From 7695340e915ee9ac5e1e5ddb3610d3ea9a25174c Mon Sep 17 00:00:00 2001 From: Israel Figueroa Date: Sun, 16 Mar 2025 05:05:13 -0300 Subject: [PATCH] s --- TrainerClass.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/TrainerClass.py b/TrainerClass.py index 0529a5a..b010479 100644 --- a/TrainerClass.py +++ b/TrainerClass.py @@ -154,7 +154,7 @@ class eNoseTrainer: return mse, mae, rmse, optimized_model, model_params - def train_and_score_conv1D_v1(self, X_train, X_test, y_train, y_test, epochs=30, num_samples=25): + def train_and_score_conv1D_v1(self, X_train, X_test, Y_train, Y_test, epochs=30, num_samples=25): ray.init(ignore_reinit_error=True) X_train_ref = ray.put(X_train) Y_train_ref = ray.put(Y_train) @@ -218,12 +218,12 @@ class eNoseTrainer: Y_test_pred = best_model.predict(X_test) mse_train = mean_squared_error(Y_train, Y_train_pred) - mae_test = mean_absolute_error(y_test, Y_test_pred) + mae_test = mean_absolute_error(Y_test, Y_test_pred) mse_test = mean_squared_error(Y_test, Y_test_pred) rmse_test = np.sqrt(mse_test) # # Calculate evaluation metrics - mse = mean_squared_error(y_test, y_pred) + mse = mean_squared_error(Y_test, y_pred) rmse = np.sqrt(mse) return mse_train, mae_test, mse_test, rmse_test, best_model, best_config