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Added Baselines + extended manual evaluation

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bs
2025-11-18 13:11:48 +01:00
parent ac57fef0e5
commit 40b32c30d3
3 changed files with 73 additions and 17 deletions
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pipeline.py
+22 -10
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@@ -1,3 +1,5 @@
import random
import keras_tuner
import numpy as np
import pandas as pd
@@ -78,6 +80,8 @@ def prepare_data_for_model(user_data, sequence_length):
x_new, y_new = make_sequences(data, sequence_length)
x = x + x_new
y = y + y_new
random.Random(17).shuffle(x)
random.Random(17).shuffle(y)
x = np.array(x)
y = np.array(y)
return x,y
@@ -205,7 +209,7 @@ 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):
def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, l_rate, reg, 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())
@@ -213,7 +217,7 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, sequence
# prepare model
def build_model():
model = Sequential()
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch))
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch, bias_regularizer=reg))
# if model_type == model_type_bilstm:
# model.add(Bidirectional(units=units_hp))
if model_type == model_type_lstm:
@@ -225,7 +229,7 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, sequence
# 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),
optimizer=Adam(learning_rate=l_rate),
loss=SparseCategoricalCrossentropy(),
metrics=[SparseCategoricalAccuracy()],
)
@@ -234,21 +238,24 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, sequence
model = build_model()
# fit model
train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list()
train_acc, test_acc, train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),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)
acc, p, r, f1 = evaluate(model, train_data, sequence_length, n_batch)
train_acc.append(acc)
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)
acc, p, r, f1 = evaluate(model, val_data, sequence_length, n_batch)
test_acc.append(acc)
test_p.append(p)
test_r.append(r)
test_f1.append(f1)
history = DataFrame()
history['train_acc'], history['test_acc'] = train_acc, test_acc
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
@@ -262,12 +269,17 @@ def evaluate(model, df, sequence_length, batch_size):
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
return eval_metrics(y_true=y_true, y_pred=y_pred_classes)
def eval_metrics(y_true, y_pred):
f1 = f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
p = precision_score(y_true=y_true, y_pred=y_pred, average='weighted')
r = recall_score(y_true=y_true, y_pred=y_pred, average='weighted')
acc = accuracy_score(y_true=y_true, y_pred=y_pred)
return acc, 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...")