Added method for manual testing of hyperparameters

main.py

@@ -13,7 +13,7 @@ from pipeline import (
     prepare_user_data,
     train_models,
     evaluate_models,
-    prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2
+    prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2, train_one_model
 )
 
 year_str = 'Year'
@@ -321,11 +321,53 @@ def test(model_type):
                             ignore_index=True)
     print(results)
 
+def manual_tuning(model_type):
+    # load dataset
+    sequence_length = 20
+    data_filename = 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'
+    timespan_id = min_timespan_str
+    threshold_id = with_threshold_str
+
+    file_path = os.path.join(dataset_path, data_filename)
+    df = load_dataset(file_path)
+    df = remove_covid_data(df)
+
+    tr, val, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=20)
+    tr = reduce_columns(tr, data_filename)
+    val = reduce_columns(val, data_filename)
+    te = reduce_columns(te, data_filename)
+
+    user_data_train = prepare_user_data(tr)
+    user_data_val = prepare_user_data(val)
+
+    # fit and evaluate model
+    # config
+    repeats = 5
+    n_batch = 4
+    n_epochs = 500
+    n_neurons = 1
+
+    history_list = list()
+    # run diagnostic tests
+    for i in range(repeats):
+        history = train_one_model(user_data_train, user_data_val, n_batch, n_epochs, n_neurons,
+                                     sequence_length=sequence_length,
+                                     model_type=model_type)
+        history_list.append(history)
+    for metric in ['p', 'r', 'f1']:
+        for history in history_list:
+            plt.plot(history['train_'+metric], color='blue')
+            plt.plot(history['test_'+metric], color='orange')
+        plt.savefig(figure_path+metric+'_epochs_diagnostic.png')
+        plt.clf()
+    print('Done')
+
 
 if __name__ == "__main__":
     # main_two_v1()
     # visualise_results_v1()
-    test(model_type=model_type_gru)
+    #test(model_type=model_type_gru)
    # main_two_v2(model_type=model_type_gru)
     #visualise_results_v2()
+    manual_tuning(model_type=model_type_lstm)
     print('Done')

pipeline.py

@@ -4,14 +4,15 @@ import pandas as pd
 import shutil
 
 from keras import Input
-from keras.src.metrics import F1Score, Precision, Recall, Accuracy
+from keras.src.losses import SparseCategoricalCrossentropy
-from pandas import ExcelWriter
+from keras.src.metrics import F1Score, Precision, Recall, Accuracy, SparseCategoricalAccuracy
+from pandas import ExcelWriter, DataFrame
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
 from keras_tuner import RandomSearch
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
 
 epochs = 5#50
 model_type_gru = 'GRU'
@@ -61,18 +62,25 @@ def prepare_user_data(df):
     users = df['user'].unique()
     return {user: df[df['user'] == user] for user in users}
 
+def make_sequences(data, sequence_length):
+    x, y = [], []
+    features = data.drop('user', axis=1).values
+    features = features.astype(int)
+    labels = data['user'].values
+    for i in range(len(features) - sequence_length):
+        x.append(features[i:i + sequence_length])
+        y.append(labels[i + sequence_length])
+    return x, y
+
 def prepare_data_for_model(user_data, sequence_length):
-    X, y = [], []
+    x, y = [], []
     for user, data in user_data.items():
-        features = data.drop('user', axis=1).values
+        x_new, y_new = make_sequences(data, sequence_length)
-        features = features.astype(int)
+        x = x + x_new
-        labels = data['user'].values
+        y = y + y_new
-        for i in range(len(features) - sequence_length):
+    x = np.array(x)
-            X.append(features[i:i + sequence_length])
-            y.append(labels[i + sequence_length])
-    X = np.array(X)
     y = np.array(y)
-    return X,y
+    return x,y
 
 # === Training & Validation ===
 def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./working/tuner", model_type=model_type_lstm):
@@ -197,6 +205,69 @@ def train_models_v2(user_data, user_data_val, sequence_length, model_type):
     return tuner.get_best_models(num_models=1)[0]
 
 
+def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, sequence_length, model_type):
+    x, y = prepare_data_for_model(user_data=train_data, sequence_length=sequence_length)
+    n_features = x.shape[2]
+    users = list(train_data.keys())
+
+    # prepare model
+    def build_model():
+        model = Sequential()
+        model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch))
+#        if model_type == model_type_bilstm:
+ #           model.add(Bidirectional(units=units_hp))
+        if model_type == model_type_lstm:
+            model.add(LSTM(n_neurons))
+  #      if model_type == model_type_gru:
+   #         model.add(GRU(units=units_hp))
+        # TODO: add another dense layer
+        #model.add(Dense(256, activation='relu'))
+       # model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.2, step=0.1)))
+        model.add(Dense(len(users), activation='softmax'))
+        model.compile(
+            optimizer=Adam(learning_rate=1e-5),
+            loss=SparseCategoricalCrossentropy(),
+            metrics=[SparseCategoricalAccuracy()],
+        )
+        return model
+
+    model = build_model()
+
+    # fit model
+    train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list()
+    for i in range(n_epochs):
+        model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False)
+        # evaluate model on train data
+        p, r, f1 = evaluate(model, train_data, sequence_length, n_batch)
+        train_p.append(p)
+        train_r.append(r)
+        train_f1.append(f1)
+        # evaluate model on test data
+        p, r, f1 = evaluate(model, val_data, sequence_length, n_batch)
+        test_p.append(p)
+        test_r.append(r)
+        test_f1.append(f1)
+
+    history = DataFrame()
+    history['train_p'], history['test_p'] = train_p, test_p
+    history['train_r'], history['test_r'] = train_r, test_r
+    history['train_f1'], history['test_f1'] = train_f1, test_f1
+    return history
+
+
+def evaluate(model, df, sequence_length, batch_size):
+    x, y = prepare_data_for_model(user_data=df, sequence_length=sequence_length)
+    x = np.array(x)
+    y_true = np.array(y)
+
+    y_pred = model.predict(x, verbose=0, batch_size=batch_size)
+    y_pred_classes = np.argmax(y_pred, axis=1)
+    f1 = f1_score(y_true=y_true, y_pred=y_pred_classes, average='weighted')
+    p = precision_score(y_true=y_true, y_pred=y_pred_classes, average='weighted')
+    r = recall_score(y_true=y_true, y_pred=y_pred_classes, average='weighted')
+    return p, r, f1
+
+
 # === Evaluation ===
 def evaluate_models(best_models, df_test, sequence_lengths, output_excel_path, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
     print("\n🧪 Evaluating on Test Data...")