Submodules
Acyclic Graph Generator.
Generates a dataset out of an acyclic FCM.
Author : Olivier Goudet and Diviyan Kalainathan
Minor update: J. Renero (due to pandas changes to as_matrix()), and bug fix in
polynomial mechanism.
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class AcyclicGraphGenerator(causal_mechanism, initial_variable_generator=<function gmm_cause>, points=500, nodes=20, timesteps=0, parents_max=5, verbose=False)[source]
Bases: object
Generates a cross-sectional dataset out of a cyclic FCM.
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__init__(causal_mechanism, initial_variable_generator=<function gmm_cause>, points=500, nodes=20, timesteps=0, parents_max=5, verbose=False)[source]
Initialize a synthetic acyclic graph generator.
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init_variables()[source]
Redefine the causes of the graph.
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generate(rescale=True)[source]
Generate data from an FCM containing cycles.
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to_csv(fname_radical, **kwargs)[source]
Save data to the csv format by default, in two separate files.
Optional keyword arguments can be passed to pandas.
Defining a set of classes that represent causal functions/ mechanisms.
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gmm_cause(n, k=4, p1=2, p2=2, verbose=False)[source]
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class LinearMechanism(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Bases: object
Linear mechanism, where Effect = alpha*Cause + Noise.
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__init__(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Init the mechanism.
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__call__(causes, verbose=False)[source]
Run the mechanism.
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class Polynomial_Mechanism(ncauses, points, d=2, noise_coeff=0.7, verbose=False)[source]
Bases: object
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__init__(ncauses, points, d=2, noise_coeff=0.7, verbose=False)[source]
Init the mechanism.
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mechanism(x, par)[source]
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__call__(causes, verbose=False)[source]
Run the mechanism.
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class SigmoidAM_Mechanism(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Bases: object
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__init__(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Init the mechanism.
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mechanism(x)[source]
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__call__(causes, verbose=False)[source]
Run the mechanism.
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class SigmoidMix_Mechanism(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Bases: object
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__init__(ncauses, points, d=4, noise_coeff=0.7, verbose=False)[source]
Init the mechanism.
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mechanism(causes)[source]
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__call__(causes, verbose=False)[source]
Run the mechanism.
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computeGaussKernel(x)[source]
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class GaussianProcessAdd_Mechanism(ncauses, points, verbose=False)[source]
Bases: object
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__init__(ncauses, points, verbose=False)[source]
Init the mechanism.
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mechanism(x)[source]
Run the mechanism.
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__call__(causes, verbose=False)[source]
Run the mechanism.
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class GaussianProcessMix_Mechanism(ncauses, points, verbose=False)[source]
Bases: object
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__init__(ncauses, points, verbose=False)[source]
Init the mechanism.
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mechanism(x)[source]
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__call__(causes, verbose=False)[source]
Run the mechanism.
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gaussian_cause(n)[source]
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noise(n, v)[source]