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mlflow

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Szymon Bartanowicz 2024-05-18 19:42:40 +02:00
parent 57def16f1a
commit 248ef29471
53 changed files with 156 additions and 20 deletions
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mlflow/MLproject
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name: MLflow_s464937
conda_env: conda.yaml
entry_points:
optimal_parameters:
main:
parameters:
epochs: { type: int, default: 20 }
command: 'python mlflow_model.py {epochs}'

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mlflow/mlflow_model.py
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@ -7,6 +7,8 @@ from sklearn.pipeline import Pipeline
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import tensorflow as tf
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
from math import sqrt
import mlflow
@ -14,41 +16,63 @@ mlflow.set_tracking_uri("http://localhost:5000")
def main():
data = pd.read_csv('./data/train.csv')
data = pd.read_csv('../openpowerlifting.csv')
data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
data['Age'] = pd.to_numeric(data['Age'], errors='coerce')
data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce')
data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce')
features = data[['Sex', 'Age', 'BodyweightKg']]
target = data['TotalKg']
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), ['Age', 'BodyweightKg']),
('cat', OneHotEncoder(), ['Sex'])
],
)
with mlflow.start_run() as run:
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), ['Age', 'BodyweightKg']),
('cat', OneHotEncoder(), ['Sex'])
],
)
pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('model', Sequential([
model = Sequential([
Dense(64, activation='relu', input_dim=5),
Dense(64, activation='relu'),
Dense(1)
]))
])
])
pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('model', model)
])
X_train_excluded = X_train.iloc[1:]
y_train_excluded = y_train.iloc[1:]
X_train_excluded = X_train.iloc[1:]
y_train_excluded = y_train.iloc[1:]
pipeline.fit(X_train_excluded, y_train_excluded, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
pipeline['model'].save('powerlifting_model.h5')
loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
pipeline.fit(X_train_excluded, y_train_excluded, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
test_data = pd.read_csv('openpowerlifting.csv')
test_data = test_data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
test_data['Age'] = pd.to_numeric(test_data['Age'], errors='coerce')
test_data['BodyweightKg'] = pd.to_numeric(test_data['BodyweightKg'], errors='coerce')
test_data['TotalKg'] = pd.to_numeric(test_data['TotalKg'], errors='coerce')
test_features = test_data[['Sex', 'Age', 'BodyweightKg']]
test_target = test_data['TotalKg']
pipeline['model'].save('powerlifting_model.h5')
X_test_transformed = preprocessor.transform(test_features)
predictions = loaded_model.predict(X_test_transformed)
predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
predictions_df['actual_TotalKg'] = test_target.reset_index(drop=True)
predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)
data = pd.read_csv('powerlifting_test_predictions.csv')
y_pred = data['predicted_TotalKg']
y_test = data['actual_TotalKg']
rmse = sqrt(mean_squared_error(y_test, y_pred))
mlflow.log_param("epochs", int(sys.argv[1]))
mlflow.log_metric("rmse", rmse)
if __name__ == '__main__':

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