36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import pandas as pd
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import tensorflow as tf
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import train_test_split
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from keras.metrics import MeanSquaredError
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loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
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data = pd.read_csv('openpowerlifting.csv')
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data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
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data['Age'] = pd.to_numeric(data['Age'], errors='coerce')
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data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce')
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data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce')
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features = data[['Sex', 'Age', 'BodyweightKg']]
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target = data['TotalKg']
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X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
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preprocessor = ColumnTransformer(
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transformers=[
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('num', StandardScaler(), ['Age', 'BodyweightKg']),
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('cat', OneHotEncoder(), ['Sex'])
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]
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)
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X_test_transformed = preprocessor.fit_transform(X_test)
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predictions = loaded_model.predict(X_test_transformed)
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predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
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predictions_df['actual_TotalKg'] = y_test.reset_index(drop=True)
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predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)
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