CODEWITHSUNDEEP
pythonscikit-learnsklearn-pandas
Bowers
Bowers

Reputation: 846

Increase performance of Random Forest Regressor in sklearn

There is an optimization problem where I have to call the predict function of a Random Forest Regressor several thousand times. 

from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor(n_estimators=10)
rfr = rfr.fit(X, Y)
for iteration in range(0, 100000):
    # code that adapts the input data according to fitness of the last output
    output_data = rfr.predict(input_data)
    # code that evaluates the fitness of output data

Is there a way to increase the speed of the predict function in this case? Possibly by using Cython?

Upvotes: 4

Views: 572

Answers (1)

green1111
green1111

Reputation: 231

You can convert it to C or C++ Code with SKompiler (https://github.com/konstantint/SKompiler) and then run it there.

from skompiler import skompile
expr = skompile(rfr.predict)
with open("output.cpp", "w") as text_file: print(expr.to('sympy/cxx'), file=text_file)

Upvotes: 5

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