Concrete ML fully supports Pandas, allowing built-in models such as linear and tree-based models to use Pandas dataframes and series just as they would be used with NumPy arrays.
The table below summarizes current compatibility:
Methods
Support Pandas dataframe
fit
✓
compile
✓
predict (fhe="simulate")
✓
predict (fhe="execute")
✓
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=250,
n_features=30,
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=8)
# 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)
# Perform the inference in FHE
y_pred_fhe = model.predict(X_test, fhe="execute")
# Assert that FHE predictions are the same as the clear predictions
print(
f"{(y_pred_fhe == y_pred_clear).sum()} "
f"examples over {len(y_pred_fhe)} have an FHE inference equal to the clear inference."
)
# Output:
# 100 examples over 100 have an FHE inference equal to the clear inference.
