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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 name: MLflow_s464937
conda_env: conda.yaml conda_env: conda.yaml
entry_points: entry_points:
optimal_parameters: main:
parameters: parameters:
epochs: { type: int, default: 20 } epochs: { type: int, default: 20 }
command: 'python mlflow_model.py {epochs}' 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.models import Sequential
from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dense
import tensorflow as tf import tensorflow as tf
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
from math import sqrt
import mlflow import mlflow
@ -14,41 +16,63 @@ mlflow.set_tracking_uri("http://localhost:5000")
def main(): def main():
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']
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
preprocessor = ColumnTransformer( with mlflow.start_run() as run:
transformers=[ preprocessor = ColumnTransformer(
('num', StandardScaler(), ['Age', 'BodyweightKg']), transformers=[
('cat', OneHotEncoder(), ['Sex']) ('num', StandardScaler(), ['Age', 'BodyweightKg']),
], ('cat', OneHotEncoder(), ['Sex'])
) ],
)
pipeline = Pipeline(steps=[ model = Sequential([
('preprocessor', preprocessor),
('model', Sequential([
Dense(64, activation='relu', input_dim=5), Dense(64, activation='relu', input_dim=5),
Dense(64, activation='relu'), Dense(64, activation='relu'),
Dense(1) 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:] X_train_excluded = X_train.iloc[1:]
y_train_excluded = y_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__': if __name__ == '__main__':

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mlflow/mlruns/0/meta.yaml Normal file
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