feat: clean the code (#48)

* fix: update trainer and tuner

* chore: update pyproject to support python3.11

* fix: update pyproject

* fix: pyproject

Makefile

@@ -5,7 +5,7 @@ check_dirs := tfts examples tests
 # run checks on all files and potentially modifies some of them
 
 style:
-	black --preview $(check_dirs)
+	black $(check_dirs)
 	isort $(check_dirs)
 	flake8
 	pre-commit run --all-files

requirements.txt

@@ -1,6 +1,6 @@
 tensorflow>=2.3.1
-optuna>=2.0
-pandas>=1.0
+optuna>=2.3
+pandas>=1.2
 scikit-learn>0.23
 joblib
 matplotlib

tfts/trainer.py

@@ -166,7 +166,7 @@ def train_step(self, x_train, y_train):
         return y_pred, loss
 
     def valid_loop(self, valid_loader):
-        valid_loss = 0.0
+        valid_loss: float = 0.0
         y_valid_trues, y_valid_preds = [], []
 
         for valid_step, (x_valid, y_valid) in enumerate(valid_loader):
@@ -221,18 +221,21 @@ def __init__(
         optimizer: tf.keras.optimizers = tf.keras.optimizers.Adam(0.003),
         lr_scheduler: Optional[tf.keras.optimizers.Optimizer] = None,
         strategy: Optional[tf.keras.optimizers.schedules.LearningRateSchedule] = None,
+        run_eagerly: bool = True,
         **kwargs: Dict
     ) -> None:
         """
         model: tf.keras.Model instance
         loss: loss function
         optimizer: tf.keras.Optimizer instance
+        run_eargely: it depends which one is much faster
         """
         self.model = model
         self.loss_fn = loss_fn
         self.optimizer = optimizer
         self.lr_scheduler = lr_scheduler
         self.strategy = strategy
+        self.run_eargely = run_eagerly
 
         for key, value in kwargs.items():
             setattr(self, key, value)
@@ -298,7 +301,9 @@ def train(
             self.model = self.model.build_model(inputs=inputs)
 
         # print(self.model.summary())
-        self.model.compile(loss=self.loss_fn, optimizer=self.optimizer, metrics=callback_metrics, run_eagerly=False)
+        self.model.compile(
+            loss=self.loss_fn, optimizer=self.optimizer, metrics=callback_metrics, run_eagerly=self.run_eargely
+        )
         if isinstance(train_dataset, (list, tuple)):
             x_train, y_train = train_dataset
 

tfts/tuner.py

@@ -1,5 +1,7 @@
 """tfts auto tuner"""
 
+from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Type, Union
+
 import numpy as np
 import optuna
 
@@ -15,8 +17,8 @@ class AutoTuner(object):
     def __init__(self, use_model: str) -> None:
         self.use_model = use_model
 
-    def generate_parameter(self):
-        pass
+    def generate_parameter(self) -> None:
+        return
 
-    def run(self, config, direction="maximize"):
-        pass
+    def run(self, config, direction: str = "maximize") -> None:
+        return