Source code for zfit.minimizers.fitresult

#  Copyright (c) 2024 zfit

from __future__ import annotations

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    import zfit
    from .evaluation import LossEval

from collections.abc import Mapping
from collections.abc import Callable
from collections.abc import Iterable

import collections
import contextlib
import itertools
import math
import warnings
from collections import OrderedDict

import colored
import iminuit
import numpy as np
import scipy.optimize
import scipy.stats
from colorama import Style, init
from ordered_set import OrderedSet
from scipy.optimize import LbfgsInvHessProduct
from tabulate import tabulate

if TYPE_CHECKING:
    try:
        import ipyopt  # for type checking
    except ImportError:
        pass

from .errors import (
    compute_errors,
    covariance_with_weights,
    dict_to_matrix,
    matrix_to_dict,
)
from ..z import numpy as znp
from .interface import ZfitMinimizer, ZfitResult
from .termination import ConvergenceCriterion
from ..core.interfaces import (
    ZfitIndependentParameter,
    ZfitLoss,
    ZfitParameter,
    ZfitData,
)
from ..core.parameter import set_values
from ..settings import run
from ..util.container import convert_to_container
from ..util.deprecation import deprecated_args, deprecated
from ..util.warnings import ExperimentalFeatureWarning, warn_changed_feature
from ..util.ztyping import ParamsTypeOpt

init(autoreset=True)


class Approximations:
    def __init__(
        self,
        params: list[ZfitParameter],
        gradient: np.ndarray | None = None,
        hessian: np.ndarray | None = None,
        inv_hessian: np.ndarray | None = None,
    ) -> None:
        """Holds different approximations after the minimisation and/or calculates them.

        Args:
            params: List of parameters the approximations (gradient, hessian, ...) were calculated with.
            gradient: Gradient
            hessian: Hessian Matrix
            inv_hessian: Inverse of the Hessian Matrix
        """
        self._params = params
        self._gradient = gradient
        self._hessian = hessian
        self._inv_hessian = inv_hessian
        super().__init__()

    @property
    def params(self):
        return self._params

    def gradient(
        self, params: ZfitParameter | Iterable[ZfitParameter] | None = None
    ) -> np.ndarray | None:
        """Return an approximation of the gradient _if available_.

        Args:
            params: Parameters to which the gradients should be returned

        Returns:
            Array with gradients or ``None``
        """
        grad = self._gradient
        if grad is None:
            return None

        if params is not None:
            params = convert_to_container(params, container=tuple)
            params_mapped = {
                i: params.index(param)
                for i, param in enumerate(self.params)
                if param in params
            }
            indices = sorted(params_mapped, key=lambda x: params_mapped[x])
            grad = grad[indices]
        return grad

    def hessian(self, invert: bool = True) -> np.ndarray | None:
        """Return an approximation of the hessian _if available_.

        Args:
            invert: If a _hessian approximation_ is not available but an inverse hessian is, invert the latter to
                obtain the hessian approximation.

        Returns:
            Array with hessian matrix or ``None``
        """
        hess = self._hessian
        if hess is None and invert:
            inv_hess = self._inv_hessian
            if inv_hess is not None:
                hess = np.linalg.inv(inv_hess)
                self._hessian = hess
        return hess

    def inv_hessian(self, invert: bool = True) -> None | np.ndarray:
        """Return an approximation of the inverse hessian _if available_.

        Args:
            invert: If an _inverse hessian approximation_ is not available but a hessian is, invert the latter to
                obtain the inverse hessian approximation.

        Returns:
            Array with the inverse of the hessian matrix or ``None``
        """
        inv_hess = self._inv_hessian
        if inv_hess is None and invert:
            hess = self._hessian
            if hess is not None:
                inv_hess = np.linalg.inv(hess)
                self._inv_hessian = inv_hess
        return inv_hess

    def freeze(self):
        self._params = [p.name for p in self.params]


def _minos_minuit(result, params, cl=None):
    minuit_minimizer = result._create_minuit_instance()

    try:
        minuit_minimizer.minos(*(p.name for p in params), cl=cl)
        # Minuit seems very ustable on this and the call can fail after a few trials
    except RuntimeError as error:
        if "Function minimum is not valid." not in error.args[0]:
            raise
        minuit_minimizer.reset()
        minuit_minimizer.minos(*(p.name for p in params), cl=cl)

    merror_result = minuit_minimizer.merrors  # returns every var
    attrs = [
        "lower",
        "upper",
        "is_valid",
        "upper_valid",
        "lower_valid",
        "at_lower_limit",
        "at_upper_limit",
        "nfcn",
    ]
    errors = {}
    for p in params:
        error_res = merror_result[p.name]
        errors[p] = {attr: getattr(error_res, attr) for attr in attrs}
        errors[p]["original"] = error_res
    new_result = None
    return errors, new_result


def _covariance_minuit(result, params):
    minuit = result._create_minuit_instance()

    _ = minuit.hesse()  # make sure to have an accurate covariance

    covariance = minuit.covariance

    covariance_dict = {}
    if covariance is None:
        warnings.warn(
            "minuit failed to calculate the covariance matrix or similar when calling `hesse`."
            "Try to use `hesse_np` as the method instead and try again."
            "This is unexpected and may has to do with iminuitV2. Either way, please fill an issue if"
            " this is not expected to fail for you."
        )
    else:
        for p1 in params:
            for p2 in params:
                key = (p1, p2)
                covariance_dict[key] = covariance[tuple(k.name for k in key)]

    return covariance_dict


def _covariance_np(result, params):
    if any(
        isinstance(data, ZfitData) and data.weights is not None
        for data in result.loss.data
    ):
        warnings.warn(
            "The computation of the covariance matrix with weights is still experimental.",
            ExperimentalFeatureWarning,
        )

    _, gradient, hessian = result.loss.value_gradient_hessian(params)
    covariance = znp.linalg.inv(hessian)

    return matrix_to_dict(params, covariance)


def _covariance_approx(result, params):
    if any(
        isinstance(data, ZfitData) and data.weights is not None
        for data in result.loss.data
    ):
        warnings.warn(
            "Approximate covariance/hesse estimation with weights is not supported, returning None",
            RuntimeWarning,
        )

    inv_hessian = result.approx.inv_hessian(invert=True)
    if inv_hessian is None:
        return {}

    params_approx = list(result.params)
    param_indices = [params_approx.index(p) for p in params]
    covariance_dict = {
        (p1, p2): inv_hessian[(p1_index, p2_index)]
        for (p1, p1_index), (p2, p2_index) in itertools.product(
            zip(params, param_indices), zip(params, param_indices)
        )
    }
    return covariance_dict


class ParamToNameGetitem:
    __slots__ = ()

    def __getitem__(self, item):
        if isinstance(item, ZfitParameter):
            item = item.name
        return super().__getitem__(item)


class NameToParamGetitem:
    __slots__ = ()

    def __getitem__(self, item):
        if isinstance(item, ZfitParameter):
            item = item.name
        for param in self.keys():
            name = param.name if isinstance(param, ZfitParameter) else param
            if name == item:
                item = param
                break
        return super().__getitem__(item)  # raises key error if not there, which is good

    def __contains__(self, item):
        try:
            self[item]
        except KeyError:
            return False
        except Exception as error:
            raise RuntimeError(
                "Unknown exception occurred! This should not happen."
            ) from error
        else:
            return True


[docs] class FitResult(ZfitResult): _default_hesse = "minuit_hesse" _hesse_methods = { "minuit_hesse": _covariance_minuit, "hesse_np": _covariance_np, "approx": _covariance_approx, } _default_error = "minuit_minos" _error_methods = { "minuit_minos": _minos_minuit, "zfit_error": compute_errors, "zfit_errors": compute_errors, } def __init__( self, loss: ZfitLoss, params: dict[ZfitParameter, float], minimizer: ZfitMinimizer, valid: bool, edm: float, fminopt: float, criterion: ConvergenceCriterion | None, status: int | None = None, converged: bool | None = None, message: str | None = None, info: Mapping | None = None, approx: Mapping | Approximations | None = None, niter: int | None = None, evaluator: LossEval = None, ) -> None: """Create a ``FitResult`` from a minimization. Store parameter values, minimization infos and calculate errors. Any errors calculated are saved under ``self.params`` dictionary with:: {parameter: {error_name1: {'low': value, 'high': value or similar}} Args: loss: |@doc:result.init.loss| The loss function that was minimized. Usually, but not necessary, contains also the pdf, data and constraints. |@docend:result.init.loss| params: |@doc:result.init.params| Result of the fit where each :py:class:`~zfit.Parameter` key has the value from the minimum found by the minimizer. |@docend:result.init.params| minimizer: |@doc:result.init.minimizer| Minimizer that was used to obtain this ``FitResult`` and will be used to calculate certain errors. If the minimizer is state-based (like "iminuit"), then this is a copy and the state of other ``FitResults`` or of the *actual* minimizer that performed the minimization won't be altered. |@docend:result.init.minimizer| valid: |@doc:result.init.valid| Indicating whether the result is valid or not. This is the strongest indication and serves as the global flag. The reasons why a result may be invalid can be arbitrary, including but not exclusive: - parameter(s) at the limit - maxiter reached without proper convergence - the minimizer maybe even converged but it is known that this is only a local minimum To indicate the reason for the invalidity, pass a message. |@docend:result.init.valid| edm: |@doc:result.init.edm| The estimated distance to minimum which is the criterion value at the minimum. |@docend:result.init.edm| fminopt: |@doc:result.init.fmin| Value of the function at the minimum. |@docend:result.init.fmin| criterion: |@doc:result.init.criterion| Criterion that was used during the minimization. This determines the estimated distance to the minimum (edm) |@docend:result.init.criterion| status: |@doc:result.init.status| A status code (if available) that describes the minimization termination. 0 means a valid termination. |@docend:result.init.status| converged: |@doc:result.init.converged| Whether the fit has successfully converged or not. The result itself can still be an invalid minimum such as if the parameters are at or close to the limits or in case another minimum is found. |@docend:result.init.converged| message: |@doc:result.init.message| Human-readable message to indicate the reason if the fitresult is not valid. If the fit is valid, the message (should) be an empty string (or None), otherwise, it should denote the reason for the invalidity. |@docend:result.init.message| info: |@doc:result.init.info| Additional information (if available) such as *number of gradient function calls* or the original minimizer return message. This is a relatively free field and _no single field_ in it is guaranteed to be stable. Some recommended fields: - *original*: contains the original returned object by the minimizer used internally. - *optimizer*: the actual instance of the wrapped optimizer (if available) |@docend:result.init.info| approx: |@doc:result.init.approx| Collection of approximations found during the minimization process such as gradient and hessian. |@docend:result.init.approx| niter: |@doc:result.init.niter| Approximate number of iterations ~= number of function evaluations ~= number of gradient evaluations. This is an approximated value and the exact meaning can differ between different minimizers. |@docend:result.init.niter| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| """ super().__init__() if status is None: status = 0 if valid else -999 if converged is None and valid: converged = True if message is None: if valid: message = "" else: message = "Invalid, unknown reason (not specified)" info = {} if info is None else info approx = self._input_convert_approx(approx, evaluator, info, params) if evaluator is not None: niter = evaluator.niter if niter is None else niter param_at_limit = any(param.at_limit for param in params) if param_at_limit: valid = False if message: message += " AND " message += "parameter(s) at their limit." self._cache_minuit = None # in case used in errors self._evaluator = evaluator # keep private for now self._niter = niter # keep private for now self._approx = approx self._status = status self._message = "" if message is None else message self._converged = converged self._params = self._input_convert_params(params) self._values = ValuesHolder(params) self._params_at_limit = param_at_limit self._edm = edm self._criterion = criterion self._fminopt = fminopt self._info = info self._loss = loss self._minimizer = minimizer self._valid = valid self._covariance_dict = {} try: fminfull = loss.value(full=True) except Exception as error: warnings.warn( f"Could not calculate fminfull due to {error}. Setting to 0. This is a new feature and is caught to not break backwards compatibility." ) fminfull = 0 self._fmin = float(fminfull) def _input_convert_approx(self, approx, evaluator, info, params): """Convert approx (if a Mapping) to an `Approximation` using the information provided. Args: approx: |@doc:result.init.approx| Collection of approximations found during the minimization process such as gradient and hessian. |@docend:result.init.approx| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| info: |@doc:result.init.info| Additional information (if available) such as *number of gradient function calls* or the original minimizer return message. This is a relatively free field and _no single field_ in it is guaranteed to be stable. Some recommended fields: - *original*: contains the original returned object by the minimizer used internally. - *optimizer*: the actual instance of the wrapped optimizer (if available) |@docend:result.init.info| params: |@doc:result.init.params| Result of the fit where each :py:class:`~zfit.Parameter` key has the value from the minimum found by the minimizer. |@docend:result.init.params| Returns: The created approximation. """ approx = {} if approx is None else approx if isinstance(approx, collections.abc.Mapping): if "params" not in approx: approx["params"] = params if info: if "gradient" not in approx: approx["gradient"] = info.get("grad", info.get("gradient")) if "hessian" not in approx: approx["hessian"] = info.get( "hess", info.get("hesse", info.get("hessian")) ) if "inv_hessian" not in approx: approx["inv_hessian"] = info.get( "inv_hess", info.get("inv_hesse", info.get("inv_hessian")) ) if evaluator is not None: if "gradient" not in approx: approx["gradient"] = evaluator.last_gradient if "hessian" not in approx: approx["hessian"] = evaluator.last_hessian approx = Approximations(**approx) return approx def _input_convert_params(self, params): return ParamHolder((p, {"value": v}) for p, v in params.items()) def _check_get_uncached_params(self, params, method_name, cl): uncached = [] for p in params: errordict = self.params[p].get(method_name) # cl is < 1 and gets very close. The closer, the more it matters -> scale tolerance by it if errordict is not None and not math.isclose( errordict["cl"], cl, abs_tol=3e-3 * (1 - cl) ): raise NameError( f"Error with name {method_name} already exists in {repr(self)} with a different" f" convidence level of {errordict['cl']} instead of the requested {cl}." f" Use a different name.", ) else: uncached.append(p) return uncached def _create_minuit_instance(self): minuit = self._cache_minuit from zfit.minimizers.minimizer_minuit import Minuit if minuit is None: if isinstance(self.minimizer, Minuit): minuit = self.minimizer._minuit_minimizer else: minimizer = Minuit( tol=self.minimizer.tol, verbosity=0, name="ZFIT_TMP_UNCERTAINITIES" ) minuit, _, _ = minimizer._make_minuit( loss=self.loss, params=self.params, init=self ) self._cache_minuit = minuit return minuit
[docs] @classmethod def from_ipopt( cls, loss: ZfitLoss, params: Iterable[ZfitParameter], problem: "ipyopt.Problem", minimizer: "zfit.minimize.IpyoptV1", valid: bool, values: np.ndarray, message: str | None, converged: bool | None, edm: zfit.minimizers.termination.CriterionNotAvailable | float, niter: int | None, fminopt: float | None, status: int | None, criterion: zfit.minimizers.termination.ConvergenceCriterion, evaluator: zfit.minimizers.evaluation.LossEval | None, ) -> FitResult: """Create a ``FitResult`` from an ipopt minimization. Args: loss: |@doc:result.init.loss| The loss function that was minimized. Usually, but not necessary, contains also the pdf, data and constraints. |@docend:result.init.loss| params: |@doc:result.init.params| Result of the fit where each :py:class:`~zfit.Parameter` key has the value from the minimum found by the minimizer. |@docend:result.init.params| problem: |@doc:result.init.problem||@docend:result.init.problem| minimizer: |@doc:result.init.minimizer| Minimizer that was used to obtain this ``FitResult`` and will be used to calculate certain errors. If the minimizer is state-based (like "iminuit"), then this is a copy and the state of other ``FitResults`` or of the *actual* minimizer that performed the minimization won't be altered. |@docend:result.init.minimizer| valid: |@doc:result.init.valid| Indicating whether the result is valid or not. This is the strongest indication and serves as the global flag. The reasons why a result may be invalid can be arbitrary, including but not exclusive: - parameter(s) at the limit - maxiter reached without proper convergence - the minimizer maybe even converged but it is known that this is only a local minimum To indicate the reason for the invalidity, pass a message. |@docend:result.init.valid| values: |@doc:result.init.values| Values of the parameters at the found minimum. |@docend:result.init.values| message: |@doc:result.init.message| Human-readable message to indicate the reason if the fitresult is not valid. If the fit is valid, the message (should) be an empty string (or None), otherwise, it should denote the reason for the invalidity. |@docend:result.init.message| converged: |@doc:result.init.converged| Whether the fit has successfully converged or not. The result itself can still be an invalid minimum such as if the parameters are at or close to the limits or in case another minimum is found. |@docend:result.init.converged| edm: |@doc:result.init.edm| The estimated distance to minimum which is the criterion value at the minimum. |@docend:result.init.edm| niter: |@doc:result.init.niter| Approximate number of iterations ~= number of function evaluations ~= number of gradient evaluations. This is an approximated value and the exact meaning can differ between different minimizers. |@docend:result.init.niter| fminopt: |@doc:result.init.fmin| Value of the function at the minimum. |@docend:result.init.fmin| status: |@doc:result.init.status| A status code (if available) that describes the minimization termination. 0 means a valid termination. |@docend:result.init.status| criterion: |@doc:result.init.criterion| Criterion that was used during the minimization. This determines the estimated distance to the minimum (edm) |@docend:result.init.criterion| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| Returns: ``zfit.minimize.FitResult``: """ info = {"problem": problem} params = dict(zip(params, values)) valid = valid if converged is None else valid and converged if evaluator is not None: valid = valid and not evaluator.maxiter_reached return cls( params=params, loss=loss, fminopt=fminopt, edm=edm, message=message, criterion=criterion, info=info, valid=valid, converged=converged, niter=niter, status=status, minimizer=minimizer, evaluator=evaluator, )
[docs] @classmethod def from_minuit( cls, loss: ZfitLoss, params: Iterable[ZfitParameter], minuit: iminuit.Minuit, minimizer: ZfitMinimizer | iminuit.Minuit, valid: bool | None, values: np.ndarray | None = None, message: str | None = None, converged: bool | None = None, edm: None | (zfit.minimizers.termination.CriterionNotAvailable | float) = None, niter: int | None = None, fminopt: float | None = None, status: int | None = None, criterion: zfit.minimizers.termination.ConvergenceCriterion | None = None, evaluator: zfit.minimizers.evaluation.LossEval | None = None, ) -> FitResult: """Create a `FitResult` from a :py:class:`~iminuit.util.MigradResult` returned by :py:meth:`iminuit.Minuit.migrad` and a iminuit :py:class:`~iminuit.Minuit` instance with the corresponding zfit objects. Args: loss: zfit Loss that was minimized. params: Iterable of the zfit parameters that were floating during the minimization. minuit: Return value of the iminuit migrad command, the instance of :class:`iminuit.Minuit` minimizer: Instance of the zfit Minuit minimizer that was used to minimize the loss. valid: |@doc:result.init.valid| Indicating whether the result is valid or not. This is the strongest indication and serves as the global flag. The reasons why a result may be invalid can be arbitrary, including but not exclusive: - parameter(s) at the limit - maxiter reached without proper convergence - the minimizer maybe even converged but it is known that this is only a local minimum To indicate the reason for the invalidity, pass a message. |@docend:result.init.valid| values: |@doc:result.init.values| Values of the parameters at the found minimum. |@docend:result.init.values| message: |@doc:result.init.message| Human-readable message to indicate the reason if the fitresult is not valid. If the fit is valid, the message (should) be an empty string (or None), otherwise, it should denote the reason for the invalidity. |@docend:result.init.message| converged: |@doc:result.init.converged| Whether the fit has successfully converged or not. The result itself can still be an invalid minimum such as if the parameters are at or close to the limits or in case another minimum is found. |@docend:result.init.converged| edm: |@doc:result.init.edm| The estimated distance to minimum which is the criterion value at the minimum. |@docend:result.init.edm| niter: |@doc:result.init.niter| Approximate number of iterations ~= number of function evaluations ~= number of gradient evaluations. This is an approximated value and the exact meaning can differ between different minimizers. |@docend:result.init.niter| fminopt: |@doc:result.init.fmin| Value of the function at the minimum. |@docend:result.init.fmin| status: |@doc:result.init.status| A status code (if available) that describes the minimization termination. 0 means a valid termination. |@docend:result.init.status| criterion: |@doc:result.init.criterion| Criterion that was used during the minimization. This determines the estimated distance to the minimum (edm) |@docend:result.init.criterion| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| Returns: ``zfit.minimize.FitResult``: A `FitResult` as if zfit Minuit was used. """ from .minimizer_minuit import Minuit from .termination import EDM if not isinstance(minimizer, Minuit): if isinstance(minimizer, iminuit.Minuit): minimizer_new = Minuit() minimizer_new._minuit_minimizer = minimizer minimizer = minimizer_new else: raise ValueError( f"Minimizer {minimizer} not supported. Use `Minuit` from zfit or from iminuit." ) params_result = [p_dict for p_dict in minuit.params] fmin_object = minuit.fmin minuit_converged = not fmin_object.is_above_max_edm converged = ( minuit_converged if converged is None else (converged and minuit_converged) ) niter = fmin_object.nfcn if niter is None else niter info = { "n_eval": niter, # 'grad': result['jac'], # 'message': result['message'], "minuit": minuit, "original": fmin_object, } if fmin_object.has_covariance: info["inv_hessian"] = np.array(minuit.covariance) edm = fmin_object.edm if edm is None else edm if criterion is None: criterion = EDM(tol=minimizer.tol, loss=loss, params=params) criterion.last_value = edm fminopt = fmin_object.fval if fminopt is None else fminopt minuit_valid = fmin_object.is_valid valid = minuit_valid if valid is None else minuit_valid and valid if evaluator is not None: valid = valid and not evaluator.maxiter_reached if values is None: values = (res.value for res in params_result) params = dict(zip(params, values)) return cls( params=params, edm=edm, fminopt=fminopt, info=info, loss=loss, niter=niter, converged=converged, status=status, message=message, valid=valid, criterion=criterion, minimizer=minimizer, evaluator=evaluator, )
[docs] @classmethod def from_scipy( cls, loss: ZfitLoss, params: Iterable[ZfitParameter], result: scipy.optimize.OptimizeResult, minimizer: ZfitMinimizer, message: str | None, valid: bool, criterion: ConvergenceCriterion, edm: float | None = None, niter: int | None = None, evaluator: zfit.minimize.LossEval | None = None, ) -> FitResult: """Create a ``FitResult from a SciPy `~scipy.optimize.OptimizeResult`. Args: loss: |@doc:result.init.loss| The loss function that was minimized. Usually, but not necessary, contains also the pdf, data and constraints. |@docend:result.init.loss| params: |@doc:result.init.params| Result of the fit where each :py:class:`~zfit.Parameter` key has the value from the minimum found by the minimizer. |@docend:result.init.params| result: Result of the SciPy optimization. minimizer: |@doc:result.init.minimizer| Minimizer that was used to obtain this ``FitResult`` and will be used to calculate certain errors. If the minimizer is state-based (like "iminuit"), then this is a copy and the state of other ``FitResults`` or of the *actual* minimizer that performed the minimization won't be altered. |@docend:result.init.minimizer| message: |@doc:result.init.message| Human-readable message to indicate the reason if the fitresult is not valid. If the fit is valid, the message (should) be an empty string (or None), otherwise, it should denote the reason for the invalidity. |@docend:result.init.message| edm: |@doc:result.init.edm| The estimated distance to minimum which is the criterion value at the minimum. |@docend:result.init.edm| niter: |@doc:result.init.niter| Approximate number of iterations ~= number of function evaluations ~= number of gradient evaluations. This is an approximated value and the exact meaning can differ between different minimizers. |@docend:result.init.niter| valid: |@doc:result.init.valid| Indicating whether the result is valid or not. This is the strongest indication and serves as the global flag. The reasons why a result may be invalid can be arbitrary, including but not exclusive: - parameter(s) at the limit - maxiter reached without proper convergence - the minimizer maybe even converged but it is known that this is only a local minimum To indicate the reason for the invalidity, pass a message. |@docend:result.init.valid| criterion: |@doc:result.init.criterion| Criterion that was used during the minimization. This determines the estimated distance to the minimum (edm) |@docend:result.init.criterion| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| Returns: `zfit.minimize.FitResult`: """ result_values = result["x"] if niter is None: niter = result.get("nit") converged = result.get("success", valid) status = result["status"] if message is None and (not converged or not valid): message = result.get("message") grad = result.get("grad") info = { "n_eval": result["nfev"], "n_iter": niter, "niter": niter, "grad": result.get("jac") if grad is None else grad, "message": message, "evaluator": evaluator, "original": result, } approx = dict( params=params, gradient=info.get("grad"), ) if info.get("niter", 0) > 25: # unreliable if too few iterations, fails for EDM inv_hesse = result.get("hess_inv") if isinstance(inv_hesse, LbfgsInvHessProduct): inv_hesse = inv_hesse.todense() hesse = info.get("hesse") info["inv_hesse"] = inv_hesse info["hesse"] = hesse approx["hessian"] = hesse approx["inv_hessian"] = inv_hesse fminopt = result["fun"] params = dict(zip(params, result_values)) if evaluator is not None: valid = valid and not evaluator.maxiter_reached fitresult = cls( params=params, edm=edm, fminopt=fminopt, info=info, approx=approx, converged=converged, status=status, message=message, valid=valid, niter=niter, loss=loss, minimizer=minimizer, criterion=criterion, evaluator=evaluator, ) return fitresult
[docs] @classmethod def from_nlopt( cls, loss: ZfitLoss, opt, params: Iterable[ZfitParameter], minimizer: ZfitMinimizer | iminuit.Minuit, valid: bool | None, values: np.ndarray | None = None, message: str | None = None, converged: bool | None = None, edm: None | (zfit.minimizers.termination.CriterionNotAvailable | float) = None, niter: int | None = None, fminopt: float | None = None, status: int | None = None, criterion: zfit.minimizers.termination.ConvergenceCriterion | None = None, evaluator: zfit.minimizers.evaluation.LossEval | None = None, inv_hessian: np.ndarray | None = None, hessian: np.ndarray | None = None, ) -> FitResult: """Create a ``FitResult`` from an NLopt optimizer. Args: loss: |@doc:result.init.loss| The loss function that was minimized. Usually, but not necessary, contains also the pdf, data and constraints. |@docend:result.init.loss| opt: Optimizer instance of NLopt params: |@doc:result.init.params| Result of the fit where each :py:class:`~zfit.Parameter` key has the value from the minimum found by the minimizer. |@docend:result.init.params| minimizer: |@doc:result.init.minimizer| Minimizer that was used to obtain this ``FitResult`` and will be used to calculate certain errors. If the minimizer is state-based (like "iminuit"), then this is a copy and the state of other ``FitResults`` or of the *actual* minimizer that performed the minimization won't be altered. |@docend:result.init.minimizer| valid: |@doc:result.init.valid| Indicating whether the result is valid or not. This is the strongest indication and serves as the global flag. The reasons why a result may be invalid can be arbitrary, including but not exclusive: - parameter(s) at the limit - maxiter reached without proper convergence - the minimizer maybe even converged but it is known that this is only a local minimum To indicate the reason for the invalidity, pass a message. |@docend:result.init.valid| values: |@doc:result.init.values| Values of the parameters at the found minimum. |@docend:result.init.values| message: |@doc:result.init.message| Human-readable message to indicate the reason if the fitresult is not valid. If the fit is valid, the message (should) be an empty string (or None), otherwise, it should denote the reason for the invalidity. |@docend:result.init.message| converged: |@doc:result.init.converged| Whether the fit has successfully converged or not. The result itself can still be an invalid minimum such as if the parameters are at or close to the limits or in case another minimum is found. |@docend:result.init.converged| edm: |@doc:result.init.edm| The estimated distance to minimum which is the criterion value at the minimum. |@docend:result.init.edm| niter: |@doc:result.init.niter| Approximate number of iterations ~= number of function evaluations ~= number of gradient evaluations. This is an approximated value and the exact meaning can differ between different minimizers. |@docend:result.init.niter| fminopt: |@doc:result.init.fmin| Value of the function at the minimum. |@docend:result.init.fmin| status: |@doc:result.init.status| A status code (if available) that describes the minimization termination. 0 means a valid termination. |@docend:result.init.status| criterion: |@doc:result.init.criterion| Criterion that was used during the minimization. This determines the estimated distance to the minimum (edm) |@docend:result.init.criterion| evaluator: |@doc:result.init.evaluator| Loss evaluator that was used during the minimization and that may contain information about the last evaluations of the gradient etc. which can serve as approximations. |@docend:result.init.evaluator| inv_hessian: The (approximated) inverse hessian matrix. hessian: The (approximated) hessian matrix. Returns: zfit.minimizers.fitresult.FitResult: """ converged = converged if converged is None else bool(converged) param_dict = {p: v for p, v in zip(params, values)} if fminopt is None: fminopt = opt.last_optimum_value() status_nlopt = opt.last_optimize_result() if status is None: status = status_nlopt niter = opt.get_numevals() if niter is None else niter converged = 1 <= status_nlopt <= 4 and converged is not False messages = { 1: "NLOPT_SUCCESS", 2: "NLOPT_STOPVAL_REACHED", 3: "NLOPT_FTOL_REACHED", 4: "NLOPT_XTOL_REACHED", 5: "NLOPT_MAXEVAL_REACHED", 6: "NLOPT_MAXTIME_REACHED", -1: "NLOPT_FAILURE", -2: "NLOPT_INVALID_ARGS", -3: "NLOPT_OUT_OF_MEMORY", -4: "NLOPT_ROUNDOFF_LIMITED", -5: "NLOPT_FORCED_STOP", } message_nlopt = messages[status_nlopt] info = { "n_eval": niter, "niter": niter, "message": message_nlopt, "original": status, "evaluator": evaluator, "status": status, } if message is None: message = message_nlopt valid = valid and converged if evaluator is not None: valid = valid and not evaluator.maxiter_reached approx = {} if inv_hessian is None: if hessian is None and evaluator is not None: hessian = evaluator.last_hessian # if hessian is not None: # TODO: remove? # inv_hessian = np.linalg.inv(hessian) if inv_hessian is not None: info["inv_hesse"] = inv_hessian approx["inv_hessian"] = inv_hessian return cls( params=param_dict, edm=edm, fminopt=fminopt, status=status, converged=converged, info=info, niter=niter, valid=valid, loss=loss, minimizer=minimizer, criterion=criterion, message=message, evaluator=evaluator, )
@property def approx(self) -> Approximations: return self._approx @property def params( self, ) -> Mapping[ZfitIndependentParameter, Mapping[str, Mapping[str, object]]]: return self._params @property def values(self) -> Mapping[str | ZfitParameter, float]: return self._values @property def criterion(self) -> ConvergenceCriterion: return self._criterion @property def message(self) -> str: return self._message @property def edm(self) -> float: """The estimated distance to the minimum. Returns: Numeric """ return self._edm @property def minimizer(self) -> ZfitMinimizer: return self._minimizer @property def loss(self) -> ZfitLoss: # TODO(Mayou36): this is currently a reference, should be a copy of the loss? return self._loss @property def fminopt(self) -> float: """Function value with possible optimizations at the minimum, namely constant subtraction. Returns: Numeric """ return self._fminopt @property def fmin(self) -> float: """Function value, fully evaluated, at the minimum. Returns: Numeric """ return self._fmin @property @deprecated( None, "Use `fmin` instead which now returns the full minimum value. This will be removed in the future.", ) def fminfull(self) -> float: """Function value, fully evaluated, at the minimum. Returns: Numeric """ return self.fmin @property def status(self): return self._status @property def info(self) -> Mapping[str, object]: return self._info @property def converged(self) -> bool: return self._converged @property def valid(self) -> bool: return self._valid and not self.params_at_limit and self.converged @property def params_at_limit(self) -> bool: return self._params_at_limit @contextlib.contextmanager def _input_check_reset_params(self, params): params = self._input_check_params(params=params) old_values = run(params) try: yield params except Exception: warnings.warn( "Exception occurred, parameter values are not reset and in an arbitrary, last" " used state. If this happens during normal operation, make sure you reset the values.", RuntimeWarning, ) raise set_values( params=params, values=old_values, allow_partial=True ) # TODO: or set? def _input_check_params(self, params): if params is not None: params = convert_to_container(params) else: params = list(self.params.keys()) return params
[docs] @deprecated_args(None, "Use `name` instead", "error_name") def hesse( self, params: ParamsTypeOpt = None, method: str | Callable = None, cl: float | None = None, name: str | bool | None = None, # DEPRECATED error_name: str | None = None, ) -> dict[ZfitIndependentParameter, dict]: r"""Calculate for `params` the symmetric error using the Hessian/covariance matrix. This method estimates the covariance matric using the inverse of the Hessian matrix. The assumption is that the loss profile - usually a likelihood or a :math:\chi^2 - is hyperbolic. This is usually the case for fits with many observations, i.e. it is exact in the asymptotic limit. If the loss profile is not hyperbolic, another method, "zfit_error" or "minuit_minos" should be used. **Weights** Weighted likelihoods are a special class of likelihoods as they are not an actual likelihood. However, the minimum is still valid, however the profile is not a proper likelihood. Therefore, corrections will be automatically applied to the Hessian uncertainty estimation in order to correct for the effects in the weights. The corrections used are "asymptotically correct" and are described in `Parameter uncertainties in weighted unbinned maximum likelihood fits`<https://doi.org/10.1140/epjc/s10052-022-10254-8>` by Christoph Langenbruch. Since this method uses the jacobian matrix, it takes significantly longer to calculate than witout weights. Args: params: The parameters to calculate the Hessian symmetric error. If None, use all parameters. method: the method to calculate the covariance matrix. Can be {'minuit_hesse', 'hesse_np', 'approx'} or a callable. cl: Confidence level for the error. If None, use the default value of 0.68. name: The name for the error in the dictionary. This will be added to the information collected in params under ``params[p][name]`` where p is a Parameter. If the name is `False`, it won't be added and only returned. Defaulst to `'hesse'`. Returns: Result of the hessian (symmetric) error as dict with each parameter holding the error dict {'error': sym_error}. So given param_a (from zfit.Parameter(.)) `error_a = result.hesse(params=param_a)[param_a]['error']` error_a is the hessian error. """ # for compatibility with `errors` cl = 0.68268949 if cl is None else cl # scipy.stats.chi2(1).cdf(1) if cl >= 1: raise ValueError(f"cl is the confidence limit and has to be < 1, not {cl}") if method is None: # LEGACY START method = self._default_hesse from zfit.minimizers.minimizer_minuit import Minuit if isinstance(self.minimizer, Minuit): method = "minuit_hesse" # LEGACY END # Deprecated name if error_name is not None: name = error_name name_warning_triggered = False if name is None: if not isinstance(method, str): raise ValueError("Need to specify `name` or use a string as `method`") message = ( "Default name of hesse (which is currently the method name such as `minuit_hesse`" "or `hesse_np`) has changed to `hesse` (it still adds the old one as well. This will" " be removed in the future). " "INSTRUCTIONS: to stay compatible, " " change wherever you access the error to 'hesse' (if you don't explicitly specify the name" " in hesse(...)." ) warn_changed_feature(message, "hesse_name") name_warning_triggered = True name = "hesse" with self._input_check_reset_params(params) as params: uncached_params = self._check_get_uncached_params( params=params, method_name=name, cl=cl ) if uncached_params: error_dict = self._hesse(params=uncached_params, method=method, cl=cl) if any(val["error"] is None for val in error_dict.values()): return {} for p in error_dict: error_dict[p]["cl"] = cl if name: self._cache_errors(name=name, errors=error_dict) else: error_dict = {} error_dict.update( {p: self.params[p][name] for p in params if p not in uncached_params} ) if name_warning_triggered: error_dict.update( {p: self.params[p][method] for p in params if p not in uncached_params} ) return {p: error_dict[p] for p in params}
def _cache_errors(self, name, errors): for param, error in errors.items(): self.params[param][name] = error def _hesse(self, params, method, cl): pseudo_sigma = scipy.stats.chi2(1).ppf(cl) ** 0.5 covariance_dict = self.covariance(params, method, as_dict=True) return { p: { "error": ( float(covariance_dict[(p, p)]) ** 0.5 * pseudo_sigma if covariance_dict[(p, p)] is not None else None ) } for p in params }
[docs] def error( self, params: ParamsTypeOpt = None, method: str | Callable = None, error_name: str = None, sigma: float = 1.0, ) -> OrderedDict: r""".. deprecated:: unknown Use :func:`errors` instead. Args: params: The parameters or their names to calculate the errors. If `params` is `None`, use all *floating* parameters. method: The method to use to calculate the errors. Valid choices are {'minuit_minos'} or a Callable. sigma: Errors are calculated with respect to `sigma` std deviations. The definition of 1 sigma depends on the loss function and is defined there. For example, the negative log-likelihood (without the factor of 2) has a correspondents of :math:`\Delta` NLL of 1 corresponds to 1 std deviation. error_name: The name for the error in the dictionary. Returns: A `OrderedDict` containing as keys the parameter names and as value a `dict` which contains (next to probably more things) two keys 'lower' and 'upper', holding the calculated errors. Example: result['par1']['upper'] -> the asymmetric upper error of 'par1' """ warnings.warn( "`error` is deprecated, use `errors` instead. This will return not only the errors but also " "(a possible) new FitResult if a minimum was found. So change" "errors = result.error()" "to" "errors, new_res = result.errors()", DeprecationWarning, ) return self.errors(params=params, method=method, name=error_name, sigma=sigma)[ 0 ]
[docs] @deprecated_args(None, "Use name instead.", "error_name") def errors( self, params: ParamsTypeOpt = None, method: str | Callable = None, name: str = None, cl: float | None = None, sigma=None, error_name: str = None, ) -> tuple[OrderedDict, None | FitResult]: r"""Calculate and set for `params` the asymmetric error using the set error method. Args: params: The parameters or their names to calculate the errors. If `params` is `None`, use all *floating* parameters. method: The method to use to calculate the errors. Valid choices are {'minuit_minos', 'zfit_errors'} or a Callable. cl: Uncertainties are calculated with respect to the confidence level cl. The default is 68.3%. For example, the negative log-likelihood (without the factor of 2) has a correspondents of :math:`\Delta` NLL of 1 corresponds to 1 std deviation. sigma: Errors are calculated with respect to `sigma` std deviations. The definition of 1 sigma depends on the loss function and is defined there. name: The name for the error in the dictionary. Defaults to `errors` Returns: A `OrderedDict` containing as keys the parameter and as value a `dict` which contains (next to often more things) two keys 'lower' and 'upper', holding the calculated errors. Furthermore, it has `cl` to indicate the convidence level the uncertainty was calculated with. Example: result[par1]['upper'] -> the asymmetric upper error of 'par1' """ # Deprecated name if error_name is not None: name = error_name if sigma is not None: if cl is not None: raise ValueError("Cannot define sigma and cl, use only one.") else: cl = scipy.stats.chi2(1).cdf(sigma) if cl is None: cl = 0.68268949 # scipy.stats.chi2(1).cdf(1) if method is None: # TODO: legacy, remove 0.6 from zfit.minimize import Minuit if isinstance(self.minimizer, Minuit): method = "minuit_minos" warnings.warn( "'minuit_minos' will be changed as the default errors method to a custom implementation" "with the same functionality. If you want to make sure that 'minuit_minos' will be used " "in the future, add it explicitly as in `errors(method='minuit_minos')`", FutureWarning, ) else: method = self._default_error name_warning_triggered = False if name is None: if not isinstance(method, str): raise ValueError("Need to specify `name` or use a string as `method`") message = ( "Default name of errors (which is currently the method name such as `minuit_minos`" "or `zfit_errors`) has changed to `errors`. Old names are still added as well for compatibility" " but will be removed in the future. " "INSTRUCTIONS: to stay compatible," " change wherever you access the error to 'errors' or specify the name explicitly in" " errors(...)." ) warn_changed_feature(message, "errors_name") name_warning_triggered = True name = "errors" if method == "zfit_error": warnings.warn( "'zfit_error' is still rather new. If it fails, please report it here:" " https://github.com/zfit/zfit/issues/new?assignees=&labels=bug&template" "=bug_report.md&title=zfit%20error%20fails.", ExperimentalFeatureWarning, stacklevel=2, ) params = self._input_check_params(params) with self._input_check_reset_params(self.params.keys()): uncached_params = self._check_get_uncached_params( params=params, method_name=name, cl=cl ) new_result = None if uncached_params: error_dict, new_result = self._error( params=uncached_params, method=method, cl=cl ) for p in error_dict: error_dict[p]["cl"] = cl self._cache_errors(name=name, errors=error_dict) if new_result is not None: msg = "Invalid, a new minimum was found." self._cache_errors(name=name, errors={p: msg for p in params}) self._valid = False self._message = msg new_result._cache_errors(name=name, errors=error_dict) all_errors = {p: self.params[p][name] for p in params} if name_warning_triggered: self._cache_errors(name=method, errors=error_dict) return all_errors, new_result
def _error(self, params, method, cl): if not callable(method): try: method = self._error_methods[method] except KeyError: raise KeyError( f"The following method is not a valid, implemented method: {method}. Use one of {self._error_methods.keys()}" ) return method(result=self, params=params, cl=cl)
[docs] def covariance( self, params: ParamsTypeOpt = None, method: str | Callable = None, as_dict: bool = False, ): """Calculate the covariance matrix for `params`. Args: params: The parameters to calculate the covariance matrix. If `params` is `None`, use all *floating* parameters. method: The method to use to calculate the covariance matrix. Valid choices are {'minuit_hesse', 'hesse_np'} or a Callable. as_dict: Default `False`. If `True` then returns a dictionnary. Returns: 2D `numpy.array` of shape (N, N); `dict`(param1, param2) -> covariance if `as_dict == True`. """ if method is None: # LEGACY START method = self._default_hesse from zfit.minimizers.minimizer_minuit import Minuit if isinstance(self.minimizer, Minuit): method = "minuit_hesse" # LEGACY END if method not in self._covariance_dict: with self._input_check_reset_params(params) as params: self._covariance_dict[method] = self._covariance(method=method) params = self._input_check_params(params) covariance = { k: self._covariance_dict[method].get(k) for k in itertools.product(params, params) } if as_dict: return covariance else: return dict_to_matrix(params, covariance)
def _covariance(self, method): if not callable(method): if method not in self._hesse_methods: raise KeyError( f"The following method is not a valid, implemented method: {method}. Use one of {self._hesse_methods.keys()}" ) method = self._hesse_methods[method] params = list(self.params.keys()) if any( isinstance(data, ZfitData) and data.weights is not None for data in self.loss.data ): return covariance_with_weights(method=method, result=self, params=params) else: return method(result=self, params=params)
[docs] def correlation( self, params: ParamsTypeOpt = None, method: str | Callable = None, as_dict: bool = False, ): """Calculate the correlation matrix for `params`. Args: params: The parameters to calculate the correlation matrix. If `params` is `None`, use all *floating* parameters. method: The method to use to calculate the correlation matrix. Valid choices are {'minuit_hesse', 'hesse_np'} or a Callable. as_dict: Default `False`. If `True` then returns a dictionnary. Returns: 2D `numpy.array` of shape (N, N); `dict`(param1, param2) -> correlation if `as_dict == True`. """ covariance = self.covariance(params=params, method=method, as_dict=False) correlation = covariance_to_correlation(covariance) if as_dict: params = self._input_check_params(params) return matrix_to_dict(params, correlation) else: return correlation
[docs] def freeze(self): """Freeze the result to make it pickleable and convert all TensorFlow elements to names (parameters) or arrays. After this, no more uncertainties or covariances can be calculated. The already calculated ones remain however. Parameters can be accessed by their string name. """ self._loss = self.loss.name self._minimizer = self.minimizer.name self._criterion = self.criterion.name self._evaluator = None self.approx.freeze() self._covariance_dict = { k: {(p[0].name, p[1].name): v for p, v in d.items()} for k, d in self._covariance_dict.items() } self._values = ValuesHolder({p.name: self.values[p] for p in self.params}) self._params = ParamHolder({k.name: v for k, v in self.params.items()}) if "minuit" in self.info: self.info["minuit"] = "Minuit_frozen" if "problem" in self.info: try: import ipyopt except ImportError: pass else: if isinstance(self.info["problem"], ipyopt.Problem): self.info["problem"] = "ipyopt_frozen" if "evaluator" in self.info: self.info["evaluator"] = "evaluator_frozen" self._cache_minuit = None
def __str__(self): string = ( Style.BRIGHT + "FitResult" + Style.NORMAL + f" of\n{self.loss} \nwith\n{self.minimizer}\n\n" ) string += tabulate( [ [ color_on_bool(self.valid), color_on_bool(self.converged, on_true=False), color_on_bool( self.params_at_limit, on_true=colored.bg(9), on_false=False ), format_value(self.edm, highprec=False), f" {self._fmin:.2f} | {format_value(self.fminopt)}", ] ], [ "valid", "converged", "param at limit", "edm", "approx. fmin (full | opt.)", ], tablefmt="fancy_grid", disable_numparse=True, colalign=["center", "center", "center", "center", "right"], ) string += "\n\n" + Style.BRIGHT + "Parameters\n" + Style.NORMAL string += str(self.params) return string def _repr_pretty_(self, p, cycle): if cycle: p.text(self.__repr__()) return p.text(self.__str__())
def covariance_to_correlation(covariance): diag = np.diag(1 / np.diag(covariance) ** 0.5) return np.matmul(diag, np.matmul(covariance, diag)) def format_value(value, highprec=True): m_error_class = ( iminuit.util.MError ) # if iminuit is not available (maybe in the future?), use dict instead if isinstance(value, dict) and "error" in value: value = value["error"] value = f"{value:> 6.2g}" value = f'+/-{" " * (8 - len(value))}' + value if isinstance(value, m_error_class) or ( isinstance(value, dict) and "lower" in value and "upper" in value ): if isinstance(value, m_error_class): lower = value.lower upper = value.upper else: lower = value["lower"] upper = value["upper"] lower_sign = f"{np.sign(lower): >+}"[0] upper_sign = f"{np.sign(upper): >+}"[0] lower, upper = f"{np.abs(lower): >6.2g}", f"{upper: >6.2g}" lower = lower_sign + " " * (7 - len(lower)) + lower upper = upper_sign + " " * (7 - len(upper)) + upper # lower += " t" * (11 - len(lower)) value = lower + " " * 3 + upper if isinstance(value, float): if highprec: value = f"{value:> 6.7g}" else: value = f"{value:> 6.2g}" return value def color_on_bool(value, on_true=colored.bg(10), on_false=colored.bg(9)): if not value and on_false: value_add = on_false elif value and on_true: value_add = on_true else: value_add = "" value = value_add + str(value) + Style.RESET_ALL return value class ListWithKeys(collections.UserList): __slots__ = ("_initdict",) def __init__(self, initdict) -> None: super().__init__(initlist=initdict.values()) self._initdict = initdict def __getitem__(self, item): if isinstance(item, ZfitParameter): return self._initdict[item] return super().__getitem__(item) def keys(self): return self._initdict.keys() class ValuesHolder(NameToParamGetitem, ListWithKeys): __slots__ = () class ParamHolder(NameToParamGetitem, collections.UserDict): def __str__(self) -> str: order_keys = ["value", "hesse"] keys = OrderedSet() for pdict in self.values(): keys.update(OrderedSet(pdict)) order_keys = OrderedSet([key for key in order_keys if key in keys]) order_keys.update(keys) rows = [] for param, pdict in self.items(): name = param.name if isinstance(param, ZfitParameter) else param row = [name] row.extend(format_value(pdict.get(key, " ")) for key in order_keys) if isinstance(param, ZfitParameter): row.append( color_on_bool( run(param.at_limit), on_true=colored.bg("light_red"), on_false=False, ) ) rows.append(row) order_keys = ["name"] + list(order_keys) + ["at limit"] order_keys[order_keys.index("value")] = "value (rounded)" table = tabulate( rows, order_keys, numalign="right", stralign="right", colalign=("left",) ) return table