Added more tests

.gitignore

@@ -141,3 +141,4 @@ cython_debug/
 .idea
 working/tuner
 working
+figures

main.py

@@ -68,11 +68,11 @@ def split_data_by_month_percentage(df, percentages):
     tr, va, te = np.split(ids, [int((train_p/100) * len(ids)), int(((train_p + valid_p)/100) * len(ids))])
     return df.merge(tr, on=[year_str, month_str], how='inner'), df.merge(va, on=[year_str, month_str], how='inner'), df.merge(te, on=[year_str, month_str], how='inner')
 
-def split_data_by_userdata_percentage(df, percentages):
+def split_data_by_userdata_percentage(df, percentages, sample):
     train_p, valid_p, test_p = percentages
     tr, va, te = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
     for user_id in df[user_str].unique():
-        user_data = df[df[user_str]==user_id].sort_values([year_str, month_str])
+        user_data = df[df[user_str]==user_id].sample(frac=sample/ 100).sort_values([year_str, month_str])
         u_tr, u_va, u_te = np.split(user_data, [int((train_p/100)*len(user_data)), int(((train_p+valid_p)/100)*len(user_data))])
         tr = pd.concat([tr, u_tr], ignore_index=True)
         va = pd.concat([va, u_va], ignore_index=True)
@@ -285,9 +285,47 @@ def visualise_results_v2():
     # Fazit: keine eindeutig besseren Versionen erkennbar
 
 
+def test(model_type):
+    sequence_length = 20
+    data_filename = os.listdir(dataset_path)[0]
+    timespan_id = hour_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)
+    results = pd.DataFrame()
+
+    for percentage in [33,66,100]:
+        print('Percentage:', percentage)
+        tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10),sample=percentage)
+        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_model = train_models_v2(user_data_train, user_data_val,
+                                      sequence_length=sequence_length,
+                                      model_type=model_type)
+
+        results = pd.concat([results,
+                             evaluate_model_on_test_data(model=best_model,
+                                                         test_df=te,
+                                                         sequence_length=sequence_length,
+                                                         time_span_id=timespan_id,
+                                                         threshold_id=threshold_id,
+                                                         model_type=model_type,
+                                                         split_id=data_split_str)],
+                            ignore_index=True)
+    print(results)
+
+
 if __name__ == "__main__":
     # main_two_v1()
     # visualise_results_v1()
-    main_two_v2(model_type=model_type_gru)
+    test(model_type=model_type_gru)
+   # main_two_v2(model_type=model_type_gru)
     #visualise_results_v2()
     print('Done')

pipeline.py

@@ -4,7 +4,7 @@ import pandas as pd
 import shutil
 
 from keras import Input
-from keras.src.metrics import F1Score
+from keras.src.metrics import F1Score, Precision, Recall, Accuracy
 from pandas import ExcelWriter
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU
@@ -13,7 +13,7 @@ from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
 from keras_tuner import RandomSearch
 from sklearn.metrics import accuracy_score
 
-epochs = 50
+epochs = 5#50
 model_type_gru = 'GRU'
 model_type_lstm = 'LSTM'
 model_type_bilstm = 'BiLSTM'
@@ -57,14 +57,15 @@ def filter_test_data(df, scenario):
     return pd.concat(data_parts, ignore_index=True)
 
 def prepare_user_data(df):
-    df_sorted = df.sort_values(by='user').reset_index(drop=True)
-    users = df_sorted['user'].unique()
-    return {user: df_sorted[df_sorted['user'] == user] for user in users}
+    #df_sorted = df.sort_values(by='user').reset_index(drop=True)
+    users = df['user'].unique()
+    return {user: df[df['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
+        features = features.astype(int)
         labels = data['user'].values
         for i in range(len(features) - sequence_length):
             X.append(features[i:i + sequence_length])
@@ -144,9 +145,11 @@ def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./workin
 # === Training & Validation ===
 def train_models_v2(user_data, user_data_val, sequence_length, model_type):
     tuner_dir = "./working/tuner/"+model_type
+    #val_metric = 'val_f1'
+    val_metric = 'val_precision'
 
-    early_stopping = EarlyStopping(monitor='val_f1', patience=3, restore_best_weights=True)
-    lr_scheduler = ReduceLROnPlateau(monitor='val_f1', factor=0.5, patience=2)
+    early_stopping = EarlyStopping(monitor=val_metric, patience=3, restore_best_weights=True)
+    lr_scheduler = ReduceLROnPlateau(monitor=val_metric, factor=0.5, patience=2)
 
     shutil.rmtree(tuner_dir, ignore_errors=True)
 
@@ -156,11 +159,12 @@ def train_models_v2(user_data, user_data_val, sequence_length, model_type):
     n_features = x.shape[2]
     users = list(user_data.keys())
 
-    y_val = np.array(y_val).reshape(-1, 1)
-    y = np.array(y).reshape(-1, 1)
+    #y_val = np.array(y_val).reshape(-1, 1)
+    #y = np.array(y).reshape(-1, 1)
 
     def build_model(hp):
-        units_hp = hp.Int('units', 2, 256, step=2, sampling="log")
+        units_hp = hp.Int('units', 2, 8, step=2, sampling="log")
+#        units_hp = hp.Int('units', 2, 256, step=2, sampling="log")
 
         model = Sequential()
         model.add(Input((sequence_length, n_features)))
@@ -170,18 +174,20 @@ def train_models_v2(user_data, user_data_val, sequence_length, model_type):
             model.add(LSTM(units=units_hp))
         if model_type==model_type_gru:
             model.add(GRU(units=units_hp))
-        model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1)))
+        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=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
+            optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-5])),
             loss='sparse_categorical_crossentropy',
-            metrics=[F1Score(name='f1', average='weighted')]
+            metrics=[#F1Score(name='f1', average='weighted'),
+                     Precision(), #Recall(), Accuracy()
+            ]
         )
         return model
 
     tuner = RandomSearch(
         build_model,
-        objective=keras_tuner.Objective("val_f1", direction="max"),
+        objective=keras_tuner.Objective(val_metric, direction="max"),
         max_trials=120,
         directory=tuner_dir,
     )