118 строки
4.7 KiB
Python
118 строки
4.7 KiB
Python
"""
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Tests sklearn MLP models (MLPClassifier, MLPRegressor) converters.
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"""
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import unittest
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import warnings
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import numpy as np
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import torch
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from sklearn.neural_network import MLPClassifier, MLPRegressor
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import hummingbird.ml
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from hummingbird.ml import constants
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from hummingbird.ml._utils import tvm_installed
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class TestSklearnMLPClassifier(unittest.TestCase):
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# MLPClassifier test function to be parameterized
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def _test_mlp_classifer(self, num_classes, activation="relu", labels_shift=0, backend="torch", extra_config={}):
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model = MLPClassifier(hidden_layer_sizes=(100, 100, 50,), activation=activation)
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np.random.seed(0)
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X = np.random.rand(100, 200)
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X = np.array(X, dtype=np.float32)
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y = np.random.randint(num_classes, size=100) + labels_shift
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model.fit(X, y)
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torch_model = hummingbird.ml.convert(model, backend, X, extra_config=extra_config)
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self.assertTrue(torch_model is not None)
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np.testing.assert_allclose(model.predict_proba(X), torch_model.predict_proba(X), rtol=1e-6, atol=1e-6)
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# MLPClassifier binary
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def test_mlp_classifer_bi(self):
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self._test_mlp_classifer(2)
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# MLPClassifier multi-class
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def test_mlp_classifer_multi(self):
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self._test_mlp_classifer(3)
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# MLPClassifier multi-class w/ shifted labels
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def test_mlp_classifer_multi_shifted_labels(self):
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self._test_mlp_classifer(3, labels_shift=3)
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# MLPClassifier multi-class w/ tanh activation
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def test_mlp_classifer_multi_logistic(self):
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self._test_mlp_classifer(3, activation="tanh")
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# MLPClassifier multi-class w/ logistic activation
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def test_mlp_classifer_multi_tanh(self):
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self._test_mlp_classifer(3, activation="logistic")
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# MLPClassifier multi-class w/ identity activation
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def test_mlp_classifer_multi_identity(self):
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self._test_mlp_classifer(3, activation="identity")
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# Test TVM backend
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# MLPClassifier binary
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_bi_tvm(self):
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self._test_mlp_classifer(2, backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPClassifier multi-class
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_multi_tvm(self):
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self._test_mlp_classifer(3, backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPClassifier multi-class w/ shifted labels
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_multi_shifted_labels_tvm(self):
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self._test_mlp_classifer(3, labels_shift=3, backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPClassifier multi-class w/ tanh activation
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_multi_logistic_tvm(self):
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self._test_mlp_classifer(3, activation="tanh", backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPClassifier multi-class w/ logistic activation
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_multi_tanh_tvm(self):
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self._test_mlp_classifer(3, activation="logistic", backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPClassifier multi-class w/ identity activation
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@unittest.skipIf(not (tvm_installed()), reason="TVM tests require TVM")
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def test_mlp_classifer_multi_identity_tvm(self):
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self._test_mlp_classifer(3, activation="identity", backend="tvm", extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
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# MLPRegressor test function to be parameterized
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def _test_mlp_regressor(self, activation="relu"):
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model = MLPRegressor(hidden_layer_sizes=(100, 100, 50,), activation=activation)
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np.random.seed(0)
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X = np.random.rand(100, 200)
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X = np.array(X, dtype=np.float32)
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y = np.random.rand(100)
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model.fit(X, y)
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torch_model = hummingbird.ml.convert(model, "torch")
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self.assertTrue(torch_model is not None)
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np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-6, atol=1e-6)
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# MLPRegressor
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def test_mlp_regressor(self):
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self._test_mlp_regressor()
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# MLPRegressor w/ tanh activation
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def test_mlp_regressor_multi_logistic(self):
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self._test_mlp_regressor(activation="tanh")
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# MLPRegressor w/ logistic activation
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def test_mlp_regressor_multi_tanh(self):
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self._test_mlp_regressor(activation="logistic")
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# MLPRegressor multi-class w/ identity activation
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def test_mlp_regressor_multi_identity(self):
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self._test_mlp_regressor(activation="identity")
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if __name__ == "__main__":
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unittest.main()
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