<reponame>LukasSp/pyPESTO<filename>pypesto/objective/history.py
import numpy as np
import pandas as pd
import time
import os
from .constants import MODE_FUN, MODE_RES, FVAL, GRAD, HESS, RES, SRES
from .util import res_to_chi2, sres_to_schi2
from .options import ObjectiveOptions


class ObjectiveHistory:
    """
    Objective call history. Also handles saving of intermediate results.

    Parameteters
    ------------

    options: ObjectiveOptions, optional
        Values needed for creating a history are extracted.

    Attributes
    ----------

    n_fval, n_grad, n_hess, n_res, n_sres: int
        Counters of function values, gradients and hessians,
        residuals and residual sensitivities.

    trace: list
        List containing history of function values and parameters if
        options.tr_record is True.

    start_time: float
        Reference start time.

    fval0, fval_min: float
        Initial and best function value found.

    x0, x_min: np.ndarray
        Initial and best parameters found.

    index: str
        Id identifying the history object when called in a multistart
        setting.
    """

    def __init__(self, options=None):

        if options is None:
            options = ObjectiveOptions()
        self.options = options

        self.n_fval = None
        self.n_grad = None
        self.n_hess = None
        self.n_res = None
        self.n_sres = None

        self.trace = None
        self.start_time = None

        self.fval_min = None
        self.x_min = None
        self.fval0 = None
        self.x0 = None

        self.index = None

        self.reset()

    def reset(self, index=None):
        """
        Reset all counters, the trace, and start the timer, and create
        directory for trace file.
        """
        self.n_fval = 0
        self.n_grad = 0
        self.n_hess = 0
        self.n_res = 0
        self.n_sres = 0

        self.trace = None
        self.start_time = time.time()

        self.fval0 = None
        self.x0 = None
        self.fval_min = np.inf
        self.x_min = None

        self.index = index

        # create trace file dirs
        if self.options.trace_file is not None:
            dirname = os.path.dirname(self.options.trace_file)
            if not os.path.exists(dirname):
                os.makedirs(dirname)

    def update(self, x, sensi_orders, mode, result):
        """
        Update the history.

        Parameters
        ----------
        x: np.ndarray
            The current parameter.
        sensi_orders: tuple
            The sensitivity orders.
        mode: str
            As in constants.MODE_.
        result: dict
            The result for x.
        """
        self._update_counts(sensi_orders, mode)
        self._update_trace(x, sensi_orders, mode, result)
        self._update_vals(x, sensi_orders, mode, result)

    def finalize(self):
        """
        Save the trace to file if options.trace_save is True.
        """
        self._save_trace(finalize=True)

    def _update_counts(self, sensi_orders, mode):
        """
        Update the counters.
        """
        if mode == MODE_FUN:
            if 0 in sensi_orders:
                self.n_fval += 1
            if 1 in sensi_orders:
                self.n_grad += 1
            if 2 in sensi_orders:
                self.n_hess += 1
        elif mode == MODE_RES:
            if 0 in sensi_orders:
                self.n_res += 1
            if 1 in sensi_orders:
                self.n_sres += 1

    def _update_trace(self, x, sensi_orders, mode, result):
        """
        Update and possibly store the trace.
        """

        if not self.options.trace_record:
            return

        # init trace
        if self.trace is None:
            columns = ['time',
                       'n_fval', 'n_grad', 'n_hess', 'n_res', 'n_sres',
                       'fval', 'grad', 'hess', 'res', 'sres', 'chi2', 'schi2',
                       'x']
            self.trace = pd.DataFrame(columns=columns)

        # extract function values
        if mode == MODE_FUN:
            fval = None if 0 not in sensi_orders \
                else result.get(FVAL, None)
            grad = None if not self.options.trace_record_grad \
                or 1 not in sensi_orders \
                else result.get(GRAD, None)
            hess = None if not self.options.trace_record_hess \
                or 2 not in sensi_orders \
                else result.get(HESS, None)
            res = None
            sres = None
            chi2 = None
            schi2 = None
        else:  # mode == MODE_RES
            fval = None
            grad = None
            hess = None
            res_result = result.get(RES, None)
            sres_result = result.get(SRES, None)
            res = None if not self.options.trace_record_res \
                or 0 not in sensi_orders \
                else res_result
            sres = None if not self.options.trace_record_sres \
                or 1 not in sensi_orders \
                else sres_result
            chi2 = None if not self.options.trace_record_chi2 \
                or 0 not in sensi_orders \
                else res_to_chi2(res_result)
            schi2 = None if not self.options.trace_record_schi2 \
                or 0 not in sensi_orders or 1 not in sensi_orders \
                else sres_to_schi2(res_result, sres_result)

        # check whether to append to trace
        if not self.options.trace_all and fval >= self.fval_min:
            return

        used_time = time.time() - self.start_time

        # create table row
        values = [
            used_time,
            self.n_fval, self.n_grad, self.n_hess, self.n_res, self.n_sres,
            fval, grad, hess, res, sres, chi2, schi2,
            x
        ]

        # append to trace
        self.trace.loc[len(self.trace)] = values

        # save trace to file
        self._save_trace()

    def _save_trace(self, finalize=False):
        """
        Save to file via pd.DataFrame.to_csv() if options.trace_file is
        not None and other conditions apply.
        Format might be revised when storage is implemented.
        """
        if self.options.trace_file is None:
            return

        if finalize \
           or (len(self.trace) > 0 and len(self.trace) %
               self.options.trace_save_iter == 0):
            filename = self.options.trace_file
            if self.index is not None:
                filename = filename.replace("{index}", str(self.index))
            # save
            self.trace.to_csv(filename)

    def _update_vals(self, x, sensi_orders, mode, result):
        """
        Update initial and best function values. Must be called after
        update_trace().
        """

        # update initial point
        if self.fval0 is None and 0 in sensi_orders:
            if mode == MODE_FUN:
                self.fval0 = result[FVAL]
                self.x0 = x
            else:  # mode == MODE_RES:
                self.fval0 = res_to_chi2(result[RES])
                self.x0 = x

        # update best point
        fval = np.inf
        if 0 in sensi_orders:
            if mode == MODE_FUN:
                fval = result[FVAL]
            else:  # mode == MODE_RES:
                fval = res_to_chi2(result[RES])
        if fval < self.fval_min:
            self.fval_min = fval
            self.x_min = x

import numpy as np
import pandas as pd
import time
import os
from .constants import MODE_FUN, MODE_RES, FVAL, GRAD, HESS, RES, SRES
from .util import res_to_chi2, sres_to_schi2
from .options import ObjectiveOptions


class ObjectiveHistory:
    """
    Objective call history. Also handles saving of intermediate results.

    Parameteters
    ------------

    options: ObjectiveOptions, optional
        Values needed for creating a history are extracted.

    Attributes
    ----------

    n_fval, n_grad, n_hess, n_res, n_sres: int
        Counters of function values, gradients and hessians,
        residuals and residual sensitivities.

    trace: list
        List containing history of function values and parameters if
        options.tr_record is True.

    start_time: float
        Reference start time.

    fval0, fval_min: float
        Initial and best function value found.

    x0, x_min: np.ndarray
        Initial and best parameters found.

    index: str
        Id identifying the history object when called in a multistart
        setting.
    """

    def __init__(self, options=None):

        if options is None:
            options = ObjectiveOptions()
        self.options = options

        self.n_fval = None
        self.n_grad = None
        self.n_hess = None
        self.n_res = None
        self.n_sres = None

        self.trace = None
        self.start_time = None

        self.fval_min = None
        self.x_min = None
        self.fval0 = None
        self.x0 = None

        self.index = None

        self.reset()

    def reset(self, index=None):
        """
        Reset all counters, the trace, and start the timer, and create
        directory for trace file.
        """
        self.n_fval = 0
        self.n_grad = 0
        self.n_hess = 0
        self.n_res = 0
        self.n_sres = 0

        self.trace = None
        self.start_time = time.time()

        self.fval0 = None
        self.x0 = None
        self.fval_min = np.inf
        self.x_min = None

        self.index = index

        # create trace file dirs
        if self.options.trace_file is not None:
            dirname = os.path.dirname(self.options.trace_file)
            if not os.path.exists(dirname):
                os.makedirs(dirname)

    def update(self, x, sensi_orders, mode, result):
        """
        Update the history.

        Parameters
        ----------
        x: np.ndarray
            The current parameter.
        sensi_orders: tuple
            The sensitivity orders.
        mode: str
            As in constants.MODE_.
        result: dict
            The result for x.
        """
        self._update_counts(sensi_orders, mode)
        self._update_trace(x, sensi_orders, mode, result)
        self._update_vals(x, sensi_orders, mode, result)

    def finalize(self):
        """
        Save the trace to file if options.trace_save is True.
        """
        self._save_trace(finalize=True)

    def _update_counts(self, sensi_orders, mode):
        """
        Update the counters.
        """
        if mode == MODE_FUN:
            if 0 in sensi_orders:
                self.n_fval += 1
            if 1 in sensi_orders:
                self.n_grad += 1
            if 2 in sensi_orders:
                self.n_hess += 1
        elif mode == MODE_RES:
            if 0 in sensi_orders:
                self.n_res += 1
            if 1 in sensi_orders:
                self.n_sres += 1

    def _update_trace(self, x, sensi_orders, mode, result):
        """
        Update and possibly store the trace.
        """

        if not self.options.trace_record:
            return

        # init trace
        if self.trace is None:
            columns = ['time',
                       'n_fval', 'n_grad', 'n_hess', 'n_res', 'n_sres',
                       'fval', 'grad', 'hess', 'res', 'sres', 'chi2', 'schi2',
                       'x']
            self.trace = pd.DataFrame(columns=columns)

        # extract function values
        if mode == MODE_FUN:
            fval = None if 0 not in sensi_orders \
                else result.get(FVAL, None)
            grad = None if not self.options.trace_record_grad \
                or 1 not in sensi_orders \
                else result.get(GRAD, None)
            hess = None if not self.options.trace_record_hess \
                or 2 not in sensi_orders \
                else result.get(HESS, None)
            res = None
            sres = None
            chi2 = None
            schi2 = None
        else:  # mode == MODE_RES
            fval = None
            grad = None
            hess = None
            res_result = result.get(RES, None)
            sres_result = result.get(SRES, None)
            res = None if not self.options.trace_record_res \
                or 0 not in sensi_orders \
                else res_result
            sres = None if not self.options.trace_record_sres \
                or 1 not in sensi_orders \
                else sres_result
            chi2 = None if not self.options.trace_record_chi2 \
                or 0 not in sensi_orders \
                else res_to_chi2(res_result)
            schi2 = None if not self.options.trace_record_schi2 \
                or 0 not in sensi_orders or 1 not in sensi_orders \
                else sres_to_schi2(res_result, sres_result)

        # check whether to append to trace
        if not self.options.trace_all and fval >= self.fval_min:
            return

        used_time = time.time() - self.start_time

        # create table row
        values = [
            used_time,
            self.n_fval, self.n_grad, self.n_hess, self.n_res, self.n_sres,
            fval, grad, hess, res, sres, chi2, schi2,
            x
        ]

        # append to trace
        self.trace.loc[len(self.trace)] = values

        # save trace to file
        self._save_trace()

    def _save_trace(self, finalize=False):
        """
        Save to file via pd.DataFrame.to_csv() if options.trace_file is
        not None and other conditions apply.
        Format might be revised when storage is implemented.
        """
        if self.options.trace_file is None:
            return

        if finalize \
           or (len(self.trace) > 0 and len(self.trace) %
               self.options.trace_save_iter == 0):
            filename = self.options.trace_file
            if self.index is not None:
                filename = filename.replace("{index}", str(self.index))
            # save
            self.trace.to_csv(filename)

    def _update_vals(self, x, sensi_orders, mode, result):
        """
        Update initial and best function values. Must be called after
        update_trace().
        """

        # update initial point
        if self.fval0 is None and 0 in sensi_orders:
            if mode == MODE_FUN:
                self.fval0 = result[FVAL]
                self.x0 = x
            else:  # mode == MODE_RES:
                self.fval0 = res_to_chi2(result[RES])
                self.x0 = x

        # update best point
        fval = np.inf
        if 0 in sensi_orders:
            if mode == MODE_FUN:
                fval = result[FVAL]
            else:  # mode == MODE_RES:
                fval = res_to_chi2(result[RES])
        if fval < self.fval_min:
            self.fval_min = fval
            self.x_min = x
Objective call history. Also handles saving of intermediate results.      Parameteters     ------------      options: ObjectiveOptions, optional         Values needed for creating a history are extracted.      Attributes     ----------      n_fval, n_grad, n_hess, n_res, n_sres: int         Counters of function values, gradients and hessians,         residuals and residual sensitivities.      trace: list         List containing history of function values and parameters if         options.tr_record is True.      start_time: float         Reference start time.      fval0, fval_min: float         Initial and best function value found.      x0, x_min: np.ndarray         Initial and best parameters found.      index: str         Id identifying the history object when called in a multistart         setting. Reset all counters, the trace, and start the timer, and create         directory for trace file. # create trace file dirs Update the history.          Parameters         ----------         x: np.ndarray             The current parameter.         sensi_orders: tuple             The sensitivity orders.         mode: str             As in constants.MODE_.         result: dict             The result for x. Save the trace to file if options.trace_save is True. Update the counters. Update and possibly store the trace. # init trace # extract function values # mode == MODE_RES # check whether to append to trace # create table row # append to trace # save trace to file Save to file via pd.DataFrame.to_csv() if options.trace_file is         not None and other conditions apply.         Format might be revised when storage is implemented. # save Update initial and best function values. Must be called after         update_trace(). # update initial point # mode == MODE_RES: # update best point # mode == MODE_RES: