9 lines
296 B
Python
9 lines
296 B
Python
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import mlflow
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import numpy as np
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model = mlflow.pyfunc.load_model('/mlruns/1/e435ee5c0c5a468c99eb43c13df4a94b/artifacts/s444421')
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with open('input_example.json') as f:
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input = json.load(f)
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y_predicted = model.predict(np.array([data['inputs']]).reshape(-1, 2))
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print(y_predicted[:5])
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