wehub-resource-sync
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import pandas as pd
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import requests
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from sktime.datasets import load_longley
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from sktime.forecasting.model_selection import temporal_train_test_split
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y, X = load_longley()
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y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
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# Define local host and endpoint url
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host = "127.0.0.1"
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url = f"http://{host}:5000/invocations"
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# Model scoring via REST API requires transforming the configuration DataFrame
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# into JSON format. As numpy ndarray type is not JSON serializable we need to
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# convert the exogenous regressor into a list. The wrapper instance will convert
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# the list back to ndarray type as required by sktime predict methods. For more
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# details read the MLflow deployment API reference.
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# (https://mlflow.org/docs/latest/models.html#deploy-mlflow-models)
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X_test_list = X_test.to_numpy().tolist()
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predict_conf = pd.DataFrame([
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{
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"fh": [1, 2, 3, 4],
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"predict_method": "predict_interval",
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"coverage": [0.9, 0.95],
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"X": X_test_list,
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}
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])
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# Create dictionary with pandas DataFrame in the split orientation
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json_data = {"dataframe_split": predict_conf.to_dict(orient="split")}
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# Score model
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response = requests.post(url, json=json_data)
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print(f"\nPyfunc 'predict_interval':\n${response.json()}")
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