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* feat: adds iRProp+ optimiser * removes redundant checks implemented in parent class, adds coverage * fix: avoids potential multistart infeasible condition * restores examples, updates to tell() comments * tests: updates parameter range to align with default pybamm parameter object * tests: updates priors for x0 within bound clipping for RandomSearch test * docs: adds additional information to the iRProp+ docstring * adds changelog entry
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,188 @@ | ||
| # | ||
| # Initially based of Pints' IRProp- class. | ||
| # | ||
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| import numpy as np | ||
| from pints import Optimiser as PintsOptimiser | ||
| from pints import RectangularBoundaries | ||
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| class IRPropPlusImpl(PintsOptimiser): | ||
| """ | ||
| The iRprop+ algorithm adjusts step sizes based on the sign of the gradient | ||
| in each dimension, increasing step sizes when the sign remains consistent | ||
| and decreasing when the sign changes. This implementation includes | ||
| weight (parameter) backtracking that reverts the previous step in the | ||
| event of a gradient sign changing. | ||
| References: | ||
| - [1] Igel and Hüsken (2003): Empirical Evaluation of Improved Rprop Algorithms. | ||
| - [2] Riedmiller and Braun (1993): A Direct Adaptive Method for Faster Backpropagation. | ||
| - [3] Igel and Husk (2003): Improving the Rprop Learning Algorithm. | ||
| Parameters | ||
| ---------- | ||
| x0 : array-like | ||
| Initial starting point for the optimisation. | ||
| sigma0 : float or array-like, optional | ||
| Initial step size(s). If a scalar is provided, it is applied to all dimensions. | ||
| Default is 0.05. | ||
| boundaries : pints.Boundaries, optional | ||
| Boundary constraints for the optimisation. If None, no boundaries are applied. | ||
| Attributes | ||
| ---------- | ||
| eta_min : float | ||
| Factor by which the step size is reduced when the gradient sign changes. | ||
| Default is 0.5. | ||
| eta_max : float | ||
| Factor by which the step size is increased when the gradient sign remains consistent. | ||
| Default is 1.2. | ||
| step_min : float | ||
| Minimum allowable step size. Default is 1e-3 * min(sigma0). | ||
| step_max : float or None | ||
| Maximum allowable step size. Default is None (unlimited). | ||
| """ | ||
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| def __init__(self, x0, sigma0=0.05, boundaries=None): | ||
| super().__init__(x0, sigma0, boundaries) | ||
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| # Set hypers | ||
| self.eta_min = 0.5 | ||
| self.eta_max = 1.2 | ||
| self.step_min = 1e-4 * np.min(self._sigma0) | ||
| self.step_max = None | ||
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| # Store the previous update for backtracking | ||
| self._update_previous = np.zeros_like(x0, dtype=float) | ||
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| # Internal states | ||
| self._x_current = np.array(x0, dtype=float) | ||
| self._gradient_previous = None | ||
| self._step_sizes = np.copy(self._sigma0) | ||
| self._f_current = np.inf | ||
| self._x_best = np.array(x0, dtype=float) | ||
| self._f_best = np.inf | ||
| self._running = False | ||
| self._ready_for_tell = False | ||
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| # Boundaries | ||
| self._boundaries = boundaries | ||
| if isinstance(boundaries, RectangularBoundaries): | ||
| self._lower = boundaries.lower() | ||
| self._upper = boundaries.upper() | ||
| else: | ||
| self._lower = self._upper = None | ||
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| # Set proposals | ||
| self._proposed = np.copy(self._x_current) | ||
| self._proposed.setflags(write=False) | ||
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| def ask(self): | ||
| """ | ||
| Proposes the next point for evaluation. | ||
| Returns | ||
| ------- | ||
| list | ||
| A list containing the proposed point. | ||
| """ | ||
| if not self._running: | ||
| if self.step_min is not None and self.step_max is not None: | ||
| if self.step_min >= self.step_max: | ||
| raise ValueError( | ||
| f"Minimum step size ({self.step_min}) must be less than " | ||
| f"maximum step size ({self.step_max})." | ||
| ) | ||
| self._running = True | ||
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| self._ready_for_tell = True | ||
| return [self._proposed] | ||
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| def tell(self, reply): | ||
| """ | ||
| Updates the optimiser with the function value and gradient at the proposed point. | ||
| Parameters | ||
| ---------- | ||
| reply : list | ||
| A list containing a tuple of (function value, gradient) at the proposed point. | ||
| Raises | ||
| ------ | ||
| RuntimeError | ||
| If `ask()` was not called before `tell()`. | ||
| """ | ||
| if not self._ready_for_tell: | ||
| raise RuntimeError("ask() must be called before tell().") | ||
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| self._ready_for_tell = False | ||
| f_new, gradient_new = reply[0] | ||
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| # Setup for first iteration | ||
| if self._gradient_previous is None: | ||
| self._gradient_previous = gradient_new | ||
| self._f_current = f_new | ||
| return | ||
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| # Compute element-wise gradient product | ||
| grad_product = gradient_new * self._gradient_previous | ||
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| # Update step sizes, and bound them | ||
| self._step_sizes[grad_product > 0] *= self.eta_max | ||
| self._step_sizes[grad_product < 0] *= self.eta_min | ||
| self._step_sizes = np.clip(self._step_sizes, self.step_min, self.step_max) | ||
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| # Handle weight backtracking, | ||
| # Reverting last update where gradient sign changed | ||
| gradient_new[grad_product < 0] = 0 | ||
| self._x_current[grad_product < 0] -= self._update_previous[grad_product < 0] | ||
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| # Update the current position | ||
| self._x_current = np.copy(self._proposed) | ||
| self._f_current = f_new | ||
| self._gradient_previous = gradient_new | ||
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| # Compute the new update and store for back-tracking | ||
| update = -self._step_sizes * np.sign(gradient_new) | ||
| self._update_previous = update | ||
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| # Step in the direction of the negative gradient | ||
| proposed = self._x_current + update | ||
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| # Boundaries | ||
| if self._lower is not None: | ||
| # Rectangular boundaries | ||
| while np.any(proposed < self._lower) or np.any(proposed >= self._upper): | ||
| mask = np.logical_or(proposed < self._lower, proposed >= self._upper) | ||
| self._step_sizes[mask] *= self.eta_min | ||
| proposed = self._x_current - self._step_sizes * np.sign(gradient_new) | ||
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| # Update proposed attribute | ||
| self._proposed = proposed | ||
| self._proposed.setflags(write=False) | ||
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| # Update best solution | ||
| if f_new < self._f_best: | ||
| self._f_best = f_new | ||
| self._x_best = self._x_current | ||
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| def running(self): | ||
| """Returns the state of the optimiser""" | ||
| return self._running | ||
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| def f_best(self): | ||
| """Returns the best function value found so far.""" | ||
| return self._f_best | ||
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| def x_best(self): | ||
| """Returns the best position found so far.""" | ||
| return self._x_best | ||
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| def name(self): | ||
| """Returns the name of the optimiser.""" | ||
| return "iRprop+" | ||
|
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| def needs_sensitivities(self): | ||
| """Indicates that this optimiser requires gradient information.""" | ||
| return True |
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