skpro.survival.ensemble.SurvivalForestXtraSkSurv#
- class skpro.survival.ensemble.SurvivalForestXtraSkSurv(n_estimators=100, max_depth=None, min_samples_split=6, min_samples_leaf=3, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=None, warm_start=False, max_samples=None, verbose=0, random_state=None)[source]#
Survival random forest with extra randomization-averaging, from scikit-survival.
Direct interface to
sksurv.ensemble.ExtraSurvivalTrees
, bysebp
.This class implements a meta estimator that fits a number of randomized survival trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if
bootstrap=True
(default).In each randomized survival tree, the quality of a split is measured by the log-rank splitting rule.
Compared to
RandomSurvivalForest
, randomness goes one step further in the way splits are computed. As inRandomSurvivalForest
, a random subset of candidate features is used, but instead of looking for the most discriminative thresholds, thresholds are drawn at random for each candidate feature and the best of these randomly-generated thresholds is picked as the splitting rule.- Parameters:
- n_estimatorsinteger, optional, default: 100
The number of trees in the forest.
- max_depthint or None, optional, default: None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
- min_samples_splitint, float, optional, default: 6
The minimum number of samples required to split an internal node:
If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
- min_samples_leafint, float, optional, default: 3
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least
min_samples_leaf
training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
- min_weight_fraction_leaffloat, optional, default: 0.
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
- max_featuresint, float, string or None, optional, default: None
The number of features to consider when looking for the best split:
If int, then consider max_features features at each split.
If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features.- max_leaf_nodesint or None, optional, default: None
Grow a tree with
max_leaf_nodes
in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.- bootstrapboolean, optional, default: True
Whether bootstrap samples are used when building trees. If False, the whole datset is used to build each tree.
- oob_scorebool, default: False
Whether to use out-of-bag samples to estimate the generalization accuracy.
- n_jobsint or None, optional, default: None
The number of jobs to run in parallel.
fit
,predict
,decision_path
andapply
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors.- warm_startbool, optional, default: False
When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.- max_samplesint or float, optional, default: None
If bootstrap is True, the number of samples to draw from X to train each base estimator.
If None (default), then draw X.shape[0] samples.
If int, then draw max_samples samples.
If float, then draw max_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0.0, 1.0].
- verboseint, optional, default: 0
Controls the verbosity when fitting and predicting.
- random_stateint, RandomState instance or None, optional, default: None
Controls both the randomness of the bootstrapping of the samples used when building trees (if
bootstrap=True
) and the sampling of the features to consider when looking for the best split at each node (ifmax_features < n_features
).
- Attributes:
- estimators_list of SurvivalTree instances
The collection of fitted sub-estimators.
- unique_times_array of shape = (n_unique_times,)
Unique time points.
- oob_score_float
Concordance index of the training dataset obtained using an out-of-bag estimate.
Methods
Check if the estimator has been fitted.
clone
()Obtain a clone of the object with same hyper-parameters.
clone_tags
(estimator[, tag_names])Clone tags from another estimator as dynamic override.
create_test_instance
([parameter_set])Construct Estimator instance if possible.
create_test_instances_and_names
([parameter_set])Create list of all test instances and a list of names for them.
fit
(X, y[, C])Fit regressor to training data.
get_class_tag
(tag_name[, tag_value_default])Get a class tag's value.
Get class tags from the class and all its parent classes.
Get config flags for self.
get_fitted_params
([deep])Get fitted parameters.
Get object's parameter defaults.
Get object's parameter names.
get_params
([deep])Get a dict of parameters values for this object.
get_tag
(tag_name[, tag_value_default, ...])Get tag value from estimator class and dynamic tag overrides.
get_tags
()Get tags from estimator class and dynamic tag overrides.
get_test_params
([parameter_set])Return testing parameter settings for the estimator.
Check if the object is composed of other BaseObjects.
predict
(X)Predict labels for data from features.
predict_interval
([X, coverage])Compute/return interval predictions.
Predict distribution over labels for data from features.
predict_quantiles
([X, alpha])Compute/return quantile predictions.
predict_var
([X])Compute/return variance predictions.
reset
()Reset the object to a clean post-init state.
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 dynamic tags to given values.
- classmethod get_test_params(parameter_set='default')[source]#
Return testing parameter settings for the estimator.
- 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:
- paramsdict or list of dict, default = {}
Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params
- check_is_fitted()[source]#
Check if the estimator has been fitted.
Inspects object’s _is_fitted attribute that should initialize to False during object construction, and be set to True in calls to an object’s fit method.
- Raises:
- NotFittedError
If the estimator has not been fitted yet.
- clone()[source]#
Obtain a clone of the object with same hyper-parameters.
A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.
- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
Notes
If successful, equal in value to
type(self)(**self.get_params(deep=False))
.
- clone_tags(estimator, tag_names=None)[source]#
Clone tags from another estimator as dynamic override.
- Parameters:
- estimatorestimator inheriting from :class:BaseEstimator
- tag_namesstr or list of str, default = None
Names of tags to clone. If None then all tags in estimator are used as tag_names.
- Returns:
- Self
Reference to self.
Notes
Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.
- classmethod create_test_instance(parameter_set='default')[source]#
Construct Estimator instance if possible.
- 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
Notes
get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.
- 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 convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}
- fit(X, y, C=None)[source]#
Fit regressor to training data.
- Writes to self:
Sets fitted model attributes ending in “_”.
Changes state to “fitted” = sets is_fitted flag to True
- Parameters:
- Xpandas DataFrame
feature instances to fit regressor to
- ypd.DataFrame, must be same length as X
labels to fit regressor to
- Cpd.DataFrame, optional (default=None)
censoring information for survival analysis, should have same column name as y, same length as X and y should have entries 0 and 1 (float or int) 0 = uncensored, 1 = (right) censored if None, all observations are assumed to be uncensored
- Returns:
- selfreference to self
- classmethod get_class_tag(tag_name, tag_value_default=None)[source]#
Get a class tag’s value.
Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.
- Parameters:
- tag_namestr
Name of tag value.
- tag_value_defaultany
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 the class and all its parent classes.
Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.
- Returns:
- collected_tagsdict
Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.
- get_config()[source]#
Get config flags for self.
- 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.
- get_fitted_params(deep=True)[source]#
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
Whether to return fitted parameters of components.
If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.
- Returns:
- fitted_paramsdict with str-valued keys
Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:
always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
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
- 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()[source]#
Get object’s parameter names.
- Returns:
- param_names: list[str]
Alphabetically sorted list of parameter names of cls.
- 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_tag(tag_name, tag_value_default=None, raise_error=True)[source]#
Get tag value from estimator class and dynamic tag overrides.
- 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, returns an error if raise_error is True, otherwise it returns tag_value_default.
- Raises:
- ValueError if raise_error is True i.e. if tag_name is not in
- self.get_tags().keys()
- get_tags()[source]#
Get tags from estimator class and dynamic tag overrides.
- 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.
- 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 BaseObjects.
- property is_fitted[source]#
Whether fit has been called.
Inspects object’s _is_fitted attribute that should initialize to False during object construction, and be set to True in calls to an object’s fit method.
- Returns:
- bool
Whether the estimator has been fit.
- predict(X)[source]#
Predict labels for data from features.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”
- Parameters:
- Xpandas DataFrame, must have same columns as X in fit
data to predict labels for
- Returns:
- ypandas DataFrame, same length as X
labels predicted for X
- predict_interval(X=None, coverage=0.9)[source]#
Compute/return interval predictions.
If coverage is iterable, multiple intervals will be calculated.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”.
- Parameters:
- Xpandas DataFrame, must have same columns as X in fit
data to predict labels for
- coveragefloat or list of float of unique values, optional (default=0.90)
nominal coverage(s) of predictive interval(s)
- Returns:
- pred_intpd.DataFrame
Column has multi-index: first level is variable name from
y
in fit, second level coverage fractions for which intervals were computed, in the same order as in input coverage. Third level is string “lower” or “upper”, for lower/upper interval end. Row index is equal to row index ofX
. Entries are lower/upper bounds of interval predictions, for var in col index, at nominal coverage in second col index, lower/upper depending on third col index, for the row index. Upper/lower interval end are equivalent to quantile predictions at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.
- predict_proba(X)[source]#
Predict distribution over labels for data from features.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”
- Parameters:
- Xpandas DataFrame, must have same columns as X in fit
data to predict labels for
- Returns:
- yskpro BaseDistribution, same length as X
labels predicted for X
- predict_quantiles(X=None, alpha=None)[source]#
Compute/return quantile predictions.
If alpha is iterable, multiple quantiles will be calculated.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”.
- Parameters:
- Xpandas DataFrame, must have same columns as X in fit
data to predict labels for
- alphafloat or list of float of unique values, optional (default=[0.05, 0.95])
A probability or list of, at which quantile predictions are computed.
- Returns:
- quantilespd.DataFrame
Column has multi-index: first level is variable name from
y
in fit, second level being the values of alpha passed to the function. Row index is equal to row index ofX
. Entries are quantile predictions, for var in col index, at quantile probability in second col index, for the row index.
- predict_var(X=None)[source]#
Compute/return variance predictions.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”.
- Parameters:
- Xpandas DataFrame, must have same columns as X in fit
data to predict labels for
- Returns:
- pred_varpd.DataFrame
Column names are exactly those of
y
passed infit
. Row index is equal to row index ofX
. Entries are variance prediction, for var in col index. A variance prediction for given variable and fh index is a predicted variance for that variable and index, given observed data.
- reset()[source]#
Reset the object to a clean post-init state.
Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:
hyper-parameters = arguments of __init__
object attributes containing double-underscores, i.e., the string “__”
Class and object methods, and class attributes are also unaffected.
- Returns:
- self
Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.
Notes
Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))
- set_config(**config_dict)[source]#
Set config flags to given values.
- 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 estimators 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 viaestimator.get_params
, and sets them to integers derived fromrandom_state
viaset_params
. These integers are sampled from chain hashing viasample_dependent_seed
, and guarantee pseudo-random independence of seeded random generators.Applies to
random_state
parameters inestimator
depending onself_policy
, and remaining component estimators if and only ifdeep=True
.Note: calls
set_params
even ifself
does not have arandom_state
, or none of the components have arandom_state
parameter. Therefore,set_random_state
will reset anyscikit-base
estimator, even those without arandom_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 sub-estimators. If False, will set only
self
’srandom_state
parameter, if exists. If True, will setrandom_state
parameters in sub-estimators as well.- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
estimator.random_state
is set to inputrandom_state
“keep” :
estimator.random_state
is kept as is“new” :
estimator.random_state
is set to a new random state,
derived from input
random_state
, and in general different from it
- Returns:
- selfreference to self