Added new evaluation method

.gitignore

@@ -139,3 +139,4 @@ dmypy.json
 cython_debug/
 
 .idea
+working/tuner

main.py

@@ -1,5 +1,9 @@
+import json
+import os
+
 import numpy as np
 import pandas as pd
+import sklearn
 
 from pipeline import (
     load_dataset,
@@ -9,17 +13,28 @@ from pipeline import (
     train_models,
     evaluate_models,
     display_warning_about_2020_data,
-    display_warnings_for_scenarios
+    display_warnings_for_scenarios, prepare_data_for_model
 )
 
 year_str = 'Year'
 month_str = 'Month'
 user_str = 'user'
+split_str = 'split type'
+threshold_str = 'threshold used'
+timespan_str = 'time used'
+sequence_length_str = 'sequence length'
+precision_str = 'precision'
+recall_str = 'recall'
+f1_string = 'f1 score'
+weak_column_names = ['DayOfWeek_'+day for day in
+                     ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]]
 
 # === Configurable Parameters ===
-DATA_PATH = './Datasets/ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
+dataset_path = './Datasets/'
+DATA_PATH = dataset_path +'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
 OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx'
-SEQUENCE_LENGTHS = [20]  # You can add more: [20, 25, 30]
+result_filename = './working/evaluation_results.json'
+SEQUENCE_LENGTHS = [20, 15, 10, 5, 1]  # 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])]
@@ -67,9 +82,6 @@ def main():
 
     # === Load and preprocess ===
     df = load_dataset(DATA_PATH)
-    removed = remove_covid_data(df)
-    tr,val,te = split_data_by_userdata_percentage(df, (80,10,10))
-    tr_2, val_2, te_2 = split_data_by_month_percentage(df, (80, 10, 10))
 
     ALLUSERS32_15MIN_WITHOUTTHREHOLD = False
     if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH):
@@ -90,5 +102,68 @@ def main():
 
     print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}")
 
+
+def reduce_columns(df, filename):
+    if '15MIN' in filename:
+        return df.drop(columns=['Month', 'Year', 'date']+weak_column_names)
+    else:
+        return df.drop(columns=['Month', 'Year', 'date'])
+
+def main_two():
+    results = pd.DataFrame()
+    if os.path.exists(result_filename):
+        results = pd.DataFrame(json.load(open(result_filename)))
+    for sequence_length in SEQUENCE_LENGTHS:
+        for data_filename in os.listdir(dataset_path):
+            for split_id, split_method in [('data percentages', split_data_by_userdata_percentage),('month percentages', split_data_by_month_percentage)]:
+                timespan_id = '1HR'
+                threshold_id = 'WITH'
+                if '15MIN' in data_filename:
+                    timespan_id = '15MIN'
+                if 'WITHOUT' in data_filename:
+                    threshold_id = 'WITHOUT'
+                if len(results) > 0:
+                    if len(results[(results[split_str]==split_id) &
+                                   (results[timespan_str]==timespan_id) &
+                                   (results[threshold_str]==threshold_id) &
+                                   (results[sequence_length_str]==sequence_length)]) > 0:
+                        continue
+
+                file_path = os.path.join(dataset_path, data_filename)
+                df = load_dataset(file_path)
+                df = remove_covid_data(df)
+                tr,val,te = split_method(df, percentages=(80,10,10))
+                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)
+
+                best_models = train_models(user_data_train, user_data_val, sequence_lengths=[sequence_length])
+
+                results = pd.concat([results,
+                                     evaluate_model_on_test_data(model=best_models[sequence_length]['model'], test_df=te, split_id=split_id,
+                                     sequence_length=sequence_length, time_span_id=timespan_id, threshold_id=threshold_id)], ignore_index=True)
+                results.to_json(result_filename)
+
+
+# === Evaluation ===
+def evaluate_model_on_test_data(model, test_df,sequence_length, split_id, threshold_id, time_span_id):
+    user_data = prepare_user_data(test_df)
+    x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
+
+    y_pred = model.predict(x, verbose=0)
+    y_pred_classes = np.argmax(y_pred, axis=1)
+
+    recall = sklearn.metrics.recall_score(y, y_pred_classes, average='weighted')
+    precision = sklearn.metrics.precision_score(y, y_pred_classes, average='weighted')
+    f1_score = sklearn.metrics.f1_score(y, y_pred_classes, average='weighted')
+    return pd.DataFrame({split_str:[split_id], threshold_str:[threshold_id], timespan_str:[time_span_id],
+                         sequence_length_str:[sequence_length], recall_str:[recall],
+                         precision_str:[precision], f1_string:[f1_score]})
+
+
 if __name__ == "__main__":
-    main()
+    main_two()
+    print('Done')

pipeline.py

@@ -54,6 +54,18 @@ def prepare_user_data(df):
     users = df_sorted['user'].unique()
     return {user: df_sorted[df_sorted['user'] == user] for user in users}
 
+def prepare_data_for_model(user_data, sequence_length):
+    X, y = [], []
+    for user, data in user_data.items():
+        features = data.drop('user', axis=1).values
+        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])
+    X = np.array(X)
+    y = np.array(y)
+    return X,y
+
 # === Training & Validation ===
 def train_models(user_data, user_data_val, sequence_lengths=[20], tuner_dir="./working/tuner"):
     best_models = {}
@@ -65,25 +77,8 @@ def train_models(user_data, user_data_val, sequence_lengths=[20], tuner_dir="./w
 
     for sequence_length in sequence_lengths:
         print(f"\n=== Training for Sequence Length: {sequence_length} ===")
-        X, y = [], []
-        for user, data in user_data.items():
-            features = data.drop('user', axis=1).values
-            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])
-        X = np.array(X)
-        y = np.array(y)
-
-        X_val, y_val = [], []
-        for user, data in user_data_val.items():
-            features = data.drop('user', axis=1).values
-            labels = data['user'].values
-            for i in range(len(features) - sequence_length):
-                X_val.append(features[i:i + sequence_length])
-                y_val.append(labels[i + sequence_length])
-        X_val = np.array(X_val)
-        y_val = np.array(y_val)
+        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)
 
         if X.shape[0] == 0 or X_val.shape[0] == 0:
             print(f"⚠️ Skipped sequence length {sequence_length} due to insufficient data.")