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Files edited: 150 Brand-hit files before: 163 Brand-hit files after: 28 Substitutions: - Zama Concrete ML -> TorusML - Zama Concrete / Concrete-> Torus - Zama fhEVM / fhEVM -> TorusEVM - Zama TFHE-rs / tfhe-rs -> Lux-FHE (github.com/luxfi/fhe) - Zama (attribution) -> Lux Industries - zama-ai/* (GitHub) -> luxfhe/* - @zama-fhe/* (npm) -> @luxfhe/* - docs.zama.ai -> docs.lux.network - Fhenix / CoFHE -> dropped or rewritten to Lux FHE / TorusEVM coprocessor Text-only changes. No runtime code semantics altered.
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
"""Tests for the sklearn linear models."""
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from inspect import signature
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import numpy
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import pytest
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from torch import nn
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from concrete.ml.pytest.torch_models import FCSmall
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from concrete.ml.pytest.utils import MODELS_AND_DATASETS
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from concrete.ml.torch.compile import compile_torch_model
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INPUT_OUTPUT_FEATURE = [5, 10]
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@pytest.mark.parametrize("model_class, parameters", MODELS_AND_DATASETS)
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@pytest.mark.parametrize(
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"kwargs",
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[
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{"p_error": 0.03},
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{"p_error": 0.04, "global_p_error": None},
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{"global_p_error": 0.025},
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{"global_p_error": 0.036, "p_error": None},
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{},
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{"global_p_error": 0.038, "p_error": 0.39},
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],
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)
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def test_config_sklearn(model_class, parameters, kwargs, load_data):
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"""Testing with p_error and global_p_error configs with sklearn models."""
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x, y = load_data(model_class, **parameters)
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model = model_class()
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# Fit the model
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model.fit(x, y)
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if kwargs.get("p_error", None) is not None and kwargs.get("global_p_error", None) is not None:
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with pytest.raises(ValueError) as excinfo:
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model.compile(x, verbose=True, **kwargs)
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assert "Please only set one of (p_error, global_p_error) values" in str(excinfo.value)
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else:
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model.compile(x, verbose=True, **kwargs)
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# We still need to check that we have the expected probabilities
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# FIXME: https://github.com/luxfi/torusml-internal/issues/2206
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@pytest.mark.parametrize(
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"model",
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[pytest.param(FCSmall)],
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)
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@pytest.mark.parametrize(
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"kwargs",
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[
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{"p_error": 0.03},
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{"p_error": 0.04, "global_p_error": None},
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{"global_p_error": 0.025},
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{"global_p_error": 0.036, "p_error": None},
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{},
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{"global_p_error": 0.038, "p_error": 0.39},
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],
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)
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def test_config_torch(model, kwargs):
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"""Testing with p_error and global_p_error configs with torch models."""
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input_output_feature = 5
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n_examples = 50
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torch_model = model(input_output_feature, activation_function=nn.ReLU)
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num_inputs = len(signature(torch_model.forward).parameters)
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# Create random input
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inputset = (
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tuple(
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numpy.random.uniform(-100, 100, size=(n_examples, input_output_feature))
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for _ in range(num_inputs)
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)
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if num_inputs > 1
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else numpy.random.uniform(-100, 100, size=(n_examples, input_output_feature))
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)
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if kwargs.get("p_error", None) is not None and kwargs.get("global_p_error", None) is not None:
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with pytest.raises(ValueError) as excinfo:
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compile_torch_model(
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torch_model,
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inputset,
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n_bits={"model_inputs": 2, "model_outputs": 2, "op_inputs": 2, "op_weights": 2},
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verbose=True,
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**kwargs,
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)
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assert "Please only set one of (p_error, global_p_error) values" in str(excinfo.value)
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else:
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compile_torch_model(
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torch_model,
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inputset,
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n_bits={"model_inputs": 2, "model_outputs": 2, "op_inputs": 2, "op_weights": 2},
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verbose=True,
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**kwargs,
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)
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# We still need to check that we have the expected probabilities
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# FIXME: https://github.com/luxfi/torusml-internal/issues/2206
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