Concrete-ML provides partial support for Pandas, with most available models (linear and tree-based models) usable on Pandas dataframes the same way they would be used with NumPy arrays.
The table below summarizes the current compatibility:
Methods
Support Pandas dataframe
fit
✓
compile
✗
predict (execute_in_fhe=False)
✓
predict (execute_in_fhe=True)
✓
Example
The following example considers a LogisticRegression model on a simple classification problem. A more advanced example can be found in the , which considers a XGBClassifier.
import numpy as np
import pandas as pd
from concrete.ml.sklearn import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# Create the data set as a Pandas dataframe
X, y = make_classification(
n_samples=100,
n_features=2,
n_redundant=0,
random_state=2,
)
X, y = pd.DataFrame(X), pd.DataFrame(y)
# Retrieve train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
# Instantiate the model
model = LogisticRegression(n_bits=2)
# Fit the model
model.fit(X_train, y_train)
# Evaluate the model on the test set in clear
y_pred_clear = model.predict(X_test)
# Compile the model
model.compile(X_train.to_numpy())
# Perform the inference in FHE
# Warning: this will take a while. It is recommended to run this with a very small batch of
# examples first (e.g. N_TEST_FHE = 1)
# Note that here the encryption and decryption is done behind the scenes.
N_TEST_FHE = 1
y_pred_fhe = model.predict(X_test.head(N_TEST_FHE), execute_in_fhe=True)
# 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 a FHE inference equal to the clear inference.")
# Output:
# 1 examples over 1 have a FHE inference equal to the clear inference
