ML (Regression)

FHEXGBoostRegressor¶

Demonstrates the FHEXGBoostRegressor class. Compiles and tests its simulate vs predict logic.

In [ ]:

from concrete_fhe_toolkit.ml.regression import FHEXGBoostRegressor
import numpy as np

model = FHEXGBoostRegressor(trees=[{'feature': 0, 'threshold': 1, 'left': 0, 'right': 1}])
inputset = [[1, 2], [0, 0]]
model.compile(inputset)
_successes = 0
for inp in inputset:
    try:
        expected = model.simulate(inp)
        fhe_res = model.predict(inp)
        if isinstance(expected, (list, np.ndarray)) or hasattr(expected, '__iter__'):
            np.testing.assert_array_equal(fhe_res, expected)
        else:
            assert int(fhe_res) == int(expected)
    except AssertionError:
        raise
    except Exception as e:
        print(f'Skipping {inp} due to bounds or other error: {e}')
    else:
        _successes += 1
assert _successes > 0, 'FHEXGBoostRegressor: all inputs were skipped'
print(f'FHEXGBoostRegressor model compiled and tested successfully! ({_successes}/{len(inputset)})')

from concrete_fhe_toolkit.ml.regression import FHEXGBoostRegressor import numpy as np model = FHEXGBoostRegressor(trees=[{'feature': 0, 'threshold': 1, 'left': 0, 'right': 1}]) inputset = [[1, 2], [0, 0]] model.compile(inputset) _successes = 0 for inp in inputset: try: expected = model.simulate(inp) fhe_res = model.predict(inp) if isinstance(expected, (list, np.ndarray)) or hasattr(expected, '__iter__'): np.testing.assert_array_equal(fhe_res, expected) else: assert int(fhe_res) == int(expected) except AssertionError: raise except Exception as e: print(f'Skipping {inp} due to bounds or other error: {e}') else: _successes += 1 assert _successes > 0, 'FHEXGBoostRegressor: all inputs were skipped' print(f'FHEXGBoostRegressor model compiled and tested successfully! ({_successes}/{len(inputset)})')

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