From 88b15508c96c1c63f6b3d2333d6cd30005fd5b59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C5=82=20Dudziak?= Date: Thu, 11 May 2023 13:11:28 +0200 Subject: [PATCH] =?UTF-8?q?Usu=C5=84=20'predict.py'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- predict.py | 31 ------------------------------- 1 file changed, 31 deletions(-) delete mode 100644 predict.py diff --git a/predict.py b/predict.py deleted file mode 100644 index 3777e19..0000000 --- a/predict.py +++ /dev/null @@ -1,31 +0,0 @@ -import tensorflow as tf -import pandas as pd -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 - -feature_cols = ['year', 'mileage', 'vol_engine'] - -model = load_model('model.h5') -test_data = pd.read_csv('test.csv') - -predictions = model.predict(test_data[feature_cols]) -predicted_prices = [p[0] for p in predictions] - - -results = pd.DataFrame({'id': test_data['id'], 'year': test_data['year'], 'mileage': test_data['mileage'], 'vol_engine': test_data['vol_engine'], 'predicted_price': predicted_prices}) -results.to_csv('predictions.csv', index=False) - -y_true = test_data['price'] -y_pred = y_pred = [round(p[0]) for p in predictions] - -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') - -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")