Source code for zfit.minimizers.baseminimizer

Definition of minimizers, wrappers etc.


#  Copyright (c) 2020 zfit
import abc
import collections
import copy
import warnings
from abc import abstractmethod
from collections import OrderedDict
from typing import List, Union, Iterable, Mapping

import numpy as np
import texttable as tt

from .fitresult import FitResult
from .interface import ZfitMinimizer
from ..core.interfaces import ZfitLoss, ZfitParameter
from ..settings import run
from ..util import ztyping
from ..util.container import convert_to_container
from ..util.exception import MinimizeNotImplementedError, MinimizeStepNotImplementedError

[docs]class FailMinimizeNaN(Exception): pass
[docs]class ZfitStrategy(abc.ABC):
[docs] @abstractmethod def minimize_nan(self, loss: ZfitLoss, params: ztyping.ParamTypeInput, minimizer: ZfitMinimizer, values: Mapping = None) -> float: raise NotImplementedError
[docs]class BaseStrategy(ZfitStrategy): def __init__(self) -> None: self.fit_result = None self.error = None super().__init__()
[docs] def minimize_nan(self, loss: ZfitLoss, params: ztyping.ParamTypeInput, minimizer: ZfitMinimizer, values: Mapping = None) -> float: return self._minimize_nan(loss=loss, params=params, minimizer=minimizer, values=values)
def _minimize_nan(self, loss: ZfitLoss, params: ztyping.ParamTypeInput, minimizer: ZfitMinimizer, values: Mapping = None) -> float: print("The minimization failed due to too many NaNs being produced in the loss." "This is most probably caused by negative" " values returned from the PDF. Changing the initial values/stepsize of the parameters can solve this" " problem. Also check your model (if custom) for problems. For more information," " visit") raise FailMinimizeNaN() def __str__(self) -> str: return repr(self.__class__)[:-2].split(".")[-1]
[docs]class ToyStrategyFail(BaseStrategy): def __init__(self) -> None: super().__init__() self.fit_result = FitResult(params={}, edm=-999, fmin=-999, status=-999, converged=False, info={}, loss=None, minimizer=None) def _minimize_nan(self, loss: ZfitLoss, params: ztyping.ParamTypeInput, minimizer: ZfitMinimizer, values: Mapping = None) -> float: param_vals = run(params) param_vals = OrderedDict((param, value) for param, value in zip(params, param_vals)) self.fit_result = FitResult(params=param_vals, edm=-999, fmin=-999, status=9, converged=False, info={}, loss=loss, minimizer=minimizer) raise FailMinimizeNaN()
[docs]class PushbackStrategy(BaseStrategy): def __init__(self, nan_penalty: Union[float, int] = 100, nan_tolerance: int = 30, **kwargs): """Pushback by adding `nan_penalty * counter` to the loss if NaNs are encountered. The counter indicates how many NaNs occurred in a row. The `nan_tolerance` is the upper limit, if this is exceeded, the fallback will be used and an error is raised. Args: nan_penalty: Value to add to the previous loss in order to penalize the step taken. nan_tolerance: If the number of NaNs encountered in a row exceeds this number, the fallback is used. """ super().__init__(**kwargs) self.nan_penalty = nan_penalty self.nan_tolerance = nan_tolerance def _minimize_nan(self, loss: ZfitLoss, params: ztyping.ParamTypeInput, minimizer: ZfitMinimizer, values: Mapping = None) -> float: assert 'nan_counter' in values, "'nan_counter' not in values, minimizer not correctly implemented" nan_counter = values['nan_counter'] if nan_counter < self.nan_tolerance: last_loss = values.get('old_loss') if last_loss is not None: loss_evaluated = last_loss + self.nan_penalty*nan_counter else: loss_evaluated = values.get('loss') if isinstance(loss_evaluated, str): raise RuntimeError("Loss starts already with NaN, cannot minimize.") return loss_evaluated else: super()._minimize_nan(loss=loss, params=params, minimizer=minimizer, values=values)
DefaultStrategy = PushbackStrategy
[docs]class DefaultToyStrategy(DefaultStrategy, ToyStrategyFail): """Same as :py:class:`DefaultStrategy`, but does not raise an error on full failure, instead return an invalid FitResult. This can be useful for toy studies, where multiple fits are done and a failure should simply be counted as a failure instead of rising an error. """
[docs]class BaseMinimizer(ZfitMinimizer): """Minimizer for loss functions. Additional `minimizer_options` (given as **kwargs) can be accessed and changed via the attribute (dict) `minimizer.minimizer_options`. """ _DEFAULT_TOLERANCE = 1e-3 def __init__(self, name, tolerance, verbosity, minimizer_options, strategy=None, **kwargs): super().__init__(**kwargs) if name is None: name = repr(self.__class__)[:-2].split(".")[-1] if strategy is None: strategy = DefaultStrategy() if not isinstance(strategy, ZfitStrategy): raise TypeError(f"strategy {strategy} is not an instance of ZfitStrategy.") self.strategy = strategy = name if tolerance is None: tolerance = self._DEFAULT_TOLERANCE self.tolerance = tolerance self.verbosity = verbosity if minimizer_options is None: minimizer_options = {} self.minimizer_options = minimizer_options self._max_steps = 5000 def _check_input_params(self, loss: ZfitLoss, params, only_floating=True): params = convert_to_container(params) if params is None: params = loss.get_params(only_floating=only_floating) params = list(params) else: params_indep = [] for param in params: if param.independent: params_indep.append(param) else: params_indep.extend(param.get_params(only_floating=only_floating)) params = params_indep if only_floating: params = self._filter_floating_params(params) if not params: raise RuntimeError("No parameter for minimization given/found. Cannot minimize.") return params @staticmethod def _filter_floating_params(params): non_floating = [param for param in params if not param.floating] if non_floating: # legacy warning warnings.warn(f"CHANGED BEHAVIOR! Non-floating parameters {non_floating} will not be used in the " f"minimization.") return [param for param in params if param.floating] @staticmethod def _extract_load_method(params): return [param.load for param in params] @staticmethod def _extract_param_names(params): return [ for param in params] def _check_gradients(self, params, gradients): non_dependents = [param for param, grad in zip(params, gradients) if grad is None] if non_dependents: raise ValueError("Invalid gradients for the following parameters: {}" "The function does not depend on them. Probably a Tensor" "instead of a `CompositeParameter` was created implicitly.".format(non_dependents)) @property def tolerance(self): return self._tolerance @tolerance.setter def tolerance(self, tolerance): self._tolerance = tolerance @staticmethod def _extract_start_values(params): """Extract the current value if defined, otherwise random. Arguments: params: Return: list(const): the current value of parameters """ values = [p for p in params] return values @staticmethod def _update_params(params: Union[Iterable[ZfitParameter]], values: Union[Iterable[float], np.ndarray]) -> List[ ZfitParameter]: """Update `params` with `values`. Returns the assign op (if `use_op`, otherwise use a session to load the value. Args: params: The parameters to be updated values: New values for the parameters. Returns: List of assign operations if `use_op`, otherwise empty. The output can therefore be directly used as argument to :py:func:`~tf.control_dependencies`. """ if len(params) == 1 and len(values) > 1: values = (values,) # iteration will be correctly for param, value in zip(params, values): param.set_value(value) return params
[docs] def step(self, loss, params: ztyping.ParamsOrNameType = None): """Perform a single step in the minimization (if implemented). Args: params: Returns: Raises: MinimizeStepNotImplementedError: if the `step` method is not implemented in the minimizer. """ params = self._check_input_params(loss, params) return self._step(loss, params=params)
[docs] def minimize(self, loss: ZfitLoss, params: ztyping.ParamsTypeOpt = None) -> FitResult: """Fully minimize the `loss` with respect to `params`. Args: loss: Loss to be minimized. params: The parameters with respect to which to minimize the `loss`. If `None`, the parameters will be taken from the `loss`. Returns: The fit result. """ params = self._check_input_params(loss=loss, params=params, only_floating=True) try: return self._hook_minimize(loss=loss, params=params) except (FailMinimizeNaN, RuntimeError) as error: # iminuit raises RuntimeError if user raises Error fail_result = self.strategy.fit_result if fail_result is not None: return fail_result else: raise
def _hook_minimize(self, loss, params): return self._call_minimize(loss=loss, params=params) def _call_minimize(self, loss, params): try: return self._minimize(loss=loss, params=params) except MinimizeNotImplementedError as error: try: return self._minimize_with_step(loss=loss, params=params) except MinimizeStepNotImplementedError: raise error def _minimize_with_step(self, loss, params): # TODO improve n_old_vals = 10 changes = collections.deque(np.ones(n_old_vals)) last_val = -10 n_steps = 0 def step_fn(loss, params): try: self._step_tf(loss=loss.value, params=params) except MinimizeStepNotImplementedError: self.step(loss, params) return loss.value() while sum(sorted(changes)[-3:]) > self.tolerance and n_steps < self._max_steps: # TODO: improve condition cur_val = step_fn(loss=loss, params=params) changes.popleft() changes.append(abs(cur_val - last_val)) last_val = cur_val n_steps += 1 fmin = cur_val edm = -999 # TODO: get edm # compose fit result message = "successful finished" are_unique = len(set([float(change.numpy()) for change in changes])) > 1 # values didn't change... if not are_unique: message = "Loss unchanged for last {} steps".format(n_old_vals) success = are_unique status = 0 if success else 10 info = {'success': success, 'message': message} # TODO: create status param_values = [float(p.numpy()) for p in params] params = OrderedDict((p, val) for p, val in zip(params, param_values)) return FitResult(params=params, edm=edm, fmin=fmin, info=info, converged=success, status=status, loss=loss, minimizer=self.copy())
[docs] def copy(self): return copy.copy(self)
def _minimize(self, loss, params): raise MinimizeNotImplementedError def _step_tf(self, loss, params): raise MinimizeStepNotImplementedError def _step(self, loss, params): raise MinimizeStepNotImplementedError def __str__(self) -> str: string = f'<{} strategy={self.strategy} tolerance={self.tolerance}>' return string