PSO/pso/optimizer.py

import gc
import json
import os
import sys
from datetime import datetime

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tqdm.auto import tqdm

from .particle import Particle

gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
    try:
        tf.config.experimental.set_memory_growth(gpus[0], True)
    except RuntimeError as r:
        print(r)


class Optimizer:
    """
    particle swarm optimization
    PSO 실행을 위한 클래스
    """

    def __init__(
        self,
        model: keras.models,
        loss="mean_squared_error",
        n_particles: int = 10,
        c0=0.5,
        c1=1.5,
        w_min=0.5,
        w_max=1.5,
        negative_swarm: float = 0,
        mutation_swarm: float = 0,
        np_seed: int = None,
        tf_seed: int = None,
        random_state: tuple = None,
        particle_min: float = -0.3,
        particle_max: float = 0.3,
        convergence_reset: bool = False,
        convergence_reset_patience: int = 10,
        convergence_reset_min_delta: float = 0.0001,
        convergence_reset_monitor: str = "loss",
    ):
        """
        particle swarm optimization

        Args:
            model (keras.models): 모델 구조 - keras.models.model_from_json 을 이용하여 생성
            loss (str): 손실함수 - keras.losses 에서 제공하는 손실함수 사용
            n_particles (int): 파티클 개수
            c0 (float): local rate - 지역 최적값 관성 수치
            c1 (float): global rate - 전역 최적값 관성 수치
            w_min (float): 최소 관성 수치
            w_max (float): 최대 관성 수치
            negative_swarm (float): 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
            mutation_swarm (float): 돌연변이가 일어날 확률
            np_seed (int, optional): numpy seed. Defaults to None.
            tf_seed (int, optional): tensorflow seed. Defaults to None.
            particle_min (float, optional): 가중치 초기화 최소값. Defaults to -5.
            particle_max (float, optional): 가중치 초기화 최대값. Defaults to 5.
            convergence_reset (bool, optional): early stopping 사용 여부. Defaults to False.
            convergence_reset_patience (int, optional): early stopping 사용시 얼마나 기다릴지. Defaults to 10.
            convergence_reset_min_delta (float, optional): early stopping 사용시 얼마나 기다릴지. Defaults to 0.0001.
            convergence_reset_monitor (str, optional): early stopping 사용시 어떤 값을 기준으로 할지. Defaults to "loss".
        """
        if np_seed is not None:
            np.random.seed(np_seed)
        if tf_seed is not None:
            tf.random.set_seed(tf_seed)

        self.random_state = np.random.get_state()

        if random_state is not None:
            np.random.set_state(random_state)

        model.compile(loss=loss, optimizer="sgd", metrics=["accuracy"])
        self.model = model  # 모델 구조
        self.loss = loss  # 손실함수
        self.n_particles = n_particles  # 파티클 개수
        self.particles = [None] * n_particles  # 파티클 리스트
        self.c0 = c0  # local rate - 지역 최적값 관성 수치
        self.c1 = c1  # global rate - 전역 최적값 관성 수치
        self.w_min = w_min  # 최소 관성 수치
        self.w_max = w_max  # 최대 관성 수치
        self.negative_swarm = negative_swarm  # 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
        self.mutation_swarm = mutation_swarm  # 관성을 추가로 사용할 파티클 비율 - 0 ~ 1 사이의 값
        self.particle_min = particle_min  # 가중치 초기화 최소값
        self.particle_max = particle_max
        self.g_best_score = [0, np.inf]  # 최고 점수 - 시작은 0으로 초기화
        self.g_best = None  # 최고 점수를 받은 가중치
        self.g_best_ = None  # 최고 점수를 받은 가중치 - 값의 분산을 위한 변수
        self.avg_score = 0  # 평균 점수
        self.sigma = 1.0

        self.save_path = None  # 저장 위치
        self.renewal = "acc"
        self.dispersion = False
        self.day = datetime.now().strftime("%Y%m%d-%H%M%S")

        self.empirical_balance = False

        negative_count = 0

        self.train_summary_writer = [None] * self.n_particles

        try:
            print(f"start running time : {self.day}")
            for i in tqdm(range(self.n_particles), desc="Initializing Particles"):

                self.particles[i] = Particle(
                    model,
                    self.loss,
                    negative=True if i < self.negative_swarm * self.n_particles else False,
                    mutation=self.mutation_swarm,
                    converge_reset=convergence_reset,
                    converge_reset_patience=convergence_reset_patience,
                    converge_reset_monitor=convergence_reset_monitor,
                    converge_reset_min_delta=convergence_reset_min_delta,
                )

                if i < self.negative_swarm * self.n_particles:
                    negative_count += 1
                # del m, init_weights, w_, sh_, len_
                gc.collect()
                tf.keras.backend.reset_uids()
                tf.keras.backend.clear_session()

                # del model_

            print(f"negative swarm : {negative_count} / {n_particles}")
            print(f"mutation swarm : {mutation_swarm * 100}%")

            gc.collect()
            tf.keras.backend.reset_uids()
            tf.keras.backend.clear_session()
        except KeyboardInterrupt:
            sys.exit("Ctrl + C : Stop Training")
        except MemoryError:
            sys.exit("Memory Error : Stop Training")

    def __del__(self):
        del self.model
        del self.loss
        del self.n_particles
        del self.particles
        del self.c0
        del self.c1
        del self.w_min
        del self.w_max
        del self.negative_swarm
        del self.g_best_score
        del self.g_best
        del self.g_best_
        del self.avg_score

        gc.collect()
        tf.keras.backend.reset_uids()
        tf.keras.backend.clear_session()

    def _encode(self, weights):
        """
        가중치를 1차원으로 풀어서 반환

        Args:
            weights (list) : keras model의 가중치
        Returns:
            (numpy array) : 가중치 - 1차원으로 풀어서 반환
            (list) : 가중치의 원본 shape
            (list) : 가중치의 원본 shape의 길이
        """
        w_gpu = np.array([])
        length = []
        shape = []
        for layer in weights:
            shape.append(layer.shape)
            w_tmp = layer.reshape(-1)
            length.append(len(w_tmp))
            w_gpu = np.append(w_gpu, w_tmp)

        del weights

        return w_gpu, shape, length

    def _decode(self, weight, shape, length):
        """
        _encode 로 인코딩된 가중치를 원본 shape으로 복원
        파라미터는 encode의 리턴값을 그대로 사용을 권장

        Args:
            weight (numpy array): 가중치 - 1차원으로 풀어서 반환
            shape (list): 가중치의 원본 shape
            length (list): 가중치의 원본 shape의 길이
        Returns:
            (list) : 가중치 원본 shape으로 복원
        """
        weights = []
        start = 0
        for i in range(len(shape)):
            end = start + length[i]
            w_tmp = weight[start:end]
            w_tmp = np.reshape(w_tmp, shape[i])
            weights.append(w_tmp)
            start = end

        del weight, shape, length
        del start, end, w_tmp

        return weights

    def _f(self, x, y, weights):
        """
        EBPSO의 목적함수 (예상)

        Args:
            x (list): 입력 데이터
            y (list): 출력 데이터
            weights (list): 가중치

        Returns:
            (float): 목적 함수 값
        """
        self.model.set_weights(weights)
        score = self.model.evaluate(x, y, verbose=0)
        if self.renewal == "acc":
            score_ = score[1]
        else:
            score_ = score[0]

        if score_ > 0:
            return 1 / (1 + score_)
        else:
            return 1 + np.abs(score_)

    class _batch_generator:
        def __init__(self, x, y, batch_size: int = 32):
            self.batch_size = batch_size
            self.index = 0
            self.x = x
            self.y = y
            self.setBatchSize(batch_size)

        def next(self):
            self.index += 1
            if self.index >= self.max_index:
                self.index = 0
                self.__getBatchSlice__(self.batch_size)
            return self.dataset[self.index][0], self.dataset[self.index][1]

        def getMaxIndex(self):
            return self.max_index

        def getIndex(self):
            return self.index

        def setIndex(self, index):
            self.index = index

        def getBatchSize(self):
            return self.batch_size

        def setBatchSize(self, batch_size):
            self.batch_size = batch_size
            if self.batch_size > len(self.x):
                self.batch_size = len(self.x)
            print(f"batch size : {self.batch_size}")
            self.dataset = self.__getBatchSlice__(self.batch_size)
            self.max_index = len(self.dataset)

        def __getBatchSlice__(self, batch_size):
            return list(
                tf.data.Dataset.from_tensor_slices(
                    (self.x, self.y)).shuffle(len(self.x)).batch(batch_size)
            )

        def getDataset(self):
            return self.dataset

    def fit(
        self,
        x,
        y,
        epochs: int = 100,
        log: int = 0,
        log_name: str = None,
        save_info: bool = False,
        save_path: str = "./result",
        renewal: str = "acc",
        empirical_balance: bool = False,
        dispersion: bool = False,
        check_point: int = None,
        batch_size: int = None,
        validate_data: any = None,
    ):
        """
        # Args:
            x : numpy array,
            y : numpy array,
            epochs : int,
            log : int - 0 : log 기록 안함, 1 : csv, 2 : tensorboard,
            save_info : bool - 종료시 학습 정보 저장 여부 default : False,
            save_path : str - ex) "./result",
            renewal : str ex) "acc" or "loss" or "both",
            empirical_balance : bool - True : EBPSO, False : PSO,
            dispersion : bool - True : g_best 의 값을 분산시켜 전역해를 찾음, False : g_best 의 값만 사용
            check_point : int - 저장할 위치 - None : 저장 안함
            batch_size : int - batch size default : None => len(x) // 10
                batch_size > len(x) : auto max batch size
        """
        try:
            if x.shape[0] != y.shape[0]:
                raise ValueError("x, y shape error")

            if log not in [0, 1, 2]:
                raise ValueError("log not in [0, 1, 2]")

            if save_info and save_path is None:
                raise ValueError("save_path is None")

            if renewal not in ["acc", "loss", "both"]:
                raise ValueError("renewal not in ['acc', 'loss', 'both']")

            if check_point is not None and save_path is None:
                raise ValueError("save_path is None")

        except ValueError as ve:
            sys.exit(ve)

        self.save_path = save_path
        self.empirical_balance = empirical_balance
        self.dispersion = dispersion

        if batch_size is None:
            batch_size = len(x) // 10

        self.renewal = renewal
        particle_sum = 0  # x_j
        try:
            train_log_dir = "logs/fit/" + self.day
            if log == 2:
                assert log_name is not None, "log_name is None"

                train_log_dir = f"logs/{log_name}/{self.day}/train"
                for i in range(self.n_particles):
                    self.train_summary_writer[i] = tf.summary.create_file_writer(
                        train_log_dir + f"/{i}"
                    )

            elif check_point is not None or log == 1:
                if save_path is None:
                    raise ValueError("save_path is None")
                else:
                    self.save_path = save_path
                    if not os.path.exists(f"{save_path}/{self.day}"):
                        os.makedirs(f"{save_path}/{self.day}", exist_ok=True)
        except ValueError as ve:
            sys.exit(ve)

        model_ = keras.models.model_from_json(self.model.to_json())
        model_.compile(loss=self.loss, optimizer="adam", metrics=["accuracy"])
        model_.fit(x, y, epochs=1, verbose=0)
        score = model_.evaluate(x, y, verbose=1)

        if renewal == "acc":
            self.g_best_score[0] = score[1]
            self.g_best_score[1] = score[0]
        else:
            self.g_best_score[0] = score[0]
            self.g_best_score[1] = score[1]

        self.g_best = model_.get_weights()
        self.g_best_ = model_.get_weights()

        del model_

        dataset = self._batch_generator(x, y, batch_size=batch_size)

        for i in tqdm(range(self.n_particles), desc="Initializing velocity"):
            p = self.particles[i]

            x_batch, y_batch = dataset.next()
            local_score = p.get_score(x_batch, y_batch, renewal=renewal)

            particle_sum += local_score[1]

            if renewal == "acc":
                if local_score[1] > self.g_best_score[0]:
                    self.g_best_score[0] = local_score[1]
                    self.g_best_score[1] = local_score[0]
                    self.g_best = p.get_best_weights()
                    self.g_best_ = p.get_best_weights()
            elif renewal == "loss":
                if local_score[0] < self.g_best_score[1]:
                    self.g_best_score[1] = local_score[0]
                    self.g_best_score[0] = local_score[1]
                    self.g_best = p.get_best_weights()
                    self.g_best_ = p.get_best_weights()
            elif renewal == "both":
                if local_score[1] > self.g_best_score[0]:
                    self.g_best_score[0] = local_score[1]
                    self.g_best_score[1] = local_score[0]
                    self.g_best = p.get_best_weights()
                    self.g_best_ = p.get_best_weights()

            if log == 1:
                with open(
                    f"./{save_path}/{self.day}/{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv",
                    "a",
                ) as f:
                    f.write(f"{local_score[0]}, {local_score[1]}")
                    if i != self.n_particles - 1:
                        f.write(", ")
                    else:
                        f.write("\n")

            elif log == 2:
                with self.train_summary_writer[i].as_default():
                    tf.summary.scalar("loss", local_score[0], step=0)
                    tf.summary.scalar("accuracy", local_score[1], step=0)

            del local_score
            # gc.collect()
            # tf.keras.backend.reset_uids()
            # tf.keras.backend.clear_session()
        print(
            f"initial g_best_score : {self.g_best_score[0] if self.renewal == 'acc' else self.g_best_score[1]}"
        )

        try:
            epoch_sum = 0
            epochs_pbar = tqdm(
                range(epochs),
                desc=f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}",
                ascii=True,
                leave=True,
                position=0,
            )
            for epoch in epochs_pbar:
                particle_avg = particle_sum / self.n_particles  # x_j
                particle_sum = 0
                max_score = 0
                min_loss = np.inf
                # epoch_particle_sum = 0
                part_pbar = tqdm(
                    range(len(self.particles)),
                    desc=f"acc : {max_score:.4f} loss : {min_loss:.4f}",
                    ascii=True,
                    leave=False,
                    position=1,
                )
                w = self.w_max - (self.w_max - self.w_min) * epoch / epochs
                for i in part_pbar:
                    part_pbar.set_description(
                        f"acc : {max_score:.4f} loss : {min_loss:.4f}"
                    )

                    g_best = self.g_best

                    x_batch, y_batch = dataset.next()

                    if dispersion:
                        ts = self.particle_min + np.random.rand() * (
                            self.particle_max - self.particle_min
                        )
                        g_, g_sh, g_len = self._encode(self.g_best)
                        decrement = (epochs - epoch + 1) / epochs
                        g_ = (1 - decrement) * g_ + decrement * ts
                        self.g_best_ = self._decode(g_, g_sh, g_len)
                        g_best = self.g_best_

                    if empirical_balance:
                        if np.random.rand() < np.exp(-(epoch) / epochs):
                            w_p_ = self._f(
                                x, y, self.particles[i].get_best_weights()
                            )
                            w_g_ = self._f(x, y, self.g_best)
                            w_p = w_p_ / (w_p_ + w_g_)
                            w_g = w_p_ / (w_p_ + w_g_)

                            del w_p_
                            del w_g_

                        else:
                            p_b = self.particles[i].get_best_score()
                            g_a = self.avg_score
                            l_b = p_b - g_a
                            sigma_post = np.sqrt(np.power(l_b, 2))
                            sigma_pre = (
                                1
                                / (
                                    self.n_particles
                                    * np.linalg.norm(
                                        self.particle_max - self.particle_min
                                    )
                                )
                                * sigma_post
                            )
                            p_ = np.exp(-1 * sigma_pre * sigma_post)

                            # p_ = (
                            #     1
                            #     / (self.n_particles * np.linalg.norm(self.particle_max - self.particle_min))
                            #     * np.exp(
                            #         -np.power(l_b, 2) / (2 * np.power(self.sigma, 2))
                            #     )
                            # )
                            # g_ = (
                            #     1
                            #     / np.linalg.norm(self.c1 - self.c0)
                            #     * np.exp(
                            #         -np.power(l_b, 2) / (2 * np.power(self.sigma, 2))
                            #     )
                            # )
                            # w_p = p_ / (p_ + g_)
                            # w_g = g_ / (p_ + g_)
                            w_p = p_
                            w_g = 1 - p_

                            del p_b
                            del g_a
                            del l_b
                            del p_

                        score = self.particles[i].step_w(
                            x_batch,
                            y_batch,
                            self.c0,
                            self.c1,
                            w,
                            g_best,
                            w_p,
                            w_g,
                            renewal=renewal,
                        )
                        epoch_sum += np.power(score[1] - particle_avg, 2)

                    else:
                        score = self.particles[i].step(
                            x_batch, y_batch, self.c0, self.c1, w, g_best, renewal=renewal
                        )

                    if log == 2:
                        with self.train_summary_writer[i].as_default():
                            tf.summary.scalar("loss", score[0], step=epoch + 1)
                            tf.summary.scalar(
                                "accuracy", score[1], step=epoch + 1
                            )

                    if renewal == "acc":
                        if score[1] >= max_score:
                            max_score = score[1]
                            min_loss = score[0]

                        if score[1] >= self.g_best_score[0]:
                            if score[1] > self.g_best_score[0]:
                                self.g_best_score[0] = score[1]
                                self.g_best = self.particles[i].get_best_weights(
                                )
                            else:
                                if score[0] < self.g_best_score[1]:
                                    self.g_best_score[1] = score[0]
                                    self.g_best = self.particles[i].get_best_weights(
                                    )
                            epochs_pbar.set_description(
                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
                            )
                    elif renewal == "loss":
                        if score[0] <= min_loss:
                            min_loss = score[0]
                            max_score = score[1]

                        if score[0] <= self.g_best_score[1]:
                            if score[0] < self.g_best_score[1]:
                                self.g_best_score[1] = score[0]
                                self.g_best = self.particles[i].get_best_weights(
                                )
                            else:
                                if score[1] > self.g_best_score[0]:
                                    self.g_best_score[0] = score[1]
                                    self.g_best = self.particles[i].get_best_weights(
                                    )
                            epochs_pbar.set_description(
                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
                            )
                    elif renewal == "both":
                        if score[0] <= min_loss:
                            min_loss = score[0]
                        if score[1] >= self.g_best_score[0]:
                            self.g_best_score[0] = score[1]
                            self.g_best = self.particles[i].get_best_weights()
                            epochs_pbar.set_description(
                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
                            )
                        if score[1] >= max_score:
                            max_score = score[1]
                        if score[0] <= self.g_best_score[1]:
                            self.g_best_score[1] = score[0]
                            self.g_best = self.particles[i].get_best_weights()
                            epochs_pbar.set_description(
                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
                            )
                    particle_sum += score[1]

                    if log == 1:
                        with open(
                            f"./{save_path}/{self.day}/{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv",
                            "a",
                        ) as f:
                            f.write(f"{score[0]}, {score[1]}")
                            if i != self.n_particles - 1:
                                f.write(", ")
                            else:
                                f.write("\n")
                part_pbar.refresh()

                if check_point is not None:
                    if epoch % check_point == 0:
                        os.makedirs(f"./{save_path}/{self.day}", exist_ok=True)
                        self._check_point_save(
                            f"./{save_path}/{self.day}/ckpt-{epoch}")

                gc.collect()
                tf.keras.backend.reset_uids()
                tf.keras.backend.clear_session()

        except KeyboardInterrupt:
            print("Ctrl + C : Stop Training")

        except MemoryError:
            print("Memory Error : Stop Training")

        except Exception as e:
            print(e)

        finally:
            self.model_save(x, y, save_path)
            print("model save")
            if save_info:
                self.save_info(save_path)
                print("save info")

            return self.g_best_score

    def get_best_model(self):
        """
        최고 점수를 받은 모델을 반환

        Returns:
            (keras.models): 모델
        """
        model = keras.models.model_from_json(self.model.to_json())
        model.set_weights(self.g_best)
        model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])

        return model

    def get_best_score(self):
        """
        최고 점수를 반환

        Returns:
            (float): 점수
        """
        return self.g_best_score

    def get_best_weights(self):
        """
        최고 점수를 받은 가중치를 반환

        Returns:
            (float): 가중치
        """
        return self.g_best

    def save_info(self, path: str = "./result"):
        """
        학습 정보를 저장

        Args:
            path (str, optional): 저장 위치. Defaults to "./result".
        """
        json_save = {
            "name": f"{self.day}/{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5",
            "n_particles": self.n_particles,
            "score": self.g_best_score,
            "c0": self.c0,
            "c1": self.c1,
            "w_min": self.w_min,
            "w_max": self.w_max,
            "loss_method": self.loss,
            "empirical_balance": self.empirical_balance,
            "dispersion": self.dispersion,
            "negative_swarm": self.negative_swarm,
            "mutation_swarm": self.mutation_swarm,
            "random_state_0": self.random_state[0],
            "random_state_1": self.random_state[1].tolist(),
            "random_state_2": self.random_state[2],
            "random_state_3": self.random_state[3],
            "random_state_4": self.random_state[4],
            "renewal": self.renewal,
        }

        with open(
            f"./{path}/{self.day}/{self.loss}_{self.g_best_score}.json",
            "a",
        ) as f:
            json.dump(json_save, f, indent=4)

    def _check_point_save(self, save_path: str = f"./result/check_point"):
        """
        중간 저장

        Args:
            save_path (str, optional): checkpoint 저장 위치 및 이름. Defaults to f"./result/check_point".
        """
        model = self.get_best_model()
        model.save_weights(save_path)

    def model_save(self, x, y, save_path: str = "./result"):
        """
        최고 점수를 받은 모델 저장

        Args:
            save_path (str, optional): 모델의 저장 위치. Defaults to "./result".

        Returns:
            (keras.models): 모델
        """
        model = self.get_best_model()
        score = model.evaluate(x, y, verbose=1)
        print(f"model acc : {score[1]}, loss : {score[0]}")
        model.save(
            f"./{save_path}/{self.day}/{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5"
        )
        return model