mlprodict¶
Links: github, documentation, mlprodict, blog
mlprodict explores couple of ways to compute predictions faster than the library used to build the machine learning model. One way is to convert the prediction function into C.
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from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 1
lr = LogisticRegression()
lr.fit(X, y)
############################
# Conversion into a graph.
from mlprodict.grammar_sklearn import sklearn2graph
gr = sklearn2graph(lr, output_names=['Prediction', 'Score'])
######################################
# Conversion into C
ccode = gr.export(lang='c')
# We print after a little bit of cleaning (remove all comments)
print("\n".join(_ for _ in ccode['code'].split("\n") if "//" not in _))
>>>
/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
int LogisticRegression (float* pred, float* Features)
{
float pred0c0c00c0[2] = {(float)2.4896657466888428, (float)-3.9989070892333984};
float* pred0c0c00c1 = Features;
float pred0c0c00;
adot_float_float(&pred0c0c00, pred0c0c00c0, pred0c0c00c1, 2);
float pred0c0c01 = (float)-0.8005923628807068;
float pred0c0c0 = pred0c0c00 + pred0c0c01;
float pred0c0;
sign_float(&pred0c0, pred0c0c0);
float pred0[2];
concat_float_float(pred0, pred0c0, pred0c0c0);
memcpy(pred, pred0, 2*sizeof(float));
return 0;
}
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