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zeekay
bde2e2531b
Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
"""Tests common to all sklearn models."""
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import inspect
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import numpy
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import pytest
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from concrete.ml.common.utils import get_model_class
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from concrete.ml.pytest.utils import MODELS_AND_DATASETS
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from concrete.ml.sklearn import _get_sklearn_all_models
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def test_sklearn_args():
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"""Check that all arguments from the underlying sklearn model are exposed."""
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test_counter = 0
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for model_class in _get_sklearn_all_models():
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model_class = get_model_class(model_class)
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# For Neural Network models, we manually fix the module parameter to
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# SparseQuantNeuralNetImpl. It is therefore not exposed to the users.
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assert (
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not set(inspect.getfullargspec(model_class.sklearn_model_class).args)
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- set(inspect.getfullargspec(model_class).args)
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- {"module"}
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)
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test_counter += 1
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assert test_counter == 21
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@pytest.mark.parametrize("model_class, parameters", MODELS_AND_DATASETS)
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def test_seed_sklearn(model_class, parameters, load_data):
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"""Tests the random_state parameter."""
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x, y = load_data(model_class, **parameters)
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if "random_state" not in inspect.getfullargspec(model_class).args:
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pytest.skip(f"Model class: `{model_class}` has no arguments random_state.")
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random_state_constructor = numpy.random.randint(0, 2**15)
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random_state_user = numpy.random.randint(0, 2**15)
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# First case: user gives his own random_state
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model = model_class(random_state=random_state_constructor)
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# Fit the model
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model, sklearn_model = model.fit_benchmark(x, y, random_state=random_state_user)
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assert (
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model.random_state == random_state_user and sklearn_model.random_state == random_state_user
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)
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# Second case: user does not give random_state but seeds the constructor
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model = model_class(random_state=random_state_constructor)
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# Fit the model
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model, sklearn_model = model.fit_benchmark(x, y)
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assert (model.random_state == random_state_constructor) and (
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sklearn_model.random_state == random_state_constructor
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)
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# Third case: user does not provide any seed
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model = model_class(random_state=None)
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assert model.random_state is None
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# Fit the model
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model, sklearn_model = model.fit_benchmark(x, y)
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# model.random_state and sklearn_model.random_state should now be seeded with the same value
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assert model.random_state is not None and sklearn_model.random_state is not None
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assert model.random_state == sklearn_model.random_state
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