wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import json
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import flavor
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import pandas as pd
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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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from sktime.forecasting.naive import NaiveForecaster
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from sktime.performance_metrics.forecasting import (
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mean_absolute_error,
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mean_absolute_percentage_error,
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)
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import mlflow
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with mlflow.start_run() as run:
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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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forecaster = NaiveForecaster()
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forecaster.fit(
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y_train,
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X=X_train,
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)
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# Extract parameters
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parameters = forecaster.get_params()
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# Evaluate model
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y_pred = forecaster.predict(fh=[1, 2, 3, 4], X=X_test)
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metrics = {
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"mae": mean_absolute_error(y_test, y_pred),
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"mape": mean_absolute_percentage_error(y_test, y_pred),
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}
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print(f"Parameters: \n{json.dumps(parameters, indent=2)}")
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print(f"Metrics: \n{json.dumps(metrics, indent=2)}")
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# Log parameters and metrics
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mlflow.log_params(parameters)
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mlflow.log_metrics(metrics)
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# Log model using custom model flavor with pickle serialization (default).
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# Note that pickle serialization requires using the same python environment
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# (version) in whatever environment you're going to use this model for
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# inference to ensure that the model will load with appropriate version of
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# pickle.
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model_info = flavor.log_model(
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sktime_model=forecaster,
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artifact_path="sktime_model",
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serialization_format="pickle",
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)
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model_uri = model_info.model_uri
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# Load model in native sktime flavor and pyfunc flavor
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loaded_model = flavor.load_model(model_uri=model_uri)
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loaded_pyfunc = flavor.pyfunc.load_model(model_uri=model_uri)
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# Convert test data to 2D numpy array so it can be passed to pyfunc predict using
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# a single-row Pandas DataFrame configuration argument
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X_test_array = X_test.to_numpy()
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# Create configuration DataFrame for interval forecast with nominal coverage
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# value [0.9,0.95], future forecast horizon of 4 periods, and exogenous regressor.
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# Read more in the flavor.py module docstrings about the possible configurations.
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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_array,
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}
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])
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# Generate interval forecasts with native sktime flavor and pyfunc flavor
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print(
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f"\nNative sktime 'predict_interval':\n${loaded_model.predict_interval(fh=[1, 2, 3], X=X_test, coverage=[0.9, 0.95])}"
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)
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print(f"\nPyfunc 'predict_interval':\n${loaded_pyfunc.predict(predict_conf)}")
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# Print the run id which is used for serving the model to a local REST API endpoint
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# in the score_model.py module
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print(f"\nMLflow run id:\n{run.info.run_id}")
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