iris.py 파일 삭제

pso/optimizer.py

@@ -13,7 +13,7 @@ from sklearn.model_selection import train_test_split
 from tensorboard.plugins.hparams import api as hp
 from tensorflow import keras
 from tqdm.auto import tqdm
-from typing import Any
+from typing import Any, List
 from .particle import Particle
 
 
@@ -159,13 +159,17 @@ class Optimizer:
             tf.keras.backend.reset_uids()
             tf.keras.backend.clear_session()
         except KeyboardInterrupt:
-            sys.exit("Ctrl + C : Stop Training")
+            print("Ctrl + C : Stop Training")
+            sys.exit(1)
         except MemoryError:
-            sys.exit("Memory Error : Stop Training")
+            print("Memory Error : Stop Training")
-        except ValueError as ve:
+            sys.exit(12)
-            sys.exit(ve)
+        except ValueError:
+            print("Value Error : Stop Training")
+            sys.exit(11)
         except Exception as e:
-            sys.exit(e)
+            print(e)
+            sys.exit(10)
 
     def __del__(self):
         del self.model
@@ -246,7 +250,7 @@ class Optimizer:
             (float): 목적 함수 값
         """
         self.model.set_weights(weights)
-        score = self.model.evaluate(x, y, verbose=0)
+        score = self.model.evaluate(x, y, verbose=0)  # type: ignore
         if self.renewal == "loss":
             score_ = score[0]
         elif self.renewal == "acc":
@@ -276,70 +280,58 @@ class Optimizer:
         return weight_min, weight_max
 
     class batch_generator:
-        def __init__(self, x, y, batch_size: int = None):
+        def __init__(self, x, y, batch_size: int = 0):
             self.index = 0
             self.x = x
             self.y = y
-            self.setBatchSize(batch_size)
+            self.set_batch_size(batch_size)
 
         def next(self):
             self.index += 1
             if self.index > self.max_index:
                 self.index = 0
-                self.__getBatchSlice(self.batch_size)
+                self.__get_batch_slice(self.batch_size)
             return self.dataset[self.index - 1][0], self.dataset[self.index - 1][1]
 
-        def getMaxIndex(self):
+        def get_max_index(self):
             return self.max_index
 
-        def getIndex(self):
+        def get_index(self):
             return self.index
 
-        def setIndex(self, index):
+        def set_index(self, index):
             self.index = index
 
-        def getBatchSize(self):
+        def get_batch_size(self):
             return self.batch_size
 
-        def setBatchSize(self, batch_size: int = None):
+        def set_batch_size(self, batch_size: int = 0):
-            if batch_size is None:
+            if batch_size == -1 or batch_size > len(self.x):
-                batch_size = len(self.x) // 10
-            elif batch_size > len(self.x):
                 batch_size = len(self.x)
+            elif batch_size == 0:
+                batch_size = len(self.x) // 10
 
             self.batch_size = batch_size
 
             print(f"batch size : {self.batch_size}")
-            self.dataset = self.__getBatchSlice(self.batch_size)
+            self.dataset = self.__get_batch_slice(self.batch_size)
             self.max_index = len(self.dataset)
 
-        def __getBatchSlice(self, batch_size):
+        def __get_batch_slice(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):
+        def get_dataset(self):
             return self.dataset
 
     def fit(
         self,
         x,
         y,
-        epochs: int = 1,
+        **kwargs,
-        log: int = 0,
-        log_name: str = None,
-        save_info: bool = None,
-        renewal: str = None,
-        empirical_balance: bool = None,
-        dispersion: bool = None,
-        check_point: int = None,
-        batch_size: int = None,
-        validate_data: tuple = None,
-        validation_split: float = None,
-        back_propagation: bool = False,
-        weight_reduction: int = None,
     ):
         """
         # Args:
@@ -360,6 +352,20 @@ class Optimizer:
             weight_reduction : int - 가중치 감소 초기화 주기 default : None => epochs
         """
         try:
+            epochs = kwargs.get("epochs", 10)
+            log = kwargs.get("log", 0)
+            log_name = kwargs.get("log_name", None)
+            save_info = kwargs.get("save_info", False)
+            renewal = kwargs.get("renewal", "acc")
+            empirical_balance = kwargs.get("empirical_balance", False)
+            dispersion = kwargs.get("dispersion", False)
+            check_point = kwargs.get("check_point", None)
+            batch_size = kwargs.get("batch_size", None)
+            validate_data = kwargs.get("validate_data", None)
+            validation_split = kwargs.get("validation_split", None)
+            back_propagation = kwargs.get("back_propagation", False)
+            weight_reduction = kwargs.get("weight_reduction", None)
+
             if x.shape[0] != y.shape[0]:
                 raise ValueError("x, y shape error")
 
@@ -393,13 +399,13 @@ class Optimizer:
             ):
                 raise ValueError("validate_data shape error")
             else:
-                validate_data = (x, y)
+                validate_data = [x, y]
 
             if validation_split is not None:
                 if validation_split < 0 or validation_split > 1:
                     raise ValueError("validation_split not in [0, 1]")
 
-                x, validate_data[0], y, validate_data[1] = train_test_split(
+                [x, validate_data[0], y, validate_data[1]] = train_test_split(
                     x, y, test_size=validation_split, shuffle=True
                 )
 
@@ -413,15 +419,16 @@ class Optimizer:
                 weight_reduction = epochs
 
         except ValueError as ve:
-            sys.exit(ve)
+            print(ve)
+            sys.exit(11)
         except Exception as e:
-            sys.exit(e)
+            print(e)
+            sys.exit(10)
 
         self.empirical_balance = empirical_balance
         self.dispersion = dispersion
 
         self.renewal = renewal
-        particle_sum = 0  # x_j
 
         try:
             if log_name is None:
@@ -452,9 +459,11 @@ class Optimizer:
                 if not os.path.exists(self.log_path):
                     os.makedirs(self.log_path, exist_ok=True)
         except ValueError as ve:
-            sys.exit(ve)
+            print(ve)
+            sys.exit(11)
         except Exception as e:
-            sys.exit(e)
+            print(e)
+            sys.exit(10)
 
         try:
             dataset = self.batch_generator(x, y, batch_size=batch_size)
@@ -466,8 +475,8 @@ class Optimizer:
                     optimizer="adam",
                     metrics=["accuracy", "mse"],
                 )
-                model_.fit(x, y, epochs=1, verbose=0)
+                model_.fit(x, y, epochs=1, verbose=0)  # type: ignore
-                score = model_.evaluate(x, y, verbose=1)
+                score = model_.evaluate(x, y, verbose="auto")
 
                 Particle.g_best_score = score
 
@@ -489,7 +498,6 @@ class Optimizer:
 
             print("best score init complete" + str(Particle.g_best_score))
 
-            epoch_sum = 0
             epochs_pbar = tqdm(
                 range(epochs),
                 desc=f"best - loss: {Particle.g_best_score[0]:.4f} - acc: {Particle.g_best_score[1]:.4f} - mse: {Particle.g_best_score[2]:.4f}",
@@ -499,8 +507,8 @@ class Optimizer:
             )
             for epoch in epochs_pbar:
                 # 이번 epoch의 평균 점수
-                particle_avg = particle_sum / self.n_particles  # x_j
+                # particle_avg = particle_sum / self.n_particles  # x_j
-                particle_sum = 0
+                # particle_sum = 0
                 # 각 최고 점수, 최저 loss, 최저 mse
                 max_acc = 0
                 min_loss = np.inf
@@ -522,6 +530,7 @@ class Optimizer:
                     * (epoch % weight_reduction)
                     / weight_reduction
                 )
+                rng = np.random.default_rng()
                 for i in part_pbar:
                     part_pbar.set_description(
                         f"loss: {min_loss:.4f} acc: {max_acc:.4f} mse: {min_mse:.4f}"
@@ -531,9 +540,8 @@ class Optimizer:
                     x_batch, y_batch = dataset.next()
 
                     weight_min, weight_max = self.__weight_range()
-
                     if dispersion:
-                        ts = weight_min + np.random.rand() * (weight_max - weight_min)
+                        ts = weight_min + rng.random() * (weight_max - weight_min)
 
                         g_, g_sh, g_len = self._encode(Particle.g_best_weights)
                         decrement = (epochs - epoch + 1) / epochs
@@ -541,7 +549,7 @@ class Optimizer:
                         g_best = self._decode_(g_, g_sh, g_len)
 
                     if empirical_balance:
-                        if np.random.rand() < np.exp(-(epoch) / epochs):
+                        if rng.random() < np.exp(-(epoch) / epochs):
                             w_p_ = self._f(
                                 x_batch, y_batch, self.particles[i].get_best_weights()
                             )
@@ -585,7 +593,6 @@ class Optimizer:
                             w_g,
                             renewal=renewal,
                         )
-                        epoch_sum += np.power(score[1] - particle_avg, 2)
 
                     else:
                         score = self.particles[i].step(
@@ -594,8 +601,8 @@ class Optimizer:
 
                     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)
+                            tf.summary.scalar("loss", score[0], step=epoch + 1)
                             tf.summary.scalar("mse", score[2], step=epoch + 1)
 
                     if renewal == "loss":
@@ -633,7 +640,6 @@ class Optimizer:
                                 min_loss, max_acc, min_mse = score
 
                                 best_particle_index = i
-                    particle_sum += score[1]
 
                     if log == 1:
                         with open(
@@ -647,15 +653,13 @@ class Optimizer:
                                 f.write("\n")
                 part_pbar.refresh()
                 # 한번 epoch 가 끝나고 갱신을 진행해야 순간적으로 높은 파티클이 발생해도 오류가 생기지 않음
-                if renewal == "loss":
+                if renewal == "loss" and min_loss <= Particle.g_best_score[0]:
-                    if min_loss <= Particle.g_best_score[0]:
                     if min_loss < Particle.g_best_score[0]:
                         self.particles[best_particle_index].update_global_best()
                     else:
                         if max_acc > Particle.g_best_score[1]:
                             self.particles[best_particle_index].update_global_best()
-                elif renewal == "acc":
+                elif renewal == "acc" and max_acc >= Particle.g_best_score[1]:
-                    if max_acc >= Particle.g_best_score[1]:
                     # 최고 점수 보다 높을 경우
                     if max_acc > Particle.g_best_score[1]:
                         # 최고 점수 갱신
@@ -665,8 +669,7 @@ class Optimizer:
                         # 최저 loss 보다 낮을 경우
                         if min_loss < Particle.g_best_score[0]:
                             self.particles[best_particle_index].update_global_best()
-                elif renewal == "mse":
+                elif renewal == "mse" and min_mse <= Particle.g_best_score[2]:
-                    if min_mse <= Particle.g_best_score[2]:
                     if min_mse < Particle.g_best_score[2]:
                         self.particles[best_particle_index].update_global_best()
                     else:
@@ -677,20 +680,19 @@ class Optimizer:
                     f"best - loss: {Particle.g_best_score[0]:.4f} - acc: {Particle.g_best_score[1]:.4f} - mse: {Particle.g_best_score[2]:.4f}"
                 )
 
-                if check_point is not None:
+                if check_point is not None and epoch % check_point == 0:
-                    if epoch % check_point == 0:
                     os.makedirs(
                         f"./logs/{log_name}/{self.day}",
                         exist_ok=True,
                     )
-                        self._check_point_save(
+                    self._check_point_save(f"./logs/{log_name}/{self.day}/ckpt-{epoch}")
-                            f"./logs/{log_name}/{self.day}/ckpt-{epoch}"
-                        )
 
                 tf.keras.backend.reset_uids()
                 tf.keras.backend.clear_session()
                 gc.collect()
 
+            return Particle.g_best_score
+
         except KeyboardInterrupt:
             print("Ctrl + C : Stop Training")
 
@@ -707,8 +709,6 @@ class Optimizer:
                 self.save_info()
                 print("save info")
 
-            return Particle.g_best_score
-
     def get_best_model(self):
         """
         최고 점수를 받은 모델을 반환
@@ -778,7 +778,7 @@ class Optimizer:
         ) as f:
             json.dump(json_save, f, indent=4)
 
-    def _check_point_save(self, save_path: str = f"./result/check_point"):
+    def _check_point_save(self, save_path: str = "./result/check_point"):
         """
         중간 저장
 
@@ -788,7 +788,7 @@ class Optimizer:
         model = self.get_best_model()
         model.save_weights(save_path)
 
-    def model_save(self, valid_data: tuple = None):
+    def model_save(self, valid_data: List):
         """
         최고 점수를 받은 모델 저장
 
@@ -800,9 +800,15 @@ class Optimizer:
         """
         x, y = valid_data
         model = self.get_best_model()
-        score = model.evaluate(x, y, verbose=1)
+        score = model.evaluate(x, y, verbose=1)  # type: ignore
         print(f"model score - loss: {score[0]} - acc: {score[1]} - mse: {score[2]}")
-        model.save(
+
-            f"./{self.log_path}/model_{score[0 if self.renewal == 'loss' else 1 if self.renewal == 'acc'  else 2 ]}.h5"
+        if self.renewal == "loss":
-        )
+            index = 0
+        elif self.renewal == "acc":
+            index = 1
+        else:
+            index = 2
+
+        model.save(f"./{self.log_path}/model_{score[index]}.h5")
         return model

pso/particle.py

@@ -18,6 +18,8 @@ class Particle:
     g_best_weights = None
     count = 0
 
+    MODEL_IS_NONE = "model is None"
+
     def __init__(
         self,
         model: keras.Model,
@@ -60,12 +62,10 @@ class Particle:
             exit(1)
 
         self.__reset_particle()
-        self.best_weights = self.model.get_weights()
+        self.best_weights = self.get_weights()
-        # self.before_best = self.model.get_weights()
         self.negative = negative
         self.mutation = mutation
         self.best_score = [np.inf, 0, np.inf]
-        # self.before_w = 0
         self.score_history = []
         self.converge_reset = converge_reset
         self.converge_reset_patience = converge_reset_patience
@@ -131,6 +131,28 @@ class Particle:
 
         return weights
 
+    def get_model(self):
+        if self.model is None:
+            raise ValueError(self.MODEL_IS_NONE)
+
+        return self.model
+
+    def set_model(self, model: keras.Model):
+        self.model = model
+        self.__reset_particle()
+
+    def get_weights(self):
+        if self.model is None:
+            raise ValueError(self.MODEL_IS_NONE)
+
+        return self.model.get_weights()
+
+    def evaluate(self, x, y):
+        if self.model is None:
+            raise ValueError(self.MODEL_IS_NONE)
+
+        return self.model.evaluate(x, y, verbose=0)  # type: ignore
+
     def get_score(self, x, y, renewal: str = "acc"):
         """
         모델의 성능을 평가하여 점수를 반환
@@ -144,19 +166,19 @@ class Particle:
             (float): 점수
         """
 
-        score = self.model.evaluate(x, y, verbose=0)
+        score = self.evaluate(x, y)
         if renewal == "loss":
             if score[0] < self.best_score[0]:
                 self.best_score = score
-                self.best_weights = self.model.get_weights()
+                self.best_weights = self.get_weights()
         elif renewal == "acc":
             if score[1] > self.best_score[1]:
                 self.best_score = score
-                self.best_weights = self.model.get_weights()
+                self.best_weights = self.get_weights()
         elif renewal == "mse":
             if score[2] < self.best_score[2]:
                 self.best_score = score
-                self.best_weights = self.model.get_weights()
+                self.best_weights = self.get_weights()
         else:
             raise ValueError("renewal must be 'acc' or 'loss' or 'mse'")
 
@@ -196,14 +218,16 @@ class Particle:
         return False
 
     def __reset_particle(self):
+
         self.model = keras.models.model_from_json(self.model.to_json())
         self.model.compile(
             optimizer="adam",
             loss=self.loss,
             metrics=["accuracy", "mse"],
         )
-        i_w_, i_s, i_l = self._encode(self.model.get_weights())
+        i_w_, i_s, i_l = self._encode(self.get_weights())
-        i_w_ = np.random.uniform(-0.1, 0.1, len(i_w_))
+        rng = np.random.default_rng()
+        i_w_ = rng.uniform(-0.1, 0.1, len(i_w_))
         self.velocities = self._decode(i_w_, i_s, i_l)
 
         del i_w_, i_s, i_l
@@ -218,24 +242,14 @@ class Particle:
             global_rate (float): 전역 최적해의 영향력
             w (float): 현재 속도의 영향력 - 관성 | 0.9 ~ 0.4 이 적당
         """
-        encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights())
+        encode_w, w_sh, w_len = self._encode(weights=self.get_weights())
         encode_v, v_sh, v_len = self._encode(weights=self.velocities)
         encode_p, p_sh, p_len = self._encode(weights=self.best_weights)
         encode_g, g_sh, g_len = self._encode(weights=Particle.g_best_weights)
-        # encode_before, before_sh, before_len = self._encode(
-        # weights=self.before_best
-        # )
-        r_0 = np.random.rand()
-        r_1 = np.random.rand()
 
-        # 이전 전역 최적해와 현재 전역 최적해가 다르면 관성을 순간적으로 증가 - 값이 바뀔 경우 기존 관성을 특정 기간동안 유지
+        rng = np.random.default_rng()
-        # if not np.array_equal(encode_before, encode_g, equal_nan=True):
+        r_0 = rng.random()
-        # 이전 가중치 중요도의 1.5 배로 관성을 증가
+        r_1 = rng.random()
-        # self.before_w = w * 0.5
-        # w = w + self.before_w
-        # else:
-        # self.before_w *= 0.75
-        # w = w + self.before_w
 
         if self.negative:
             # 지역 최적해와 전역 최적해를 음수로 사용하여 전역 탐색을 유도
@@ -257,8 +271,8 @@ class Particle:
                 + global_rate * r_1 * (encode_g - encode_w)
             )
 
-        if np.random.rand() < self.mutation:
+        if rng.random() < self.mutation:
-            m_v = np.random.uniform(-0.1, 0.1, len(encode_v))
+            m_v = rng.uniform(-0.1, 0.1, len(encode_v))
             new_v = m_v
 
         self.velocities = self._decode(new_v, w_sh, w_len)
@@ -281,22 +295,14 @@ class Particle:
             w_p (float): 지역 최적해의 분산 정도
             w_g (float): 전역 최적해의 분산 정도
         """
-        encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights())
+        encode_w, w_sh, w_len = self._encode(weights=self.get_weights())
         encode_v, v_sh, v_len = self._encode(weights=self.velocities)
         encode_p, p_sh, p_len = self._encode(weights=self.best_weights)
         encode_g, g_sh, g_len = self._encode(weights=Particle.g_best_weights)
-        # encode_before, before_sh, before_len = self._encode(
-        # weights=self.before_best
-        # )
-        r_0 = np.random.rand()
-        r_1 = np.random.rand()
 
-        # if not np.array_equal(encode_before, encode_g, equal_nan=True):
+        rng = np.random.default_rng()
-        # self.before_w = w * 0.5
+        r_0 = rng.random()
-        # w = w + self.before_w
+        r_1 = rng.random()
-        # else:
-        # self.before_w *= 0.75
-        # w = w + self.before_w
 
         if self.negative:
             new_v = (
@@ -311,8 +317,8 @@ class Particle:
                 + global_rate * r_1 * (w_g * encode_g - encode_w)
             )
 
-        if np.random.rand() < self.mutation:
+        if rng.random() < self.mutation:
-            m_v = np.random.uniform(-0.1, 0.1, len(encode_v))
+            m_v = rng.uniform(-0.1, 0.1, len(encode_v))
             new_v = m_v
 
         self.velocities = self._decode(new_v, w_sh, w_len)
@@ -327,7 +333,7 @@ class Particle:
         """
         가중치 업데이트
         """
-        encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights())
+        encode_w, w_sh, w_len = self._encode(weights=self.get_weights())
         encode_v, v_sh, v_len = self._encode(weights=self.velocities)
         new_w = encode_w + encode_v
         self.model.set_weights(self._decode(new_w, w_sh, w_len))
@@ -382,17 +388,6 @@ class Particle:
             self.__reset_particle()
             score = self.get_score(x, y, renewal)
 
-        # # score 가 inf 이면 가중치를 초기화
-        # # score 가 nan 이면 가중치를 초기화
-        # # score 가 0 이면 가중치를 초기화
-        # if np.isinf(score[0]) or np.isinf(score[1]) or np.isinf(score[2]) or np.isnan(score[0]) or np.isnan(score[1]) or np.isnan(score[2]) or score[0] == 0 or score[1] == 0 or score[2] == 0:
-        #     self.__reset_particle()
-        #     score = self.get_score(x, y, renewal)
-        # # score 가 상식적인 범위를 벗어나면 가중치를 초기화
-        # if score[0] > 1000 or score[1] > 1 or score[2] > 1000:
-        #     self.__reset_particle()
-        #     score = self.get_score(x, y, renewal)
-
         return score
 
     def step_w(self, x, y, local_rate, global_rate, w, w_p, w_g, renewal: str = "acc"):
@@ -445,17 +440,6 @@ class Particle:
             self.__reset_particle()
             score = self.get_score(x, y, renewal)
 
-        # # score 가 inf 이면 가중치를 초기화
-        # # score 가 nan 이면 가중치를 초기화
-        # # score 가 0 이면 가중치를 초기화
-        # if np.isinf(score[0]) or np.isinf(score[1]) or np.isinf(score[2]) or np.isnan(score[0]) or np.isnan(score[1]) or np.isnan(score[2]) or score[0] == 0 or score[1] == 0 or score[2] == 0:
-        #     self.__reset_particle()
-        #     score = self.get_score(x, y, renewal)
-        # # score 가 상식적인 범위를 벗어나면 가중치를 초기화
-        # if score[0] > 1000 or score[1] > 1 or score[2] > 1000:
-        #     self.__reset_particle()
-        #     score = self.get_score(x, y, renewal)
-
         return score
 
     def get_best_score(self):
@@ -490,12 +474,9 @@ class Particle:
         self.set_global_weights()
 
     def check_global_best(self, renewal: str = "loss"):
-        if renewal == "loss":
+        if (
-            if self.best_score[0] < Particle.g_best_score[0]:
+            (renewal == "loss" and self.best_score[0] < Particle.g_best_score[0])
-                self.update_global_best()
+            or (renewal == "acc" and self.best_score[1] > Particle.g_best_score[1])
-        elif renewal == "acc":
+            or (renewal == "mse" and self.best_score[2] < Particle.g_best_score[2])
-            if self.best_score[1] > Particle.g_best_score[1]:
+        ):
-                self.update_global_best()
-        elif renewal == "mse":
-            if self.best_score[2] < Particle.g_best_score[2]:
             self.update_global_best()