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evaluation metrics plot

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Szymon Bartanowicz 2024-05-15 00:53:08 +02:00
parent 17be57bcd3
commit 8d92919488
4 changed files with 20 additions and 25 deletions
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33
metrics.py
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@ -1,24 +1,17 @@
# import pandas as pd import pandas as pd
# from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
# from math import sqrt from math import sqrt
# import sys import sys
#
# data = pd.read_csv('powerlifting_test_predictions.csv') data = pd.read_csv('powerlifting_test_predictions.csv')
# y_pred = data['Predictions'] y_pred = data['predicted_TotalKg']
# y_test = data['Actual'] y_test = data['actual_TotalKg']
# y_test_binary = (y_test >= 3).astype(int) y_test_binary = (y_test >= 3).astype(int)
#
# build_number = sys.argv[1] # build_number = sys.argv[1]
# build_number = 1
# accuracy = accuracy_score(y_test_binary, y_pred.round())
# precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro') rmse = sqrt(mean_squared_error(y_test, y_pred))
# rmse = sqrt(mean_squared_error(y_test, y_pred))
#
# print(f'Accuracy: {accuracy}')
# print(f'Micro-avg Precision: {precision}')
# print(f'Micro-avg Recall: {recall}')
# print(f'F1 Score: {f1}')
# print(f'RMSE: {rmse}')
with open(r"metrics.txt", "a") as f: with open(r"metrics.txt", "a") as f:
f.write(f"{123},{1}\n") f.write(f"{123},{1}\n")

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metrics.txt Normal file
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4
plot.py
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@ -11,8 +11,8 @@ def main():
plt.plot(build_numbers, accuracy) plt.plot(build_numbers, accuracy)
plt.xlabel("Build Number") plt.xlabel("Build Number")
plt.ylabel("Accuracy") plt.ylabel("RMSE")
plt.title("Accuracy of the model over time") plt.title("RMSE of the model over time")
plt.xticks(range(min(build_numbers), max(build_numbers) + 1)) plt.xticks(range(min(build_numbers), max(build_numbers) + 1))
plt.show() plt.show()

8
predict.py
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@ -6,15 +6,16 @@ from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from keras.metrics import MeanSquaredError from keras.metrics import MeanSquaredError
loaded_model = tf.keras.models.load_model('powerlifting_model.h5') loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
data = pd.read_csv('./data/train.csv') data = pd.read_csv('openpowerlifting.csv')
data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna() data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
data['Age'] = pd.to_numeric(data['Age'], errors='coerce') data['Age'] = pd.to_numeric(data['Age'], errors='coerce')
data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce') data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce')
data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce') data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce')
features = data[['Sex', 'Age', 'BodyweightKg']] features = data[['Sex', 'Age', 'BodyweightKg']]
target = data['TotalKg'] target = data['TotalKg']
@ -26,8 +27,9 @@ preprocessor = ColumnTransformer(
('cat', OneHotEncoder(), ['Sex']) ('cat', OneHotEncoder(), ['Sex'])
] ]
) )
X_test_transformed = preprocessor.fit_transform(X_test)
X_test_transformed = preprocessor.fit_transform(X_test)
predictions = loaded_model.predict(X_test_transformed) predictions = loaded_model.predict(X_test_transformed)
predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg']) predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
predictions_df['actual_TotalKg'] = y_test.reset_index(drop=True)
predictions_df.to_csv('powerlifting_test_predictions.csv', index=False) predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)