Concrete-ML provides several of the most popular classification and regression tree models that can be found in Scikit-learn:
Concrete-ML
scikit-learn
In addition to support for scikit-learn, Concrete-ML also supports XGBoost 's XGBClassifier:
Concrete-ML
XGboost
Example
Here's an example of how to use this model in FHE on a popular data-set using some of scikit-learn's pre-processing tools. A more complete example can be found in the XGBClassifier notebook.
from sklearn.datasets import load_breast_cancerfrom sklearn.decomposition import PCAfrom sklearn.model_selection import GridSearchCV, train_test_splitfrom sklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScalerfrom concrete.ml.sklearn.xgb import XGBClassifier# Get data-set and split into train and testX, y =load_breast_cancer(return_X_y=True)# Split the train and test setX_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.2, random_state=0)# Define our modelmodel =XGBClassifier(n_jobs=1, n_bits=3)# Define the pipeline# We will normalize the data and apply a PCA before fitting the modelpipeline =Pipeline( [("standard_scaler", StandardScaler()), ("pca", PCA(random_state=0)), ("model", model)])# Define the parameters to tuneparam_grid ={"pca__n_components": [2,5,10,15],"model__max_depth": [2,3,5],"model__n_estimators": [5,10,20],}# Instantiate the grid search with 5-fold cross validation on all available coresgrid =GridSearchCV(pipeline, param_grid, cv=5, n_jobs=-1, scoring="accuracy")# Launch the grid searchgrid.fit(X_train, y_train)# Print the best parameters foundprint(f"Best parameters found: {grid.best_params_}")# Output:# Best parameters found: {'model__max_depth': 5, 'model__n_estimators': 10, 'pca__n_components': 5}# Currently we only focus on model inference in FHE# The data transformation will be done in clear (client machine)# while the model inference will be done in FHE on a server.# The pipeline can be split into 2 parts:# 1. data transformation# 2. estimatorbest_pipeline = grid.best_estimator_data_transformation_pipeline = best_pipeline[:-1]model = best_pipeline[-1]# Transform test setX_train_transformed = data_transformation_pipeline.transform(X_train)X_test_transformed = data_transformation_pipeline.transform(X_test)# Evaluate the model on the test set in cleary_pred_clear = model.predict(X_test_transformed)print(f"Test accuracy in clear: {(y_pred_clear == y_test).mean():0.2f}")# Output:# Test accuracy: 0.98# Compile the model to FHEmodel.compile(X_train_transformed)# Perform the inference in FHE# Warning: this will take a while. It is recommended to run this with a very small batch of# example first (e.g. N_TEST_FHE = 1)# Note that here the encryption and decryption is done behind the scene.N_TEST_FHE =1y_pred_fhe = model.predict(X_test_transformed[:N_TEST_FHE], execute_in_fhe=True)# Assert that FHE predictions are the same as the clear predictionsprint(f"{(y_pred_fhe == y_pred_clear[:N_TEST_FHE]).sum()} "f"examples over {N_TEST_FHE} have a FHE inference equal to the clear inference.")# Output:# 1 examples over 1 have a FHE inference equal to the clear inference
Using the above example, we can then plot how the model classifies the inputs and then compare those results with the XGBoost model executed in clear. A 6-bits model is also given in order to better understand the impact of quantization on classification. Similar plots can be found in the Classifier Comparison notebook.
This graph shows the impact of quantization over the decision boundaries in the Concrete-ML FHE decision tree models. In the 3-bits model, only a rough, highly-discrete decision function is observed. This results in a small decrease of accuracy of about 7% compared to the initial XGBoost classifier. Besides, using 6-bits of quantization makes the model reach 93% accuracy, drastically reducing this difference to only 1.7 percentage points.
In fact, the quantization process may sometimes create some artifacts that could lead to a decrease in performance. Still, as the quantization is done individually on each input feature, the artifacts are minor when considering small tree-based models with 5-6 bits quantization. Thus, FHE tree-based models reach similar scores as their equivalent floating point ones.
The following graph shows that using 5-6 bits of quantization is usually sufficient to reach the performance of a non-quantized XGBoost model on floating point data. The metrics plotted are accuracy and F1-score on the spambase data-set.
