added new version to run with adjusted training process

pipeline.py

@@ -1,7 +1,10 @@
+import keras_tuner
 import numpy as np
 import pandas as pd
 import shutil
 import os
+
+from keras.src.metrics import F1Score
 from pandas import ExcelWriter
 import keras_tuner as kt
 from tensorflow.keras.models import Sequential
@@ -11,7 +14,7 @@ from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
 from keras_tuner import RandomSearch
 from sklearn.metrics import accuracy_score
 
-epochs = 50
+epochs = 30
 model_type_gru = 'GRU'
 model_type_lstm = 'LSTM'
 model_type_bilstm = 'BiLSTM'
@@ -139,6 +142,58 @@ def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./workin
 
     return best_models
 
+# === Training & Validation ===
+def train_models_v2(user_data, user_data_val, sequence_length, model_type):
+    tuner_dir = "./working/tuner"
+
+    early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
+    lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2)
+
+    shutil.rmtree(tuner_dir, ignore_errors=True)
+
+    x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
+    x_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
+
+    n_features = x.shape[2]
+    users = list(user_data.keys())
+
+    def build_model(hp):
+        model = Sequential()
+        if model_type==model_type_bilstm:
+            model.add(Bidirectional(LSTM(units=hp.Int('units', 32, 256, step=2),
+                                         input_shape=(sequence_length, n_features))))
+        if model_type==model_type_lstm:
+            model.add(LSTM(units=hp.Int('units', 32, 256, step=2),
+                                         input_shape=(sequence_length, n_features)))
+        if model_type==model_type_gru:
+            model.add(GRU(units=hp.Int('units', 32, 256, step=2),
+                                         input_shape=(sequence_length, n_features)))
+        model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1)))
+        model.add(Dense(len(users), activation='softmax'))
+        model.compile(
+            optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
+            loss='sparse_categorical_crossentropy',
+            metrics=['accuracy']
+        )
+        return model
+
+    tuner = RandomSearch(
+        build_model,
+        objective='val_loss',
+        max_trials=100,
+        directory=tuner_dir,
+    )
+
+    tuner.search(x, y, epochs=epochs, validation_data=(x_val, y_val),
+                 callbacks=[early_stopping, lr_scheduler])
+
+    best_hps = tuner.get_best_hyperparameters(1)[0]
+    best_model = tuner.hypermodel.build(best_hps)
+    best_model.fit(x, y, epochs=epochs, validation_data=(x_val, y_val),
+                   callbacks=[early_stopping, lr_scheduler])
+    return best_model
+
+
 # === Evaluation ===
 def evaluate_models(best_models, df_test, sequence_lengths, output_excel_path, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
     print("\n🧪 Evaluating on Test Data...")