docs/ml/advanced_examples/ImportingFromScikitLearn.ipynb

Importing models from scikit-learn

Concrete ML provides a from_sklearn_model to import an already trained model to FHE. This feature is implemented for two types of models, linear models and tree based models.

In [1]:

from functools import partial

# The simulation mode allows to measure the impact of FHE execution on accuracy
# without paying the cost of FHE computations.
# However, data is not encrypted when using the simulation: the model performs inference
# on clear data.


def make_classifier_comparison_from_sklearn(*args, **kwargs):
    return args, kwargs


%run utils/classifier_comparison_utils.py

In [2]:

from concrete.ml.sklearn import (
    DecisionTreeClassifier,
    LinearSVC,
    LogisticRegression,
    RandomForestClassifier,
    XGBClassifier,
)

Linear models

In [3]:

%%time

linear_classifiers = [
    (partial(LinearSVC, C=0.025), "Linear SVC"),
    (LogisticRegression, "Logistic Regression"),
]

# pylint: disable-next=undefined-variable
make_classifier_comparison_from_sklearn(
    "Linear Classifiers", linear_classifiers, 0, simulate=True, h=1
)  # noqa

CPU times: user 27 s, sys: 5.96 s, total: 32.9 s
Wall time: 6.2 s

Tree-based-models

In [4]:

%%time

tree_classifiers = [
    (partial(DecisionTreeClassifier, max_depth=5), "Decision Tree"),
    (partial(RandomForestClassifier, max_depth=4, n_estimators=5), "Random Forest"),
    (partial(XGBClassifier, n_jobs=1, max_depth=4, n_estimators=5), "XGB"),
]

# pylint: disable-next=undefined-variable
make_classifier_comparison_from_sklearn(  # noqa
    "Tree-Based Classifiers", tree_classifiers, 0.5, simulate=True, h=0.1
)

CPU times: user 1d 3h 26min 25s, sys: 51min 48s, total: 1d 4h 18min 14s
Wall time: 14min 6s