neuraxle.hyperparams.scipy_distributions¶
Module-level documentation for neuraxle.hyperparams.scipy_distributions. Here is an inheritance diagram, including dependencies to other base modules of Neuraxle:
Functions
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Classes
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Gaussian distribution that inherits from scipy.stats.rv_continuous |
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Histogram distribution that inherits from scipy.stats.rv_histogram |
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Poisson distribution that inherits from scipy.stats.rv_discrete |
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Base class for a distribution that wraps a scipy distribution. |
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Get a LogUniform distribution. |
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Get a normal distribution from std and min. |
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class
neuraxle.hyperparams.scipy_distributions.
ScipyDistributionWrapper
(scipy_distribution, **scipy_distribution_arguments)[source]¶ Bases:
abc.ABC
Base class for a distribution that wraps a scipy distribution.
Usage example:
distribution = ScipyDistributionWrapper( scipy_distribution=rv_histogram(histogram=histogram), null_default_value=null_default_value )
See also
HyperparameterDistribution
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__init__
(scipy_distribution, **scipy_distribution_arguments)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
ScipyContinuousDistributionWrapper
(scipy_distribution, null_default_value=None, **kwargs)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.ScipyDistributionWrapper
,neuraxle.hyperparams.distributions.ContinuousHyperparameterDistribution
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__init__
(scipy_distribution, null_default_value=None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
ScipyDiscreteDistributionWrapper
(scipy_distribution, null_default_value=None, **kwargs)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.ScipyDistributionWrapper
,neuraxle.hyperparams.distributions.DiscreteHyperparameterDistribution
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__init__
(scipy_distribution, null_default_value=None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
BaseCustomDiscreteScipyDistribution
(name, min_included, max_included, null_default_value, **kwargs)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.ScipyDiscreteDistributionWrapper
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__init__
(name, min_included, max_included, null_default_value, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
Distribution
(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None, seed=None)[source]¶
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class
neuraxle.hyperparams.scipy_distributions.
BaseCustomContinuousScipyDistribution
(name, min_included, max_included, null_default_value, **kwargs)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.ScipyContinuousDistributionWrapper
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__init__
(name, min_included, max_included, null_default_value, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
ScipyLogUniform
(min_included: float, max_included: float, null_default_value=None)[source]¶ Bases:
neuraxle.hyperparams.distributions.LogSpaceDistributionMixin
,neuraxle.hyperparams.scipy_distributions.BaseCustomContinuousScipyDistribution
Get a LogUniform distribution.
Refer to:
scipy.stats.loguniform
.See also
set_hyperparams_space()
,ScipyDistributionWrapper
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__init__
(min_included: float, max_included: float, null_default_value=None)[source]¶ Create a quantized random log uniform distribution. A random float between the two values inclusively will be returned.
- Parameters
min_included (
float
) – minimum integer, should be somehow included.max_included (
float
) – maximum integer, should be somehow included.null_default_value (int) – null default value for distribution. if None, take the min_included
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_pdf
(x) → float[source]¶ Calculate the logUniform probability distribution value at position x.
- Return type
float
- Parameters
x – value where the probability distribution function is evaluated.
- Returns
value of the probability distribution function.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
StdMeanLogNormal
(log2_space_mean: float, log2_space_std: float, hard_clip_min: float, hard_clip_max: float, null_default_value: float = None)[source]¶ Bases:
neuraxle.hyperparams.distributions.LogSpaceDistributionMixin
,neuraxle.hyperparams.scipy_distributions.BaseCustomContinuousScipyDistribution
Get a normal distribution from std and min.
See also
NormalScipyDistribution
,set_hyperparams_space()
,HyperparameterDistribution
,ScipyDistributionWrapper
,neuraxle.hyperparams.space.HyperparameterSamples
,neuraxle.hyperparams.space.HyperparameterSpace
,neuraxle.base.BaseStep
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__init__
(log2_space_mean: float, log2_space_std: float, hard_clip_min: float, hard_clip_max: float, null_default_value: float = None)[source]¶ Create a normal distribution from mean and standard deviation.
- Parameters
log2_space_mean (float) – the most common value to pop
log2_space_std (float) – the standard deviation (that is, the sqrt of the variance).
hard_clip_min (float) – if not none, rvs will return max(result, hard_clip_min).
hard_clip_max (float) – if not none, rvs will return min(result, hard_clip_min).
null_default_value (float) – if none, null default value will be set to hard_clip_min.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
Gaussian
(min_included: int, max_included: int, null_default_value: float = None)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.BaseCustomContinuousScipyDistribution
Gaussian distribution that inherits from scipy.stats.rv_continuous
Example usage :
gaussian_distribution = Gaussian( min_included=0, max_included=10, null_default_value=0.0 ) assert 0.0 <= gaussian_distribution.rvs() <= 10.0 assert gaussian_distribution.pdf(10) < 0.001 assert gaussian_distribution.pdf(0) < 0.42 assert 0.55 > gaussian_distribution.cdf(5.0) > 0.45 assert gaussian_distribution.cdf(0) == 0.0
See also
GaussianScipyDistribution
,ScipyDistributionWrapper
,set_hyperparams_space()
,HyperparameterDistribution
,neuraxle.hyperparams.space.HyperparameterSamples
,neuraxle.hyperparams.space.HyperparameterSpace
,neuraxle.base.BaseStep
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__init__
(min_included: int, max_included: int, null_default_value: float = None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
Poisson
(min_included: float, max_included: float, null_default_value: float = None, mu=0.6)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.BaseCustomDiscreteScipyDistribution
Poisson distribution that inherits from scipy.stats.rv_discrete
Example usage :
poisson_distribution = Poisson( min_included=0.0, max_included=10.0, null_default_value=0.0, mu=5.0 ) rvs = [poisson_distribution.rvs() for i in range(10)] assert not all(x == rvs[0] for x in rvs) assert 0.0 <= poisson_distribution.rvs() <= 10.0 assert poisson_distribution.pdf(10) == 0.01813278870782187 assert np.isclose(poisson_distribution.pdf(0), 0.006737946999085467) assert poisson_distribution.cdf(5.0) == 0.6159606548330632 assert poisson_distribution.cdf(0) == 0.006737946999085467
See also
set_hyperparams_space()
,PoissonScipyDistribution
,HyperparameterDistribution
,ScipyDistributionWrapper
,HyperparameterSamples
,HyperparameterSpace
,BaseStep
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__init__
(min_included: float, max_included: float, null_default_value: float = None, mu=0.6)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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class
neuraxle.hyperparams.scipy_distributions.
Histogram
(histogram: numpy.histogram, null_default_value: float = None, **kwargs)[source]¶ Bases:
neuraxle.hyperparams.scipy_distributions.ScipyDiscreteDistributionWrapper
Histogram distribution that inherits from scipy.stats.rv_histogram
Example usage :
hist_dist = Histogram( histogram=np.histogram(data, bins=100), null_default_value=0.0 ) assert min(data) <= hist_dist.rvs() <= max(data) assert 1.0 > hist_dist.pdf(x=1.0) > 0.0 assert hist_dist.pdf(x=np.max(data)) == 0.0 assert hist_dist.pdf(x=np.min(data)) < 0.001 assert hist_dist.cdf(x=np.max(data)) == 1.0 assert 0.55 > hist_dist.cdf(x=np.median(data)) > 0.45 assert hist_dist.cdf(x=np.min(data)) == 0.0
See also
HyperparameterDistribution
,ScipyDistributionWrapper
,Poisson
,Gaussian
,set_hyperparams_space()
,HyperparameterSamples
,HyperparameterSpace
,BaseStep
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__init__
(histogram: numpy.histogram, null_default_value: float = None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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_abc_impl
= <_abc_data object>¶
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