Survival

class rulekit.survival.SurvivalRules(survival_time_attr: str | None = None, minsupp_new: int = 0.05, max_growing: int = 0.0, enable_pruning: bool = True, ignore_missing: bool = False, max_uncovered_fraction: float = 0.0, select_best_candidate: bool = False, complementary_conditions: bool = False, max_rule_count: int = 0)

Survival model.

Parameters:

  • survival_time_attr (str) – name of column containing survival time data (use when data passed to model is padnas dataframe).

  • minsupp_new (float = 5.0) –

    a minimum number (or fraction, if value < 1.0) of previously uncovered examples

    to be covered by a new rule (positive examples for classification problems); default: 5,

  • max_growing (int = 0.0) – non-negative integer representing maximum number of conditions which can be added to the rule in the growing phase (use this parameter for large datasets if execution time is prohibitive); 0 indicates no limit; default: 0,

  • enable_pruning (bool = True) – enable or disable pruning, default is True.

  • ignore_missing (bool = False) –

    boolean telling whether missing values should be ignored (by default, a missing value

    of given attribute is always considered as not fulfilling the condition build upon that

    attribute); default: False.

  • max_uncovered_fraction (float = 0.0) –

    Floating-point number from [0,1] interval representing maximum fraction of examples

    that may remain uncovered by the rule set, default: 0.0.

  • select_best_candidate (bool = False) –

    Flag determining if best candidate should be selected from growing phase;

    default: False.

  • complementary_conditions (bool = False) – If enabled, complementary conditions in the form a = !{value} for nominal attributes are supported.

  • max_rule_count (int = 0) –

    Maximum number of rules to be generated (for classification data sets it applies

    to a single class); 0 indicates no limit.

add_event_listener(listener: RuleInductionProgressListener)
Add event listener object to the operator which allows to monitor

rule induction progress.

Example

>>> from rulekit.events import RuleInductionProgressListener
>>> from rulekit.classification import RuleClassifier
>>>
>>> class MyEventListener(RuleInductionProgressListener):
>>>     def on_new_rule(self, rule):
>>>         print('Do something with new rule', rule)
>>>
>>> operator = RuleClassifier()
>>> operator.add_event_listener(MyEventListener())
Parameters:

listener (RuleInductionProgressListener) – listener object

fit(values: ndarray | DataFrame | list, labels: ndarray | DataFrame | list, survival_time: ndarray | DataFrame | list | None = None) → SurvivalRules

Train model on given dataset.

Parameters:
Returns:

self

Return type:

SurvivalRules

get_coverage_matrix(values: ndarray | DataFrame | list) → ndarray

Calculates coverage matrix for ruleset.

Parameters:

values (rulekit.operator.Data) – dataset

Returns:

coverage_matrix – Each row of the matrix represent single example from dataset and every column represent on rule from rule set. Value 1 in the matrix cell means that rule covered certain

example, value 0 means that it doesn’t.

Return type:

np.ndarray

get_metadata_routing() → None

Warning

Scikit-learn metadata routing is not supported yet.

Raises:

NotImplementedError – _description_

get_params(deep: bool = True) → dict[str, Any]
Parameters:

deep (rulekit.operator.Data) – Parameter for scikit-learn compatibility. Not used.

Returns:

hyperparameters – Dictionary containing model hyperparameters.

Return type:

np.ndarray

predict(values: ndarray | DataFrame | list) → ndarray

Perform prediction and return estimated survival function for each example.

Parameters:

values (rulekit.operator.Data) – attributes

Returns:

result – Each row represent single example from dataset and contains estimated survival function for that example. Estimated survival function is returned as a dictionary containing

times and corresponding probabilities.

Return type:

np.ndarray

score(values: ndarray | DataFrame | list, labels: ndarray | DataFrame | list, survival_time: ndarray | DataFrame | list | None = None) → float
Return the Integrated Brier Score on the given dataset and labels

(event status indicator).

Integrated Brier Score (IBS) - the Brier score (BS) represents the squared difference

between true event status at time T and predicted event status at that time; the Integrated Brier score summarizes the prediction error over all observations and over all times in a test set.

Parameters:
Returns:

score – Integrated Brier Score of self.predict(values) wrt. labels.

Return type:

float

set_params(**kwargs) → object

Set models hyperparameters. Parameters are the same as in constructor.

class rulekit.survival.ExpertSurvivalRules(survival_time_attr: str | None = None, minsupp_new: float = 0.05, max_growing: int = 0.0, enable_pruning: bool = True, ignore_missing: bool = False, max_uncovered_fraction: float = 0.0, select_best_candidate: bool = False, complementary_conditions: bool = False, extend_using_preferred: bool = False, extend_using_automatic: bool = False, induce_using_preferred: bool = False, induce_using_automatic: bool = False, preferred_conditions_per_rule: int = 2147483647, preferred_attributes_per_rule: int = 2147483647, max_rule_count: int = 0)

Expert Survival model.

Parameters:

  • minsupp_new (float = 5.0) –

    a minimum number (or fraction, if value < 1.0) of previously uncovered examples

    to be covered by a new rule (positive examples for classification problems); default: 5,

  • survival_time_attr (str) – name of column containing survival time data (use when data passed to model is pandas dataframe).

  • max_growing (int = 0.0) – non-negative integer representing maximum number of conditions which can be added to the rule in the growing phase (use this parameter for large datasets if execution time is prohibitive); 0 indicates no limit; default: 0,

  • enable_pruning (bool = True) – enable or disable pruning, default is True.

  • ignore_missing (bool = False) – boolean telling whether missing values should be ignored (by default, a missing value of given attribute is always considered as not fulfilling the condition build upon that attribute); default: False.

  • max_uncovered_fraction (float = 0.0) –

    Floating-point number from [0,1] interval representing maximum fraction of examples

    that may remain uncovered by the rule set, default: 0.0.

  • select_best_candidate (bool = False) –

    Flag determining if best candidate should be selected from growing phase;

    default: False.

  • complementary_conditions (bool = False) – If enabled, complementary conditions in the form a = !{value} for nominal attributes are supported.

  • max_rule_count (int = 0) –

    Maximum number of rules to be generated (for classification data sets it applies

    to a single class); 0 indicates no limit.

  • extend_using_preferred (bool = False) – boolean indicating whether initial rules should be extended with a use of preferred conditions and attributes; default is False

  • extend_using_automatic (bool = False) – boolean indicating whether initial rules should be extended with a use of automatic conditions and attributes; default is False

  • induce_using_preferred (bool = False) –

    boolean indicating whether new rules should be induced with a use of preferred

    conditions and attributes; default is False

  • induce_using_automatic (bool = False) –

    boolean indicating whether new rules should be induced with a use of automatic

    conditions and attributes; default is False

  • preferred_conditions_per_rule (int = None) – maximum number of preferred conditions per rule; default: unlimited,

  • preferred_attributes_per_rule (int = None) – maximum number of preferred attributes per rule; default: unlimited.

add_event_listener(listener: RuleInductionProgressListener)
Add event listener object to the operator which allows to monitor

rule induction progress.

Example

>>> from rulekit.events import RuleInductionProgressListener
>>> from rulekit.classification import RuleClassifier
>>>
>>> class MyEventListener(RuleInductionProgressListener):
>>>     def on_new_rule(self, rule):
>>>         print('Do something with new rule', rule)
>>>
>>> operator = RuleClassifier()
>>> operator.add_event_listener(MyEventListener())
Parameters:

listener (RuleInductionProgressListener) – listener object

fit(values: ndarray | DataFrame | list, labels: ndarray | DataFrame | list, survival_time: ndarray | DataFrame | list | None = None, expert_rules: list[str | tuple[str, str]] | None = None, expert_preferred_conditions: list[str | tuple[str, str]] | None = None, expert_forbidden_conditions: list[str | tuple[str, str]] | None = None) → ExpertSurvivalRules

Train model on given dataset.

Parameters:

  • values (rulekit.operator.Data) – attributes

  • labels (Data) – survival status

  • survival_time (rulekit.operator.Data) – data about survival time. Could be omitted when survival_time_attr parameter was specified.

  • expert_rules (List[Union[str, Tuple[str, str]]]) – set of initial rules, either passed as a list of strings representing rules or as list of tuples where first element is name of the rule and second one is rule string.

  • expert_preferred_conditions (List[Union[str, Tuple[str, str]]]) –

    multiset of preferred conditions (used also for specifying preferred attributes by

    using special value Any). Either passed as a list of strings representing rules or as

    list of tuples where first element is name of the rule and second one is rule string.

  • expert_forbidden_conditions (List[Union[str, Tuple[str, str]]]) –

    set of forbidden conditions (used also for specifying forbidden attributes by using

    special valye Any). Either passed as a list of strings representing rules or as list

    of tuples where first element is name of the rule and second one is rule string.

Returns:

self

Return type:

ExpertSurvivalRules

get_coverage_matrix(values: ndarray | DataFrame | list) → ndarray

Calculates coverage matrix for ruleset.

Parameters:

values (rulekit.operator.Data) – dataset

Returns:

coverage_matrix – Each row of the matrix represent single example from dataset and every column represent on rule from rule set. Value 1 in the matrix cell means that rule covered certain

example, value 0 means that it doesn’t.

Return type:

np.ndarray

get_metadata_routing() → None

Warning

Scikit-learn metadata routing is not supported yet.

Raises:

NotImplementedError – _description_

get_params(deep: bool = True) → dict[str, Any]
Parameters:

deep (rulekit.operator.Data) – Parameter for scikit-learn compatibility. Not used.

Returns:

hyperparameters – Dictionary containing model hyperparameters.

Return type:

np.ndarray

predict(values: ndarray | DataFrame | list) → ndarray

Perform prediction and return estimated survival function for each example.

Parameters:

values (rulekit.operator.Data) – attributes

Returns:

result – Each row represent single example from dataset and contains estimated survival function for that example. Estimated survival function is returned as a dictionary containing

times and corresponding probabilities.

Return type:

np.ndarray

score(values: ndarray | DataFrame | list, labels: ndarray | DataFrame | list, survival_time: ndarray | DataFrame | list | None = None) → float
Return the Integrated Brier Score on the given dataset and labels

(event status indicator).

Integrated Brier Score (IBS) - the Brier score (BS) represents the squared difference

between true event status at time T and predicted event status at that time; the Integrated Brier score summarizes the prediction error over all observations and over all times in a test set.

Parameters:
Returns:

score – Integrated Brier Score of self.predict(values) wrt. labels.

Return type:

float

set_params(**kwargs) → object

Set models hyperparameters. Parameters are the same as in constructor.