task.set_train_task(func=f_train, data=train, epoch=args.epoch, name='train')
task.set_valid_task(func=f_score,
data=valid,
freq=training.Timer(percentage=0.8),
name='valid')
task.run()
# ===========================================================================
# Prediction
# ===========================================================================
y_pred_proba, Z1_test, Z2_test, Z3_test = make_dnn_prediction(
functions=[f_pred_proba, f_z1, f_z2, f_z3], X=X_test_data, title='TEST')
print("Test Latent:", Z1_test.shape, Z2_test.shape, Z3_test.shape)
y_pred = np.argmax(y_pred_proba, axis=-1)
evaluate(y_true=X_test_true,
y_pred_proba=y_pred_proba,
labels=labels,
title="Test set (Deep prediction)",
path=os.path.join(EXP_DIR, 'test_deep.pdf'))
# ====== make a streamline classifier ====== #
# training PLDA
Z3_train, y_train = make_dnn_prediction(f_z3, X=train, title="TRAIN")
print("Z3_train:", Z3_train.shape, y_train.shape)
Z3_valid, y_valid = make_dnn_prediction(f_z3, X=valid, title="VALID")
print("Z3_valid:", Z3_valid.shape, y_valid.shape)
plda = PLDA(n_phi=200,
random_state=K.get_rng().randint(10e8),
n_iter=12,
labels=labels,
verbose=0)
plda.fit(np.concatenate([Z3_train, Z3_valid], axis=0),
np.concatenate([y_train, y_valid], axis=0))
])
task.set_train_task(func=f_train, data=train,
epoch=args.epoch, name='train')
task.set_valid_task(func=f_score, data=valid,
freq=training.Timer(percentage=0.8),
name='valid')
task.run()
# ===========================================================================
# Prediction
# ===========================================================================
y_pred_proba, Z1_test, Z2_test, Z3_test = make_dnn_prediction(
functions=[f_pred_proba, f_z1, f_z2, f_z3], X=X_test_data, title='TEST')
print("Test Latent:", Z1_test.shape, Z2_test.shape, Z3_test.shape)
y_pred = np.argmax(y_pred_proba, axis=-1)
evaluate(y_true=X_test_true, y_pred_proba=y_pred_proba, labels=labels,
title="Test set (Deep prediction)",
path=os.path.join(EXP_DIR, 'test_deep.pdf'))
# ====== make a streamline classifier ====== #
# training PLDA
Z3_train, y_train = make_dnn_prediction(f_z3, X=train, title="TRAIN")
print("Z3_train:", Z3_train.shape, y_train.shape)
Z3_valid, y_valid = make_dnn_prediction(f_z3, X=valid, title="VALID")
print("Z3_valid:", Z3_valid.shape, y_valid.shape)
plda = PLDA(n_phi=200, random_state=K.get_rng().randint(10e8),
n_iter=12, labels=labels, verbose=0)
plda.fit(np.concatenate([Z3_train, Z3_valid], axis=0),
np.concatenate([y_train, y_valid], axis=0))
y_pred_log_proba = plda.predict_log_proba(Z3_test)
evaluate(y_true=X_test_true, y_pred_log_proba=y_pred_log_proba, labels=labels,
title="Test set (PLDA - Latent prediction)",
path=os.path.join(EXP_DIR, 'test_latent.pdf'))