From 470c4453aeef91afc09ac4e690bc12c2e3396fff Mon Sep 17 00:00:00 2001
From: Israel Figueroa <israel.figueroa@usm.cl>
Date: Mon, 17 Mar 2025 16:06:17 -0300
Subject: [PATCH] long run

---
 TrainerClass.py   | 47 +----------------------------------------------
 train_sequence.py | 25 +++++++++++++------------
 2 files changed, 14 insertions(+), 58 deletions(-)

diff --git a/TrainerClass.py b/TrainerClass.py
index 1aba047..5d3321b 100644
--- a/TrainerClass.py
+++ b/TrainerClass.py
@@ -97,9 +97,7 @@ class eNoseTrainer:
 
     def wrap_and_save(self):
         self.logger.info("{:=^60}".format(' Saving Summary and Wrap the output in a ZipFile '))
-
-
-        with zipfile.ZipFile('{}-{}.zip'.format(self.name, self.start), 'w', zipfile.ZIP_DEFLATED) as zipf:
+        with zipfile.ZipFile('{}-{}.zip'.format(self.name, self.start), 'w', zipfile.ZIP_LZMA) as zipf:
             for root, dirs, files in os.walk(self.name):
                 for file in files:
                     zipf.write(os.path.join(root, file))
@@ -109,49 +107,6 @@ class eNoseTrainer:
         self.logger.debug(f'Looking for {dataset}, {model}, {self.loader.target}, {self.ratio} => {search_result} {self.ledger.shape}')
         return search_result
 
-    def model_A(self, hp):
-
-        model = Sequential()
-        model.add(Dense(units=hp.Int('units_input', min_value=48, max_value=56, step=8), input_dim=self.nvars, activation='relu'))
-        model.add(BatchNormalization())
-        model.add(Dropout(rate=hp.Float('dropout_input', min_value=0.1, max_value=0.1, step=0.1)))
-
-        model.add(Dense(units=hp.Int('units_hidden', min_value=32, max_value=48, step=8), activation='relu'))
-        model.add(BatchNormalization())
-        model.add(Dropout(rate=hp.Float('dropout_hidden', min_value=0.4, max_value=0.4, step=0.1)))
-
-        model.add(Dense(1, activation='sigmoid'))
-
-        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', AUC()])
-
-        return model
-
-    def train_and_score_model_keras(self, X_train, X_test, Y_train, Y_test, seed, label):
-#        set_random_seed(seed)
-        ntrials = 6
-        tuner = RandomSearch(
-            self.get_model_train_keras,
-            objective='val_loss', #val_loss
-#            seed=seed,
-            max_trials=ntrials,
-#            executions_per_trial=1,  # Número de ejecuciones por cada configuración
-            directory=self.name,
-            project_name='{}-{}'.format(label,seed))
-
-        self.logger.info("{:~^60}".format(' {}-{} '.format(label,seed)))
-
-
-        search_dir = "{}/keras-tuner-{}/".format(self.name,label)
-        os.makedirs(search_dir, exist_ok=True)
-        search_callback = TensorBoard(log_dir=search_dir)
-        early_stopping_search = EarlyStopping(monitor='val_loss', patience=13, min_delta=0.005, start_from_epoch=7, restore_best_weights=True)
-        tuner.search(X_train, Y_train, epochs=150, batch_size=10, validation_data=(X_test, Y_test), callbacks=[early_stopping_search, search_callback])
-        best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
-
-        self.trained += 1
-        self.bar.update()
-        return mse, mae, rmse, optimized_model, model_params
-
     def get_model_train(self):
         return [
             XGBRegressor(objective='reg:squarederror'),
diff --git a/train_sequence.py b/train_sequence.py
index 1f0d2c0..47fa6f0 100644
--- a/train_sequence.py
+++ b/train_sequence.py
@@ -8,20 +8,21 @@ source_channels=["MQ 8", "MQ 9", "MQ 135", "TGS 813", "TGS 821", "TGS 2600", "TG
 target_variables=['C2H2', 'CH4', 'C3H6', 'CO', 'C2H6', 'C3H8', 'C2H4', 'H2', 'O2']
 
 eNoseLoader = GasSensorDataLoader("enose_dataset", threshold=0.85, source_channels=source_channels, target_list=target_variables, debug=False)
-eNose = eNoseTrainer(eNoseLoader, test_size=0.2, debug=True)
+eNose = eNoseTrainer(eNoseLoader, test_size=0.2, debug=False)
+
 eNoseLoader.target_list=['H2',]
 eNose.fit()
-# eNoseLoader.target_list=['C2H2',]
-# eNose.fit()
-# eNoseLoader.target_list=['CH4',]
-# eNose.fit()
-# eNoseLoader.target_list=['C2H4',]
-# eNose.fit()
-# eNoseLoader.target_list=['C2H6',]
-# eNose.fit()
-# eNoseLoader.target_list=['H2', 'C2H2', 'CH4', 'C2H4', 'C2H6',]
-# eNose.fit()
-# eNose.wrap_and_save()
+eNoseLoader.target_list=['C2H2',]
+eNose.fit()
+eNoseLoader.target_list=['CH4',]
+eNose.fit()
+eNoseLoader.target_list=['C2H4',]
+eNose.fit()
+eNoseLoader.target_list=['C2H6',]
+eNose.fit()
+eNoseLoader.target_list=['H2', 'C2H2', 'CH4', 'C2H4', 'C2H6',]
+eNose.fit()
+eNose.wrap_and_save()
 
 # eNoseLoader.target_list=['H2',]
 # eNose.gen_plots('Tabular-conv3','XGBRegressor_0')