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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
111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
"""Tests for the torch to numpy module."""
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import inspect
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import random
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import numpy
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import pytest
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from sklearn.tree import plot_tree
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from concrete.ml.pytest.utils import MODELS_AND_DATASETS
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def test_seed_1():
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"""Test python and numpy seeding."""
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# Python random
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for _ in range(10):
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print(random.randint(0, 1000))
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# Numpy random
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for _ in range(10):
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print(numpy.random.randint(0, 1000))
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print(numpy.random.uniform(-100, 100, size=(3, 3)))
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def test_seed_2():
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"""Test python and numpy seeding."""
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# Python random
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for _ in range(20):
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print(random.randint(0, 100))
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# Numpy random
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for _ in range(20):
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print(numpy.random.randint(0, 100))
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print(numpy.random.uniform(-10, 100, size=(3, 3)))
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@pytest.mark.parametrize("random_inputs_1", [numpy.random.randint(0, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_2", [numpy.random.randint(-1000, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_3", [numpy.random.randint(-100, 2**15, size=20)])
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def test_seed_needing_randomly_seed_arg_1(random_inputs_1, random_inputs_2, random_inputs_3):
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"""Test python and numpy seeding for pytest parameters.
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Remark this test needs an extra --randomly-seed argument for reproducibility
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"""
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print("Random inputs", random_inputs_1)
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print("Random inputs", random_inputs_2)
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print("Random inputs", random_inputs_3)
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@pytest.mark.parametrize("random_inputs_1", [numpy.random.uniform(0, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_2", [numpy.random.uniform(-1000, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_3", [numpy.random.uniform(-100, 2**15, size=20)])
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def test_seed_needing_randomly_seed_arg_2(random_inputs_1, random_inputs_2, random_inputs_3):
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"""Test python and numpy seeding for pytest parameters.
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Remark this test needs an extra --randomly-seed argument for reproducibility
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"""
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print("Random inputs", random_inputs_1)
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print("Random inputs", random_inputs_2)
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print("Random inputs", random_inputs_3)
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@pytest.mark.parametrize("random_inputs_1", [numpy.random.randint(0, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_2", [numpy.random.uniform(-1000, 2**15, size=20)])
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@pytest.mark.parametrize("random_inputs_3", [numpy.random.randint(-100, 2**15, size=20)])
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def test_seed_needing_randomly_seed_arg_3(random_inputs_1, random_inputs_2, random_inputs_3):
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"""Test python and numpy seeding for pytest parameters.
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Remark this test needs an extra --randomly-seed argument for reproducibility
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"""
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print("Random inputs", random_inputs_1)
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print("Random inputs", random_inputs_2)
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print("Random inputs", random_inputs_3)
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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, default_configuration):
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"""Test seeding of sklearn models"""
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x, y = load_data(model_class, **parameters)
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# Force "random_state": if it was there, it is overwritten; if it was not there, it is added
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model_params = {}
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if "random_state" in inspect.signature(model_class).parameters:
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model_params["random_state"] = 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(**model_params)
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# Fit the model
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model, sklearn_model = model.fit_benchmark(x, y)
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lpvoid_ptr_plot_tree = getattr(model, "plot_tree", None)
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if callable(lpvoid_ptr_plot_tree):
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print("model", plot_tree(model.sklearn_model))
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print(f"sklearn_model = {sklearn_model}")
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# Test the determinism of our package (even if the bit-width may be too large)
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try:
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model.compile(x, configuration=default_configuration, show_mlir=True)
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except RuntimeError as err:
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print(err)
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except AssertionError as err:
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print(err)
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