46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
import sys
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import pandas as pd
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from sklearn.model_selection import train_test_split
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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 tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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import tensorflow as tf
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# data = pd.read_csv('./data/train.csv')
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data = pd.read_csv('./openpowerlifting.csv')
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print(data)
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data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
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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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pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', Sequential([
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Dense(64, activation='relu', input_dim=4),
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Dense(64, activation='relu'),
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Dense(1)
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]))
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])
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pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
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X_train_excluded = X_train.iloc[1:]
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y_train_excluded = y_train.iloc[1:]
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pipeline.fit(X_train_excluded, y_train_excluded, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
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pipeline['model'].save('powerlifting_model.h5')
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