sklearn binary classifier: compiled and Python model discrepancies?

[blokhin]

Dear treelite community,

what could be the reasons of discrepancies in predictions of a sklearn random forest binary classifier, between the original Python code and the compiled code?

I used gallery interface for compilation and treelite.runtime for deployment, if that may matter.

PS thanks for treelite, I really enjoy 10-15x speedup in my compiled regression models (they work just fine)

[hcho3]

@blokhin Is it possible to post your model for debugging purposes? If not, can you make a toy example to demonstrate the issue?

[hcho3]

@blokhin Any updates?

[blokhin]

@hcho3 I figured out the compiled random forest binary classifier outputs not the 0 vs. 1 (as expected), but the float in the range between 0 and 1. Not a big deal, as this float can be correctly rounded then. That was actually a reason of discrepancy.

[hcho3]

@blokhin Treelite will produce a number between 0 and 1, representing the fraction of the votes among the decision trees for the positive class (e.g. 0.75 means 75% of the trees predicted the positive class).

[blokhin]

This is a sample code to reproduce:

import random
import numpy as np

from sklearn.ensemble import RandomForestClassifier

import treelite.gallery.sklearn
import treelite.runtime


# First, generate sample data
my_range = range(1, 100)
X_data = [[random.choice(my_range) for _ in range(125)] for _ in range(76)]
X_test = X_data.pop()
y_data = [random.choice([0, 1]) for _ in range(75)]

# Second, prepare a classifier and compile it
model_py = RandomForestClassifier(
    n_estimators=100,
    max_features=2,
    max_depth=None,
    min_samples_split=2,
    min_samples_leaf=5,
    bootstrap=True,
    n_jobs=-1
)
model_py.fit(X_data, y_data)

model_file = './compiled.so'
icompiler = treelite.gallery.sklearn.import_model(model_py)
icompiler.export_lib(toolchain='clang', libpath=model_file, verbose=True, params={'parallel_comp': 8})
model_tr = treelite.runtime.Predictor(model_file, verbose=True)

# Third, compare: py vs. tr
result_py = model_py.predict([X_test])[0]

batch = treelite.runtime.Batch.from_npy2d(np.array([X_test]))
result_tr = model_tr.predict(batch)

print(result_py, result_tr)
assert result_py == result_tr # I would expect that, but...

[hcho3]

@blokhin Also, try using predict_proba() to get probability values from scikit-learn

[blokhin]

OK, got it!