Added a main for the model training and evaluation. The code from the jupyter notebook has been split up into functions in pipeline.py. User input has been removed and instead replaced with function arguments, to change at the start of main.py.

2025-07-28 15:30:27 +02:00
parent b5794122c1
commit 85022f9fcc
2 changed files with 279 additions and 0 deletions
@@ -0,0 +1,62 @@
+from pipeline import (
+    load_dataset,
+    filter_data,
+    filter_test_data,
+    prepare_user_data,
+    train_models,
+    evaluate_models,
+    display_warning_about_2020_data,
+    display_warnings_for_scenarios
+)
+
+# === Configurable Parameters ===
+DATA_PATH = './Datasets/ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
+OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx'
+SEQUENCE_LENGTHS = [20]  # You can add more: [20, 25, 30]
+
+TRAINING_SCENARIO = [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]
+VALIDATION_SCENARIO = [(2019, [10, 11, 12])]
+TEST_SCENARIO = [(2020, [1, 2])]  # Jan–Feb 2020 only
+
+# === Optional display only ===
+predefined_training_scenarios = {
+    "Scenario 1": {"years_months": [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]},
+    "Scenario 2": {"years_months": [(2017, list(range(1, 13))), (2018, list(range(1, 13))), (2019, list(range(1, 10)))]}
+}
+predefined_validation_scenarios = {
+    "Scenario A": {"years_months": [(2019, [10, 11, 12])]}
+}
+
+def main():
+    print("=== Training Scenario Setup ===")
+    display_warning_about_2020_data()
+    display_warnings_for_scenarios("training", predefined_training_scenarios, predefined_validation_scenarios)
+
+    print("\n=== Validation Scenario Setup ===")
+    display_warning_about_2020_data()
+    display_warnings_for_scenarios("validation", predefined_training_scenarios, predefined_validation_scenarios)
+
+    # === Load and preprocess ===
+    df = load_dataset(DATA_PATH)
+
+    ALLUSERS32_15MIN_WITHOUTTHREHOLD = False
+    if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH):
+        ALLUSERS32_15MIN_WITHOUTTHREHOLD = True
+
+    training_data = filter_data(df, TRAINING_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
+    validation_data = filter_data(df, VALIDATION_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
+
+    user_data_train = prepare_user_data(training_data)
+    user_data_val = prepare_user_data(validation_data)
+
+    # === Train models ===
+    best_models = train_models(user_data_train, user_data_val, sequence_lengths=SEQUENCE_LENGTHS)
+
+    # === Load and evaluate test ===
+    test_df = filter_test_data(df, TEST_SCENARIO)
+    evaluate_models(best_models, test_df, SEQUENCE_LENGTHS, OUTPUT_EXCEL_PATH, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
+
+    print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}")
+
+if __name__ == "__main__":
+    main()