wehub-resource-sync
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
import argparse
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import lightgbm as lgb
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import matplotlib as mpl
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from sklearn import datasets
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from sklearn.metrics import accuracy_score, log_loss
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from sklearn.model_selection import train_test_split
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import mlflow
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import mlflow.lightgbm
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mpl.use("Agg")
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def parse_args():
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parser = argparse.ArgumentParser(description="LightGBM example")
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parser.add_argument(
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"--learning-rate",
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type=float,
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default=0.1,
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help="learning rate to update step size at each boosting step (default: 0.3)",
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)
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parser.add_argument(
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"--colsample-bytree",
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type=float,
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default=1.0,
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help="subsample ratio of columns when constructing each tree (default: 1.0)",
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)
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parser.add_argument(
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"--subsample",
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type=float,
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default=1.0,
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help="subsample ratio of the training instances (default: 1.0)",
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)
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return parser.parse_args()
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def main():
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# parse command-line arguments
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args = parse_args()
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# prepare train and test data
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# enable auto logging
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mlflow.lightgbm.autolog()
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train_set = lgb.Dataset(X_train, label=y_train)
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with mlflow.start_run():
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# train model
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params = {
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"objective": "multiclass",
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"num_class": 3,
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"learning_rate": args.learning_rate,
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"metric": "multi_logloss",
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"colsample_bytree": args.colsample_bytree,
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"subsample": args.subsample,
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"seed": 42,
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}
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model = lgb.train(
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params, train_set, num_boost_round=10, valid_sets=[train_set], valid_names=["train"]
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)
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# evaluate model
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y_proba = model.predict(X_test)
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y_pred = y_proba.argmax(axis=1)
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loss = log_loss(y_test, y_proba)
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acc = accuracy_score(y_test, y_pred)
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# log metrics
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mlflow.log_metrics({"log_loss": loss, "accuracy": acc})
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if __name__ == "__main__":
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main()
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