모델과 파티클 클래스에 타입 힌트 추가

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 .particle import Particle
 
 
@@ -33,22 +33,9 @@ class Optimizer:
 
     def __init__(
         self,
-        model: keras.models,
-        loss: any = None,
-        n_particles: int = None,
-        c0: float = 0.5,
-        c1: float = 0.3,
-        w_min: float = 0.1,
-        w_max: float = 0.9,
-        negative_swarm: float = 0,
-        mutation_swarm: float = 0,
-        np_seed: int = None,
-        tf_seed: int = None,
-        random_state: tuple = None,
-        convergence_reset: bool = False,
-        convergence_reset_patience: int = 10,
-        convergence_reset_min_delta: float = 0.0001,
-        convergence_reset_monitor: str = "loss",
+        model: keras.Model,
+        loss: Any,
+        **kwargs,
     ):
         """
         particle swarm optimization
@@ -63,15 +50,33 @@ class Optimizer:
             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.
-            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". - "loss" or "acc" or "mse"
+            np_seed (int | None): numpy seed. Defaults to None.
+            tf_seed (int | None): tensorflow seed. Defaults to None.
+            random_state (tuple): numpy random state. Defaults to None.
+            convergence_reset (bool): early stopping 사용 여부. Defaults to False.
+            convergence_reset_patience (int): early stopping 사용시 얼마나 기다릴지. Defaults to 10.
+            convergence_reset_min_delta (float): early stopping 사용시 얼마나 기다릴지. Defaults to 0.0001.
+            convergence_reset_monitor (str): early stopping 사용시 어떤 값을 기준으로 할지. Defaults to "loss". - "loss" or "acc" or "mse"
         """
 
         try:
+            n_particles = kwargs.get("n_particles", 10)
+            c0 = kwargs.get("c0", 0.5)
+            c1 = kwargs.get("c1", 0.3)
+            w_min = kwargs.get("w_min", 0.1)
+            w_max = kwargs.get("w_max", 0.9)
+            negative_swarm = kwargs.get("negative_swarm", 0)
+            mutation_swarm = kwargs.get("mutation_swarm", 0)
+            np_seed = kwargs.get("np_seed", None)
+            tf_seed = kwargs.get("tf_seed", None)
+            random_state = kwargs.get("random_state", None)
+            convergence_reset = kwargs.get("convergence_reset", False)
+            convergence_reset_patience = kwargs.get("convergence_reset_patience", 10)
+            convergence_reset_min_delta = kwargs.get(
+                "convergence_reset_min_delta", 0.0001
+            )
+            convergence_reset_monitor = kwargs.get("convergence_reset_monitor", "loss")
+
             if model is None:
                 raise ValueError("model is None")
             elif model is not None and not isinstance(model, keras.models.Model):

pso/particle.py

@@ -1,5 +1,6 @@
 import numpy as np
 from tensorflow import keras
+from typing import Any
 
 
 class Particle:
@@ -19,8 +20,8 @@ class Particle:
 
     def __init__(
         self,
-        model: keras.models,
-        loss: any = None,
+        model: keras.Model,
+        loss: Any = None,
         negative: bool = False,
         mutation: float = 0,
         converge_reset: bool = False,
@@ -142,6 +143,7 @@ class Particle:
         Returns:
             (float): 점수
         """
+
         score = self.model.evaluate(x, y, verbose=0)
         if renewal == "loss":
             if score[0] < self.best_score[0]:
@@ -163,7 +165,7 @@ class Particle:
     def __check_converge_reset(
         self,
         score,
-        monitor: str = None,
+        monitor: str = "auto",
         patience: int = 10,
         min_delta: float = 0.0001,
     ):
@@ -176,7 +178,7 @@ class Particle:
             patience (int, optional): early stop을 위한 기다리는 횟수. Defaults to 10.
             min_delta (float, optional): early stop을 위한 최소 변화량. Defaults to 0.0001.
         """
-        if monitor is None:
+        if monitor == "auto":
             monitor = "acc"
         if monitor in ["loss"]:
             self.score_history.append(score[0])

test/iris.py

@@ -41,7 +41,7 @@ x_train, x_test, y_train, y_test = load_data()
 
 
 pso_iris = optimizer(
-    model,
+    model=model,
     loss="categorical_crossentropy",
     n_particles=100,
     c0=0.5,