Пример #1
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def get_pyrbm(n_features, rng):
    pyvotune.dense_input(PyRBMFeatureExtractor)
    pyvotune.non_terminal(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='learning_rate',
                    rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='momentum',
                    rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='l2_weight',
                    rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='sparsity',
                    rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='scale',
                    rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pbool(name='binary', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pbool(name='reconstruction', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 10), name='n_training_epochs',
                  rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 20), name='n_gibbs',
                  rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='batch_size',
                  rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(5, 1500), name='n_hidden',
                  rng=rng)(PyRBMFeatureExtractor)

    return [PyRBMFeatureExtractor]
Пример #2
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def get_decomposers(n_features, rng):
    pyvotune.dense_input(PCA)
    pyvotune.non_terminal(PCA)
    pyvotune.pbool(name='whiten', rng=rng)(PCA)

    pyvotune.non_terminal(ProjectedGradientNMF)
    pyvotune.pint(range=(1, n_features), name='n_components',
                  rng=rng)(ProjectedGradientNMF)
    pyvotune.choice(choices=['nndsvd', 'nndsvda', 'nndsvdar'],
                    name='init',
                    rng=rng)(ProjectedGradientNMF)
    pyvotune.choice(choices=['data', 'components', None],
                    name='sparseness',
                    rng=rng)(ProjectedGradientNMF)
    pyvotune.pfloat(range=(1, 10), name='beta', rng=rng)(ProjectedGradientNMF)
    pyvotune.pfloat(range=(0.01, 10), name='eta',
                    rng=rng)(ProjectedGradientNMF)
    pyvotune.pint(range=(1, 600), name='max_iter',
                  rng=rng)(ProjectedGradientNMF)
    pyvotune.pint(range=(100, 4000), name='nls_max_iter',
                  rng=rng)(ProjectedGradientNMF)

    # PCA & ProbabilisticPCA are initialized together since ProbabilisticPCA
    # wraps PCA
    return [PCA, ProbabilisticPCA, ProjectedGradientNMF]
Пример #3
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def get_decomposers(n_features, rng):
    pyvotune.dense_input(PCA)
    pyvotune.non_terminal(PCA)
    pyvotune.pbool(name='whiten', rng=rng)(PCA)

    pyvotune.non_terminal(ProjectedGradientNMF)
    pyvotune.pint(
        range=(1, n_features), name='n_components', rng=rng)(ProjectedGradientNMF)
    pyvotune.choice(
        choices=['nndsvd', 'nndsvda', 'nndsvdar'],
        name='init', rng=rng)(ProjectedGradientNMF)
    pyvotune.choice(
        choices=['data', 'components', None],
        name='sparseness', rng=rng)(ProjectedGradientNMF)
    pyvotune.pfloat(
        range=(1, 10), name='beta', rng=rng)(ProjectedGradientNMF)
    pyvotune.pfloat(
        range=(0.01, 10), name='eta', rng=rng)(ProjectedGradientNMF)
    pyvotune.pint(
        range=(1, 600), name='max_iter', rng=rng)(ProjectedGradientNMF)
    pyvotune.pint(
        range=(100, 4000), name='nls_max_iter', rng=rng)(ProjectedGradientNMF)

    # PCA & ProbabilisticPCA are initialized together since ProbabilisticPCA
    # wraps PCA
    return [PCA, ProbabilisticPCA, ProjectedGradientNMF]
Пример #4
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def get_theano(n_features, rng):
    pyvotune.dense_input(TheanoRBMFeatureExtractor)
    pyvotune.non_terminal(TheanoRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='learning_rate', rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 10), name='training_epochs', rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='batch_size', rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='n_resamples', rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(5, 1500), name='n_hidden', rng=rng)(TheanoRBMFeatureExtractor)

    return [TheanoRBMFeatureExtractor]
Пример #5
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def get_theano(n_features, rng):
    pyvotune.dense_input(TheanoRBMFeatureExtractor)
    pyvotune.non_terminal(TheanoRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='learning_rate',
                    rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 10), name='training_epochs',
                  rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='batch_size',
                  rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='n_resamples',
                  rng=rng)(TheanoRBMFeatureExtractor)
    pyvotune.pint(range=(5, 1500), name='n_hidden',
                  rng=rng)(TheanoRBMFeatureExtractor)

    return [TheanoRBMFeatureExtractor]
Пример #6
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def get_pyrbm(n_features, rng):
    pyvotune.dense_input(PyRBMFeatureExtractor)
    pyvotune.non_terminal(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='learning_rate', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='momentum', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='l2_weight', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='sparsity', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pfloat(range=(0., 1.0), name='scale', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pbool(name='binary', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pbool(name='reconstruction', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 10), name='n_training_epochs', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 20), name='n_gibbs', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(1, 1000), name='batch_size', rng=rng)(PyRBMFeatureExtractor)
    pyvotune.pint(range=(5, 1500), name='n_hidden', rng=rng)(PyRBMFeatureExtractor)

    return [PyRBMFeatureExtractor]
Пример #7
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def get_image_features(n_features, rng):
    pyvotune.dense_input(PatchExtractor)
    pyvotune.non_terminal(PatchExtractor)
    pyvotune.param(
        typename="patchsize",
        checker_fn=patchsize_checker,
        checker_args={
            'min_width': 2,
            'max_width': 200,
            'min_height': 2,
            'max_height': 200
        },
        generator_fn=patchsize_generator,
        generator_args={
            'min_width': 2,
            'max_width': 200,
            'min_height': 2,
            'max_height': 200
        },
        name="patch_size")(PatchExtractor)
    pyvotune.pint(range=(1, 100), name='max_patches', rng=rng)(PatchExtractor)

    return [PatchExtractor]
Пример #8
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def get_preprocessors(n_features, rng):
    pyvotune.dense_input(Scaler)
    pyvotune.non_terminal(Scaler)
    pyvotune.pbool(name="with_std", rng=rng)(Scaler)

    pyvotune.non_terminal(Normalizer)
    pyvotune.choice(choices=["l1", "l2"], name="norm", rng=rng)(Normalizer)

    pyvotune.non_terminal(Binarizer)
    pyvotune.pfloat(range=(0, 10000), name="threshold", rng=rng)(Binarizer)

    return [Scaler, Normalizer, Binarizer]