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 Titanic use case notebook, which considers a XGBClassifier.

import numpy as npimport pandas as pdfrom concrete.ml.sklearn import LogisticRegressionfrom sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_split# Create the data-set as a Pandas dataframeX, 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 setsX_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.4, random_state=42)# Instantiate the modelmodel =LogisticRegression(n_bits=8)# Fit the modelmodel.fit(X_train, y_train)# Evaluate the model on the test set in cleary_pred_clear = model.predict(X_test)# Compile the modelmodel.compile(X_train)# Perform the inference in FHEy_pred_fhe = model.predict(X_test, fhe="execute")# Assert that FHE predictions are the same as the clear predictionsprint(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.