From 0405205089d9a30355fae893549382316789ec61 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C5=82=20Dudziak?= Date: Thu, 11 May 2023 13:55:56 +0200 Subject: [PATCH] Zaktualizuj 'predict.py' --- predict.py | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/predict.py b/predict.py index f8ab15e..23ad3ef 100644 --- a/predict.py +++ b/predict.py @@ -4,7 +4,18 @@ import numpy as np import sklearn import sklearn.model_selection from tensorflow.keras.models import load_model -from sklearn.metrics import accuracy_score, precision_score, f1_score +from sklearn.metrics import mean_absolute_error, mean_squared_error + +import mlflow + +# Wskazujemy ścieżkę do folderu, gdzie zostaną zapisane wyniki MLflow +mlflow.set_tracking_uri("file:/mlflow") + +# Ustawiamy nazwę eksperymentu +mlflow.set_experiment("nazwa eksperymentu") + +feature_cols = ['year', 'mileage', 'vol_engine'] + feature_cols = ['year', 'mileage', 'vol_engine'] @@ -19,16 +30,13 @@ results = pd.DataFrame({'id': test_data['id'], 'year': test_data['year'], 'milea results.to_csv('predictions.csv', index=False) y_true = test_data['price'] -y_pred = y_pred = [round(p[0]) for p in predictions] +y_pred = [round(p[0]) for p in predictions] -print(y_pred) -print(y_true) - -accuracy = accuracy_score(y_true, y_pred) -precision = precision_score(y_true, y_pred, average='micro') -f1 = f1_score(y_true, y_pred, average='micro') +mae = mean_absolute_error(y_true, y_pred) +mse = mean_squared_error(y_true, y_pred) +rmse = np.sqrt(mse) with open('metrics.txt', 'w') as f: - f.write(f"Accuracy: {accuracy:.4f}\n") - f.write(f"Micro-average Precision: {precision:.4f}\n") - f.write(f"Micro-average F1-score: {f1:.4f}\n") + f.write(f"MAE: {mae:.4f}\n") + f.write(f"MSE: {mse:.4f}\n") + f.write(f"RMSE: {rmse:.4f}\n") \ No newline at end of file