sklearn.gaussian_process.kernels import DotProduct, Kernel, Hyperparameter | ||
|
|
||
|
|
||
| def solver(X, y, regressor: Optional[str] = "NormalizedGaussianProcess", **kwargs): | ||
| if regressor == "GaussianProcess": | ||
| return gp(X, y, **kwargs) | ||
| elif regressor == "NormalizedGaussianProcess": | ||
| return normalized_gp(X, y, **kwargs) | ||
| else: | ||
| raise NotImplementedError(f"{regressor} is not implemented") | ||
|
|
||
|
|
||
| def normalized_gp(X, y, **kwargs): | ||
| feature_rms = 1.0 / np.sqrt(np.average(X**2, axis=0)) | ||
| feature_rms = np.nan_to_num(feature_rms, 1) | ||
| y_mean = np.sum(y) / np.sum(X) | ||
| mean, std = base_gp( | ||
| X, | ||
| y - (np.sum(X, axis=1) * y_mean).reshape(y.shape), | ||
| NormalizedDotProduct, | ||
| {"diagonal_elements": feature_rms}, | ||
| **kwargs, | ||
| ) | ||
| return mean + y_mean, std | ||
|
|
||
|
|
||
| def gp(X, y, **kwargs): | ||
| return base_gp( | ||
| X, y, DotProduct, {"sigma_0": 0, "sigma_0_bounds": "fixed"}, **kwargs | ||
| ) | ||
|
|
||
|
|
||
| def base_gp( | ||
| X, | ||
| y, | ||
| kernel, | ||
| kernel_kwargs, | ||
| alpha: Optional[float] = 0.1, | ||
| max_iteration: int = 20, | ||
| stride: Optional[int] = None, | ||
| ): | ||
|
|
||
| if len(y.shape) == 1: | ||
| y = y.reshape([-1, 1]) | ||
|
|
||
| if stride is not None: | ||
| X = X[::stride] | ||
| y = y[::stride] | ||
|
|
||
| not_fit = True | ||
| iteration = 0 | ||
| mean = None | ||
| std = None | ||
| while not_fit: | ||
| logging.debug(f"GP fitting iteration {iteration} {alpha}") | ||
| try: | ||
| _kernel = kernel(**kernel_kwargs) | ||
| gpr = GaussianProcessRegressor(kernel=_kernel, random_state=0, alpha=alpha) | ||
| gpr = gpr.fit(X, y) | ||
|
|
||
| vec = np.diag(np.ones(X.shape[1])) | ||
| mean, std = gpr.predict(vec, return_std=True) | ||
|
|
||
| mean = np.reshape(mean, -1) | ||
| # ignore all the off-diagonal terms | ||
| std = np.reshape(std, -1) | ||
| likelihood = gpr.log_marginal_likelihood() | ||
|
|
||
| res = np.sqrt(np.mean(np.square(np.matmul(X, mean.reshape([-1, 1])) - y))) | ||
|
|
||
| logging.debug( | ||
| f"GP fitting: alpha {alpha}:\n" | ||
| f" residue {res}\n" | ||
| f" mean {mean} std {std}\n" | ||
| f" log marginal likelihood {likelihood}" | ||
| ) | ||
| not_fit = False | ||
|
|
||
| except Exception as e: | ||
| logging.info(f"GP fitting failed for alpha={alpha} and {e.args}") | ||
| if alpha == 0 or alpha is None: | ||
| logging.info("try a non-zero alpha") | ||
| not_fit = False | ||
| raise ValueError( | ||
| f"Please set the {alpha} to non-zero value. \n" | ||
| "The dataset energy is rank deficient to be solved with GP" | ||
| ) | ||
| else: | ||
| alpha = alpha * 2 | ||
| iteration += 1 | ||
| logging.debug(f" increase alpha to {alpha}") | ||
|
|
||
| if iteration >= max_iteration or not_fit is False: | ||
| raise ValueError( | ||
| "Please set the per species shift and scale to zeros and ones. \n" | ||
| "The dataset energy is to diverge to be solved with GP" | ||
| ) | ||
|
|
||
| return mean, std | ||
|
|
||
|
|
||
| class NormalizedDotProduct(Kernel): | ||
| r"""Dot-Product kernel. | ||
| .. math:: | ||
| k(x_i, x_j) = x_i \cdot A \cdot x_j | ||
| """ | ||
|
|
||
| def __init__(self, diagonal_elements): | ||
| # TO DO: check shape | ||
| self.diagonal_elements = diagonal_elements | ||
| self.A = np.diag(diagonal_elements) | ||
|
|
||
| def __call__(self, X, Y=None, eval_gradient=False): | ||
| """Return the kernel k(X, Y) and optionally its gradient. | ||
| Parameters | ||
| ---------- | ||
| X : ndarray of shape (n_samples_X, n_features) | ||
| Left argument of the returned kernel k(X, Y) | ||
| Y : ndarray of shape (n_samples_Y, n_features), default=None | ||
| Right argument of the returned kernel k(X, Y). If None, k(X, X) | ||
| if evaluated instead. | ||
| eval_gradient : bool, default=False | ||
| Determines whether the gradient with respect to the log of | ||
| the kernel hyperparameter is computed. | ||
| Only supported when Y is None. | ||
| Returns | ||
| ------- | ||
| K : ndarray of shape (n_samples_X, n_samples_Y) | ||
| Kernel k(X, Y) | ||
| K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\ | ||
| optional | ||
| The gradient of the kernel k(X, X) with respect to the log of the | ||
| hyperparameter of the kernel. Only returned when `eval_gradient` | ||
| is True. | ||
| """ | ||
| X = np.atleast_2d(X) | ||
| if Y is None: | ||
| K = (X.dot(self.A)).dot(X.T) | ||
| else: | ||
| if eval_gradient: | ||
| raise ValueError("Gradient can only be evaluated when Y is None.") | ||
| K = (X.dot(self.A)).dot(Y.T) | ||
|
|
||
| if eval_gradient: | ||
| return K, np.empty((X.shape[0], X.shape[0], 0)) | ||
| else: | ||
| return K | ||
|
|
||
| def diag(self, X): | ||
| """Returns the diagonal of the kernel k(X, X). | ||
| The result of this method is identical to np.diag(self(X)); however, | ||
| it can be evaluated more efficiently since only the diagonal is | ||
| evaluated. | ||
| Parameters | ||
| ---------- | ||
| X : ndarray of shape (n_samples_X, n_features) | ||
| Left argument of the returned kernel k(X, Y). | ||
| Returns | ||
| ------- | ||
| K_diag : ndarray of shape (n_samples_X,) | ||
| Diagonal of kernel k(X, X). | ||
| """ | ||
| return np.einsum("ij,ij,jj->i", X, X, self.A) | ||
|
|
||
| def __repr__(self): | ||
| return "" | ||
|
|
||
| def is_stationary(self): | ||
| """Returns whether the kernel is stationary.""" | ||
| return False | ||
|
|
||
| @property | ||
| def hyperparameter_diagonal_elements(self): | ||
| return Hyperparameter("diagonal_elements", "numeric", "fixed") |