skpro.distributions.Skellam#

class skpro.distributions.Skellam(mu1, mu2, index=None, columns=None)[source]#

Skellam probability distribution.

The Skellam distribution is a discrete probability distribution that describes the difference between two independent Poisson-distributed random variables with means $mu_1$ and $mu_2$. Its probability mass function (PMF) is:

\[P(k; \mu_1, \mu_2) = e^{-(\mu_1 + \mu_2)} \left( \frac{\mu_1}{\mu_2} \right)^{k/2} I_{|k|}(2 \sqrt{\mu_1 \mu_2})\]

where $I_{|k|}$ is the modified Bessel function of the first kind.

Parameters:

mu1float: Mean of the first Poisson distribution
mu2float: Mean of the second Poisson distribution

Attributes:

at: Integer location indexer, for single index.
iat: Integer location indexer, for single index.
iloc: Integer location indexer, for groups of indices.
loc: Location indexer, for groups of indices.
name: Return the name of the object or estimator.
ndim: Number of dimensions of self.
shape: Shape of self, a pair (2-tuple).

Methods

`cdf`(x)	Cumulative distribution function.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`energy`([x])	Energy of self, w.r.t.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_params_df`()	Return distribution parameters in a dict of DataFrame.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return test parameters for Skellam.
`haz`(x)	Hazard function.
`head`([n])	Return the first n rows.
`is_composite`()	Check if the object is composed of other BaseObjects.
`log_pdf`(x)	Logarithmic probability density function.
`log_pmf`(x)	Logarithmic probability mass function.
`mean`()	Return expected value of the distribution.
`pdf`(x)	Probability density function.
`pdfnorm`([a])	a-norm of pdf, defaults to 2-norm.
`plot`([fun, ax])	Plot the distribution.
`pmf`(x)	Probability mass function.
`ppf`(p)	Quantile function = percent point function = inverse cdf.
`quantile`(alpha)	Return entry-wise quantiles, in Proba/pred_quantiles mtype format.
`reset`()	Reset the object to a clean post-init state.
`sample`([n_samples])	Sample from the distribution.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`surv`(x)	Survival function.
`tail`([n])	Return the last n rows.
`to_df`()	Return distribution parameters as a single DataFrame.
`to_str`()	Return string representation of self.
`var`()	Return element/entry-wise variance of the distribution.

classmethod get_test_params(parameter_set='default')[source]#: Return test parameters for Skellam.

property at[source]#

Integer location indexer, for single index.

Use my_distribution.at[index] for pandas-like row/column subsetting of BaseDistribution descendants.

index can be any pandas at compatible index subsetter.

my_distribution.at[index] or my_distribution.at[row_index, col_index] subset my_distribution to the row selected by row_index, col by col_index, to exactly the same col/rows as pandas at would subset rows in my_distribution.index and columns in my_distribution.columns.

cdf(x)[source]#

Cumulative distribution function.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $F_{X_{ij}}$, denote the marginal cdf of $X$ at the $(i,j)$-th entry, i.e., $F_{X_{ij}}(t) = \mathbb{P}(X_{ij} \leq t)$.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $F_{X_{ij}}(x_{ij})$.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $F_{X_{ij}}(x_{ij})$, as above

clone()[source]#

Obtain a clone of the object with same hyper-parameters and config.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Equivalent to constructing a new instance of type(self), with parameters of self, that is, type(self)(**self.get_params(deep=False)).

If configs were set on self, the clone will also have the same configs as the original, equivalent to calling cloned_self.set_config(**self.get_config()).

Also equivalent in value to a call of self.reset, with the exception that clone returns a new object, instead of mutating self like reset.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

clone_tags(estimator, tag_names=None)[source]#

Clone tags from another object as dynamic override.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

clone_tags sets dynamic tag overrides from another object, estimator.

The clone_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

The dynamic tags are set to the values of the tags in estimator, with the names specified in tag_names.

The default of tag_names writes all tags from estimator to self.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

estimatorAn instance of :class:BaseObject or derived class
tag_namesstr or list of str, default = None: Names of tags to clone. The default (None) clones all tags from estimator.

Returns:

self: Reference to self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct an instance of the class, using first test parameter set.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests. The naming convention is {cls.__name__}-{i} if more than one instance, otherwise {cls.__name__}

energy(x=None)[source]#

Energy of self, w.r.t. self or a constant frame x.

Let $X, Y$ be i.i.d. random variables with the distribution of self.

If x is None, returns $\mathbb{E}[|X-Y|]$ (per row), “self-energy”. If x is passed, returns $\mathbb{E}[|X-x|]$ (per row), “energy wrt x”.

The CRPS is related to energy: it holds that $\mbox{CRPS}(\mbox{self}, y)$ = self.energy(y) - 0.5 * self.energy().

Parameters:

xNone or pd.DataFrame, optional, default=None: if pd.DataFrame, must have same rows and columns as self

Returns:

pd.DataFrame with same rows as self, single column "energy": each row contains one float, self-energy/energy as described above.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get class tag value from class, with tag level inheritance from parents.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tag method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns the value of the tag with name tag_name from the object, taking into account tag overrides, in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

To retrieve tag values with potential instance overrides, use the get_tag method instead.

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from class, with tag level inheritance from parent classes.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tags method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns a dictionary with keys being keys of any attribute of _tags set in the class or any of its parent classes.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Instances can override these tags depending on hyper-parameters.

To retrieve tags with potential instance overrides, use the get_tags method instead.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

For including overrides from dynamic tags, use get_tags.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or clone_tags.

get_config()[source]#

Get config flags for self.

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

get_config returns dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

Returns:

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

classmethod get_param_defaults()[source]#

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]: Keys are all parameters of cls that have a default defined in __init__. Values are the defaults, as defined in __init__.

classmethod get_param_names(sort=True)[source]#

Get object’s parameter names.

Parameters:

sortbool, default=True: Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]: List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

get_params_df()[source]#

Return distribution parameters in a dict of DataFrame.

Available only for simple parametric distributions, i.e., distributions with tag “distr:paramtype” having value “parametric”.

Returns:

dict of pd.DataFrame: Dictionary with all distribution parameters, as pd.DataFrame. Keys are the parameter names, values are the pd.DataFrame. Each DataFrame has the same index as self and columns as self. Entries are the values of the distribution parameters.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tag method retrieves the value of a single tag with name tag_name from the instance, taking into account tag overrides, in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Parameters:

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny: Value of the tag_name tag in self. If not found, raises an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError, if raise_error is True.: The ValueError is then raised if tag_name is not in self.get_tags().keys().

get_tags()[source]#

Get tags from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tags method returns a dictionary of tags, with keys being keys of any attribute of _tags set in the class or any of its parent classes, or tags set via set_tags or clone_tags.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

haz(x)[source]#

Hazard function.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $h_{X_{ij}}$, denote the marginal hazard of $X$ at the $(i,j)$-th entry, i.e., $h_{X_{ij}}(t) = \frac{f_{X_{ij}}(t)}{S_{X_{ij}}(t)}$, where $f_{X_{ij}}$ is the marginal pdf, and $S_{X_{ij}}$ is the marginal survival function at the $(i,j)$-th entry.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $h_{X_{ij}}(x_{ij})$.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $h_{X_{ij}}(x_{ij})$, as above

head(n=5)[source]#

Return the first n rows.

If there are less than n rows in self, returns clone of self.

For negative n, returns all rows except the last n.

Parameters:

nint, default=5: Number of rows to return.

Returns:

self subset to the first n rows, i.e., self.iloc[0:min(n, len(self))]

property iat[source]#

Integer location indexer, for single index.

Use my_distribution.iat[index] for pandas-like row/column subsetting of BaseDistribution descendants.

index can be any pandas iat compatible index subsetter.

my_distribution.iat[index] or my_distribution.iat[row_index, col_index] subset my_distribution to the row selected by row_index, col by col_index, to exactly the same col/rows as pandas iat would subset rows in my_distribution.index and columns in my_distribution.columns.

property iloc[source]#

Integer location indexer, for groups of indices.

Use my_distribution.iloc[index] for pandas-like row/column subsetting of BaseDistribution descendants.

index can be any pandas iloc compatible index subsetter.

my_distribution.iloc[index] or my_distribution.iloc[row_index, col_index] subset my_distribution to rows selected by row_index, cols by col_index, to exactly the same cols/rows as pandas iloc would subset rows in my_distribution.index and columns in my_distribution.columns.

is_composite()[source]#

Check if the object is composed of other BaseObjects.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether an object has any parameters whose values are BaseObject descendant instances.

property loc[source]#

Location indexer, for groups of indices.

Use my_distribution.loc[index] for pandas-like row/column subsetting of BaseDistribution descendants.

index can be any pandas iloc compatible index subsetter.

my_distribution.loc[index] or my_distribution.loc[row_index, col_index] subset my_distribution to rows selected by row_index, cols by col_index, to exactly the same cols/rows as pandas loc would subset rows in my_distribution.index and columns in my_distribution.columns.

log_pdf(x)[source]#

Logarithmic probability density function.

Numerically more stable than calling pdf and then taking logarithms.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $p_{X_{ij}}$, denote the marginal pdf of $X$ at the $(i,j)$-th entry.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $\log p_{X_{ij}}(x_{ij})$.

If self has a mixed or discrete distribution, this returns the weighted continuous part of self’s distribution instead of the pdf, i.e., the marginal pdf integrate to the weight of the continuous part.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $\log p_{X_{ij}}(x_{ij})$, as above

log_pmf(x)[source]#

Logarithmic probability mass function.

Numerically more stable than calling pmf and then taking logarithms.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $m_{X_{ij}}$, denote the marginal pdf of $X$ at the $(i,j)$-th entry, i.e., $m_{X_{ij}}(x_{ij}) = \mathbb{P}(X_{ij} = x_{ij})$.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $\log m_{X_{ij}}(x_{ij})$.

If self has a mixed or discrete distribution, this returns the weighted continuous part of self’s distribution instead of the pdf, i.e., the marginal pdf integrate to the weight of the continuous part.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $\log m_{X_{ij}}(x_{ij})$, as above

mean()[source]#

Return expected value of the distribution.

Let $X$ be a random variable with the distribution of self. Returns the expectation $\mathbb{E}[X]$

Returns:

pd.DataFrame with same rows, columns as self: expected value of distribution (entry-wise)

property name[source]#: Return the name of the object or estimator.

property ndim[source]#: Number of dimensions of self. 2 if array, 0 if scalar.

pdf(x)[source]#

Probability density function.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $p_{X_{ij}}$, denote the marginal pdf of $X$ at the $(i,j)$-th entry.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $p_{X_{ij}}(x_{ij})$.

If self has a mixed or discrete distribution, this returns the weighted continuous part of self’s distribution instead of the pdf, i.e., the marginal pdf integrate to the weight of the continuous part.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $p_{X_{ij}}(x_{ij})$, as above

pdfnorm(a=2)[source]#

a-norm of pdf, defaults to 2-norm.

computes a-norm of the entry marginal pdf, i.e., $\mathbb{E}[p_X(X)^{a-1}] = \int p(x)^a dx$, where $X$ is a random variable distributed according to the entry marginal of self, and $p_X$ is its pdf

Parameters:

a: int or float, optional, default=2

Returns:

pd.DataFrame with same rows and columns as self
each entry is $\mathbb{E}[p_X(X)^{a-1}] = \int p(x)^a dx$, see above

plot(fun=None, ax=None, **kwargs)[source]#

Plot the distribution.

Different distribution defining functions can be selected for plotting via the fun parameter. The functions available are the same as the methods of the distribution class, e.g., "pdf", "cdf", "ppf".

For array distribution, the marginal distribution at each entry is plotted, as a separate subplot.

Parameters:

funstr, optional, default=”pdf” for continuous distributions, otherwise “cdf”: the function to plot, one of “pdf”, “cdf”, “ppf”
axmatplotlib Axes object, optional: matplotlib Axes to plot in if not provided, defaults to current axes (plot.gca)
kwargskeyword arguments: passed to the plotting function

Returns:

figmatplotlib.Figure, only returned if self is array distribution: matplotlig Figure object for subplots
axmatplotlib.Axes: the axis or axes on which the plot is drawn

pmf(x)[source]#

Probability mass function.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $m_{X_{ij}}$, denote the marginal mass of $X$ at the $(i,j)$-th entry, i.e., $m_{X_{ij}}(x_{ij}) = \mathbb{P}(X_{ij} = x_{ij})$.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $m_{X_{ij}}(x_{ij})$.

If self has a mixed or discrete distribution, this returns the weighted continuous part of self’s distribution instead of the pdf, i.e., the marginal pdf integrate to the weight of the continuous part.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $p_{X_{ij}}(x_{ij})$, as above

ppf(p)[source]#

Quantile function = percent point function = inverse cdf.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $F_{X_{ij}}$, denote the marginal cdf of $X$ at the $(i,j)$-th entry.

The output of this method, for input p representing $p$, is a DataFrame with same columns and indices as self, and entries $F^{-1}_{X_{ij}}(p_{ij})$.

Parameters:

ppandas.DataFrame or 2D np.ndarray: representing $p$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $F_{X_{ij}}(x_{ij})$, as above

quantile(alpha)[source]#

Return entry-wise quantiles, in Proba/pred_quantiles mtype format.

This method broadcasts as follows: for a scalar alpha, computes the alpha-quantile entry-wise, and returns as a pd.DataFrame with same index, and columns as in return. If alpha is iterable, multiple quantiles will be calculated, and the result will be concatenated column-wise (axis=1).

The ppf method also computes quantiles, but broadcasts differently, in numpy style closer to tensorflow. In contrast, this quantile method broadcasts as sktime forecaster predict_quantiles, i.e., columns first.

Parameters:

alphafloat or list of float of unique values: A probability or list of, at which quantiles are computed.

Returns:

quantilespd.DataFrame: Column has multi-index: first level is variable name from self.columns, second level being the values of alpha passed to the function. Row index is self.index. Entries in the i-th row, (j, p)-the column is the p-th quantile of the marginal of self at index (i, j).

reset()[source]#

Reset the object to a clean post-init state.

Results in setting self to the state it had directly after the constructor call, with the same hyper-parameters. Config values set by set_config are also retained.

A reset call deletes any object attributes, except:

hyper-parameters = arguments of __init__ written to self, e.g., self.paramname where paramname is an argument of __init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of get_config before and after reset are equal.

Class and object methods, and class attributes are also unaffected.

Equivalent to clone, with the exception that reset mutates self instead of returning a new object.

After a self.reset() call, self is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

sample(n_samples=None)[source]#

Sample from the distribution.

Parameters:

n_samplesint, optional, default = None: number of samples to draw from the distribution

Returns:

pd.DataFrame

samples from the distribution

if n_samples is None:

returns a sample that contains a single sample from self, in pd.DataFrame mtype format convention, with index and columns as self * if n_samples is int: returns a pd.DataFrame that contains n_samples i.i.d. samples from self, in pd-multiindex mtype format convention, with same columns as self, and row MultiIndex that is product of RangeIndex(n_samples) and self.index

set_config(**config_dict)[source]#

Set config flags to given values.

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

set_config sets dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

Parameters:

config_dictdict: Dictionary of config name : config value pairs.

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple skbase objects as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via self.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in self, depending on self_policy, and remaining component objects if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base object, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in skbase object valued parameters, i.e., component estimators.

If False, will set only self’s random_state parameter, if exists.
If True, will set random_state parameters in component objects as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : self.random_state is set to input random_state
“keep” : self.random_state is kept as is
“new” : self.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

set_tags(**tag_dict)[source]#

Set instance level tag overrides to given values.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

set_tags sets dynamic tag overrides to the values as specified in tag_dict, with keys being the tag name, and dict values being the value to set the tag to.

The set_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

**tag_dictdict: Dictionary of tag name: tag value pairs.

Returns:

Self: Reference to self.

property shape[source]#: Shape of self, a pair (2-tuple).

surv(x)[source]#

Survival function.

Let $X$ be a random variables with the distribution of self, taking values in (N, n) DataFrame-s Let $x\in \mathbb{R}^{N\times n}$. By $S_{X_{ij}}$, denote the marginal survival of $X$ at the $(i,j)$-th entry, i.e., $S_{X_{ij}}(t) = \mathbb{P}(X_{ij} \gneq t)$.

The output of this method, for input x representing $x$, is a DataFrame with same columns and indices as self, and entries $F_{X_{ij}}(x_{ij})$.

Parameters:

xpandas.DataFrame or 2D np.ndarray: representing $x$, as above

Returns:

pd.DataFrame with same columns and index as self: containing $S_{X_{ij}}(x_{ij})$, as above

tail(n=5)[source]#

Return the last n rows.

If there are less than n rows in self, returns clone of self.

For negative n, returns all rows except the first n.

Parameters:

nint, default=5: Number of rows to return.

Returns:

self subset to the last n rows, i.e., self.iloc[max(len(self) - n, 0):]

to_df()[source]#

Return distribution parameters as a single DataFrame.

Available only for simple parametric distributions, i.e., distributions with tag “distr:paramtype” having value “parametric”.

Returns:

pd.DataFrame: DataFrame with all distribution parameters. column is a MultiIndex (paramname, varname). row index is the index of the distribution. Entries are the values of the distribution parameters.

to_str()[source]#: Return string representation of self.

var()[source]#

Return element/entry-wise variance of the distribution.

Let $X$ be a random variable with the distribution of self. Returns $\mathbb{V}[X] = \mathbb{E}\left(X - \mathbb{E}[X]\right)^2$, where the square is element-wise.

Returns:

pd.DataFrame with same rows, columns as self: variance of distribution (entry-wise)