mlprodict/_doc/examples/plot_speedup_pca.py at loop · sdpython/mlprodict · GitHub

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"""

.. _l-Speedup-pca:

Speed up scikit-learn inference with ONNX

=========================================

Is it possible to make :epkg:`scikit-learn` faster with ONNX?

That's question this example tries to answer. The scenario is

is the following:

* a model is trained

* it is converted into ONNX for inference

* it selects a runtime to compute the prediction

The following runtime are tested:

* `python`: python runtime for ONNX

* `onnxruntime1`: :epkg:`onnxruntime`

* `numpy`: the ONNX graph is converted into numpy code

* `numba`: the numpy code is accelerated with :epkg:`numba`.

.. contents::

:local:

PCA

+++

Let's look at a very simple model, a PCA.

"""

import numpy

from pandas import DataFrame

import matplotlib.pyplot as plt

from sklearn.datasets import make_regression

from sklearn.decomposition import PCA

from pyquickhelper.pycode.profiling import profile

from mlprodict.sklapi import OnnxSpeedupTransformer

from cpyquickhelper.numbers.speed_measure import measure_time

from tqdm import tqdm

################################

# Data and models to test.

data, _ = make_regression(1000, n_features=20)

data = data.astype(numpy.float32)

models = [

('sklearn', PCA(n_components=10)),

('python', OnnxSpeedupTransformer(

PCA(n_components=10), runtime='python')),

('onnxruntime1', OnnxSpeedupTransformer(

PCA(n_components=10), runtime='onnxruntime1')),

('numpy', OnnxSpeedupTransformer(

PCA(n_components=10), runtime='numpy')),

('numba', OnnxSpeedupTransformer(

PCA(n_components=10), runtime='numba'))]

#################################

# Training.

for name, model in tqdm(models):

model.fit(data)

#################################

# Profiling of runtime `onnxruntime1`.

def fct():

for i in range(1000):

models[2][1].transform(data)

res = profile(fct, pyinst_format="text")

print(res[1])

#################################

# Profiling of runtime `numpy`.

def fct():

for i in range(1000):

models[3][1].transform(data)

res = profile(fct, pyinst_format="text")

print(res[1])

#################################

# The class *OnnxSpeedupTransformer* converts the PCA

# into ONNX and then converts it into a python code using

# *numpy*. The code is the following.

print(models[3][1].numpy_code_)

#################################

# Benchmark.

bench = []

for name, model in tqdm(models):

for size in (1, 10, 100, 1000, 10000, 100000, 200000):

data, _ = make_regression(size, n_features=20)

data = data.astype(numpy.float32)

# We run it a first time (numba compiles

# the function during the first execution).

model.transform(data)

res = measure_time(

lambda: model.transform(data), div_by_number=True,

context={'data': data, 'model': model})

res['name'] = name

res['size'] = size

bench.append(res)

df = DataFrame(bench)

piv = df.pivot("size", "name", "average")

piv

######################################

# Graph.

fig, ax = plt.subplots(1, 2, figsize=(10, 4))

piv.plot(title="Speedup PCA with ONNX (lower better)",

logx=True, logy=True, ax=ax[0])

piv2 = piv.copy()

for c in piv2.columns:

piv2[c] /= piv['sklearn']

print(piv2)

piv2.plot(title="baseline=scikit-learn (lower better)",

logx=True, logy=True, ax=ax[1])

plt.show()

mlprodict/_doc/examples/plot_speedup_pca.py at loop · sdpython/mlprodict · GitHub

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