def _predict():
"""予測。"""
logger = tk.log.get(__name__)
X_test = _data.load_test_data()
threshold = float((MODELS_DIR / 'threshold.txt').read_text())
logger.info(f'threshold = {threshold:.3f}')
pred_list = predict_all('test', X_test)
pred = np.mean(pred_list, axis=0) > threshold
_data.save_submission(MODELS_DIR / 'submission.csv', pred)
def _predict():
"""予測。"""
X_test = _data.load_test_data()
pred = predict_all('test', X_test)
_data.save_submission(MODELS_DIR / 'submission.csv', pred > 0.50)
_data.save_submission(MODELS_DIR / 'submission_0.40.csv', pred > 0.40)
_data.save_submission(MODELS_DIR / 'submission_0.45.csv', pred > 0.45)
_data.save_submission(MODELS_DIR / 'submission_0.55.csv', pred > 0.55)
_data.save_submission(MODELS_DIR / 'submission_0.60.csv', pred > 0.60)
def _predict():
"""予測。"""
X_test = _data.load_test_data()
pred_list = predict_all('test', X_test)
pred = np.mean(pred_list, axis=0) > 0.5
_data.save_submission(MODELS_DIR / 'submission.csv', pred)
