# Train, convert and predict with ONNX Runtime — ONNX Runtime 1.14.0 documentation

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# Train, convert and predict with ONNX Runtime

This example demonstrates an end to end scenario starting with the training of a scikit-learn pipeline which takes as inputs not a regular vector but a dictionary `{ int: float }` as its first step is a DictVectorizer.

- Train a pipeline

- Conversion to ONNX format

## Train a pipeline

The first step consists in retrieving the boston datset.

    import pandas
    from sklearn.datasets import load_boston

    boston = load_boston()
    X, y = boston.data, boston.target

    from sklearn.model_selection import train_test_split

    X_train, X_test, y_train, y_test = train_test_split(X, y)
    X_train_dict = pandas.DataFrame(X_train[:, 1:]).T.to_dict().values()
    X_test_dict = pandas.DataFrame(X_test[:, 1:]).T.to_dict().values()

    /home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.

        The Boston housing prices dataset has an ethical problem. You can refer to
        the documentation of this function for further details.

        The scikit-learn maintainers therefore strongly discourage the use of this
        dataset unless the purpose of the code is to study and educate about
        ethical issues in data science and machine learning.

        In this special case, you can fetch the dataset from the original
        source::

            import pandas as pd
            import numpy as np

            data_url = "http://lib.stat.cmu.edu/datasets/boston"
            raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
            data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
            target = raw_df.values[1::2, 2]

        Alternative datasets include the California housing dataset (i.e.
        :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
        dataset. You can load the datasets as follows::

            from sklearn.datasets import fetch_california_housing
            housing = fetch_california_housing()

        for the California housing dataset and::

            from sklearn.datasets import fetch_openml
            housing = fetch_openml(name="house_prices", as_frame=True)

        for the Ames housing dataset.
      warnings.warn(msg, category=FutureWarning)

We create a pipeline.

    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.feature_extraction import DictVectorizer
    from sklearn.pipeline import make_pipeline

    pipe = make_pipeline(DictVectorizer(sparse=False), GradientBoostingRegressor())

    pipe.fit(X_train_dict, y_train)

    Pipeline(steps=[('dictvectorizer', DictVectorizer(sparse=False)),
                    ('gradientboostingregressor', GradientBoostingRegressor())])

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Pipeline

    Pipeline(steps=[('dictvectorizer', DictVectorizer(sparse=False)),
                    ('gradientboostingregressor', GradientBoostingRegressor())])

DictVectorizer

    DictVectorizer(sparse=False)

GradientBoostingRegressor

    GradientBoostingRegressor()

We compute the prediction on the test set and we show the confusion matrix.

    from sklearn.metrics import r2_score

    pred = pipe.predict(X_test_dict)
    print(r2_score(y_test, pred))

    0.8778842817557302

## Conversion to ONNX format

We use module sklearn-onnx to convert the model into ONNX format.

    from skl2onnx import convert_sklearn
    from skl2onnx.common.data_types import DictionaryType, FloatTensorType, Int64TensorType, SequenceType

    # initial_type = [('float_input', DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
    initial_type = [("float_input", DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
    onx = convert_sklearn(pipe, initial_types=initial_type)
    with open("pipeline_vectorize.onnx", "wb") as f:
        f.write(onx.SerializeToString())

We load the model with ONNX Runtime and look at its input and output.

    import onnxruntime as rt
    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument

    sess = rt.InferenceSession("pipeline_vectorize.onnx", providers=rt.get_available_providers())

    import numpy

    inp, out = sess.get_inputs()[0], sess.get_outputs()[0]
    print("input name='{}' and shape={} and type={}".format(inp.name, inp.shape, inp.type))
    print("output name='{}' and shape={} and type={}".format(out.name, out.shape, out.type))

    input name='float_input' and shape=[] and type=map(int64,tensor(float))
    output name='variable' and shape=[None, 1] and type=tensor(float)

We compute the predictions. We could do that in one call:

    try:
        pred_onx = sess.run([out.name], {inp.name: X_test_dict})[0]
    except (RuntimeError, InvalidArgument) as e:
        print(e)

    [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: ((seq(map(int64,tensor(float))))) , expected: ((map(int64,tensor(float))))

But it fails because, in case of a DictVectorizer, ONNX Runtime expects one observation at a time.

    pred_onx = [sess.run([out.name], {inp.name: row})[0][0, 0] for row in X_test_dict]

We compare them to the model’s ones.

    print(r2_score(pred, pred_onx))

    0.999999999999964

Very similar. ONNX Runtime uses floats instead of doubles, that explains the small discrepencies.

Total running time of the script: ( 0 minutes 1.044 seconds)

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