onnxruntime/python/_sources/auto_examples/plot_train_convert_predict.rst.txt

.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_plot_train_convert_predict.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_train_convert_predict.py:



.. _l-logreg-example:

Train, convert and predict with ONNX Runtime
============================================

This example demonstrates an end to end scenario
starting with the training of a machine learned model
to its use in its converted from.

.. contents::
    :local:

Train a logistic regression
+++++++++++++++++++++++++++

The first step consists in retrieving the iris datset.



.. code-block:: python


    from sklearn.datasets import load_iris
    iris = load_iris()
    X, y = iris.data, iris.target

    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X, y)







Then we fit a model.



.. code-block:: python


    from sklearn.linear_model import LogisticRegression
    clr = LogisticRegression()
    clr.fit(X_train, y_train)







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



.. code-block:: python

    from sklearn.metrics import confusion_matrix

    pred = clr.predict(X_test)
    print(confusion_matrix(y_test, pred))





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    [[11  0  0]
     [ 0 12  4]
     [ 0  0 11]]


Conversion to ONNX format
+++++++++++++++++++++++++

We use module 
`sklearn-onnx <https://github.com/onnx/sklearn-onnx>`_
to convert the model into ONNX format.



.. code-block:: python


    from skl2onnx import convert_sklearn
    from skl2onnx.common.data_types import FloatTensorType

    initial_type = [('float_input', FloatTensorType([None, 4]))]
    onx = convert_sklearn(clr, initial_types=initial_type)
    with open("logreg_iris.onnx", "wb") as f:
        f.write(onx.SerializeToString())







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



.. code-block:: python


    import onnxruntime as rt
    sess = rt.InferenceSession("logreg_iris.onnx")

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





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    input name='float_input' and shape=[None, 4]
    output name='output_label' and shape=[1]


We compute the predictions.



.. code-block:: python


    input_name = sess.get_inputs()[0].name
    label_name = sess.get_outputs()[0].name

    import numpy
    pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
    print(confusion_matrix(pred, pred_onx))





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 .. code-block:: none

    [[11  0  0]
     [ 0 12  0]
     [ 0  0 15]]


The prediction are perfectly identical.

Probabilities
+++++++++++++

Probabilities are needed to compute other
relevant metrics such as the ROC Curve.
Let's see how to get them first with
scikit-learn.



.. code-block:: python


    prob_sklearn = clr.predict_proba(X_test)
    print(prob_sklearn[:3])





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    [[1.04597336e-03 3.26972202e-01 6.71981824e-01]
     [8.07529571e-01 1.92267362e-01 2.03067523e-04]
     [3.75046145e-02 6.77776609e-01 2.84718777e-01]]


And then with ONNX Runtime.
The probabilies appear to be 



.. code-block:: python


    prob_name = sess.get_outputs()[1].name
    prob_rt = sess.run([prob_name], {input_name: X_test.astype(numpy.float32)})[0]

    import pprint
    pprint.pprint(prob_rt[0:3])





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    [{0: 0.0010459469631314278, 1: 0.32697227597236633, 2: 0.6719817519187927},
     {0: 0.807529628276825, 1: 0.19226738810539246, 2: 0.00020308367675170302},
     {0: 0.037504520267248154, 1: 0.6777766942977905, 2: 0.2847188115119934}]


Let's benchmark.



.. code-block:: python

    from timeit import Timer

    def speed(inst, number=10, repeat=20):
        timer = Timer(inst, globals=globals())
        raw = numpy.array(timer.repeat(repeat, number=number))
        ave = raw.sum() / len(raw) / number
        mi, ma = raw.min() / number, raw.max() / number
        print("Average %1.3g min=%1.3g max=%1.3g" % (ave, mi, ma))
        return ave

    print("Execution time for clr.predict")
    speed("clr.predict(X_test)")

    print("Execution time for ONNX Runtime")
    speed("sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]")





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    Execution time for clr.predict
    Average 2.35e-05 min=1.95e-05 max=4.43e-05
    Execution time for ONNX Runtime
    Average 2.9e-05 min=2.69e-05 max=4.27e-05


Let's benchmark a scenario similar to what a webservice
experiences: the model has to do one prediction at a time
as opposed to a batch of prediction.



.. code-block:: python


    def loop(X_test, fct, n=None):
        nrow = X_test.shape[0]
        if n is None:
            n = nrow
        for i in range(0, n):
            im = i % nrow
            fct(X_test[im: im+1])

    print("Execution time for clr.predict")
    speed("loop(X_test, clr.predict, 100)")

    def sess_predict(x):
        return sess.run([label_name], {input_name: x.astype(numpy.float32)})[0]

    print("Execution time for sess_predict")
    speed("loop(X_test, sess_predict, 100)")





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    Execution time for clr.predict
    Average 0.00202 min=0.00181 max=0.00239
    Execution time for sess_predict
    Average 0.00155 min=0.00128 max=0.00247


Let's do the same for the probabilities.



.. code-block:: python


    print("Execution time for predict_proba")
    speed("loop(X_test, clr.predict_proba, 100)")

    def sess_predict_proba(x):
        return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]

    print("Execution time for sess_predict_proba")
    speed("loop(X_test, sess_predict_proba, 100)")





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    Execution time for predict_proba
    Average 0.00396 min=0.0034 max=0.00516
    Execution time for sess_predict_proba
    Average 0.00171 min=0.0014 max=0.00269


This second comparison is better as 
ONNX Runtime, in this experience,
computes the label and the probabilities
in every case.


Benchmark with RandomForest
+++++++++++++++++++++++++++

We first train and save a model in ONNX format.



.. code-block:: python

    from sklearn.ensemble import RandomForestClassifier
    rf = RandomForestClassifier()
    rf.fit(X_train, y_train)

    initial_type = [('float_input', FloatTensorType([1, 4]))]
    onx = convert_sklearn(rf, initial_types=initial_type)
    with open("rf_iris.onnx", "wb") as f:
        f.write(onx.SerializeToString())







We compare.



.. code-block:: python


    sess = rt.InferenceSession("rf_iris.onnx")

    def sess_predict_proba_rf(x):
        return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]

    print("Execution time for predict_proba")
    speed("loop(X_test, rf.predict_proba, 100)")

    print("Execution time for sess_predict_proba")
    speed("loop(X_test, sess_predict_proba_rf, 100)")





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    Execution time for predict_proba
    Average 0.0578 min=0.0551 max=0.0601
    Execution time for sess_predict_proba
    Average 0.00177 min=0.00145 max=0.0029


Let's see with different number of trees.



.. code-block:: python


    measures = []

    for n_trees in range(5, 51, 5):   
        print(n_trees)
        rf = RandomForestClassifier(n_estimators=n_trees)
        rf.fit(X_train, y_train)
        initial_type = [('float_input', FloatTensorType([1, 4]))]
        onx = convert_sklearn(rf, initial_types=initial_type)
        with open("rf_iris_%d.onnx" % n_trees, "wb") as f:
            f.write(onx.SerializeToString())
        sess = rt.InferenceSession("rf_iris_%d.onnx" % n_trees)
        def sess_predict_proba_loop(x):
            return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
        tsk = speed("loop(X_test, rf.predict_proba, 100)", number=5, repeat=5)
        trt = speed("loop(X_test, sess_predict_proba_loop, 100)", number=5, repeat=5)
        measures.append({'n_trees': n_trees, 'sklearn': tsk, 'rt': trt})

    from pandas import DataFrame
    df = DataFrame(measures)
    ax = df.plot(x="n_trees", y="sklearn", label="scikit-learn", c="blue", logy=True)
    df.plot(x="n_trees", y="rt", label="onnxruntime",
                    ax=ax, c="green", logy=True)
    ax.set_xlabel("Number of trees")
    ax.set_ylabel("Prediction time (s)")
    ax.set_title("Speed comparison between scikit-learn and ONNX Runtime\nFor a random forest on Iris dataset")
    ax.legend()



.. image:: /auto_examples/images/sphx_glr_plot_train_convert_predict_001.png
    :class: sphx-glr-single-img


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    5
    Average 0.0353 min=0.0324 max=0.0386
    Average 0.00156 min=0.00135 max=0.00173
    10
    Average 0.0541 min=0.0519 max=0.0567
    Average 0.00157 min=0.00145 max=0.00167
    15
    Average 0.079 min=0.0768 max=0.0806
    Average 0.00187 min=0.00155 max=0.00216
    20
    Average 0.103 min=0.0994 max=0.109
    Average 0.0017 min=0.00168 max=0.00176
    25
    Average 0.121 min=0.117 max=0.125
    Average 0.00195 min=0.00159 max=0.00231
    30
    Average 0.149 min=0.145 max=0.153
    Average 0.00239 min=0.0016 max=0.00324
    35
    Average 0.17 min=0.163 max=0.178
    Average 0.00199 min=0.00162 max=0.00269
    40
    Average 0.191 min=0.189 max=0.192
    Average 0.00183 min=0.00171 max=0.00197
    45
    Average 0.214 min=0.212 max=0.216
    Average 0.00252 min=0.00187 max=0.00345
    50
    Average 0.236 min=0.226 max=0.243
    Average 0.00237 min=0.00182 max=0.00326


**Total running time of the script:** ( 0 minutes  49.102 seconds)


.. _sphx_glr_download_auto_examples_plot_train_convert_predict.py:


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