Tree-based models

This document introduces several scikit-learn's linear models for classification and regression tree models that Concrete ML provides.

Supported models

Concrete ML	scikit-learn
DecisionTreeClassifier	DecisionTreeClassifier
DecisionTreeRegressor	DecisionTreeRegressor
RandomForestClassifier	RandomForestClassifier
RandomForestRegressor	RandomForestRegressor

Concrete ML also supports XGBoost's XGBClassifier and XGBRegressor:

Concrete ML	XGboost
XGBClassifier	XGBClassifier
XGBRegressor	XGBRegressor

For a formal explanation of the mechanisms that enable FHE-compatible decision trees, please see the following paper: Privacy-Preserving Tree-Based Inference with Fully Homomorphic Encryption, arXiv:2303.01254

Using the maximum depth parameter of decision trees and tree-ensemble models strongly increases the number of nodes in the trees. Therefore, we recommend using the XGBoost models which achieve better performance with lower depth.

Ciphertext format compatibility

The DecisionTreeClassifier, RandomForestClassifier, and XGBClassifier support TFHE-rs radix ciphertexts when n_bits is set to 8. The other tree-based models, or different n_bits configurations only support Concrete ciphertexts.

To compile a model to use TFHE-rs ciphertexts as inputs and outputs, set ciphertext_mode=CiphertextFormat.TFHE-RS in the compile call.

Pre-trained models

You can convert an already trained scikit-learn tree-based model to a Concrete ML one by using the from_sklearn_model method.

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. You can find a more complete example in the XGBClassifier notebook.

from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

from concrete.ml.sklearn.xgb import XGBClassifier


# Get data-set and split into train and test
X, y = load_breast_cancer(return_X_y=True)

# Split the train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Define our model
model = XGBClassifier(n_jobs=1, n_bits=3)

# Define the pipeline
# We normalize the data and apply a PCA before fitting the model
pipeline = Pipeline(
    [("standard_scaler", StandardScaler()), ("pca", PCA(random_state=0)), ("model", model)]
)

# Define the parameters to tune
param_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 cores
grid = GridSearchCV(pipeline, param_grid, cv=5, n_jobs=-1, scoring="accuracy")

# Launch the grid search
grid.fit(X_train, y_train)

# Print the best parameters found
print(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 is done in clear (client machine)
# while the model inference is done in FHE on a server.
# The pipeline can be split into 2 parts:
#   1. data transformation
#   2. estimator
best_pipeline = grid.best_estimator_
data_transformation_pipeline = best_pipeline[:-1]
model = best_pipeline[-1]

# Transform test set
X_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 clear
y_pred_clear = model.predict(X_test_transformed)
print(f"Test accuracy in clear: {(y_pred_clear == y_test).mean():0.2f}")

# In the output, the Test accuracy in clear should be > 0.9

# Compile the model to FHE
model.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 = 1
y_pred_fhe = model.predict(X_test_transformed[:N_TEST_FHE], fhe="execute")

# Assert that FHE predictions are the same as the clear predictions
print(f"{(y_pred_fhe == y_pred_clear[:N_TEST_FHE]).sum()} "
      f"examples over {N_TEST_FHE} have an FHE inference equal to the clear inference.")

# Output:
#  1 examples over 1 have an FHE inference equal to the clear inference

We can plot and compare the decision boundaries of the Concrete ML model and the classical XGBoost model executed in the clear. Here we show a 6-bit model to illustrate the impact of quantization on classification. You will find similar plots in the Classifier Comparison notebook.

Comparison of clasification decision boundaries between FHE and plaintext models

Quantization parameters

When using a sufficiently high bit-width, quantization has little impact on the decision boundaries of the Concrete ML FHE decision tree model, as quantization is done individually on each input feature. It means FHE models can achieve similar accuracy levels as floating point models. Using 6 bits for quantization is effective in reaching or even exceeding floating point accuracy.

To adjust the number of bits for quantization, use the n_bits parameter. Setting n_bits to a low value may introduce artifacts, potentially reducing accuracy. However, the execution speed in FHE could improve. This adjustment allows you to manage the accuracy/speed trade-off. Additionally, you can recover some accuracy by increasing the n_estimators parameter.

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.

XGBoost n_bits comparison

FHE Inference time considerations

The inference time in FHE is strongly dependant on the maximum circuit bit-width. For trees, in most cases, the quantization bit-width will be the same as the circuit bit-width. Therefore, reducing the quantization bit-width to 4 or less will result in fast inference times. Adding more bits will increase FHE inference time exponentially.

In some rare cases, the bit-width of the circuit can be higher than the quantization bit-width. This could happen when the quantization bit-width is low but the tree-depth is high. In such cases, the circuit bit-width is upper bounded by ceil(log2(max_depth + 1) + 1).

For more information on the inference time of FHE decision trees and tree-ensemble models please see Privacy-Preserving Tree-Based Inference with Fully Homomorphic Encryption, arXiv:2303.01254.