mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-14 18:12:05 +00:00
519 lines
10 KiB
ReStructuredText
519 lines
10 KiB
ReStructuredText
.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_train_convert_predict.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_train_convert_predict.py:
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.. _l-logreg-example:
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Train, convert and predict with ONNX Runtime
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============================================
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This example demonstrates an end to end scenario
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starting with the training of a machine learned model
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to its use in its converted from.
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.. contents::
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:local:
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Train a logistic regression
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+++++++++++++++++++++++++++
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The first step consists in retrieving the iris datset.
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.. code-block:: python
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from sklearn.datasets import load_iris
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iris = load_iris()
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X, y = iris.data, iris.target
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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Then we fit a model.
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.. code-block:: python
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from sklearn.linear_model import LogisticRegression
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clr = LogisticRegression()
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clr.fit(X_train, y_train)
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We compute the prediction on the test set
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and we show the confusion matrix.
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.. code-block:: python
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from sklearn.metrics import confusion_matrix
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pred = clr.predict(X_test)
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print(confusion_matrix(y_test, pred))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[11 0 0]
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[ 0 12 4]
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[ 0 0 11]]
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Conversion to ONNX format
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+++++++++++++++++++++++++
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We use module
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`sklearn-onnx <https://github.com/onnx/sklearn-onnx>`_
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to convert the model into ONNX format.
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.. code-block:: python
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from skl2onnx import convert_sklearn
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from skl2onnx.common.data_types import FloatTensorType
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initial_type = [('float_input', FloatTensorType([None, 4]))]
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onx = convert_sklearn(clr, initial_types=initial_type)
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with open("logreg_iris.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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We load the model with ONNX Runtime and look at
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its input and output.
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.. code-block:: python
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import onnxruntime as rt
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sess = rt.InferenceSession("logreg_iris.onnx")
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print("input name='{}' and shape={}".format(
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sess.get_inputs()[0].name, sess.get_inputs()[0].shape))
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print("output name='{}' and shape={}".format(
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sess.get_outputs()[0].name, sess.get_outputs()[0].shape))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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input name='float_input' and shape=[None, 4]
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output name='output_label' and shape=[1]
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We compute the predictions.
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.. code-block:: python
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input_name = sess.get_inputs()[0].name
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label_name = sess.get_outputs()[0].name
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import numpy
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pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
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print(confusion_matrix(pred, pred_onx))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[11 0 0]
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[ 0 12 0]
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[ 0 0 15]]
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The prediction are perfectly identical.
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Probabilities
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+++++++++++++
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Probabilities are needed to compute other
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relevant metrics such as the ROC Curve.
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Let's see how to get them first with
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scikit-learn.
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.. code-block:: python
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prob_sklearn = clr.predict_proba(X_test)
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print(prob_sklearn[:3])
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[1.04597336e-03 3.26972202e-01 6.71981824e-01]
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[8.07529571e-01 1.92267362e-01 2.03067523e-04]
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[3.75046145e-02 6.77776609e-01 2.84718777e-01]]
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And then with ONNX Runtime.
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The probabilies appear to be
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.. code-block:: python
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prob_name = sess.get_outputs()[1].name
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prob_rt = sess.run([prob_name], {input_name: X_test.astype(numpy.float32)})[0]
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import pprint
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pprint.pprint(prob_rt[0:3])
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[{0: 0.0010459469631314278, 1: 0.32697227597236633, 2: 0.6719817519187927},
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{0: 0.807529628276825, 1: 0.19226738810539246, 2: 0.00020308367675170302},
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{0: 0.037504520267248154, 1: 0.6777766942977905, 2: 0.2847188115119934}]
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Let's benchmark.
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.. code-block:: python
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from timeit import Timer
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def speed(inst, number=10, repeat=20):
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timer = Timer(inst, globals=globals())
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raw = numpy.array(timer.repeat(repeat, number=number))
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ave = raw.sum() / len(raw) / number
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mi, ma = raw.min() / number, raw.max() / number
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print("Average %1.3g min=%1.3g max=%1.3g" % (ave, mi, ma))
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return ave
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print("Execution time for clr.predict")
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speed("clr.predict(X_test)")
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print("Execution time for ONNX Runtime")
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speed("sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for clr.predict
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Average 2.35e-05 min=1.95e-05 max=4.43e-05
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Execution time for ONNX Runtime
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Average 2.9e-05 min=2.69e-05 max=4.27e-05
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Let's benchmark a scenario similar to what a webservice
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experiences: the model has to do one prediction at a time
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as opposed to a batch of prediction.
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.. code-block:: python
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def loop(X_test, fct, n=None):
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nrow = X_test.shape[0]
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if n is None:
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n = nrow
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for i in range(0, n):
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im = i % nrow
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fct(X_test[im: im+1])
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print("Execution time for clr.predict")
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speed("loop(X_test, clr.predict, 100)")
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def sess_predict(x):
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return sess.run([label_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for sess_predict")
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speed("loop(X_test, sess_predict, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for clr.predict
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Average 0.00202 min=0.00181 max=0.00239
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Execution time for sess_predict
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Average 0.00155 min=0.00128 max=0.00247
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Let's do the same for the probabilities.
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.. code-block:: python
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print("Execution time for predict_proba")
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speed("loop(X_test, clr.predict_proba, 100)")
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def sess_predict_proba(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for sess_predict_proba")
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speed("loop(X_test, sess_predict_proba, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for predict_proba
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Average 0.00396 min=0.0034 max=0.00516
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Execution time for sess_predict_proba
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Average 0.00171 min=0.0014 max=0.00269
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This second comparison is better as
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ONNX Runtime, in this experience,
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computes the label and the probabilities
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in every case.
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Benchmark with RandomForest
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+++++++++++++++++++++++++++
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We first train and save a model in ONNX format.
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.. code-block:: python
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from sklearn.ensemble import RandomForestClassifier
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rf = RandomForestClassifier()
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rf.fit(X_train, y_train)
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initial_type = [('float_input', FloatTensorType([1, 4]))]
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onx = convert_sklearn(rf, initial_types=initial_type)
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with open("rf_iris.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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We compare.
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.. code-block:: python
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sess = rt.InferenceSession("rf_iris.onnx")
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def sess_predict_proba_rf(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for predict_proba")
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speed("loop(X_test, rf.predict_proba, 100)")
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print("Execution time for sess_predict_proba")
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speed("loop(X_test, sess_predict_proba_rf, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for predict_proba
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Average 0.0578 min=0.0551 max=0.0601
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Execution time for sess_predict_proba
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Average 0.00177 min=0.00145 max=0.0029
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Let's see with different number of trees.
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.. code-block:: python
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measures = []
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for n_trees in range(5, 51, 5):
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print(n_trees)
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rf = RandomForestClassifier(n_estimators=n_trees)
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rf.fit(X_train, y_train)
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initial_type = [('float_input', FloatTensorType([1, 4]))]
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onx = convert_sklearn(rf, initial_types=initial_type)
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with open("rf_iris_%d.onnx" % n_trees, "wb") as f:
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f.write(onx.SerializeToString())
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sess = rt.InferenceSession("rf_iris_%d.onnx" % n_trees)
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def sess_predict_proba_loop(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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tsk = speed("loop(X_test, rf.predict_proba, 100)", number=5, repeat=5)
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trt = speed("loop(X_test, sess_predict_proba_loop, 100)", number=5, repeat=5)
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measures.append({'n_trees': n_trees, 'sklearn': tsk, 'rt': trt})
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from pandas import DataFrame
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df = DataFrame(measures)
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ax = df.plot(x="n_trees", y="sklearn", label="scikit-learn", c="blue", logy=True)
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df.plot(x="n_trees", y="rt", label="onnxruntime",
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ax=ax, c="green", logy=True)
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ax.set_xlabel("Number of trees")
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ax.set_ylabel("Prediction time (s)")
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ax.set_title("Speed comparison between scikit-learn and ONNX Runtime\nFor a random forest on Iris dataset")
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ax.legend()
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.. image:: /auto_examples/images/sphx_glr_plot_train_convert_predict_001.png
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:class: sphx-glr-single-img
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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5
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Average 0.0353 min=0.0324 max=0.0386
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Average 0.00156 min=0.00135 max=0.00173
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10
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Average 0.0541 min=0.0519 max=0.0567
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Average 0.00157 min=0.00145 max=0.00167
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15
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Average 0.079 min=0.0768 max=0.0806
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Average 0.00187 min=0.00155 max=0.00216
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Average 0.103 min=0.0994 max=0.109
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Average 0.0017 min=0.00168 max=0.00176
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25
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Average 0.121 min=0.117 max=0.125
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Average 0.00195 min=0.00159 max=0.00231
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Average 0.149 min=0.145 max=0.153
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Average 0.00239 min=0.0016 max=0.00324
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35
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Average 0.17 min=0.163 max=0.178
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Average 0.00199 min=0.00162 max=0.00269
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40
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Average 0.191 min=0.189 max=0.192
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Average 0.00183 min=0.00171 max=0.00197
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45
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Average 0.214 min=0.212 max=0.216
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Average 0.00252 min=0.00187 max=0.00345
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50
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Average 0.236 min=0.226 max=0.243
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Average 0.00237 min=0.00182 max=0.00326
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**Total running time of the script:** ( 0 minutes 49.102 seconds)
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.. _sphx_glr_download_auto_examples_plot_train_convert_predict.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download
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:download:`Download Python source code: plot_train_convert_predict.py <plot_train_convert_predict.py>`
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.. container:: sphx-glr-download
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:download:`Download Jupyter notebook: plot_train_convert_predict.ipynb <plot_train_convert_predict.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
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