nestor/__init__.py

from __future__ import print_function, division
import os
import glob
import time
import math
import yaml
import pickle
from mpi4py import MPI


class NestedSampling:
    def __init__(self, h_param_file, nestor_restraints, rex_macro, exit_code):
        with open(h_param_file, "r") as paramf:
            self.h_params = yaml.safe_load(paramf)

        self.tic = -9999.0
        self.toc = None
        self.mcmc_step_time = None
        self.num_init_frames = self.h_params["num_init_frames"]
        self.num_frames_per_iter = self.h_params["num_frames_per_iter"]
        self.nestor_niter = self.h_params["max_nestor_iter"]
        self.rex_macro = rex_macro

        for restraint in nestor_restraints:
            if restraint.weight != 0:
                raise ValueError(
                    "Weight of all restraints in nestor_restraints "
                    "must be set to 0"
                )

        self.rex_macro.nestor_restraints = nestor_restraints
        self.rex_macro.nest = True

        self.max_plateau_hits = self.h_params["max_plateau_hits"]
        self.plateau_hits = 0
        self.max_failed_iter = self.h_params["max_failed_iterations"]
        self.failed_iter = 0

        self.Xi = 1
        self.Z = 0
        self.H = 0

        self.worst_li_list = []
        self.worst_xi_list = []
        self.log_worst_li = []
        self.log_xi = []
        self.xi = []

        self.comm_obj = MPI.COMM_WORLD
        self.finished = False
        self.termination_mode = "None"
        self.exit_code = exit_code
        self.return_vals = {}

    def sample_initial_frames(self):
        self.rex_macro.vars["number_of_frames"] = self.num_init_frames
        start_time = time.time()
        self.rex_macro.execute_macro()
        end_time = time.time()
        per_frame_sampling_time = ((end_time - start_time)
                                   / self.num_init_frames)
        self.mcmc_step_time = (
            per_frame_sampling_time / self.rex_macro.vars["monte_carlo_steps"]
        )

    def parse_likelihoods(self, iteration, fhead="likelihoods_"):
        sampled_likelihoods = []
        all_likelihood_binaries = glob.glob(f"{fhead}*")

        for binfile in all_likelihood_binaries:
            likelihoods = []
            with open(binfile, "rb") as rlif:
                likelihoods = pickle.load(rlif)
                for li in likelihoods:
                    sampled_likelihoods.append(li)
            os.remove(binfile)

        is_nan = False
        for li in sampled_likelihoods:
            if math.isnan(li):
                is_nan = True
        if is_nan:
            self.termination_mode = "Error: Nan found"
            self.exit_code = 11
            print("NaN found. Terminating...")
            for li in sampled_likelihoods:
                print(li)
            self.terminator(
                iteration=iteration,
                plateau_hits=self.plateau_hits,
                failed_iter=self.failed_iter,
            )

        return sampled_likelihoods

    def check_plateau(self):
        """
        Check if Li/Xi is plateuing  for consecutive samples, stop
        """

        previous_Li = self.worst_li_list[-2]
        current_Li = self.worst_li_list[-1]
        previous_Xi = self.worst_xi_list[-2]
        current_Xi = self.worst_xi_list[-1]

        if (current_Li / previous_Li) < (previous_Xi / current_Xi):
            self.plateau_hits += 1
            print(
                f"{'---'*20}\nPlateau detector hits: "
                f"{self.plateau_hits}/{self.max_plateau_hits}"
            )
        else:
            self.plateau_hits = 0

        if self.plateau_hits == self.max_plateau_hits:
            self.termination_mode = "MaxPlateauHits"
            self.finished = True

    def terminator(self, iteration, plateau_hits, failed_iter):
        from math import log

        self.toc = time.time()

        if "error" not in self.termination_mode.lower():
            print(f"Estimated evidence sampled: {self.Z}")
            self.exit_code = 0
            try:
                ana_unc = math.sqrt(self.H / self.num_init_frames)
            except ValueError:
                ana_unc = "Did not compute. H was negative"
                print("Math domain error")
                self.exit_code = 13

            from matplotlib import pyplot as plt

            fig, ax = plt.subplots(1)
            ax.set_xlabel("log(Xi)")
            ax.set_ylabel("log(Li)")
            ax.clear()
            ax.plot(self.log_xi, self.log_worst_li)
            fig.savefig("log_lixi.png")
            plt.close()

            fig, ax = plt.subplots(1)
            ax.plot(self.xi, self.log_worst_li)
            ax.set_xlabel("Xi")
            ax.set_ylabel("log(Li)")
            fig.savefig("lixi.png")
            plt.close()

            self.return_vals["last_iter"] = iteration
            self.return_vals["plateau_hits"] = plateau_hits
            self.return_vals["failed_iter"] = failed_iter
            self.return_vals["obtained_information"] = self.H
            self.return_vals["analytical_uncertainty"] = ana_unc
            self.return_vals["nestor_process_time"] = self.toc - self.tic
            self.return_vals["mcmc_step_time"] = self.mcmc_step_time
            self.return_vals["log_estimated_evidence"] = log(self.Z)

        else:
            self.return_vals["run_params"] = self.h_params

        self.return_vals["termination_mode"] = self.termination_mode
        self.return_vals["exit_code"] = self.exit_code

    def compute_evidence_H(self, iteration, curr_li):
        # compute Z
        curr_xi = math.exp(-iteration / self.num_init_frames)
        curr_wi = self.Xi - curr_xi
        prev_zi = self.Z
        self.Z += curr_li * curr_wi
        curr_zi = self.Z
        self.Xi = curr_xi

        # compute H
        if iteration > 1:
            first_term = ((curr_li * curr_wi) / curr_zi) * math.log(curr_li)
            second_term = (prev_zi / curr_zi) * (self.H + math.log(prev_zi))
            self.H = first_term + second_term - math.log(curr_zi)

    def execute_nested_sampling2(self):
        self.tic = time.time()
        import matplotlib.pyplot as plt

        i = 0
        true_iter = 0
        base_process = self.comm_obj.Get_rank() == 0
        self.comm_obj.Barrier()

        if "shuffle_config.err" in os.listdir("./"):
            self.exit_code = 11

        self.comm_obj.Barrier()

        print(
            f"Exit code from the macros after communication: {self.exit_code} "
            f"at rank: {self.comm_obj.Get_rank()}"
        )

        if self.exit_code is None:
            # Check for nan through small test run
            self.comm_obj.Barrier()
            if base_process:
                print(
                    f"{'-'*50}\nTest run complete, no NaN found. "
                    f"Continuing...\n{'-'*50}\n\n"
                )
            self.comm_obj.Barrier()

            self.sample_initial_frames()
            self.comm_obj.Barrier()

            if base_process:
                self.likelihoods = self.parse_likelihoods(iteration=true_iter)
            self.comm_obj.Barrier()

            self.rex_macro.vars["number_of_frames"] = self.num_frames_per_iter
            self.rex_macro.vars["replica_exchange_swap"] = True

            while true_iter < self.nestor_niter:
                self.comm_obj.Barrier()
                self.finished = self.comm_obj.bcast(self.finished, root=0)
                self.exit_code = self.comm_obj.bcast(self.exit_code, root=0)

                if self.exit_code is not None:
                    # run log will exist if
                    # a. parse_likelihoods had a nan error in the test iter,
                    #    called terminator.
                    # b. convergence criterion plateau reached, called
                    #    terminator.
                    # c. convergence criterion max_failed_iterations reached,
                    #    called terminator.
                    break

                if not self.finished:
                    # Other processes should not sample more models as
                    # convergence criteria i.e. a. max_failed_iterations
                    # or b. plateau triggered and the likelihoods list is
                    # unraveled to accumulate Z/H.
                    self.rex_macro.execute_macro()
                self.comm_obj.Barrier()

                if base_process:
                    if len(self.likelihoods) != 0:
                        Li = min(self.likelihoods)
                        if not self.finished:
                            newly_sampled_likelihoods = self.parse_likelihoods(
                                iteration=true_iter
                            )
                            candidate_li = max(newly_sampled_likelihoods)
                        else:  # unraveling
                            candidate_li = Li

                        if candidate_li >= Li:
                            self.likelihoods.remove(Li)

                            if not self.finished:
                                self.likelihoods.append(candidate_li)

                            # print(self.likelihoods, "\n", Li)
                            self.compute_evidence_H(iteration=i, curr_li=Li)
                            self.log_worst_li.append(math.log(Li))

                            self.log_xi.append(math.log(self.Xi))
                            self.xi.append(self.Xi)
                            self.worst_li_list.append(Li)
                            self.worst_xi_list.append(self.Xi)

                            if not self.finished:
                                if i > 1:
                                    self.check_plateau()
                                self.failed_iter = 0
                            i += 1

                        else:
                            self.failed_iter += 1
                            if self.failed_iter == self.max_failed_iter:
                                self.termination_mode = "MaxFailedIterations"
                                self.finished = True

                        true_iter += 1
                        print(
                            f'\n-----> True iteration: {true_iter} {" "*5} '
                            f'Calculation iteration: {i} {" "*5} '
                            f'Failed iteration: {self.failed_iter} {" "*5} '
                            f'Evidence: {self.Z} {" "*5} '
                            f'Terminating: {self.finished}\n'
                        )
                        if true_iter % 10 == 0:
                            from math import log

                            tempout = {
                                "True iteration": true_iter,
                                "Calculation iteration": i,
                                "Failed iteration": self.failed_iter,
                                "Log Evidence": log(self.Z),
                                "Plateau hits": self.plateau_hits,
                            }
                            with open("temporary_output.yaml", "w") as tof:
                                yaml.dump(tempout, tof)

                    else:
                        self.terminator(
                            iteration=true_iter,
                            plateau_hits=self.plateau_hits,
                            failed_iter=self.failed_iter,
                        )

                    live_fig, live_ax = plt.subplots(1)
                    live_ax.set_xlabel("log(Xi)")
                    live_ax.set_ylabel("log(Li)")
                    live_ax.plot(self.log_xi, self.log_worst_li)
                    live_fig.savefig("live_loglixi.png")
                    plt.close()

                self.comm_obj.Barrier()
                true_iter = self.comm_obj.bcast(true_iter, root=0)
                if true_iter == self.nestor_niter:
                    self.termination_mode = (
                        "Error: MaxIterations reached without convergence "
                        "criteria")
                    self.exit_code = 12
                    self.exit_code = self.comm_obj.bcast(self.exit_code,
                                                         root=0)
                    self.terminator(
                        iteration=true_iter,
                        plateau_hits=self.plateau_hits,
                        failed_iter=self.failed_iter,
                    )

        else:
            if base_process:
                self.termination_mode = "Error: Shuffle configuration error"
                self.exit_code = 11
                self.exit_code = self.comm_obj.bcast(self.exit_code, root=0)
                self.terminator(iteration=0, plateau_hits=0, failed_iter=0)
        self.comm_obj.Barrier()

        self.exit_code = self.comm_obj.bcast(self.exit_code, root=0)

        if base_process:
            return self.return_vals, self.exit_code
        else:
            return None, None


__version__ = "2.22.0"

def get_module_version():
    '''Return the version of this module, as a string'''
    return "2.22.0"

def get_module_name():
    '''Return the fully-qualified name of this module'''
    return "IMP::nestor"

def get_data_path(fname):
    '''Return the full path to one of this module's data files'''
    import IMP
    return IMP._get_module_data_path("nestor", fname)

def get_example_path(fname):
    '''Return the full path to one of this module's example files'''
    import IMP
    return IMP._get_module_example_path("nestor", fname)