2 """
3 from __future__ import annotations
4
- 5 from typing import Optional , Tuple , Union
- 6
- 7 import numpy as np
- 8 import pandas as pd
- 9 from jpype import JClass
- 10 from pydantic import BaseModel # pylint: disable=no-name-in-module
- 11
- 12 from rulekit . _helpers import ExampleSetFactory , PredictionResultMapper
- 13 from rulekit . _operator import BaseOperator , Data , ExpertKnowledgeOperator
- 14 from rulekit . _problem_types import ProblemType
- 15 from rulekit . kaplan_meier import KaplanMeierEstimator
- 16 from rulekit . params import ( DEFAULT_PARAMS_VALUE , ContrastSetModelParams ,
- 17 ExpertModelParams )
- 18 from rulekit . rules import RuleSet , SurvivalRule
- 19
- 20 _DEFAULT_SURVIVAL_TIME_ATTR : str = 'survival_time'
- 21
- 22
- 23 class _SurvivalModelsParams ( BaseModel ) :
- 24 survival_time_attr : Optional [ str ]
- 25 minsupp_new : Optional [ float ] = DEFAULT_PARAMS_VALUE [ 'minsupp_new' ]
- 26 max_growing : Optional [ float ] = DEFAULT_PARAMS_VALUE [ 'max_growing' ]
- 27 enable_pruning : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 'enable_pruning' ]
- 28 ignore_missing : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 'ignore_missing' ]
- 29 max_uncovered_fraction : Optional [ float ] = DEFAULT_PARAMS_VALUE [ 'max_uncovered_fraction' ]
- 30 select_best_candidate : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 'select_best_candidate' ]
- 31 complementary_conditions : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 'complementary_conditions' ]
- 32
- 33
- 34 class _SurvivalExpertModelParams ( _SurvivalModelsParams , ExpertModelParams ) :
- 35 pass
- 36
- 37
- 38 class _BaseSurvivalRulesModel :
- 39
- 40 model : RuleSet [ SurvivalRule ]
- 41
- 42 def get_train_set_kaplan_meier ( self ) -> KaplanMeierEstimator :
- 43 """Returns train set KaplanMeier estimator
- 44
- 45 Returns:
- 46 KaplanMeierEstimator: estimator
- 47 """
- 48 return KaplanMeierEstimator (
- 49 self . model . _java_object . getTrainingEstimator ( ) # pylint: disable=protected-access
- 50 )
- 51
+ 5 from typing import Optional
+ 6 from typing import Tuple
+ 7 from typing import Union
+ 8
+ 9 import numpy as np
+ 10 import pandas as pd
+ 11 from jpype import JClass
+ 12 from pydantic import BaseModel # pylint: disable=no-name-in-module
+ 13
+ 14 from rulekit . _helpers import ExampleSetFactory
+ 15 from rulekit . _helpers import PredictionResultMapper
+ 16 from rulekit . _operator import BaseOperator
+ 17 from rulekit . _operator import Data
+ 18 from rulekit . _operator import ExpertKnowledgeOperator
+ 19 from rulekit . _problem_types import ProblemType
+ 20 from rulekit . kaplan_meier import KaplanMeierEstimator
+ 21 from rulekit . params import ContrastSetModelParams
+ 22 from rulekit . params import DEFAULT_PARAMS_VALUE
+ 23 from rulekit . params import ExpertModelParams
+ 24 from rulekit . rules import RuleSet
+ 25 from rulekit . rules import SurvivalRule
+ 26
+ 27 _DEFAULT_SURVIVAL_TIME_ATTR : str = "survival_time"
+ 28
+ 29
+ 30 class _SurvivalModelsParams ( BaseModel ) :
+ 31 survival_time_attr : Optional [ str ]
+ 32 minsupp_new : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "minsupp_new" ]
+ 33 max_growing : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "max_growing" ]
+ 34 enable_pruning : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "enable_pruning" ]
+ 35 ignore_missing : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "ignore_missing" ]
+ 36 max_uncovered_fraction : Optional [ float ] = DEFAULT_PARAMS_VALUE [
+ 37 "max_uncovered_fraction"
+ 38 ]
+ 39 select_best_candidate : Optional [ bool ] = DEFAULT_PARAMS_VALUE [
+ 40 "select_best_candidate"
+ 41 ]
+ 42 complementary_conditions : Optional [ bool ] = DEFAULT_PARAMS_VALUE [
+ 43 "complementary_conditions"
+ 44 ]
+ 45
+ 46
+ 47 class _SurvivalExpertModelParams ( _SurvivalModelsParams , ExpertModelParams ) :
+ 48 pass
+ 49
+ 50
+ 51 class _BaseSurvivalRulesModel :
52
- 53 class SurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ) :
- 54 """Survival model."""
- 55
- 56 __params_class__ = _SurvivalModelsParams
+ 53 model : RuleSet [ SurvivalRule ]
+ 54
+ 55 def get_train_set_kaplan_meier ( self ) -> KaplanMeierEstimator :
+ 56 """Returns train set KaplanMeier estimator
57
- 58 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
- 59 self ,
- 60 survival_time_attr : str = None ,
- 61 minsupp_new : int = DEFAULT_PARAMS_VALUE [ 'minsupp_new' ] ,
- 62 max_growing : int = DEFAULT_PARAMS_VALUE [ 'max_growing' ] ,
- 63 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ 'enable_pruning' ] ,
- 64 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ 'ignore_missing' ] ,
- 65 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ 'max_uncovered_fraction' ] ,
- 66 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ 'select_best_candidate' ] ,
- 67 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 'complementary_conditions' ] ,
- 68 max_rule_count : int = DEFAULT_PARAMS_VALUE [ 'max_rule_count' ] ,
- 69 ) :
- 70 """
- 71 Parameters
- 72 ----------
- 73 survival_time_attr : str
- 74 name of column containing survival time data (use when data passed to model is padnas
- 75 dataframe).
- 76 minsupp_new : float = 5.0
- 77 a minimum number (or fraction, if value < 1.0) of previously uncovered examples
- 78 to be covered by a new rule (positive examples for classification problems); default: 5,
- 79 max_growing : int = 0.0
- 80 non-negative integer representing maximum number of conditions which can be added to
- 81 the rule in the growing phase (use this parameter for large datasets if execution time
- 82 is prohibitive); 0 indicates no limit; default: 0,
- 83 enable_pruning : bool = True
- 84 enable or disable pruning, default is True.
- 85 ignore_missing : bool = False
- 86 boolean telling whether missing values should be ignored (by default, a missing value
- 87 of given attribute is always considered as not fulfilling the condition build upon that
- 88 attribute); default: False.
- 89 max_uncovered_fraction : float = 0.0
- 90 Floating-point number from [0,1] interval representing maximum fraction of examples
- 91 that may remain uncovered by the rule set, default: 0.0.
- 92 select_best_candidate : bool = False
- 93 Flag determining if best candidate should be selected from growing phase;
- 94 default: False.
- 95 complementary_conditions : bool = False
- 96 If enabled, complementary conditions in the form a = !{value} for nominal attributes
- 97 are supported.
- 98 max_rule_count : int = 0
- 99 Maximum number of rules to be generated (for classification data sets it applies
- 100 to a single class); 0 indicates no limit.
- 101 """
- 102 self . _params = None
- 103 self . _rule_generator = None
- 104 self . _configurator = None
- 105 self . _initialize_rulekit ( )
- 106 self . set_params (
- 107 survival_time_attr = survival_time_attr ,
- 108 minsupp_new = minsupp_new ,
- 109 max_growing = max_growing ,
- 110 enable_pruning = enable_pruning ,
- 111 ignore_missing = ignore_missing ,
- 112 max_uncovered_fraction = max_uncovered_fraction ,
- 113 select_best_candidate = select_best_candidate ,
- 114 complementary_conditions = complementary_conditions ,
- 115 max_rule_count = max_rule_count
- 116 )
- 117 self . model : RuleSet [ SurvivalRule ] = None
- 118
- 119 def set_params (
- 120 self ,
- 121 ** kwargs
- 122 ) -> object :
- 123 """Set models hyperparameters. Parameters are the same as in constructor."""
- 124 self . survival_time_attr = kwargs . get ( 'survival_time_attr' )
- 125 return BaseOperator . set_params ( self , ** kwargs )
- 126
- 127 @ staticmethod
- 128 def _append_survival_time_columns (
- 129 values ,
- 130 survival_time : Union [ pd . Series , np . ndarray , list ]
- 131 ) -> Optional [ str ] :
- 132 survival_time_attr : str = _DEFAULT_SURVIVAL_TIME_ATTR
- 133 if isinstance ( survival_time , pd . Series ) :
- 134 if survival_time . name is None :
- 135 survival_time . name = survival_time_attr
- 136 else :
- 137 survival_time_attr = survival_time . name
- 138 values [ survival_time . name ] = survival_time
- 139 elif isinstance ( survival_time , np . ndarray ) :
- 140 np . append ( values , survival_time , axis = 1 )
- 141 elif isinstance ( survival_time , list ) :
- 142 for index , row in enumerate ( values ) :
- 143 row . append ( survival_time [ index ] )
- 144 else :
- 145 raise ValueError (
- 146 'Data values must be instance of either pandas DataFrame, numpy array or list'
- 147 )
- 148 return survival_time_attr
- 149
- 150 def _prepare_survival_attribute ( self , survival_time : Optional [ Data ] , values : Data ) -> str :
- 151 if self . survival_time_attr is None and survival_time is None :
- 152 raise ValueError (
- 153 'No "survival_time" attribute name was specified. ' +
- 154 'Specify it using method set_params'
- 155 )
- 156 if survival_time is not None :
- 157 return SurvivalRules . _append_survival_time_columns (
- 158 values , survival_time )
- 159 return self . survival_time_attr
- 160
- 161 def fit ( self , values : Data , labels : Data , survival_time : Data = None ) -> SurvivalRules : # pylint: disable=arguments-differ
- 162 """Train model on given dataset.
- 163
- 164 Parameters
- 165 ----------
- 166 values : :class:`rulekit.operator.Data`
- 167 attributes
- 168 labels : :class:`rulekit.operator.Data`
- 169 survival status
- 170 survival_time: :class:`rulekit.operator.Data`
- 171 data about survival time. Could be omitted when *survival_time_attr* parameter
- 172 was specified.
- 173
- 174 Returns
- 175 -------
- 176 self : SurvivalRules
- 177 """
- 178 survival_time_attribute = self . _prepare_survival_attribute (
- 179 survival_time , values )
- 180 super ( ) . fit ( values , labels , survival_time_attribute )
- 181 return self
- 182
- 183 def predict ( self , values : Data ) -> np . ndarray :
- 184 """Perform prediction and return estimated survival function for each example.
- 185
- 186 Parameters
- 187 ----------
- 188 values : :class:`rulekit.operator.Data`
- 189 attributes
- 190
- 191 Returns
- 192 -------
- 193 result : np.ndarray
- 194 Each row represent single example from dataset and contains estimated survival function
- 195 for that example. Estimated survival function is returned as a dictionary containing
- 196 times and corresponding probabilities.
- 197 """
- 198 return PredictionResultMapper . map_survival ( super ( ) . predict ( values ) )
+ 58 Returns:
+ 59 KaplanMeierEstimator: estimator
+ 60 """
+ 61 return KaplanMeierEstimator (
+ 62 self . model . _java_object . getTrainingEstimator ( ) # pylint: disable=protected-access
+ 63 )
+ 64
+ 65
+ 66 class SurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ) :
+ 67 """Survival model."""
+ 68
+ 69 __params_class__ = _SurvivalModelsParams
+ 70
+ 71 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
+ 72 self ,
+ 73 survival_time_attr : str = None ,
+ 74 minsupp_new : int = DEFAULT_PARAMS_VALUE [ "minsupp_new" ] ,
+ 75 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ] ,
+ 76 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ] ,
+ 77 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ] ,
+ 78 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ] ,
+ 79 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ] ,
+ 80 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
+ 81 "complementary_conditions"
+ 82 ] ,
+ 83 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ] ,
+ 84 ) :
+ 85 """
+ 86 Parameters
+ 87 ----------
+ 88 survival_time_attr : str
+ 89 name of column containing survival time data (use when data passed to model
+ 90 is padnas dataframe).
+ 91 minsupp_new : float = 5.0
+ 92 a minimum number (or fraction, if value < 1.0) of previously uncovered
+ 93 examples to be covered by a new rule (positive examples for classification
+ 94 problems); default: 5,
+ 95 max_growing : int = 0.0
+ 96 non-negative integer representing maximum number of conditions which can be
+ 97 added to the rule in the growing phase (use this parameter for large
+ 98 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
+ 99 enable_pruning : bool = True
+ 100 enable or disable pruning, default is True.
+ 101 ignore_missing : bool = False
+ 102 boolean telling whether missing values should be ignored (by default, a
+ 103 missing value of given attribute is always considered as not fulfilling the
+ 104 condition build upon that attribute); default: False.
+ 105 max_uncovered_fraction : float = 0.0
+ 106 Floating-point number from [0,1] interval representing maximum fraction of
+ 107 examples that may remain uncovered by the rule set, default: 0.0.
+ 108 select_best_candidate : bool = False
+ 109 Flag determining if best candidate should be selected from growing phase;
+ 110 default: False.
+ 111 complementary_conditions : bool = False
+ 112 If enabled, complementary conditions in the form a = !{value} for nominal
+ 113 attributes are supported.
+ 114 max_rule_count : int = 0
+ 115 Maximum number of rules to be generated (for classification data sets it
+ 116 applies to a single class); 0 indicates no limit.
+ 117 """
+ 118 self . _params = None
+ 119 self . _rule_generator = None
+ 120 self . _configurator = None
+ 121 self . _initialize_rulekit ( )
+ 122 self . set_params (
+ 123 survival_time_attr = survival_time_attr ,
+ 124 minsupp_new = minsupp_new ,
+ 125 max_growing = max_growing ,
+ 126 enable_pruning = enable_pruning ,
+ 127 ignore_missing = ignore_missing ,
+ 128 max_uncovered_fraction = max_uncovered_fraction ,
+ 129 select_best_candidate = select_best_candidate ,
+ 130 complementary_conditions = complementary_conditions ,
+ 131 max_rule_count = max_rule_count ,
+ 132 )
+ 133 self . model : RuleSet [ SurvivalRule ] = None
+ 134
+ 135 def set_params ( self , ** kwargs ) -> object :
+ 136 """Set models hyperparameters. Parameters are the same as in constructor."""
+ 137 self . survival_time_attr = kwargs . get ( "survival_time_attr" )
+ 138 return BaseOperator . set_params ( self , ** kwargs )
+ 139
+ 140 @ staticmethod
+ 141 def _append_survival_time_columns (
+ 142 values , survival_time : Union [ pd . Series , np . ndarray , list ]
+ 143 ) -> Optional [ str ] :
+ 144 survival_time_attr : str = _DEFAULT_SURVIVAL_TIME_ATTR
+ 145 if isinstance ( survival_time , pd . Series ) :
+ 146 if survival_time . name is None :
+ 147 survival_time . name = survival_time_attr
+ 148 else :
+ 149 survival_time_attr = survival_time . name
+ 150 values [ survival_time . name ] = survival_time
+ 151 elif isinstance ( survival_time , np . ndarray ) :
+ 152 np . append ( values , survival_time , axis = 1 )
+ 153 elif isinstance ( survival_time , list ) :
+ 154 for index , row in enumerate ( values ) :
+ 155 row . append ( survival_time [ index ] )
+ 156 else :
+ 157 raise ValueError (
+ 158 "Data values must be instance of either pandas DataFrame, numpy array"
+ 159 " or list"
+ 160 )
+ 161 return survival_time_attr
+ 162
+ 163 def _prepare_survival_attribute (
+ 164 self , survival_time : Optional [ Data ] , values : Data
+ 165 ) -> str :
+ 166 if self . survival_time_attr is None and survival_time is None :
+ 167 raise ValueError (
+ 168 'No "survival_time" attribute name was specified. '
+ 169 + "Specify it using method set_params"
+ 170 )
+ 171 if survival_time is not None :
+ 172 return SurvivalRules . _append_survival_time_columns ( values , survival_time )
+ 173 return self . survival_time_attr
+ 174
+ 175 def fit (
+ 176 self , values : Data , labels : Data , survival_time : Data = None
+ 177 ) -> SurvivalRules : # pylint: disable=arguments-differ
+ 178 """Train model on given dataset.
+ 179
+ 180 Parameters
+ 181 ----------
+ 182 values : :class:`rulekit.operator.Data`
+ 183 attributes
+ 184 labels : :class:`rulekit.operator.Data`
+ 185 survival status
+ 186 survival_time: :class:`rulekit.operator.Data`
+ 187 data about survival time. Could be omitted when *survival_time_attr*
+ 188 parameter was specified.
+ 189
+ 190 Returns
+ 191 -------
+ 192 self : SurvivalRules
+ 193 """
+ 194 survival_time_attribute = self . _prepare_survival_attribute (
+ 195 survival_time , values
+ 196 )
+ 197 super ( ) . fit ( values , labels , survival_time_attribute )
+ 198 return self
199
- 200 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
- 201 """Return the Integrated Brier Score on the given dataset and labels
- 202 (event status indicator).
- 203
- 204 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared difference
- 205 between true event status at time T and predicted event status at that time;
- 206 the Integrated Brier score summarizes the prediction error over all observations and over
- 207 all times in a test set.
- 208
- 209 Parameters
- 210 ----------
- 211 values : :class:`rulekit.operator.Data`
- 212 attributes
- 213 labels : :class:`rulekit.operator.Data`
- 214 survival status
- 215 survival_time: :class:`rulekit.operator.Data`
- 216 data about survival time. Could be omitted when *survival_time_attr* parameter was
- 217 specified
- 218
- 219 Returns
- 220 -------
- 221 score : float
- 222 Integrated Brier Score of self.predict(values) wrt. labels.
- 223 """
- 224
- 225 survival_time_attribute = self . _prepare_survival_attribute (
- 226 survival_time , values )
- 227 example_set = ExampleSetFactory ( self . _get_problem_type ( ) ) . make (
- 228 values , labels , survival_time_attribute = survival_time_attribute )
- 229
- 230 predicted_example_set = self . model . _java_object . apply ( # pylint: disable=protected-access
- 231 example_set
- 232 )
- 233
- 234 IntegratedBrierScore = JClass ( # pylint: disable=invalid-name
- 235 'adaa.analytics.rules.logic.performance.IntegratedBrierScore'
- 236 )
- 237 integrated_brier_score = IntegratedBrierScore ( )
- 238 ibs = integrated_brier_score . countExample (
- 239 predicted_example_set ) . getValue ( )
- 240 return float ( ibs )
+ 200 def predict ( self , values : Data ) -> np . ndarray :
+ 201 """Perform prediction and return estimated survival function for each example.
+ 202
+ 203 Parameters
+ 204 ----------
+ 205 values : :class:`rulekit.operator.Data`
+ 206 attributes
+ 207
+ 208 Returns
+ 209 -------
+ 210 result : np.ndarray
+ 211 Each row represent single example from dataset and contains estimated
+ 212 survival function for that example. Estimated survival function is returned
+ 213 as a dictionary containing times and corresponding probabilities.
+ 214 """
+ 215 return PredictionResultMapper . map_survival ( super ( ) . predict ( values ) )
+ 216
+ 217 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
+ 218 """Return the Integrated Brier Score on the given dataset and labels
+ 219 (event status indicator).
+ 220
+ 221 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared
+ 222 difference between true event status at time T and predicted event status at
+ 223 that time; the Integrated Brier score summarizes the prediction error over all
+ 224 observations and over all times in a test set.
+ 225
+ 226 Parameters
+ 227 ----------
+ 228 values : :class:`rulekit.operator.Data`
+ 229 attributes
+ 230 labels : :class:`rulekit.operator.Data`
+ 231 survival status
+ 232 survival_time: :class:`rulekit.operator.Data`
+ 233 data about survival time. Could be omitted when *survival_time_attr*
+ 234 parameter was specified
+ 235
+ 236 Returns
+ 237 -------
+ 238 score : float
+ 239 Integrated Brier Score of self.predict(values) wrt. labels.
+ 240 """
241
- 242 def _get_problem_type ( self ) -> ProblemType :
- 243 return ProblemType . SURVIVAL
- 244
- 245
- 246 class ExpertSurvivalRules ( ExpertKnowledgeOperator , SurvivalRules ) :
- 247 """Expert Survival model."""
+ 242 survival_time_attribute = self . _prepare_survival_attribute (
+ 243 survival_time , values
+ 244 )
+ 245 example_set = ExampleSetFactory ( self . _get_problem_type ( ) ) . make (
+ 246 values , labels , survival_time_attribute = survival_time_attribute
+ 247 )
248
- 249 __params_class__ = _SurvivalExpertModelParams
- 250
- 251 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals
- 252 self ,
- 253 survival_time_attr : str = None ,
- 254 minsupp_new : float = DEFAULT_PARAMS_VALUE [ 'minsupp_new' ] ,
- 255 max_growing : int = DEFAULT_PARAMS_VALUE [ 'max_growing' ] ,
- 256 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ 'enable_pruning' ] ,
- 257 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ 'ignore_missing' ] ,
- 258 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ 'max_uncovered_fraction' ] ,
- 259 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ 'select_best_candidate' ] ,
- 260 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 'complementary_conditions' ] ,
+ 249 predicted_example_set = (
+ 250 self . model . _java_object . apply ( # pylint: disable=protected-access
+ 251 example_set
+ 252 )
+ 253 )
+ 254
+ 255 IntegratedBrierScore = JClass ( # pylint: disable=invalid-name
+ 256 "adaa.analytics.rules.logic.performance.IntegratedBrierScore"
+ 257 )
+ 258 integrated_brier_score = IntegratedBrierScore ( )
+ 259 ibs = integrated_brier_score . countExample ( predicted_example_set ) . getValue ( )
+ 260 return float ( ibs )
261
- 262 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ 'extend_using_preferred' ] ,
- 263 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ 'extend_using_automatic' ] ,
- 264 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ 'induce_using_preferred' ] ,
- 265 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ 'induce_using_automatic' ] ,
- 266 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [
- 267 'preferred_conditions_per_rule' ] ,
- 268 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [
- 269 'preferred_attributes_per_rule' ] ,
- 270 max_rule_count : int = DEFAULT_PARAMS_VALUE [ 'max_rule_count' ] ,
- 271 ) :
- 272 """
- 273 Parameters
- 274 ----------
- 275 minsupp_new : float = 5.0
- 276 a minimum number (or fraction, if value < 1.0) of previously uncovered examples
- 277 to be covered by a new rule (positive examples for classification problems); default: 5,
- 278 survival_time_attr : str
- 279 name of column containing survival time data (use when data passed to model is pandas
- 280 dataframe).
- 281 max_growing : int = 0.0
- 282 non-negative integer representing maximum number of conditions which can be added to
- 283 the rule in the growing phase (use this parameter for large datasets if execution time
- 284 is prohibitive); 0 indicates no limit; default: 0,
- 285 enable_pruning : bool = True
- 286 enable or disable pruning, default is True.
- 287 ignore_missing : bool = False
- 288 boolean telling whether missing values should be ignored (by default, a missing value
- 289 of given attribute is always considered as not fulfilling the condition build upon that
- 290 attribute); default: False.
- 291 max_uncovered_fraction : float = 0.0
- 292 Floating-point number from [0,1] interval representing maximum fraction of examples
- 293 that may remain uncovered by the rule set, default: 0.0.
- 294 select_best_candidate : bool = False
- 295 Flag determining if best candidate should be selected from growing phase;
- 296 default: False.
- 297 complementary_conditions : bool = False
- 298 If enabled, complementary conditions in the form a = !{value} for nominal attributes
- 299 are supported.
- 300 max_rule_count : int = 0
- 301 Maximum number of rules to be generated (for classification data sets it applies
- 302 to a single class); 0 indicates no limit.
- 303
- 304 extend_using_preferred : bool = False
- 305 boolean indicating whether initial rules should be extended with a use of preferred
- 306 conditions and attributes; default is False
- 307 extend_using_automatic : bool = False
- 308 boolean indicating whether initial rules should be extended with a use of automatic
- 309 conditions and attributes; default is False
- 310 induce_using_preferred : bool = False
- 311 boolean indicating whether new rules should be induced with a use of preferred
- 312 conditions and attributes; default is False
- 313 induce_using_automatic : bool = False
- 314 boolean indicating whether new rules should be induced with a use of automatic
- 315 conditions and attributes; default is False
- 316 preferred_conditions_per_rule : int = None
- 317 maximum number of preferred conditions per rule; default: unlimited,
- 318 preferred_attributes_per_rule : int = None
- 319 maximum number of preferred attributes per rule; default: unlimited.
- 320 """
- 321 self . _params = None
- 322 self . _rule_generator = None
- 323 self . _configurator = None
- 324 self . _initialize_rulekit ( )
- 325 self . set_params (
- 326 survival_time_attr = survival_time_attr ,
- 327 minsupp_new = minsupp_new ,
- 328 max_growing = max_growing ,
- 329 enable_pruning = enable_pruning ,
- 330 ignore_missing = ignore_missing ,
- 331 max_uncovered_fraction = max_uncovered_fraction ,
- 332 select_best_candidate = select_best_candidate ,
- 333 extend_using_preferred = extend_using_preferred ,
- 334 extend_using_automatic = extend_using_automatic ,
- 335 induce_using_preferred = induce_using_preferred ,
- 336 induce_using_automatic = induce_using_automatic ,
- 337 preferred_conditions_per_rule = preferred_conditions_per_rule ,
- 338 preferred_attributes_per_rule = preferred_attributes_per_rule ,
- 339 complementary_conditions = complementary_conditions ,
- 340 max_rule_count = max_rule_count
- 341 )
- 342 self . model : RuleSet [ SurvivalRule ] = None
- 343
- 344 def set_params ( # pylint: disable=arguments-differ
- 345 self ,
- 346 ** kwargs
- 347 ) -> object :
- 348 self . survival_time_attr = kwargs [ 'survival_time_attr' ]
- 349 return ExpertKnowledgeOperator . set_params ( self , ** kwargs )
- 350
- 351 def fit ( # pylint: disable=arguments-differ,too-many-arguments
- 352 self ,
- 353 values : Data ,
- 354 labels : Data ,
- 355 survival_time : Data = None ,
- 356
- 357 expert_rules : list [ Union [ str , tuple [ str , str ] ] ] = None ,
- 358 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ] ] ] = None ,
- 359 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ] ] ] = None
- 360 ) -> ExpertSurvivalRules :
- 361 """Train model on given dataset.
- 362
- 363 Parameters
- 364 ----------
- 365 values : :class:`rulekit.operator.Data`
- 366 attributes
- 367 labels : Data
- 368 survival status
- 369 survival_time: :class:`rulekit.operator.Data`
- 370 data about survival time. Could be omitted when *survival_time_attr* parameter was
- 371 specified.
- 372
- 373 expert_rules : List[Union[str, Tuple[str, str]]]
- 374 set of initial rules, either passed as a list of strings representing rules or as list
- 375 of tuples where first
- 376 element is name of the rule and second one is rule string.
- 377 expert_preferred_conditions : List[Union[str, Tuple[str, str]]]
- 378 multiset of preferred conditions (used also for specifying preferred attributes by
- 379 using special value Any). Either passed as a list of strings representing rules or as
- 380 list of tuples where first element is name of the rule and second one is rule string.
- 381 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]]
- 382 set of forbidden conditions (used also for specifying forbidden attributes by using
- 383 special valye Any). Either passed as a list of strings representing rules or as list
- 384 of tuples where first element is name of the rule and second one is rule string.
- 385
- 386 Returns
- 387 -------
- 388 self : ExpertSurvivalRules
- 389 """
- 390 survival_time_attribute = SurvivalRules . _prepare_survival_attribute (
- 391 self , survival_time , values )
- 392 return ExpertKnowledgeOperator . fit (
- 393 self ,
- 394 values = values ,
- 395 labels = labels ,
- 396 survival_time_attribute = survival_time_attribute ,
- 397 expert_rules = expert_rules ,
- 398 expert_preferred_conditions = expert_preferred_conditions ,
- 399 expert_forbidden_conditions = expert_forbidden_conditions
- 400 )
- 401
- 402 def predict ( self , values : Data ) -> np . ndarray :
- 403 return PredictionResultMapper . map_survival ( ExpertKnowledgeOperator . predict ( self , values ) )
- 404
- 405 def _get_problem_type ( self ) -> ProblemType :
- 406 return ProblemType . SURVIVAL
- 407
- 408
- 409 class _SurvivalContrastSetModelParams ( ContrastSetModelParams , _SurvivalModelsParams ) :
- 410 pass
- 411
- 412
- 413 class ContrastSetSurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ) :
- 414 """Contrast set survival model."""
- 415
- 416 __params_class__ = _SurvivalContrastSetModelParams
- 417
- 418 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
- 419 self ,
- 420 minsupp_all : Tuple [ float , float , float ,
- 421 float ] = DEFAULT_PARAMS_VALUE [ 'minsupp_all' ] ,
- 422 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ 'max_neg2pos' ] ,
- 423 max_passes_count : int = DEFAULT_PARAMS_VALUE [ 'max_passes_count' ] ,
- 424 penalty_strength : float = DEFAULT_PARAMS_VALUE [ 'penalty_strength' ] ,
- 425 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ 'penalty_saturation' ] ,
- 426
- 427 survival_time_attr : str = None ,
- 428 minsupp_new : float = DEFAULT_PARAMS_VALUE [ 'minsupp_new' ] ,
- 429 max_growing : int = DEFAULT_PARAMS_VALUE [ 'max_growing' ] ,
- 430 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ 'enable_pruning' ] ,
- 431 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ 'ignore_missing' ] ,
- 432 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ 'max_uncovered_fraction' ] ,
- 433 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ 'select_best_candidate' ] ,
- 434 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 'complementary_conditions' ] ,
- 435 max_rule_count : int = DEFAULT_PARAMS_VALUE [ 'max_rule_count' ] ,
- 436 ) :
- 437 """
- 438 Parameters
- 439 ----------
- 440 minsupp_all: Tuple[float, float, float, float]
- 441 a minimum positive support of a contrast set (p/P). When multiple values are specified,
- 442 a metainduction is performed; Default and recommended sequence is: 0.8, 0.5, 0.2, 0.1
- 443 max_neg2pos: float
- 444 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5
- 445 max_passes_count: int
- 446 a maximum number of sequential covering passes for a single minsupp-all; Default is 5
- 447 penalty_strength: float
- 448 (s) - penalty strength; Default is 0.5
- 449 penalty_saturation: float
- 450 the value of p_new / P at which penalty reward saturates; Default is 0.2.
- 451 survival_time_attr : str
- 452 name of column containing survival time data (use when data passed to model is pandas
- 453 dataframe).
- 454 minsupp_new : float = 5.0
- 455 a minimum number (or fraction, if value < 1.0) of previously uncovered examples
- 456 to be covered by a new rule (positive examples for classification problems); default: 5,
- 457 max_growing : int = 0.0
- 458 non-negative integer representing maximum number of conditions which can be added to
- 459 the rule in the growing phase (use this parameter for large datasets if execution time
- 460 is prohibitive); 0 indicates no limit; default: 0,
- 461 enable_pruning : bool = True
- 462 enable or disable pruning, default is True.
- 463 ignore_missing : bool = False
- 464 boolean telling whether missing values should be ignored (by default, a missing value
- 465 of given attribute is always considered as not fulfilling the condition build upon that
- 466 attribute); default: False.
- 467 max_uncovered_fraction : float = 0.0
- 468 Floating-point number from [0,1] interval representing maximum fraction of examples
- 469 that may remain uncovered by the rule set, default: 0.0.
- 470 select_best_candidate : bool = False
- 471 Flag determining if best candidate should be selected from growing phase;
- 472 default: False.
- 473 complementary_conditions : bool = False
- 474 If enabled, complementary conditions in the form a = !{value} for nominal attributes
- 475 are supported.
- 476 max_rule_count : int = 0
- 477 Maximum number of rules to be generated (for classification data sets it applies
- 478 to a single class); 0 indicates no limit.
- 479 """
- 480 self . _params = None
- 481 self . _rule_generator = None
- 482 self . _configurator = None
- 483 self . contrast_attribute : str = None
- 484 self . _initialize_rulekit ( )
- 485 self . set_params (
- 486 minsupp_all = minsupp_all ,
- 487 max_neg2pos = max_neg2pos ,
- 488 max_passes_count = max_passes_count ,
- 489 penalty_strength = penalty_strength ,
- 490 penalty_saturation = penalty_saturation ,
- 491 survival_time_attr = survival_time_attr ,
- 492 minsupp_new = minsupp_new ,
- 493 max_growing = max_growing ,
- 494 enable_pruning = enable_pruning ,
- 495 ignore_missing = ignore_missing ,
- 496 max_uncovered_fraction = max_uncovered_fraction ,
- 497 select_best_candidate = select_best_candidate ,
- 498 complementary_conditions = complementary_conditions ,
- 499 max_rule_count = max_rule_count
- 500 )
- 501 self . model : RuleSet [ SurvivalRule ] = None
- 502
- 503 def set_params ( self , ** kwargs ) -> object :
- 504 """Set models hyperparameters. Parameters are the same as in constructor."""
- 505 # params validation
- 506 self . survival_time_attr = kwargs [ 'survival_time_attr' ]
- 507 return BaseOperator . set_params ( self , ** kwargs )
- 508
- 509 def fit ( # pylint: disable=arguments-renamed
- 510 self ,
- 511 values : Data ,
- 512 labels : Data ,
- 513 contrast_attribute : str ,
- 514 survival_time : Data = None
- 515 ) -> ContrastSetSurvivalRules :
- 516 """Train model on given dataset.
- 517
- 518 Parameters
- 519 ----------
- 520 values : :class:`rulekit.operator.Data`
- 521 attributes
- 522 labels : :class:`rulekit.operator.Data`
- 523 survival status
- 524 contrast_attribute: str
- 525 group attribute
- 526 survival_time: :class:`rulekit.operator.Data`
- 527 data about survival time. Could be omitted when *survival_time_attr* parameter
- 528 was specified.
- 529
- 530 Returns
- 531 -------
- 532 self : ContrastSetSurvivalRules
- 533 """
- 534 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access
- 535 self , survival_time , values )
- 536 super ( ) . fit (
- 537 values , labels ,
- 538 survival_time_attribute = survival_time_attribute ,
- 539 contrast_attribute = contrast_attribute )
- 540 self . contrast_attribute = contrast_attribute
- 541 return self
- 542
- 543 def predict ( self , values : Data ) -> np . ndarray :
- 544 """Perform prediction and return estimated survival function for each example.
- 545
- 546 Parameters
- 547 ----------
- 548 values : :class:`rulekit.operator.Data`
- 549 attributes
- 550
- 551 Returns
- 552 -------
- 553 result : np.ndarray
- 554 Each row represent single example from dataset and contains estimated survival function
- 555 for that example. Estimated survival function is returned as a dictionary containing
- 556 times and corresponding probabilities.
- 557 """
- 558 return PredictionResultMapper . map_survival ( super ( ) . predict ( values ) )
- 559
- 560 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
- 561 """Return the Integrated Brier Score on the given dataset and
- 562 labels(event status indicator).
- 563
- 564 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared difference
- 565 between true event status at time T and predicted event status at that time;
- 566 the Integrated Brier score summarizes the prediction error over all observations and
- 567 over all times in a test set.
- 568
- 569 Parameters
- 570 ----------
- 571 values : :class:`rulekit.operator.Data`
- 572 attributes
- 573 labels : :class:`rulekit.operator.Data`
- 574 survival status
- 575 survival_time: :class:`rulekit.operator.Data`
- 576 data about survival time. Could be omitted when *survival_time_attr* parameter was
- 577 specified
- 578
- 579 Returns
- 580 -------
- 581 score : float
- 582 Integrated Brier Score of self.predict(values) wrt. labels.
- 583 """
- 584 return SurvivalRules . score ( self , values , labels , survival_time = survival_time )
- 585
- 586 def _get_problem_type ( self ) -> ProblemType :
- 587 return ProblemType . CONTRAST_SURVIVAL
+ 262 def _get_problem_type ( self ) -> ProblemType :
+ 263 return ProblemType . SURVIVAL
+ 264
+ 265
+ 266 class ExpertSurvivalRules ( ExpertKnowledgeOperator , SurvivalRules ) :
+ 267 """Expert Survival model."""
+ 268
+ 269 __params_class__ = _SurvivalExpertModelParams
+ 270
+ 271 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals
+ 272 self ,
+ 273 survival_time_attr : str = None ,
+ 274 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ] ,
+ 275 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ] ,
+ 276 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ] ,
+ 277 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ] ,
+ 278 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ] ,
+ 279 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ] ,
+ 280 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
+ 281 "complementary_conditions"
+ 282 ] ,
+ 283 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ] ,
+ 284 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ] ,
+ 285 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ] ,
+ 286 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ] ,
+ 287 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [
+ 288 "preferred_conditions_per_rule"
+ 289 ] ,
+ 290 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [
+ 291 "preferred_attributes_per_rule"
+ 292 ] ,
+ 293 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ] ,
+ 294 ) :
+ 295 """
+ 296 Parameters
+ 297 ----------
+ 298 minsupp_new : float = 5.0
+ 299 a minimum number (or fraction, if value < 1.0) of previously uncovered
+ 300 examples to be covered by a new rule (positive examples for classification
+ 301 problems); default: 5,
+ 302 survival_time_attr : str
+ 303 name of column containing survival time data (use when data passed to model
+ 304 is pandas dataframe).
+ 305 max_growing : int = 0.0
+ 306 non-negative integer representing maximum number of conditions which can be
+ 307 added to the rule in the growing phase (use this parameter for large
+ 308 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
+ 309 enable_pruning : bool = True
+ 310 enable or disable pruning, default is True.
+ 311 ignore_missing : bool = False
+ 312 boolean telling whether missing values should be ignored (by default, a
+ 313 missing value of given attribute is always considered as not fulfilling the
+ 314 condition build upon that attribute); default: False.
+ 315 max_uncovered_fraction : float = 0.0
+ 316 Floating-point number from [0,1] interval representing maximum fraction of
+ 317 examples that may remain uncovered by the rule set, default: 0.0.
+ 318 select_best_candidate : bool = False
+ 319 Flag determining if best candidate should be selected from growing phase;
+ 320 default: False.
+ 321 complementary_conditions : bool = False
+ 322 If enabled, complementary conditions in the form a = !{value} for nominal
+ 323 attributes are supported.
+ 324 max_rule_count : int = 0
+ 325 Maximum number of rules to be generated (for classification data sets it
+ 326 applies to a single class); 0 indicates no limit.
+ 327
+ 328 extend_using_preferred : bool = False
+ 329 boolean indicating whether initial rules should be extended with a use of
+ 330 preferred conditions and attributes; default is False
+ 331 extend_using_automatic : bool = False
+ 332 boolean indicating whether initial rules should be extended with a use of
+ 333 automatic conditions and attributes; default is False
+ 334 induce_using_preferred : bool = False
+ 335 boolean indicating whether new rules should be induced with a use of
+ 336 preferred conditions and attributes; default is False
+ 337 induce_using_automatic : bool = False
+ 338 boolean indicating whether new rules should be induced with a use of
+ 339 automatic conditions and attributes; default is False
+ 340 preferred_conditions_per_rule : int = None
+ 341 maximum number of preferred conditions per rule; default: unlimited,
+ 342 preferred_attributes_per_rule : int = None
+ 343 maximum number of preferred attributes per rule; default: unlimited.
+ 344 """
+ 345 self . _params = None
+ 346 self . _rule_generator = None
+ 347 self . _configurator = None
+ 348 self . _initialize_rulekit ( )
+ 349 self . set_params (
+ 350 survival_time_attr = survival_time_attr ,
+ 351 minsupp_new = minsupp_new ,
+ 352 max_growing = max_growing ,
+ 353 enable_pruning = enable_pruning ,
+ 354 ignore_missing = ignore_missing ,
+ 355 max_uncovered_fraction = max_uncovered_fraction ,
+ 356 select_best_candidate = select_best_candidate ,
+ 357 extend_using_preferred = extend_using_preferred ,
+ 358 extend_using_automatic = extend_using_automatic ,
+ 359 induce_using_preferred = induce_using_preferred ,
+ 360 induce_using_automatic = induce_using_automatic ,
+ 361 preferred_conditions_per_rule = preferred_conditions_per_rule ,
+ 362 preferred_attributes_per_rule = preferred_attributes_per_rule ,
+ 363 complementary_conditions = complementary_conditions ,
+ 364 max_rule_count = max_rule_count ,
+ 365 )
+ 366 self . model : RuleSet [ SurvivalRule ] = None
+ 367
+ 368 def set_params ( self , ** kwargs ) -> object : # pylint: disable=arguments-differ
+ 369 self . survival_time_attr = kwargs [ "survival_time_attr" ]
+ 370 return ExpertKnowledgeOperator . set_params ( self , ** kwargs )
+ 371
+ 372 def fit ( # pylint: disable=arguments-differ,too-many-arguments
+ 373 self ,
+ 374 values : Data ,
+ 375 labels : Data ,
+ 376 survival_time : Data = None ,
+ 377 expert_rules : list [ Union [ str , tuple [ str , str ] ] ] = None ,
+ 378 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ] ] ] = None ,
+ 379 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ] ] ] = None ,
+ 380 ) -> ExpertSurvivalRules :
+ 381 """Train model on given dataset.
+ 382
+ 383 Parameters
+ 384 ----------
+ 385 values : :class:`rulekit.operator.Data`
+ 386 attributes
+ 387 labels : Data
+ 388 survival status
+ 389 survival_time: :class:`rulekit.operator.Data`
+ 390 data about survival time. Could be omitted when *survival_time_attr*
+ 391 parameter was specified.
+ 392 expert_rules : List[Union[str, Tuple[str, str]]]
+ 393 set of initial rules, either passed as a list of strings representing rules
+ 394 or as list of tuples where first element is name of the rule and second one
+ 395 is rule string.
+ 396 expert_preferred_conditions : List[Union[str, Tuple[str, str]]]
+ 397 multiset of preferred conditions (used also for specifying preferred
+ 398 attributes by using special value Any). Either passed as a list of strings
+ 399 representing rules or as list of tuples where first element is name of the
+ 400 rule and second one is rule string.
+ 401 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]]
+ 402 set of forbidden conditions (used also for specifying forbidden attributes
+ 403 by using special valye Any). Either passed as a list of strings representing
+ 404 rules or as list of tuples where first element is name of the rule and
+ 405 second one is rule string.
+ 406
+ 407 Returns
+ 408 -------
+ 409 self : ExpertSurvivalRules
+ 410 """
+ 411 survival_time_attribute = SurvivalRules . _prepare_survival_attribute (
+ 412 self , survival_time , values
+ 413 )
+ 414 return ExpertKnowledgeOperator . fit (
+ 415 self ,
+ 416 values = values ,
+ 417 labels = labels ,
+ 418 survival_time_attribute = survival_time_attribute ,
+ 419 expert_rules = expert_rules ,
+ 420 expert_preferred_conditions = expert_preferred_conditions ,
+ 421 expert_forbidden_conditions = expert_forbidden_conditions ,
+ 422 )
+ 423
+ 424 def predict ( self , values : Data ) -> np . ndarray :
+ 425 return PredictionResultMapper . map_survival (
+ 426 ExpertKnowledgeOperator . predict ( self , values )
+ 427 )
+ 428
+ 429 def _get_problem_type ( self ) -> ProblemType :
+ 430 return ProblemType . SURVIVAL
+ 431
+ 432
+ 433 class _SurvivalContrastSetModelParams ( ContrastSetModelParams , _SurvivalModelsParams ) :
+ 434 pass
+ 435
+ 436
+ 437 class ContrastSetSurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ) :
+ 438 """Contrast set survival model."""
+ 439
+ 440 __params_class__ = _SurvivalContrastSetModelParams
+ 441
+ 442 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
+ 443 self ,
+ 444 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [
+ 445 "minsupp_all"
+ 446 ] ,
+ 447 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ] ,
+ 448 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ] ,
+ 449 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ] ,
+ 450 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ] ,
+ 451 survival_time_attr : str = None ,
+ 452 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ] ,
+ 453 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ] ,
+ 454 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ] ,
+ 455 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ] ,
+ 456 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ] ,
+ 457 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ] ,
+ 458 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
+ 459 "complementary_conditions"
+ 460 ] ,
+ 461 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ] ,
+ 462 ) :
+ 463 """
+ 464 Parameters
+ 465 ----------
+ 466 minsupp_all: Tuple[float, float, float, float]
+ 467 a minimum positive support of a contrast set (p/P). When multiple values are
+ 468 specified, a metainduction is performed; Default and recommended sequence
+ 469 is: 0.8, 0.5, 0.2, 0.1
+ 470 max_neg2pos: float
+ 471 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5
+ 472 max_passes_count: int
+ 473 a maximum number of sequential covering passes for a single minsupp-all;
+ 474 Default is 5
+ 475 penalty_strength: float
+ 476 (s) - penalty strength; Default is 0.5
+ 477 penalty_saturation: float
+ 478 the value of p_new / P at which penalty reward saturates; Default is 0.2.
+ 479 survival_time_attr : str
+ 480 name of column containing survival time data (use when data passed to model
+ 481 is pandas dataframe).
+ 482 minsupp_new : float = 5.0
+ 483 a minimum number (or fraction, if value < 1.0) of previously uncovered
+ 484 examples to be covered by a new rule (positive examples for classification
+ 485 problems); default: 5,
+ 486 max_growing : int = 0.0
+ 487 non-negative integer representing maximum number of conditions which can be
+ 488 added to the rule in the growing phase (use this parameter for large
+ 489 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
+ 490 enable_pruning : bool = True
+ 491 enable or disable pruning, default is True.
+ 492 ignore_missing : bool = False
+ 493 boolean telling whether missing values should be ignored (by default, a
+ 494 missing value of given attribute is always considered as not fulfilling the
+ 495 condition build upon that attribute); default: False.
+ 496 max_uncovered_fraction : float = 0.0
+ 497 Floating-point number from [0,1] interval representing maximum fraction of
+ 498 examples that may remain uncovered by the rule set, default: 0.0.
+ 499 select_best_candidate : bool = False
+ 500 Flag determining if best candidate should be selected from growing phase;
+ 501 default: False.
+ 502 complementary_conditions : bool = False
+ 503 If enabled, complementary conditions in the form a = !{value} for nominal
+ 504 attributes are supported.
+ 505 max_rule_count : int = 0
+ 506 Maximum number of rules to be generated (for classification data sets it
+ 507 applies to a single class); 0 indicates no limit.
+ 508 """
+ 509 self . _params = None
+ 510 self . _rule_generator = None
+ 511 self . _configurator = None
+ 512 self . contrast_attribute : str = None
+ 513 self . _initialize_rulekit ( )
+ 514 self . set_params (
+ 515 minsupp_all = minsupp_all ,
+ 516 max_neg2pos = max_neg2pos ,
+ 517 max_passes_count = max_passes_count ,
+ 518 penalty_strength = penalty_strength ,
+ 519 penalty_saturation = penalty_saturation ,
+ 520 survival_time_attr = survival_time_attr ,
+ 521 minsupp_new = minsupp_new ,
+ 522 max_growing = max_growing ,
+ 523 enable_pruning = enable_pruning ,
+ 524 ignore_missing = ignore_missing ,
+ 525 max_uncovered_fraction = max_uncovered_fraction ,
+ 526 select_best_candidate = select_best_candidate ,
+ 527 complementary_conditions = complementary_conditions ,
+ 528 max_rule_count = max_rule_count ,
+ 529 )
+ 530 self . model : RuleSet [ SurvivalRule ] = None
+ 531
+ 532 def set_params ( self , ** kwargs ) -> object :
+ 533 """Set models hyperparameters. Parameters are the same as in constructor."""
+ 534 # params validation
+ 535 self . survival_time_attr = kwargs [ "survival_time_attr" ]
+ 536 return BaseOperator . set_params ( self , ** kwargs )
+ 537
+ 538 def fit ( # pylint: disable=arguments-renamed
+ 539 self ,
+ 540 values : Data ,
+ 541 labels : Data ,
+ 542 contrast_attribute : str ,
+ 543 survival_time : Data = None ,
+ 544 ) -> ContrastSetSurvivalRules :
+ 545 """Train model on given dataset.
+ 546
+ 547 Parameters
+ 548 ----------
+ 549 values : :class:`rulekit.operator.Data`
+ 550 attributes
+ 551 labels : :class:`rulekit.operator.Data`
+ 552 survival status
+ 553 contrast_attribute: str
+ 554 group attribute
+ 555 survival_time: :class:`rulekit.operator.Data`
+ 556 data about survival time. Could be omitted when *survival_time_attr*
+ 557 parameter was specified.
+ 558
+ 559 Returns
+ 560 -------
+ 561 self : ContrastSetSurvivalRules
+ 562 """
+ 563 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access
+ 564 self , survival_time , values
+ 565 )
+ 566 super ( ) . fit (
+ 567 values ,
+ 568 labels ,
+ 569 survival_time_attribute = survival_time_attribute ,
+ 570 contrast_attribute = contrast_attribute ,
+ 571 )
+ 572 self . contrast_attribute = contrast_attribute
+ 573 return self
+ 574
+ 575 def predict ( self , values : Data ) -> np . ndarray :
+ 576 """Perform prediction and return estimated survival function for each example.
+ 577
+ 578 Parameters
+ 579 ----------
+ 580 values : :class:`rulekit.operator.Data`
+ 581 attributes
+ 582
+ 583 Returns
+ 584 -------
+ 585 result : np.ndarray
+ 586 Each row represent single example from dataset and contains estimated
+ 587 survival function for that example. Estimated survival function is returned
+ 588 as a dictionary containing times and corresponding probabilities.
+ 589 """
+ 590 return PredictionResultMapper . map_survival ( super ( ) . predict ( values ) )
+ 591
+ 592 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
+ 593 """Return the Integrated Brier Score on the given dataset and
+ 594 labels(event status indicator).
+ 595
+ 596 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared
+ 597 differencebetween true event status at time T and predicted event status at that
+ 598 time; the Integrated Brier score summarizes the prediction error over all
+ 599 observations and over all times in a test set.
+ 600
+ 601 Parameters
+ 602 ----------
+ 603 values : :class:`rulekit.operator.Data`
+ 604 attributes
+ 605 labels : :class:`rulekit.operator.Data`
+ 606 survival status
+ 607 survival_time: :class:`rulekit.operator.Data`
+ 608 data about survival time. Could be omitted when *survival_time_attr*
+ 609 parameter was specified
+ 610
+ 611 Returns
+ 612 -------
+ 613 score : float
+ 614 Integrated Brier Score of self.predict(values) wrt. labels.
+ 615 """
+ 616 return SurvivalRules . score ( self , values , labels , survival_time = survival_time )
+ 617
+ 618 def _get_problem_type ( self ) -> ProblemType :
+ 619 return ProblemType . CONTRAST_SURVIVAL
diff --git a/docs/reports/flake8/flake8stats.txt b/docs/reports/flake8/flake8stats.txt
index 366abe8..5003324 100644
--- a/docs/reports/flake8/flake8stats.txt
+++ b/docs/reports/flake8/flake8stats.txt
@@ -1,34 +1 @@
-21 E501 line too long (100 > 99 characters)
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diff --git a/docs/reports/flake8/index.html b/docs/reports/flake8/index.html
index 4220f0a..2b4f00d 100644
--- a/docs/reports/flake8/index.html
+++ b/docs/reports/flake8/index.html
@@ -9,7 +9,7 @@
flake8 violations
-
Generated on 2024-11-28 13:34
+
Generated on 2024-11-28 14:18
with Installed plugins: flake8-html: 0.4.3, mccabe: 0.7.0, pycodestyle: 2.12.1, pyflakes: 3.2.0
diff --git a/docs/reports/flake8/rulekit.classification.report.html b/docs/reports/flake8/rulekit.classification.report.html
index 1e43a1a..15258a4 100644
--- a/docs/reports/flake8/rulekit.classification.report.html
+++ b/docs/reports/flake8/rulekit.classification.report.html
@@ -60,8 +60,8 @@
3 3 from __future__ import annotations
4 from __future__ import annotations 4
5 5 from enum import Enum
6 from enum import Enum 6 from numbers import Number
7 from numbers import Number 7 from typing import Tuple
8 from typing import Tuple , TypedDict , Union 8 from typing import TypedDict
9 9 from typing import Union
10 import numpy as np 10
11 import pandas as pd 11 import numpy as np
12 from jpype import JClass , JObject 12 import pandas as pd
13 from sklearn import metrics 13 from jpype import JClass
14 14 from jpype import JObject
15 from rulekit._helpers import PredictionResultMapper 15 from sklearn import metrics
16 from rulekit._operator import BaseOperator , Data , ExpertKnowledgeOperator 16
17 from rulekit._problem_types import ProblemType 17 from rulekit._helpers import PredictionResultMapper
18 from rulekit.params import ( DEFAULT_PARAMS_VALUE , ContrastSetModelParams , 18 from rulekit._operator import BaseOperator
19 ExpertModelParams , Measures , ModelsParams ) 19 from rulekit._operator import Data
20 from rulekit.rules import ClassificationRule , RuleSet 20 from rulekit._operator import ExpertKnowledgeOperator
21 21 from rulekit._problem_types import ProblemType
22 22 from rulekit.params import ContrastSetModelParams
23 class ClassificationPredictionMetrics ( TypedDict ): 23 from rulekit.params import DEFAULT_PARAMS_VALUE
24 """Stores additional metrics for classification prediction. 24 from rulekit.params import ExpertModelParams
25 25 from rulekit.params import Measures
26 Fields: 26 from rulekit.params import ModelsParams
27 * rules_per_example (float): Average number of rules per example. 27 from rulekit.rules import ClassificationRule
28 * voting_conflicts (_type_): Number of voting conflicts. 28 from rulekit.rules import RuleSet
29 """ 29
30
31 rules_per_example : float 31 class ClassificationPredictionMetrics ( TypedDict ):
32 voting_conflicts : float 32 """Stores additional metrics for classification prediction.
33
34 34 Fields:
35 class _ClassificationParams ( ModelsParams ): 35 * rules_per_example (float): Average number of rules per example.
36 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [ "control_apriori_precision" ] 36 * voting_conflicts (_type_): Number of voting conflicts.
37 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ] 37 """
38 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ] 38
39 39 rules_per_example : float
40 40 voting_conflicts : float
41 class _ClassificationExpertParams ( _ClassificationParams , ExpertModelParams ): 41
42 pass 42
43 43 class _ClassificationParams ( ModelsParams ):
44 44 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [ "control_apriori_precision" ]
45 class BaseClassifier : 45 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ]
46 """:meta private:""" 46 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ]
47
48 def __init__ ( self ): 48
49 self . _ClassificationRulesPerformance : JClass = ( 49 class _ClassificationExpertParams ( _ClassificationParams , ExpertModelParams ):
50 None # pylint: disable=invalid-name 50 pass
51 ) 51
52 self . _NegativeVotingConflictsPerformance : JClass = ( 52
53 None # pylint: disable=invalid-name 53 class BaseClassifier :
54 ) 54 """:meta private:"""
55 self . _init_classification_rule_performance_classes () 55
56 56 def __init__ ( self ):
57 class MetricTypes ( Enum ): 57 self . _ClassificationRulesPerformance : JClass = (
58 """:meta private:""" 58 None # pylint: disable=invalid-name
59 59 )
60 RulesPerExample = 1 # pylint: disable=invalid-name 60 self . _NegativeVotingConflictsPerformance : JClass = (
61 VotingConflicts = 2 # pylint: disable=invalid-name 61 None # pylint: disable=invalid-name
62 NegativeVotingConflicts = 3 # pylint: disable=invalid-name 62 )
63 63 self . _init_classification_rule_performance_classes ()
64 def _init_classification_rule_performance_classes ( self ): 64
65 self . _ClassificationRulesPerformance = JClass ( # pylint: disable=invalid-name 65 class MetricTypes ( Enum ):
66 "adaa.analytics.rules.logic.performance.ClassificationRulesPerformance" 66 """:meta private:"""
67 ) 67
68 68 RulesPerExample = 1 # pylint: disable=invalid-name
69 def _calculate_metric ( 69 VotingConflicts = 2 # pylint: disable=invalid-name
70 self , example_set : JObject , metric_type : MetricTypes 70 NegativeVotingConflicts = 3 # pylint: disable=invalid-name
71 ) -> float : 71
72 metric : JObject = self . _ClassificationRulesPerformance ( metric_type . value ) 72 def _init_classification_rule_performance_classes ( self ):
73 metric_value = float ( metric . countExample ( example_set ) . getValue ()) 73 self . _ClassificationRulesPerformance = JClass ( # pylint: disable=invalid-name
74 return metric_value 74 "adaa.analytics.rules.logic.performance.ClassificationRulesPerformance"
75 75 )
76 def _calculate_prediction_metrics ( 76
77 self , example_set 77 def _calculate_metric (
78 ) -> ClassificationPredictionMetrics : 78 self , example_set : JObject , metric_type : MetricTypes
79 return ClassificationPredictionMetrics ( 79 ) -> float :
80 rules_per_example = self . _calculate_metric ( 80 metric : JObject = self . _ClassificationRulesPerformance ( metric_type . value )
81 example_set , BaseClassifier . MetricTypes . RulesPerExample 81 metric_value = float ( metric . countExample ( example_set ) . getValue ())
82 ), 82 return metric_value
83 voting_conflicts = self . _calculate_metric ( 83
84 example_set , BaseClassifier . MetricTypes . VotingConflicts 84 def _calculate_prediction_metrics (
85 ), 85 self , example_set
86 ) 86 ) -> ClassificationPredictionMetrics :
87 87 return ClassificationPredictionMetrics (
88 88 rules_per_example = self . _calculate_metric (
89 class RuleClassifier ( BaseOperator , BaseClassifier ): 89 example_set , BaseClassifier . MetricTypes . RulesPerExample
90 """Classification model.""" 90 ),
91 91 voting_conflicts = self . _calculate_metric (
92 __params_class__ = _ClassificationParams 92 example_set , BaseClassifier . MetricTypes . VotingConflicts
93 93 ),
94 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals 94 )
95 self , 95
96 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 96
97 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 97 class RuleClassifier ( BaseOperator , BaseClassifier ):
98 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 98 """Classification model."""
99 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 99
100 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 100 __params_class__ = _ClassificationParams
101 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 101
102 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 102 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals
103 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 103 self ,
104 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 104 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
105 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 105 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
106 "complementary_conditions" 106 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
107 ], 107 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
108 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [ 108 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
109 "control_apriori_precision" 109 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
110 ], 110 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
111 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 111 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
112 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ], 112 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
113 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ], 113 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
114 ): 114 "complementary_conditions"
115 """ 115 ],
116 Parameters 116 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [
117 ---------- 117 "control_apriori_precision"
118 minsupp_new : float = 5.0 118 ],
119 a minimum number (or fraction, if value < 1.0) of previously uncovered 119 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
120 examples to be covered by a new rule (positive examples for classification 120 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ],
121 problems); default: 5, 121 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ],
122 induction_measure : :class:`rulekit.params.Measures` = :class:`rulekit.params.\ 122 ):
123 Measures.Correlation` 123 """
124 measure used during induction; default measure is correlation 124 Parameters
125 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 125 ----------
126 :class:`rulekit.params.Measures.Correlation` 126 minsupp_new : float = 5.0
127 measure used during pruning. Could be user defined (string), for example 127 a minimum number (or fraction, if value < 1.0) of previously uncovered
128 :code:`2 * p / n`; default measure is correlation 128 examples to be covered by a new rule (positive examples for classification
129 voting_measure : :class:`rulekit.params.Measures` = \ 129 problems); default: 5,
130 :class:`rulekit.params.Measures.Correlation` 130 induction_measure : :class:`rulekit.params.Measures` = :class:`rulekit.params.\
131 measure used during voting; default measure is correlation 131 Measures.Correlation`
132 max_growing : int = 0.0 132 measure used during induction; default measure is correlation
133 non-negative integer representing maximum number of conditions which can be 133 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
134 added to the rule in the growing phase (use this parameter for large 134 :class:`rulekit.params.Measures.Correlation`
135 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 135 measure used during pruning. Could be user defined (string), for example
136 enable_pruning : bool = True 136 :code:`2 * p / n`; default measure is correlation
137 enable or disable pruning, default is True. 137 voting_measure : :class:`rulekit.params.Measures` = \
138 ignore_missing : bool = False 138 :class:`rulekit.params.Measures.Correlation`
139 boolean telling whether missing values should be ignored (by default, a 139 measure used during voting; default measure is correlation
140 missing valueof given attribute is always cconsidered as not fulfilling the 140 max_growing : int = 0.0
141 condition build upon that attribute); default: False. 141 non-negative integer representing maximum number of conditions which can be
142 max_uncovered_fraction : float = 0.0 142 added to the rule in the growing phase (use this parameter for large
143 Floating-point number from [0,1] interval representing maximum fraction of 143 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
144 examples that may remain uncovered by the rule set, default: 0.0. 144 enable_pruning : bool = True
145 select_best_candidate : bool = False 145 enable or disable pruning, default is True.
146 Flag determining if best candidate should be selected from growing phase; 146 ignore_missing : bool = False
147 default: False. 147 boolean telling whether missing values should be ignored (by default, a
148 complementary_conditions : bool = False 148 missing valueof given attribute is always cconsidered as not fulfilling the
149 If enabled, complementary conditions in the form a = !{value} for nominal 149 condition build upon that attribute); default: False.
150 attributes are supported. 150 max_uncovered_fraction : float = 0.0
151 control_apriori_precision : bool = True 151 Floating-point number from [0,1] interval representing maximum fraction of
152 When inducing classification rules, verify if candidate precision is higher 152 examples that may remain uncovered by the rule set, default: 0.0.
153 than apriori precision of the investigated class. 153 select_best_candidate : bool = False
154 max_rule_count : int = 0 154 Flag determining if best candidate should be selected from growing phase;
155 Maximum number of rules to be generated (for classification data sets it 155 default: False.
156 applies to a single class); 0 indicates no limit. 156 complementary_conditions : bool = False
157 approximate_induction: bool = False 157 If enabled, complementary conditions in the form a = !{value} for nominal
158 Use an approximate induction heuristic which does not check all possible 158 attributes are supported.
159 splits; note: this is an experimental feature and currently works only for 159 control_apriori_precision : bool = True
160 classification data sets, results may change in future; 160 When inducing classification rules, verify if candidate precision is higher
161 approximate_bins_count: int = 100 161 than apriori precision of the investigated class.
162 maximum number of bins for an attribute evaluated in the approximate 162 max_rule_count : int = 0
163 induction. 163 Maximum number of rules to be generated (for classification data sets it
164 """ 164 applies to a single class); 0 indicates no limit.
165 BaseOperator . __init__ ( 165 approximate_induction: bool = False
166 self , 166 Use an approximate induction heuristic which does not check all possible
167 minsupp_new = minsupp_new , 167 splits; note: this is an experimental feature and currently works only for
168 induction_measure = induction_measure , 168 classification data sets, results may change in future;
169 pruning_measure = pruning_measure , 169 approximate_bins_count: int = 100
170 voting_measure = voting_measure , 170 maximum number of bins for an attribute evaluated in the approximate
171 max_growing = max_growing , 171 induction.
172 enable_pruning = enable_pruning , 172 """
173 ignore_missing = ignore_missing , 173 BaseOperator . __init__ (
174 max_uncovered_fraction = max_uncovered_fraction , 174 self ,
175 select_best_candidate = select_best_candidate , 175 minsupp_new = minsupp_new ,
176 complementary_conditions = complementary_conditions , 176 induction_measure = induction_measure ,
177 control_apriori_precision = control_apriori_precision , 177 pruning_measure = pruning_measure ,
178 max_rule_count = max_rule_count , 178 voting_measure = voting_measure ,
179 approximate_induction = approximate_induction , 179 max_growing = max_growing ,
180 approximate_bins_count = approximate_bins_count , 180 enable_pruning = enable_pruning ,
181 ) 181 ignore_missing = ignore_missing ,
182 BaseClassifier . __init__ ( self ) 182 max_uncovered_fraction = max_uncovered_fraction ,
183 self . _remap_to_numeric = False 183 select_best_candidate = select_best_candidate ,
184 self . label_unique_values = [] 184 complementary_conditions = complementary_conditions ,
185 self . model : RuleSet [ ClassificationRule ] = None 185 control_apriori_precision = control_apriori_precision ,
186 186 max_rule_count = max_rule_count ,
187 def _map_result ( self , predicted_example_set ) -> np . ndarray : 187 approximate_induction = approximate_induction ,
188 prediction : np . ndarray 188 approximate_bins_count = approximate_bins_count ,
189 if self . _remap_to_numeric : 189 )
190 prediction = PredictionResultMapper . map_to_numerical ( predicted_example_set ) 190 BaseClassifier . __init__ ( self )
191 else : 191 self . _remap_to_numeric = False
192 prediction = PredictionResultMapper . map_to_nominal ( predicted_example_set ) 192 self . label_unique_values = []
193 return prediction 193 self . model : RuleSet [ ClassificationRule ] = None
194
195 def _map_confidence ( self , predicted_example_set ) -> np . ndarray : 195 def _map_result ( self , predicted_example_set ) -> np . ndarray :
196 return PredictionResultMapper . map_confidence ( 196 prediction : np . ndarray
197 predicted_example_set , self . label_unique_values 197 if self . _remap_to_numeric :
198 ) 198 prediction = PredictionResultMapper . map_to_numerical ( predicted_example_set )
199 199 else :
200 def _get_unique_label_values ( self , labels : Data ): 200 prediction = PredictionResultMapper . map_to_nominal ( predicted_example_set )
201 tmp = {} 201 return prediction
202 for label_value in labels : 202
203 tmp [ label_value ] = None 203 def _map_confidence ( self , predicted_example_set ) -> np . ndarray :
204 self . label_unique_values = list ( tmp . keys ()) 204 return PredictionResultMapper . map_confidence (
205 if len ( self . label_unique_values ) > 0 and isinstance ( 205 predicted_example_set , self . label_unique_values
206 self . label_unique_values [ 0 ], bytes 206 )
207 ): 207
208 self . label_unique_values = [ 208 def _get_unique_label_values ( self , labels : Data ):
209 item . decode ( "utf-8" ) for item in self . label_unique_values 209 tmp = {}
210 ] 210 for label_value in labels :
211 211 tmp [ label_value ] = None
212 def _prepare_labels ( self , labels : Data ) -> Data : 212 self . label_unique_values = list ( tmp . keys ())
213 if isinstance ( labels , ( pd . DataFrame , pd . Series )): 213 if len ( self . label_unique_values ) > 0 and isinstance (
214 if labels . dtypes . name == "bool" : 214 self . label_unique_values [ 0 ], bytes
215 return labels . astype ( str ) 215 ):
216 if isinstance ( labels . iloc [ 0 ], Number ): 216 self . label_unique_values = [
217 self . _remap_to_numeric = True 217 item . decode ( "utf-8" ) for item in self . label_unique_values
218 return labels . astype ( str ) 218 ]
219 else : 219
220 if isinstance ( labels [ 0 ], bool ) or ( 220 def _prepare_labels ( self , labels : Data ) -> Data :
221 isinstance ( labels , np . ndarray ) and labels . dtype . name == "bool" 221 if isinstance ( labels , ( pd . DataFrame , pd . Series )):
222 ): 222 if labels . dtypes . name == "bool" :
223 return np . array ( list ( map ( str , labels ))) 223 return labels . astype ( str )
224 if isinstance ( labels [ 0 ], Number ): 224 if isinstance ( labels . iloc [ 0 ], Number ):
225 self . _remap_to_numeric = True
226 return np . array ( list ( map ( str , labels ))) 226 return labels . astype ( str )
227 return labels 227 else :
228 228 if isinstance ( labels [ 0 ], bool ) or (
229 def fit ( 229 isinstance ( labels , np . ndarray ) and labels . dtype . name == "bool"
230 self , values : Data , labels : Data 230 ):
231 ) -> RuleClassifier : # pylint: disable=arguments-differ 231 return np . array ( list ( map ( str , labels )))
232 """Train model on given dataset. 232 if isinstance ( labels [ 0 ], Number ):
233 233 self . _remap_to_numeric = True
234 Parameters 234 return np . array ( list ( map ( str , labels )))
235 ---------- 235 return labels
236 values : :class:`rulekit.operator.Data` 236
237 attributes 237 def fit (
238 labels : :class:`rulekit.operator.Data` 238 self , values : Data , labels : Data
239 labels 239 ) -> RuleClassifier : # pylint: disable=arguments-differ
240 Returns 240 """Train model on given dataset.
241 ------- 241
242 self : RuleClassifier 242 Parameters
243 """ 243 ----------
244 self . _get_unique_label_values ( labels ) 244 values : :class:`rulekit.operator.Data`
245 labels = self . _prepare_labels ( labels ) 245 attributes
246 BaseOperator . fit ( self , values , labels ) 246 labels : :class:`rulekit.operator.Data`
247 return self 247 labels
248 248 Returns
249 def predict ( 249 -------
250 self , values : Data , return_metrics : bool = False 250 self : RuleClassifier
251 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]: 251 """
252 """Perform prediction and returns predicted labels. 252 self . _get_unique_label_values ( labels )
253 253 labels = self . _prepare_labels ( labels )
254 Parameters 254 BaseOperator . fit ( self , values , labels )
255 ---------- 255 return self
256 values : :class:`rulekit.operator.Data` 256
257 attributes 257 def predict (
258 258 self , values : Data , return_metrics : bool = False
259 return_metrics: bool = False 259 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]:
260 Optional flag. If set to *True* method will calculate some additional model 260 """Perform prediction and returns predicted labels.
261 metrics. Method will then return tuple instead of just predicted labels. 261
262 262 Parameters
263 Returns 263 ----------
264 ------- 264 values : :class:`rulekit.operator.Data`
265 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\ 265 attributes
266 ClassificationPredictionMetrics`]] 266
267 If *return_metrics* flag wasn't set it will return just prediction, 267 return_metrics: bool = False
268 otherwise a tuple will be returned with first element being prediction and 268 Optional flag. If set to *True* method will calculate some additional model
269 second one being metrics. 269 metrics. Method will then return tuple instead of just predicted labels.
270 """ 270
271 result_example_set = BaseOperator . predict ( self , values ) 271 Returns
272 y_pred = self . _map_result ( result_example_set ) 272 -------
273 if return_metrics : 273 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\
274 metrics_values : dict = BaseClassifier . _calculate_prediction_metrics ( 274 ClassificationPredictionMetrics`]]
275 self , result_example_set 275 If *return_metrics* flag wasn't set it will return just prediction,
276 ) 276 otherwise a tuple will be returned with first element being prediction and
277 return ( y_pred , metrics_values ) 277 second one being metrics.
278 return y_pred 278 """
279 279 result_example_set = BaseOperator . predict ( self , values )
280 def predict_proba ( 280 y_pred = self . _map_result ( result_example_set )
281 self , values : Data , return_metrics : bool = False 281 if return_metrics :
282 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]: 282 metrics_values : dict = BaseClassifier . _calculate_prediction_metrics (
283 """Perform prediction and returns class probabilities for each example. 283 self , result_example_set
284 284 )
285 Parameters 285 return ( y_pred , metrics_values )
286 ---------- 286 return y_pred
287 values : :class:`rulekit.operator.Data` 287
288 attributes 288 def predict_proba (
289 289 self , values : Data , return_metrics : bool = False
290 return_metrics: bool = False 290 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]:
291 Optional flag. If set to *True* method will calculate some additional model 291 """Perform prediction and returns class probabilities for each example.
292 metrics. Method will then return tuple instead of just probabilities. 292
293 293 Parameters
294 Returns 294 ----------
295 ------- 295 values : :class:`rulekit.operator.Data`
296 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\ 296 attributes
297 ClassificationPredictionMetrics`]] 297
298 If *return_metrics* flag wasn't set it will return just probabilities 298 return_metrics: bool = False
299 matrix, otherwise a tuple will be returned with first element being 299 Optional flag. If set to *True* method will calculate some additional model
300 prediction and second one being metrics. 300 metrics. Method will then return tuple instead of just probabilities.
301 """ 301
302 result_example_set = BaseOperator . predict ( self , values ) 302 Returns
303 mapped_result_example_set = self . _map_confidence ( result_example_set ) 303 -------
304 if return_metrics : 304 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\
305 metrics_values : dict = BaseClassifier . _calculate_prediction_metrics ( 305 ClassificationPredictionMetrics`]]
306 self , result_example_set 306 If *return_metrics* flag wasn't set it will return just probabilities
307 ) 307 matrix, otherwise a tuple will be returned with first element being
308 return ( mapped_result_example_set , metrics_values ) 308 prediction and second one being metrics.
309 return mapped_result_example_set 309 """
310 310 result_example_set = BaseOperator . predict ( self , values )
311 def score ( self , values : Data , labels : Data ) -> float : 311 mapped_result_example_set = self . _map_confidence ( result_example_set )
312 """Return the accuracy on the given test data and labels. 312 if return_metrics :
313 313 metrics_values : dict = BaseClassifier . _calculate_prediction_metrics (
314 Parameters 314 self , result_example_set
315 ---------- 315 )
316 values : :class:`rulekit.operator.Data` 316 return ( mapped_result_example_set , metrics_values )
317 attributes 317 return mapped_result_example_set
318 labels : :class:`rulekit.operator.Data` 318
319 true labels 319 def score ( self , values : Data , labels : Data ) -> float :
320 320 """Return the accuracy on the given test data and labels.
321 Returns 321
322 ------- 322 Parameters
323 score : float 323 ----------
324 Accuracy of self.predict(values) wrt. labels. 324 values : :class:`rulekit.operator.Data`
325 """ 325 attributes
326 predicted_labels = self . predict ( values ) 326 labels : :class:`rulekit.operator.Data`
327 return metrics . accuracy_score ( labels , predicted_labels ) 327 true labels
328
329 def __getstate__ ( self ) -> dict : 329 Returns
330 return { 330 -------
331 ** BaseOperator . __getstate__ ( self ), 331 score : float
332 ** { 332 Accuracy of self.predict(values) wrt. labels.
333 "label_unique_values" : self . label_unique_values , 333 """
334 "_remap_to_numeric" : self . _remap_to_numeric , 334 predicted_labels = self . predict ( values )
335 }, 335 return metrics . accuracy_score ( labels , predicted_labels )
336 } 336
337 337 def __getstate__ ( self ) -> dict :
338 def __setstate__ ( self , state : dict ): 338 return {
339 BaseOperator . __setstate__ ( self , state ) 339 ** BaseOperator . __getstate__ ( self ),
340 self . _init_classification_rule_performance_classes () 340 ** {
341 self . label_unique_values = state [ "label_unique_values" ] 341 "label_unique_values" : self . label_unique_values ,
342 self . _remap_to_numeric = state [ "_remap_to_numeric" ] 342 "_remap_to_numeric" : self . _remap_to_numeric ,
343 343 },
344 def _get_problem_type ( self ) -> ProblemType : 344 }
345 return ProblemType . CLASSIFICATION 345
346 346 def __setstate__ ( self , state : dict ):
347 347 BaseOperator . __setstate__ ( self , state )
348 class ExpertRuleClassifier ( ExpertKnowledgeOperator , RuleClassifier ): 348 self . _init_classification_rule_performance_classes ()
349 """Classification model using expert knowledge.""" 349 self . label_unique_values = state [ "label_unique_values" ]
350 350 self . _remap_to_numeric = state [ "_remap_to_numeric" ]
351 __params_class__ = _ClassificationExpertParams 351
352 352 def _get_problem_type ( self ) -> ProblemType :
353 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals 353 return ProblemType . CLASSIFICATION
354 self , 354
355 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 355
356 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 356 class ExpertRuleClassifier ( ExpertKnowledgeOperator , RuleClassifier ):
357 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 357 """Classification model using expert knowledge."""
358 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 358
359 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 359 __params_class__ = _ClassificationExpertParams
360 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 360
361 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 361 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals
362 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 362 self ,
363 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 363 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
364 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 364 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
365 "complementary_conditions" 365 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
366 ], 366 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
367 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [ 367 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
368 "control_apriori_precision" 368 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
369 ], 369 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
370 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 370 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
371 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ], 371 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
372 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ], 372 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
373 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ], 373 "complementary_conditions"
374 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ], 374 ],
375 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ], 375 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [
376 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ], 376 "control_apriori_precision"
377 consider_other_classes : bool = DEFAULT_PARAMS_VALUE [ "consider_other_classes" ], 377 ],
378 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [ 378 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
379 "preferred_conditions_per_rule" 379 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ],
380 ], 380 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ],
381 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [ 381 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ],
382 "preferred_attributes_per_rule" 382 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ],
383 ], 383 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ],
384 ): 384 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ],
385 """ 385 consider_other_classes : bool = DEFAULT_PARAMS_VALUE [ "consider_other_classes" ],
386 Parameters 386 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [
387 ---------- 387 "preferred_conditions_per_rule"
388 minsupp_new : float = 5.0 388 ],
389 a minimum number (or fraction, if value < 1.0) of previously uncovered examples 389 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [
390 to be covered by a new rule (positive examples for classification problems); 390 "preferred_attributes_per_rule"
391 default: 5, 391 ],
392 392 ):
393 induction_measure : :class:`rulekit.params.Measures` = \ 393 """
394 :class:`rulekit.params.Measures.Correlation` 394 Parameters
395 measure used during induction; default measure is correlation 395 ----------
396 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 396 minsupp_new : float = 5.0
397 :class:`rulekit.params.Measures.Correlation` 397 a minimum number (or fraction, if value < 1.0) of previously uncovered examples
398 measure used during pruning. Could be user defined (string), for example 398 to be covered by a new rule (positive examples for classification problems);
399 :code:`2 * p / n`; default measure is correlation 399 default: 5,
400 voting_measure : :class:`rulekit.params.Measures` = \ 400
401 :class:`rulekit.params.Measures.Correlation` 401 induction_measure : :class:`rulekit.params.Measures` = \
402 measure used during voting; default measure is correlation 402 :class:`rulekit.params.Measures.Correlation`
403 max_growing : int = 0.0 403 measure used during induction; default measure is correlation
404 non-negative integer representing maximum number of conditions which can be 404 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
405 added to the rule in the growing phase (use this parameter for large 405 :class:`rulekit.params.Measures.Correlation`
406 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 406 measure used during pruning. Could be user defined (string), for example
407 enable_pruning : bool = True 407 :code:`2 * p / n`; default measure is correlation
408 enable or disable pruning, default is True. 408 voting_measure : :class:`rulekit.params.Measures` = \
409 ignore_missing : bool = False 409 :class:`rulekit.params.Measures.Correlation`
410 boolean telling whether missing values should be ignored (by default, a 410 measure used during voting; default measure is correlation
411 missing value of given attribute is always considered as not fulfilling the 411 max_growing : int = 0.0
412 condition build upon that attribute); default: False. 412 non-negative integer representing maximum number of conditions which can be
413 max_uncovered_fraction : float = 0.0 413 added to the rule in the growing phase (use this parameter for large
414 Floating-point number from [0,1] interval representing maximum fraction of 414 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
415 examples that may remain uncovered by the rule set, default: 0.0. 415 enable_pruning : bool = True
416 select_best_candidate : bool = False 416 enable or disable pruning, default is True.
417 Flag determining if best candidate should be selected from growing phase; 417 ignore_missing : bool = False
418 default: False. 418 boolean telling whether missing values should be ignored (by default, a
419 complementary_conditions : bool = False 419 missing value of given attribute is always considered as not fulfilling the
420 If enabled, complementary conditions in the form a = !{value} for nominal 420 condition build upon that attribute); default: False.
421 attributes 421 max_uncovered_fraction : float = 0.0
422 are supported. 422 Floating-point number from [0,1] interval representing maximum fraction of
423 control_apriori_precision : bool = True 423 examples that may remain uncovered by the rule set, default: 0.0.
424 When inducing classification rules, verify if candidate precision is higher 424 select_best_candidate : bool = False
425 than apriori precision of the investigated class. 425 Flag determining if best candidate should be selected from growing phase;
426 max_rule_count : int = 0 426 default: False.
427 Maximum number of rules to be generated (for classification data sets it 427 complementary_conditions : bool = False
428 applies to a single class); 0 indicates no limit. 428 If enabled, complementary conditions in the form a = !{value} for nominal
429 approximate_induction: bool = False 429 attributes
430 Use an approximate induction heuristic which does not check all possible 430 are supported.
431 splits; note: this is an experimental feature and currently works only for 431 control_apriori_precision : bool = True
432 classification data sets, results may change in future; 432 When inducing classification rules, verify if candidate precision is higher
433 approximate_bins_count: int = 100 433 than apriori precision of the investigated class.
434 maximum number of bins for an attribute evaluated in the approximate 434 max_rule_count : int = 0
435 induction. 435 Maximum number of rules to be generated (for classification data sets it
436 436 applies to a single class); 0 indicates no limit.
437 extend_using_preferred : bool = False 437 approximate_induction: bool = False
438 boolean indicating whether initial rules should be extended with a use of 438 Use an approximate induction heuristic which does not check all possible
439 preferred conditions and attributes; default is False 439 splits; note: this is an experimental feature and currently works only for
440 extend_using_automatic : bool = False 440 classification data sets, results may change in future;
441 boolean indicating whether initial rules should be extended with a use of 441 approximate_bins_count: int = 100
442 automatic conditions and attributes; default is False 442 maximum number of bins for an attribute evaluated in the approximate
443 induce_using_preferred : bool = False 443 induction.
444 boolean indicating whether new rules should be induced with a use of 444
445 preferred conditions and attributes; default is False 445 extend_using_preferred : bool = False
446 induce_using_automatic : bool = False 446 boolean indicating whether initial rules should be extended with a use of
447 boolean indicating whether new rules should be induced with a use of 447 preferred conditions and attributes; default is False
448 automatic conditions and attributes; default is False 448 extend_using_automatic : bool = False
449 consider_other_classes : bool = False 449 boolean indicating whether initial rules should be extended with a use of
450 boolean indicating whether automatic induction should be performed for 450 automatic conditions and attributes; default is False
451 classes for which no user's knowledge has been defined 451 induce_using_preferred : bool = False
452 (classification only); default is False. 452 boolean indicating whether new rules should be induced with a use of
453 preferred_conditions_per_rule : int = None 453 preferred conditions and attributes; default is False
454 maximum number of preferred conditions per rule; default: unlimited, 454 induce_using_automatic : bool = False
455 preferred_attributes_per_rule : int = None 455 boolean indicating whether new rules should be induced with a use of
456 maximum number of preferred attributes per rule; default: unlimited. 456 automatic conditions and attributes; default is False
457 """ 457 consider_other_classes : bool = False
458 self . _remap_to_numeric = False 458 boolean indicating whether automatic induction should be performed for
459 RuleClassifier . __init__ ( 459 classes for which no user's knowledge has been defined
460 self , 460 (classification only); default is False.
461 minsupp_new = minsupp_new , 461 preferred_conditions_per_rule : int = None
462 induction_measure = induction_measure , 462 maximum number of preferred conditions per rule; default: unlimited,
463 pruning_measure = pruning_measure , 463 preferred_attributes_per_rule : int = None
464 voting_measure = voting_measure , 464 maximum number of preferred attributes per rule; default: unlimited.
465 max_growing = max_growing , 465 """
466 enable_pruning = enable_pruning , 466 self . _remap_to_numeric = False
467 ignore_missing = ignore_missing , 467 RuleClassifier . __init__ (
468 max_uncovered_fraction = max_uncovered_fraction , 468 self ,
469 select_best_candidate = select_best_candidate , 469 minsupp_new = minsupp_new ,
470 complementary_conditions = complementary_conditions , 470 induction_measure = induction_measure ,
471 control_apriori_precision = control_apriori_precision , 471 pruning_measure = pruning_measure ,
472 max_rule_count = max_rule_count , 472 voting_measure = voting_measure ,
473 approximate_induction = approximate_induction , 473 max_growing = max_growing ,
474 approximate_bins_count = approximate_bins_count , 474 enable_pruning = enable_pruning ,
475 ) 475 ignore_missing = ignore_missing ,
476 ExpertKnowledgeOperator . __init__ ( 476 max_uncovered_fraction = max_uncovered_fraction ,
477 self , 477 select_best_candidate = select_best_candidate ,
478 minsupp_new = minsupp_new , 478 complementary_conditions = complementary_conditions ,
479 induction_measure = induction_measure , 479 control_apriori_precision = control_apriori_precision ,
480 pruning_measure = pruning_measure , 480 max_rule_count = max_rule_count ,
481 voting_measure = voting_measure , 481 approximate_induction = approximate_induction ,
482 max_growing = max_growing , 482 approximate_bins_count = approximate_bins_count ,
483 enable_pruning = enable_pruning , 483 )
484 ignore_missing = ignore_missing , 484 ExpertKnowledgeOperator . __init__ (
485 max_uncovered_fraction = max_uncovered_fraction , 485 self ,
486 select_best_candidate = select_best_candidate , 486 minsupp_new = minsupp_new ,
487 complementary_conditions = complementary_conditions , 487 induction_measure = induction_measure ,
488 extend_using_preferred = extend_using_preferred , 488 pruning_measure = pruning_measure ,
489 extend_using_automatic = extend_using_automatic , 489 voting_measure = voting_measure ,
490 induce_using_preferred = induce_using_preferred , 490 max_growing = max_growing ,
491 induce_using_automatic = induce_using_automatic , 491 enable_pruning = enable_pruning ,
492 consider_other_classes = consider_other_classes , 492 ignore_missing = ignore_missing ,
493 preferred_conditions_per_rule = preferred_conditions_per_rule , 493 max_uncovered_fraction = max_uncovered_fraction ,
494 preferred_attributes_per_rule = preferred_attributes_per_rule , 494 select_best_candidate = select_best_candidate ,
495 control_apriori_precision = control_apriori_precision , 495 complementary_conditions = complementary_conditions ,
496 max_rule_count = max_rule_count , 496 extend_using_preferred = extend_using_preferred ,
497 approximate_induction = approximate_induction , 497 extend_using_automatic = extend_using_automatic ,
498 approximate_bins_count = approximate_bins_count , 498 induce_using_preferred = induce_using_preferred ,
499 ) 499 induce_using_automatic = induce_using_automatic ,
500 self . model : RuleSet [ ClassificationRule ] = None 500 consider_other_classes = consider_other_classes ,
501 501 preferred_conditions_per_rule = preferred_conditions_per_rule ,
502 def fit ( # pylint: disable=arguments-differ,too-many-arguments 502 preferred_attributes_per_rule = preferred_attributes_per_rule ,
503 self , 503 control_apriori_precision = control_apriori_precision ,
504 values : Data , 504 max_rule_count = max_rule_count ,
505 labels : Data , 505 approximate_induction = approximate_induction ,
506 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None , 506 approximate_bins_count = approximate_bins_count ,
507 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 507 )
508 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 508 self . model : RuleSet [ ClassificationRule ] = None
509 ) -> ExpertRuleClassifier : 509
510 """Train model on given dataset. 510 def fit ( # pylint: disable=arguments-differ,too-many-arguments
511 511 self ,
512 Parameters 512 values : Data ,
513 ---------- 513 labels : Data ,
514 values : :class:`rulekit.operator.Data` 514 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None ,
515 attributes 515 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
516 labels : :class:`rulekit.operator.Data` 516 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
517 labels 517 ) -> ExpertRuleClassifier :
518 518 """Train model on given dataset.
519 expert_rules : List[Union[str, Tuple[str, str]]] 519
520 set of initial rules, either passed as a list of strings representing rules 520 Parameters
521 or as list of tuples where first element is name of the rule and second one 521 ----------
522 is rule string. 522 values : :class:`rulekit.operator.Data`
523 expert_preferred_conditions : List[Union[str, Tuple[str, str]]] 523 attributes
524 multiset of preferred conditions (used also for specifying preferred 524 labels : :class:`rulekit.operator.Data`
525 attributes by using special value Any). Either passed as a list of strings 525 labels
526 representing rules or as list of tuples where first element is name of the 526
527 rule and second one is rule string. 527 expert_rules : List[Union[str, Tuple[str, str]]]
528 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]] 528 set of initial rules, either passed as a list of strings representing rules
529 set of forbidden conditions (used also for specifying forbidden attributes 529 or as list of tuples where first element is name of the rule and second one
530 by using special value Any). Either passed as a list of strings representing 530 is rule string.
531 rules or as list of tuples where first element is name of the rule and 531 expert_preferred_conditions : List[Union[str, Tuple[str, str]]]
532 second one is rule string. 532 multiset of preferred conditions (used also for specifying preferred
533 Returns 533 attributes by using special value Any). Either passed as a list of strings
534 ------- 534 representing rules or as list of tuples where first element is name of the
535 self : ExpertRuleClassifier 535 rule and second one is rule string.
536 """ 536 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]]
537 if isinstance ( labels , ( pd . DataFrame , pd . Series )): 537 set of forbidden conditions (used also for specifying forbidden attributes
538 if isinstance ( labels . iloc [ 0 ], Number ): 538 by using special value Any). Either passed as a list of strings representing
539 self . _remap_to_numeric = True 539 rules or as list of tuples where first element is name of the rule and
540 labels = labels . astype ( str ) 540 second one is rule string.
541 else : 541 Returns
542 if isinstance ( labels [ 0 ], Number ): 542 -------
543 self . _remap_to_numeric = True 543 self : ExpertRuleClassifier
544 labels = list ( map ( str , labels )) 544 """
545 self . _get_unique_label_values ( labels ) 545 if isinstance ( labels , ( pd . DataFrame , pd . Series )):
546 self . _prepare_labels ( labels ) 546 if isinstance ( labels . iloc [ 0 ], Number ):
547 return ExpertKnowledgeOperator . fit ( 547 self . _remap_to_numeric = True
548 self , 548 labels = labels . astype ( str )
549 values , 549 else :
550 labels , 550 if isinstance ( labels [ 0 ], Number ):
551 expert_rules = expert_rules , 551 self . _remap_to_numeric = True
552 expert_preferred_conditions = expert_preferred_conditions , 552 labels = list ( map ( str , labels ))
553 expert_forbidden_conditions = expert_forbidden_conditions , 553 self . _get_unique_label_values ( labels )
554 ) 554 self . _prepare_labels ( labels )
555 555 return ExpertKnowledgeOperator . fit (
556 def predict ( 556 self ,
557 self , values : Data , return_metrics : bool = False 557 values ,
558 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]: 558 labels ,
559 return RuleClassifier . predict ( self , values , return_metrics ) 559 expert_rules = expert_rules ,
560 560 expert_preferred_conditions = expert_preferred_conditions ,
561 def __getstate__ ( self ) -> dict : 561 expert_forbidden_conditions = expert_forbidden_conditions ,
562 return { 562 )
563 ** BaseOperator . __getstate__ ( self ), 563
564 ** { "_remap_to_numeric" : self . _remap_to_numeric }, 564 def predict (
565 } 565 self , values : Data , return_metrics : bool = False
566 566 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]:
567 def __setstate__ ( self , state : dict ): 567 return RuleClassifier . predict ( self , values , return_metrics )
568 BaseOperator . __setstate__ ( self , state ) 568
569 self . _remap_to_numeric = state [ "_remap_to_numeric" ] 569 def __getstate__ ( self ) -> dict :
570 570 return {
571 def _get_problem_type ( self ) -> ProblemType : 571 ** BaseOperator . __getstate__ ( self ),
572 return ProblemType . CLASSIFICATION 572 ** { "_remap_to_numeric" : self . _remap_to_numeric },
573 573 }
574
575 class ContrastSetRuleClassifier ( BaseOperator , BaseClassifier ): 575 def __setstate__ ( self , state : dict ):
576 """Contrast set classification model.""" 576 BaseOperator . __setstate__ ( self , state )
577 577 self . _remap_to_numeric = state [ "_remap_to_numeric" ]
578 __params_class__ = ContrastSetModelParams 578
579 579 def _get_problem_type ( self ) -> ProblemType :
580 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals 580 return ProblemType . CLASSIFICATION
581 self , 581
582 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [ 582
583 "minsupp_all" 583 class ContrastSetRuleClassifier ( BaseOperator , BaseClassifier ):
584 ], 584 """Contrast set classification model."""
585 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ], 585
586 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ], 586 __params_class__ = ContrastSetModelParams
587 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ], 587
588 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ], 588 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals
589 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 589 self ,
590 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 590 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [
591 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 591 "minsupp_all"
592 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 592 ],
593 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 593 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ],
594 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 594 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ],
595 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 595 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ],
596 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 596 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ],
597 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 597 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
598 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 598 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
599 "complementary_conditions" 599 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
600 ], 600 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
601 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [ 601 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
602 "control_apriori_precision" 602 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
603 ], 603 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
604 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 604 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
605 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ], 605 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
606 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ], 606 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
607 ): 607 "complementary_conditions"
608 """ 608 ],
609 Parameters 609 control_apriori_precision : bool = DEFAULT_PARAMS_VALUE [
610 ---------- 610 "control_apriori_precision"
611 minsupp_all: Tuple[float, float, float, float] 611 ],
612 a minimum positive support of a contrast set (p/P). When multiple values 612 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
613 are specified, a metainduction is performed; Default and recommended 613 approximate_induction : bool = DEFAULT_PARAMS_VALUE [ "approximate_induction" ],
614 sequence is: 0.8, 0.5, 0.2, 0.1 614 approximate_bins_count : int = DEFAULT_PARAMS_VALUE [ "approximate_bins_count" ],
615 max_neg2pos: float 615 ):
616 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5 616 """
617 max_passes_count: int 617 Parameters
618 a maximum number of sequential covering passes for a single minsupp-all; 618 ----------
619 Default is 5 619 minsupp_all: Tuple[float, float, float, float]
620 penalty_strength: float 620 a minimum positive support of a contrast set (p/P). When multiple values
621 (s) - penalty strength; Default is 0.5 621 are specified, a metainduction is performed; Default and recommended
622 penalty_saturation: float 622 sequence is: 0.8, 0.5, 0.2, 0.1
623 the value of p_new / P at which penalty reward saturates; Default is 0.2. 623 max_neg2pos: float
624 minsupp_new : float = 5.0 624 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5
625 a minimum number (or fraction, if value < 1.0) of previously uncovered 625 max_passes_count: int
626 examples to be covered by a new rule (positive examples for classification 626 a maximum number of sequential covering passes for a single minsupp-all;
627 problems); default: 5, 627 Default is 5
628 induction_measure : :class:`rulekit.params.Measures` = \ 628 penalty_strength: float
629 :class:`rulekit.params.Measures.Correlation` 629 (s) - penalty strength; Default is 0.5
630 measure used during induction; default measure is correlation 630 penalty_saturation: float
631 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 631 the value of p_new / P at which penalty reward saturates; Default is 0.2.
632 :class:`rulekit.params.Measures.Correlation` 632 minsupp_new : float = 5.0
633 measure used during pruning. Could be user defined (string), for example 633 a minimum number (or fraction, if value < 1.0) of previously uncovered
634 :code:`2 * p / n`; default measure is correlation 634 examples to be covered by a new rule (positive examples for classification
635 voting_measure : :class:`rulekit.params.Measures` = \ 635 problems); default: 5,
636 :class:`rulekit.params.Measures.Correlation` 636 induction_measure : :class:`rulekit.params.Measures` = \
637 measure used during voting; default measure is correlation 637 :class:`rulekit.params.Measures.Correlation`
638 max_growing : int = 0.0 638 measure used during induction; default measure is correlation
639 non-negative integer representing maximum number of conditions which can be 639 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
640 added to the rule in the growing phase (use this parameter for large 640 :class:`rulekit.params.Measures.Correlation`
641 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 641 measure used during pruning. Could be user defined (string), for example
642 enable_pruning : bool = True 642 :code:`2 * p / n`; default measure is correlation
643 enable or disable pruning, default is True. 643 voting_measure : :class:`rulekit.params.Measures` = \
644 ignore_missing : bool = False 644 :class:`rulekit.params.Measures.Correlation`
645 boolean telling whether missing values should be ignored (by default, a 645 measure used during voting; default measure is correlation
646 missing value of given attribute is always considered as not fulfilling the 646 max_growing : int = 0.0
647 condition build upon that attribute); default: False. 647 non-negative integer representing maximum number of conditions which can be
648 max_uncovered_fraction : float = 0.0 648 added to the rule in the growing phase (use this parameter for large
649 Floating-point number from [0,1] interval representing maximum fraction of 649 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
650 examples that may remain uncovered by the rule set, default: 0.0. 650 enable_pruning : bool = True
651 select_best_candidate : bool = False 651 enable or disable pruning, default is True.
652 Flag determining if best candidate should be selected from growing phase; 652 ignore_missing : bool = False
653 default: False. 653 boolean telling whether missing values should be ignored (by default, a
654 complementary_conditions : bool = False 654 missing value of given attribute is always considered as not fulfilling the
655 If enabled, complementary conditions in the form a = !{value} for nominal 655 condition build upon that attribute); default: False.
656 attributes are supported. 656 max_uncovered_fraction : float = 0.0
657 control_apriori_precision : bool = True 657 Floating-point number from [0,1] interval representing maximum fraction of
658 When inducing classification rules, verify if candidate precision is higher 658 examples that may remain uncovered by the rule set, default: 0.0.
659 than apriori precision of the investigated class. 659 select_best_candidate : bool = False
660 max_rule_count : int = 0 660 Flag determining if best candidate should be selected from growing phase;
661 Maximum number of rules to be generated (for classification data sets it 661 default: False.
662 applies to a single class); 0 indicates no limit. 662 complementary_conditions : bool = False
663 approximate_induction: bool = False 663 If enabled, complementary conditions in the form a = !{value} for nominal
664 Use an approximate induction heuristic which does not check all possible 664 attributes are supported.
665 splits; note: this is an experimental feature and currently works only for 665 control_apriori_precision : bool = True
666 classification data sets, results may change in future; 666 When inducing classification rules, verify if candidate precision is higher
667 approximate_bins_count: int = 100 667 than apriori precision of the investigated class.
668 maximum number of bins for an attribute evaluated in the approximate 668 max_rule_count : int = 0
669 induction. 669 Maximum number of rules to be generated (for classification data sets it
670 """ 670 applies to a single class); 0 indicates no limit.
671 BaseOperator . __init__ ( 671 approximate_induction: bool = False
672 self , 672 Use an approximate induction heuristic which does not check all possible
673 minsupp_all = minsupp_all , 673 splits; note: this is an experimental feature and currently works only for
674 max_neg2pos = max_neg2pos , 674 classification data sets, results may change in future;
675 max_passes_count = max_passes_count , 675 approximate_bins_count: int = 100
676 penalty_strength = penalty_strength , 676 maximum number of bins for an attribute evaluated in the approximate
677 penalty_saturation = penalty_saturation , 677 induction.
678 minsupp_new = minsupp_new , 678 """
679 induction_measure = induction_measure , 679 BaseOperator . __init__ (
680 pruning_measure = pruning_measure , 680 self ,
681 voting_measure = voting_measure , 681 minsupp_all = minsupp_all ,
682 max_growing = max_growing , 682 max_neg2pos = max_neg2pos ,
683 enable_pruning = enable_pruning , 683 max_passes_count = max_passes_count ,
684 ignore_missing = ignore_missing , 684 penalty_strength = penalty_strength ,
685 max_uncovered_fraction = max_uncovered_fraction , 685 penalty_saturation = penalty_saturation ,
686 select_best_candidate = select_best_candidate , 686 minsupp_new = minsupp_new ,
687 complementary_conditions = complementary_conditions , 687 induction_measure = induction_measure ,
688 control_apriori_precision = control_apriori_precision , 688 pruning_measure = pruning_measure ,
689 max_rule_count = max_rule_count , 689 voting_measure = voting_measure ,
690 approximate_induction = approximate_induction , 690 max_growing = max_growing ,
691 approximate_bins_count = approximate_bins_count , 691 enable_pruning = enable_pruning ,
692 ) 692 ignore_missing = ignore_missing ,
693 BaseClassifier . __init__ ( self ) 693 max_uncovered_fraction = max_uncovered_fraction ,
694 self . contrast_attribute : str = None 694 select_best_candidate = select_best_candidate ,
695 self . _remap_to_numeric = False 695 complementary_conditions = complementary_conditions ,
696 self . label_unique_values = [] 696 control_apriori_precision = control_apriori_precision ,
697 self . model : RuleSet [ ClassificationRule ] = None 697 max_rule_count = max_rule_count ,
698 698 approximate_induction = approximate_induction ,
699 def _map_result ( self , predicted_example_set ) -> np . ndarray : 699 approximate_bins_count = approximate_bins_count ,
700 prediction : np . ndarray 700 )
701 if self . _remap_to_numeric : 701 BaseClassifier . __init__ ( self )
702 prediction = PredictionResultMapper . map_to_numerical ( predicted_example_set ) 702 self . contrast_attribute : str = None
703 else : 703 self . _remap_to_numeric = False
704 prediction = PredictionResultMapper . map_to_nominal ( predicted_example_set ) 704 self . label_unique_values = []
705 return prediction 705 self . model : RuleSet [ ClassificationRule ] = None
706
707 def _get_unique_label_values ( self , labels : Data ): 707 def _map_result ( self , predicted_example_set ) -> np . ndarray :
708 tmp = {} 708 prediction : np . ndarray
709 for label_value in labels : 709 if self . _remap_to_numeric :
710 tmp [ label_value ] = None 710 prediction = PredictionResultMapper . map_to_numerical ( predicted_example_set )
711 self . label_unique_values = list ( tmp . keys ()) 711 else :
712 if len ( self . label_unique_values ) > 0 and isinstance ( 712 prediction = PredictionResultMapper . map_to_nominal ( predicted_example_set )
713 self . label_unique_values [ 0 ], bytes 713 return prediction
714 ): 714
715 self . label_unique_values = [ 715 def _get_unique_label_values ( self , labels : Data ):
716 item . decode ( "utf-8" ) for item in self . label_unique_values 716 tmp = {}
717 ] 717 for label_value in labels :
718 718 tmp [ label_value ] = None
719 def fit ( 719 self . label_unique_values = list ( tmp . keys ())
720 self , values : Data , labels : Data , contrast_attribute : str 720 if len ( self . label_unique_values ) > 0 and isinstance (
721 ) -> ContrastSetRuleClassifier : # pylint: disable=arguments-differ 721 self . label_unique_values [ 0 ], bytes
722 """Train model on given dataset. 722 ):
723 723 self . label_unique_values = [
724 Parameters 724 item . decode ( "utf-8" ) for item in self . label_unique_values
725 ---------- 725 ]
726 values : :class:`rulekit.operator.Data` 726
727 attributes 727 def fit (
728 labels : :class:`rulekit.operator.Data` 728 self , values : Data , labels : Data , contrast_attribute : str
729 labels 729 ) -> ContrastSetRuleClassifier : # pylint: disable=arguments-differ
730 contrast_attribute: str 730 """Train model on given dataset.
731 group attribute 731
732 Returns 732 Parameters
733 ------- 733 ----------
734 self : ContrastSetRuleClassifier 734 values : :class:`rulekit.operator.Data`
735 """ 735 attributes
736 RuleClassifier . _get_unique_label_values ( # pylint: disable=protected-access 736 labels : :class:`rulekit.operator.Data`
737 self , labels 737 labels
738 ) 738 contrast_attribute: str
-
-
-
-
- E501
-
- Line too long (92 > 88 characters)
-
- 739 RuleClassifier . _prepare_labels ( # pylint: disable=protected-access,protected-access
+ class="code sev- ">
739 group attribute 740 self , labels 740 Returns
741 ) 741 -------
742 BaseOperator . fit ( self , values , labels , contrast_attribute = contrast_attribute ) 742 self : ContrastSetRuleClassifier
743 self . contrast_attribute = contrast_attribute 743 """
744 return self 744 RuleClassifier . _get_unique_label_values ( # pylint: disable=protected-access
745 745 self , labels
746 def predict ( 746 )
747 self , values : Data , return_metrics : bool = False
+ class="code sev-2 le">
+
+
+
+
+ E501
+
+ Line too long (92 > 88 characters)
+
+ 747 RuleClassifier . _prepare_labels ( # pylint: disable=protected-access,protected-access 748 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]: 748 self , labels
749 """Perform prediction and returns predicted labels. 749 )
750 750 BaseOperator . fit ( self , values , labels , contrast_attribute = contrast_attribute )
751 Parameters 751 self . contrast_attribute = contrast_attribute
752 ---------- 752 return self
753 values : :class:`rulekit.operator.Data` 753
754 attributes 754 def predict (
755 755 self , values : Data , return_metrics : bool = False
756 return_metrics: bool = False 756 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]:
757 Optional flag. If set to *True* method will calculate some additional model 757 """Perform prediction and returns predicted labels.
758 metrics. Method will then return tuple instead of just predicted labels. 758
759 759 Parameters
760 Returns 760 ----------
761 ------- 761 values : :class:`rulekit.operator.Data`
762 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\ 762 attributes
763 ClassificationPredictionMetrics`]] 763
764 If *return_metrics* flag wasn't set it will return just prediction, 764 return_metrics: bool = False
765 otherwise a tuple will be returned with first element being prediction and 765 Optional flag. If set to *True* method will calculate some additional model
766 second one being metrics. 766 metrics. Method will then return tuple instead of just predicted labels.
767 """ 767
768 return RuleClassifier . predict ( self , values , return_metrics ) 768 Returns
769 769 -------
770 def predict_proba ( 770 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\
771 self , values : Data , return_metrics : bool = False 771 ClassificationPredictionMetrics`]]
772 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]: 772 If *return_metrics* flag wasn't set it will return just prediction,
773 """Perform prediction and returns class probabilities for each example. 773 otherwise a tuple will be returned with first element being prediction and
774 774 second one being metrics.
775 Parameters 775 """
776 ---------- 776 return RuleClassifier . predict ( self , values , return_metrics )
777 values : :class:`rulekit.operator.Data` 777
778 attributes 778 def predict_proba (
779 779 self , values : Data , return_metrics : bool = False
780 return_metrics: bool = False 780 ) -> Union [ np . ndarray , tuple [ np . ndarray , ClassificationPredictionMetrics ]]:
781 Optional flag. If set to *True* method will calculate some additional model 781 """Perform prediction and returns class probabilities for each example.
782 metrics. Method will then return tuple instead of just probabilities. 782
783 783 Parameters
784 Returns 784 ----------
785 ------- 785 values : :class:`rulekit.operator.Data`
786 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\ 786 attributes
787 ClassificationPredictionMetrics`]] 787
788 If *return_metrics* flag wasn't set it will return just probabilities 788 return_metrics: bool = False
789 matrix, otherwise a tuple will be returned with first element being 789 Optional flag. If set to *True* method will calculate some additional model
790 prediction and second one being metrics. 790 metrics. Method will then return tuple instead of just probabilities.
791 """ 791
792 return RuleClassifier . predict_proba ( self , values , return_metrics ) 792 Returns
793 793 -------
794 def score ( self , values : Data , labels : Data ) -> float : 794 result : Union[np.ndarray, tuple[np.ndarray, :class:`rulekit.classification.\
795 """Return the accuracy on the given test data and labels. 795 ClassificationPredictionMetrics`]]
796 796 If *return_metrics* flag wasn't set it will return just probabilities
797 Parameters 797 matrix, otherwise a tuple will be returned with first element being
798 ---------- 798 prediction and second one being metrics.
799 values : :class:`rulekit.operator.Data` 799 """
800 attributes 800 return RuleClassifier . predict_proba ( self , values , return_metrics )
801 labels : :class:`rulekit.operator.Data` 801
802 true labels 802 def score ( self , values : Data , labels : Data ) -> float :
803 803 """Return the accuracy on the given test data and labels.
804 Returns 804
805 ------- 805 Parameters
806 score : float 806 ----------
807 Accuracy of self.predict(values) wrt. labels. 807 values : :class:`rulekit.operator.Data`
808 """ 808 attributes
809 return RuleClassifier . score ( self , values , labels ) 809 labels : :class:`rulekit.operator.Data`
810 810 true labels
811 def __getstate__ ( self ) -> dict : 811
812 return { 812 Returns
813 ** BaseOperator . __getstate__ ( self ), 813 -------
814 ** { 814 score : float
815 "label_unique_values" : self . label_unique_values , 815 Accuracy of self.predict(values) wrt. labels.
816 "_remap_to_numeric" : self . _remap_to_numeric , 816 """
817 "contrast_attribute" : self . contrast_attribute , 817 return RuleClassifier . score ( self , values , labels )
818 }, 818
819 } 819 def __getstate__ ( self ) -> dict :
820 820 return {
821 def __setstate__ ( self , state : dict ): 821 ** BaseOperator . __getstate__ ( self ),
822 BaseOperator . __setstate__ ( self , state ) 822 ** {
823 self . _init_classification_rule_performance_classes () 823 "label_unique_values" : self . label_unique_values ,
824 self . label_unique_values = state [ "label_unique_values" ] 824 "_remap_to_numeric" : self . _remap_to_numeric ,
825 self . _remap_to_numeric = state [ "_remap_to_numeric" ] 825 "contrast_attribute" : self . contrast_attribute ,
826 self . contrast_attribute = state [ "contrast_attribute" ] 826 },
827 827 }
828 def _get_problem_type ( self ) -> ProblemType : 828
829 return ProblemType . CONTRAST_CLASSIFICATION 829 def __setstate__ ( self , state : dict ):
+ 830 BaseOperator . __setstate__ ( self , state )
+ 831 self . _init_classification_rule_performance_classes ()
+ 832 self . label_unique_values = state [ "label_unique_values" ]
+ 833 self . _remap_to_numeric = state [ "_remap_to_numeric" ]
+ 834 self . contrast_attribute = state [ "contrast_attribute" ]
+ 835
+ 836 def _get_problem_type ( self ) -> ProblemType :
+ 837 return ProblemType . CONTRAST_CLASSIFICATION
diff --git a/docs/reports/flake8/rulekit.regression.report.html b/docs/reports/flake8/rulekit.regression.report.html
index 8025a76..4856698 100644
--- a/docs/reports/flake8/rulekit.regression.report.html
+++ b/docs/reports/flake8/rulekit.regression.report.html
@@ -60,8 +60,8 @@
diff --git a/docs/reports/flake8/rulekit.regression.source.html b/docs/reports/flake8/rulekit.regression.source.html
index daab125..ce23a47 100644
--- a/docs/reports/flake8/rulekit.regression.source.html
+++ b/docs/reports/flake8/rulekit.regression.source.html
@@ -23,19 +23,19 @@
class="code sev- ">2 """ 3 3 from __future__ import annotations
4 from __future__ import annotations 4
5 5 from numbers import Number
6 from numbers import Number 6 from typing import Tuple
7 from typing import Tuple , Union 7 from typing import Union
8
class="code sev- ">13 from rulekit._helpers import PredictionResultMapper
14 from rulekit._operator import BaseOperator , Data , ExpertKnowledgeOperator 14 from rulekit._operator import BaseOperator
15 from rulekit._problem_types import ProblemType 15 from rulekit._operator import Data
16 from rulekit.params import ( DEFAULT_PARAMS_VALUE , ContrastSetModelParams , 16 from rulekit._operator import ExpertKnowledgeOperator
17 ExpertModelParams , Measures , ModelsParams ) 17 from rulekit._problem_types import ProblemType
18 from rulekit.rules import RegressionRule , RuleSet 18 from rulekit.params import ContrastSetModelParams
19 19 from rulekit.params import DEFAULT_PARAMS_VALUE
20 20 from rulekit.params import ExpertModelParams
21 class _RegressionModelParams ( ModelsParams ): 21 from rulekit.params import Measures
22 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ] 22 from rulekit.params import ModelsParams
23 23 from rulekit.rules import RegressionRule
24 24 from rulekit.rules import RuleSet
25 class _RegressionExpertModelParams ( _RegressionModelParams , ExpertModelParams ): 25
26 pass 26
27 27 class _RegressionModelParams ( ModelsParams ):
28 28 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ]
29 class RuleRegressor ( BaseOperator ): 29
30 """Regression model.""" 30
31 31 class _RegressionExpertModelParams ( _RegressionModelParams , ExpertModelParams ):
32 __params_class__ = _RegressionModelParams 32 pass
33
34 def __init__ ( # pylint: disable=too-many-arguments 34
35 self , 35 class RuleRegressor ( BaseOperator ):
36 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 36 """Regression model."""
37 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 37
38 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 38 __params_class__ = _RegressionModelParams
39 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 39
40 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 40 def __init__ ( # pylint: disable=too-many-arguments
41 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 41 self ,
42 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 42 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
43 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 43 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
44 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 44 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
45 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 45 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
46 "complementary_conditions" 46 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
47 ], 47 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
48 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ], 48 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
49 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 49 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
50 ): 50 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
51 """ 51 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
52 Parameters 52 "complementary_conditions"
53 ---------- 53 ],
54 minsupp_new : float = 5.0 54 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ],
55 a minimum number (or fraction, if value < 1.0) of previously uncovered 55 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
56 examples to be covered by a new rule (positive examples for classification 56 ):
57 problems); default: 5, 57 """
58 induction_measure : :class:`rulekit.params.Measures` = \ 58 Parameters
59 :class:`rulekit.params.Measures.Correlation` 59 ----------
60 measure used during induction; default measure is correlation 60 minsupp_new : float = 5.0
61 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 61 a minimum number (or fraction, if value < 1.0) of previously uncovered
62 :class:`rulekit.params.Measures.Correlation` 62 examples to be covered by a new rule (positive examples for classification
63 measure used during pruning. Could be user defined (string), for example 63 problems); default: 5,
64 :code:`2 * p / n`; default measure is correlation 64 induction_measure : :class:`rulekit.params.Measures` = \
65 voting_measure : :class:`rulekit.params.Measures` = \ 65 :class:`rulekit.params.Measures.Correlation`
66 :class:`rulekit.params.Measures.Correlation` 66 measure used during induction; default measure is correlation
67 measure used during voting; default measure is correlation 67 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
68 max_growing : int = 0.0 68 :class:`rulekit.params.Measures.Correlation`
69 non-negative integer representing maximum number of conditions which can be 69 measure used during pruning. Could be user defined (string), for example
70 added to the rule in the growing phase (use this parameter for large 70 :code:`2 * p / n`; default measure is correlation
71 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 71 voting_measure : :class:`rulekit.params.Measures` = \
72 enable_pruning : bool = True 72 :class:`rulekit.params.Measures.Correlation`
73 enable or disable pruning, default is True. 73 measure used during voting; default measure is correlation
74 ignore_missing : bool = False 74 max_growing : int = 0.0
75 boolean telling whether missing values should be ignored (by default, a 75 non-negative integer representing maximum number of conditions which can be
76 missing value of given attribute is always considered as not fulfilling the 76 added to the rule in the growing phase (use this parameter for large
77 condition build upon that attribute); default: False. 77 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
78 max_uncovered_fraction : float = 0.0 78 enable_pruning : bool = True
79 Floating-point number from [0,1] interval representing maximum fraction of 79 enable or disable pruning, default is True.
80 examples that may remain uncovered by the rule set, default: 0.0. 80 ignore_missing : bool = False
81 select_best_candidate : bool = False 81 boolean telling whether missing values should be ignored (by default, a
82 Flag determining if best candidate should be selected from growing phase; 82 missing value of given attribute is always considered as not fulfilling the
83 default: False. 83 condition build upon that attribute); default: False.
84 complementary_conditions : bool = False 84 max_uncovered_fraction : float = 0.0
85 If enabled, complementary conditions in the form a = !{value} for nominal 85 Floating-point number from [0,1] interval representing maximum fraction of
86 attributes are supported. 86 examples that may remain uncovered by the rule set, default: 0.0.
87 mean_based_regression : bool = True 87 select_best_candidate : bool = False
88 Enable fast induction of mean-based regression rules instead of default 88 Flag determining if best candidate should be selected from growing phase;
89 median-based. 89 default: False.
90 max_rule_count : int = 0 90 complementary_conditions : bool = False
91 Maximum number of rules to be generated; 0 indicates no limit. Default: 0 91 If enabled, complementary conditions in the form a = !{value} for nominal
92 """ 92 attributes are supported.
93 super () . __init__ ( 93 mean_based_regression : bool = True
94 minsupp_new = minsupp_new , 94 Enable fast induction of mean-based regression rules instead of default
95 induction_measure = induction_measure , 95 median-based.
96 pruning_measure = pruning_measure , 96 max_rule_count : int = 0
97 voting_measure = voting_measure , 97 Maximum number of rules to be generated; 0 indicates no limit. Default: 0
98 max_growing = max_growing , 98 """
99 enable_pruning = enable_pruning , 99 super () . __init__ (
100 ignore_missing = ignore_missing , 100 minsupp_new = minsupp_new ,
101 max_uncovered_fraction = max_uncovered_fraction , 101 induction_measure = induction_measure ,
102 select_best_candidate = select_best_candidate , 102 pruning_measure = pruning_measure ,
103 complementary_conditions = complementary_conditions , 103 voting_measure = voting_measure ,
104 mean_based_regression = mean_based_regression , 104 max_growing = max_growing ,
105 max_rule_count = max_rule_count , 105 enable_pruning = enable_pruning ,
106 ) 106 ignore_missing = ignore_missing ,
107 self . model : RuleSet [ RegressionRule ] = None 107 max_uncovered_fraction = max_uncovered_fraction ,
108 108 select_best_candidate = select_best_candidate ,
109 def _validate_labels ( self , labels : Data ): 109 complementary_conditions = complementary_conditions ,
110 if isinstance ( labels , ( pd . DataFrame , pd . Series )): 110 mean_based_regression = mean_based_regression ,
111 first_label = labels . iloc [ 0 ] 111 max_rule_count = max_rule_count ,
112 else : 112 )
113 first_label = labels [ 0 ] 113 self . model : RuleSet [ RegressionRule ] = None
114 if not isinstance ( first_label , Number ): 114
115 raise ValueError ( 115 def _validate_labels ( self , labels : Data ):
116 f " { self . __class__ . __name__ } requires lables values to be numeric" 116 if isinstance ( labels , ( pd . DataFrame , pd . Series )):
117 ) 117 first_label = labels . iloc [ 0 ]
118 118 else :
119 def fit ( # pylint: disable=arguments-differ 119 first_label = labels [ 0 ]
120 self , values : Data , labels : Data 120 if not isinstance ( first_label , Number ):
121 ) -> RuleRegressor : 121 raise ValueError (
122 """Train model on given dataset. 122 f " { self . __class__ . __name__ } requires lables values to be numeric"
123 123 )
124 Parameters 124
125 ---------- 125 def fit ( # pylint: disable=arguments-differ
126 values : :class:`rulekit.operator.Data` 126 self , values : Data , labels : Data
127 attributes 127 ) -> RuleRegressor :
128 labels : :class:`rulekit.operator.Data` 128 """Train model on given dataset.
129 target values 129
130 Returns 130 Parameters
131 ------- 131 ----------
132 self : RuleRegressor 132 values : :class:`rulekit.operator.Data`
133 """ 133 attributes
134 self . _validate_labels ( labels ) 134 labels : :class:`rulekit.operator.Data`
135 super () . fit ( values , labels ) 135 target values
136 return self 136 Returns
137 137 -------
138 def predict ( self , values : Data ) -> np . ndarray : 138 self : RuleRegressor
139 """Perform prediction and returns predicted values. 139 """
140 140 self . _validate_labels ( labels )
141 Parameters 141 super () . fit ( values , labels )
142 ---------- 142 return self
143 values : :class:`rulekit.operator.Data` 143
144 attributes 144 def predict ( self , values : Data ) -> np . ndarray :
145 145 """Perform prediction and returns predicted values.
146 Returns 146
147 ------- 147 Parameters
148 result : np.ndarray 148 ----------
149 predicted values 149 values : :class:`rulekit.operator.Data`
150 """ 150 attributes
151 return self . _map_result ( super () . predict ( values )) 151
152 152 Returns
153 def score ( self , values : Data , labels : Data ) -> float : 153 -------
154 """Return the coefficient of determination R2 of the prediction 154 result : np.ndarray
155 155 predicted values
156 Parameters 156 """
157 ---------- 157 return self . _map_result ( super () . predict ( values ))
158 values : :class:`rulekit.operator.Data` 158
159 attributes 159 def score ( self , values : Data , labels : Data ) -> float :
160 labels : :class:`rulekit.operator.Data` 160 """Return the coefficient of determination R2 of the prediction
161 true target values 161
162 162 Parameters
163 Returns 163 ----------
164 ------- 164 values : :class:`rulekit.operator.Data`
165 score : float 165 attributes
166 R2 of self.predict(values) wrt. labels. 166 labels : :class:`rulekit.operator.Data`
167 """ 167 true target values
168 predicted_labels = self . predict ( values ) 168
169 return metrics . r2_score ( labels , predicted_labels ) 169 Returns
170 170 -------
171 def _map_result ( self , predicted_example_set ) -> np . ndarray : 171 score : float
172 return PredictionResultMapper . map_to_numerical ( 172 R2 of self.predict(values) wrt. labels.
173 predicted_example_set , remap = False 173 """
174 ) 174 predicted_labels = self . predict ( values )
175 175 return metrics . r2_score ( labels , predicted_labels )
176 def _get_problem_type ( self ) -> ProblemType : 176
177 return ProblemType . REGRESSION 177 def _map_result ( self , predicted_example_set ) -> np . ndarray :
178 178 return PredictionResultMapper . map_to_numerical (
179 179 predicted_example_set , remap = False
180 class ExpertRuleRegressor ( ExpertKnowledgeOperator , RuleRegressor ): 180 )
181 """Expert Regression model.""" 181
182 182 def _get_problem_type ( self ) -> ProblemType :
183 __params_class__ = _RegressionExpertModelParams 183 return ProblemType . REGRESSION
184
185 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals 185
186 self , 186 class ExpertRuleRegressor ( ExpertKnowledgeOperator , RuleRegressor ):
187 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 187 """Expert Regression model."""
188 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 188
189 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 189 __params_class__ = _RegressionExpertModelParams
190 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 190
191 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 191 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals
192 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 192 self ,
193 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 193 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
194 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 194 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
195 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 195 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
196 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 196 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
197 "complementary_conditions" 197 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
198 ], 198 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
199 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ], 199 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
200 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 200 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
201 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ], 201 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
202 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ], 202 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
203 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ], 203 "complementary_conditions"
204 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ], 204 ],
205 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [ 205 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ],
206 "preferred_conditions_per_rule" 206 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
207 ], 207 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ],
208 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [ 208 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ],
209 "preferred_attributes_per_rule" 209 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ],
210 ], 210 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ],
211 ): 211 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [
212 """ 212 "preferred_conditions_per_rule"
213 Parameters 213 ],
214 ---------- 214 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [
215 minsupp_new : float = 5.0 215 "preferred_attributes_per_rule"
216 a minimum number (or fraction, if value < 1.0) of previously uncovered 216 ],
217 examples to be covered by a new rule (positive examples for classification 217 ):
218 problems); default: 5, 218 """
219 induction_measure : :class:`rulekit.params.Measures` = \ 219 Parameters
220 :class:`rulekit.params.Measures.Correlation` 220 ----------
221 measure used during induction; default measure is correlation 221 minsupp_new : float = 5.0
222 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 222 a minimum number (or fraction, if value < 1.0) of previously uncovered
223 :class:`rulekit.params.Measures.Correlation` 223 examples to be covered by a new rule (positive examples for classification
224 measure used during pruning. Could be user defined (string), for example 224 problems); default: 5,
225 :code:`2 * p / n`; default measure is correlation 225 induction_measure : :class:`rulekit.params.Measures` = \
226 voting_measure : :class:`rulekit.params.Measures` = \ 226 :class:`rulekit.params.Measures.Correlation`
227 :class:`rulekit.params.Measures.Correlation` 227 measure used during induction; default measure is correlation
228 measure used during voting; default measure is correlation 228 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
229 max_growing : int = 0.0 229 :class:`rulekit.params.Measures.Correlation`
230 non-negative integer representing maximum number of conditions which can be 230 measure used during pruning. Could be user defined (string), for example
231 added to the rule in the growing phase (use this parameter for large 231 :code:`2 * p / n`; default measure is correlation
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232 voting_measure : :class:`rulekit.params.Measures` = \ 233 enable_pruning : bool = True 233 :class:`rulekit.params.Measures.Correlation`
234 enable or disable pruning, default is True. 234 measure used during voting; default measure is correlation
235 ignore_missing : bool = False 235 max_growing : int = 0.0
236 boolean telling whether missing values should be ignored (by default, a 236 non-negative integer representing maximum number of conditions which can be
237 missing value of given attribute is always considered as not fulfilling the 237 added to the rule in the growing phase (use this parameter for large
238 condition build upon that attribute); default: False.
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+ 238 datasets if execution time is prohibitive); 0 indicates no limit; default: 0, 239 max_uncovered_fraction : float = 0.0 239 enable_pruning : bool = True
240 Floating-point number from [0,1] interval representing maximum fraction of 240 enable or disable pruning, default is True.
241 examples that may remain uncovered by the rule set, default: 0.0. 241 ignore_missing : bool = False
242 select_best_candidate : bool = False 242 boolean telling whether missing values should be ignored (by default, a
243 Flag determining if best candidate should be selected from growing phase; 243 missing value of given attribute is always considered as not fulfilling the
244 default: False. 244 condition build upon that attribute); default: False.
245 complementary_conditions : bool = False 245 max_uncovered_fraction : float = 0.0
246 If enabled, complementary conditions in the form a = !{value} for nominal 246 Floating-point number from [0,1] interval representing maximum fraction of
247 attributes are supported. 247 examples that may remain uncovered by the rule set, default: 0.0.
248 mean_based_regression : bool = True 248 select_best_candidate : bool = False
249 Enable fast induction of mean-based regression rules instead of default 249 Flag determining if best candidate should be selected from growing phase;
250 median-based. 250 default: False.
251 max_rule_count : int = 0 251 complementary_conditions : bool = False
252 Maximum number of rules to be generated (for classification data sets it 252 If enabled, complementary conditions in the form a = !{value} for nominal
253 applies to a single class); 0 indicates no limit. 253 attributes are supported.
254 254 mean_based_regression : bool = True
255 extend_using_preferred : bool = False 255 Enable fast induction of mean-based regression rules instead of default
256 boolean indicating whether initial rules should be extended with a use of 256 median-based.
257 preferred conditions and attributes; default is False 257 max_rule_count : int = 0
258 extend_using_automatic : bool = False 258 Maximum number of rules to be generated (for classification data sets it
259 boolean indicating whether initial rules should be extended with a use of 259 applies to a single class); 0 indicates no limit.
260 automatic conditions and attributes; default is False 260
261 induce_using_preferred : bool = False 261 extend_using_preferred : bool = False
262 boolean indicating whether new rules should be induced with a use of 262 boolean indicating whether initial rules should be extended with a use of
263 preferred conditions and attributes; default is False
264 induce_using_automatic : bool = False 264 extend_using_automatic : bool = False
265 boolean indicating whether new rules should be induced with a use of 265 boolean indicating whether initial rules should be extended with a use of
266 automatic conditions and attributes; default is False
267 preferred_conditions_per_rule : int = None 267 induce_using_preferred : bool = False
268 maximum number of preferred conditions per rule; default: unlimited, 268 boolean indicating whether new rules should be induced with a use of
269 preferred_attributes_per_rule : int = None 269 preferred conditions and attributes; default is False
270 maximum number of preferred attributes per rule; default: unlimited. 270 induce_using_automatic : bool = False
271 """ 271 boolean indicating whether new rules should be induced with a use of
272 RuleRegressor . __init__ ( 272 automatic conditions and attributes; default is False
273 self , 273 preferred_conditions_per_rule : int = None
274 minsupp_new = minsupp_new , 274 maximum number of preferred conditions per rule; default: unlimited,
275 induction_measure = induction_measure , 275 preferred_attributes_per_rule : int = None
276 pruning_measure = pruning_measure , 276 maximum number of preferred attributes per rule; default: unlimited.
277 voting_measure = voting_measure , 277 """
278 max_growing = max_growing , 278 RuleRegressor . __init__ (
279 enable_pruning = enable_pruning , 279 self ,
280 ignore_missing = ignore_missing , 280 minsupp_new = minsupp_new ,
281 max_uncovered_fraction = max_uncovered_fraction , 281 induction_measure = induction_measure ,
282 select_best_candidate = select_best_candidate , 282 pruning_measure = pruning_measure ,
283 complementary_conditions = complementary_conditions , 283 voting_measure = voting_measure ,
284 mean_based_regression = mean_based_regression , 284 max_growing = max_growing ,
285 max_rule_count = max_rule_count , 285 enable_pruning = enable_pruning ,
286 ) 286 ignore_missing = ignore_missing ,
287 ExpertKnowledgeOperator . __init__ ( 287 max_uncovered_fraction = max_uncovered_fraction ,
288 self , 288 select_best_candidate = select_best_candidate ,
289 minsupp_new = minsupp_new , 289 complementary_conditions = complementary_conditions ,
290 induction_measure = induction_measure , 290 mean_based_regression = mean_based_regression ,
291 pruning_measure = pruning_measure , 291 max_rule_count = max_rule_count ,
292 voting_measure = voting_measure , 292 )
293 max_growing = max_growing , 293 ExpertKnowledgeOperator . __init__ (
294 enable_pruning = enable_pruning , 294 self ,
295 ignore_missing = ignore_missing , 295 minsupp_new = minsupp_new ,
296 max_uncovered_fraction = max_uncovered_fraction , 296 induction_measure = induction_measure ,
297 select_best_candidate = select_best_candidate , 297 pruning_measure = pruning_measure ,
298 extend_using_preferred = extend_using_preferred , 298 voting_measure = voting_measure ,
299 extend_using_automatic = extend_using_automatic , 299 max_growing = max_growing ,
300 induce_using_preferred = induce_using_preferred , 300 enable_pruning = enable_pruning ,
301 induce_using_automatic = induce_using_automatic , 301 ignore_missing = ignore_missing ,
302 preferred_conditions_per_rule = preferred_conditions_per_rule , 302 max_uncovered_fraction = max_uncovered_fraction ,
303 preferred_attributes_per_rule = preferred_attributes_per_rule , 303 select_best_candidate = select_best_candidate ,
304 complementary_conditions = complementary_conditions , 304 extend_using_preferred = extend_using_preferred ,
305 mean_based_regression = mean_based_regression , 305 extend_using_automatic = extend_using_automatic ,
306 max_rule_count = max_rule_count , 306 induce_using_preferred = induce_using_preferred ,
307 ) 307 induce_using_automatic = induce_using_automatic ,
308 self . model : RuleSet [ RegressionRule ] = None 308 preferred_conditions_per_rule = preferred_conditions_per_rule ,
309 309 preferred_attributes_per_rule = preferred_attributes_per_rule ,
310 def fit ( # pylint: disable=arguments-differ,too-many-arguments 310 complementary_conditions = complementary_conditions ,
311 self , 311 mean_based_regression = mean_based_regression ,
312 values : Data , 312 max_rule_count = max_rule_count ,
313 labels : Data , 313 )
314 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None , 314 self . model : RuleSet [ RegressionRule ] = None
315 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 315
316 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 316 def fit ( # pylint: disable=arguments-differ,too-many-arguments
317 ) -> ExpertRuleRegressor : 317 self ,
318 """Train model on given dataset. 318 values : Data ,
319 319 labels : Data ,
320 Parameters 320 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None ,
321 ---------- 321 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
322 values : :class:`rulekit.operator.Data` 322 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
323 attributes 323 ) -> ExpertRuleRegressor :
324 labels : :class:`rulekit.operator.Data` 324 """Train model on given dataset.
325 target values 325
326 326 Parameters
327 expert_rules : List[Union[str, Tuple[str, str]]] 327 ----------
328 set of initial rules, either passed as a list of strings representing rules 328 values : :class:`rulekit.operator.Data`
329 or as list of tuples where first element is name of the rule and second one 329 attributes
330 is rule string. 330 labels : :class:`rulekit.operator.Data`
331 expert_preferred_conditions : List[Union[str, Tuple[str, str]]] 331 target values
332 multiset of preferred conditions (used also for specifying preferred 332
333 attributes by using special value Any). Either passed as a list of strings 333 expert_rules : List[Union[str, Tuple[str, str]]]
334 representing rules or as list of tuples where first element is name of the 334 set of initial rules, either passed as a list of strings representing rules
335 rule and second one is rule string. 335 or as list of tuples where first element is name of the rule and second one
336 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]] 336 is rule string.
337 set of forbidden conditions (used also for specifying forbidden attributes 337 expert_preferred_conditions : List[Union[str, Tuple[str, str]]]
338 by using special valye Any). Either passed as a list of strings representing 338 multiset of preferred conditions (used also for specifying preferred
339 rules or as list of tuples where first element is name of the rule and 339 attributes by using special value Any). Either passed as a list of strings
340 second one is rule string. 340 representing rules or as list of tuples where first element is name of the
341 Returns 341 rule and second one is rule string.
342 ------- 342 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]]
343 self : ExpertRuleRegressor 343 set of forbidden conditions (used also for specifying forbidden attributes
344 """ 344 by using special valye Any). Either passed as a list of strings representing
345 self . _validate_labels ( labels ) 345 rules or as list of tuples where first element is name of the rule and
346 return ExpertKnowledgeOperator . fit ( 346 second one is rule string.
347 self , 347 Returns
348 values , 348 -------
349 labels , 349 self : ExpertRuleRegressor
350 expert_rules = expert_rules , 350 """
351 expert_preferred_conditions = expert_preferred_conditions , 351 self . _validate_labels ( labels )
352 expert_forbidden_conditions = expert_forbidden_conditions , 352 return ExpertKnowledgeOperator . fit (
353 ) 353 self ,
354 354 values ,
355 def predict ( self , values : Data ) -> np . ndarray : 355 labels ,
356 return self . _map_result ( ExpertKnowledgeOperator . predict ( self , values )) 356 expert_rules = expert_rules ,
357 357 expert_preferred_conditions = expert_preferred_conditions ,
358 def _get_problem_type ( self ) -> ProblemType : 358 expert_forbidden_conditions = expert_forbidden_conditions ,
359 return ProblemType . REGRESSION 359 )
360
361 361 def predict ( self , values : Data ) -> np . ndarray :
362 class ContrastSetRuleRegressor ( BaseOperator ): 362 return self . _map_result ( ExpertKnowledgeOperator . predict ( self , values ))
363 """Contrast set regression model.""" 363
364 364 def _get_problem_type ( self ) -> ProblemType :
365 __params_class__ = ContrastSetModelParams 365 return ProblemType . REGRESSION
366
367 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals 367
368 self , 368 class ContrastSetRuleRegressor ( BaseOperator ):
369 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [ 369 """Contrast set regression model."""
370 "minsupp_all" 370
371 ], 371 __params_class__ = ContrastSetModelParams
372 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ], 372
373 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ], 373 def __init__ ( # pylint: disable=too-many-arguments,too-many-locals
374 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ], 374 self ,
375 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ], 375 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [
376 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 376 "minsupp_all"
377 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ], 377 ],
378 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ], 378 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ],
379 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ], 379 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ],
380 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ], 380 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ],
381 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 381 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ],
382 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 382 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
383 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 383 induction_measure : Measures = DEFAULT_PARAMS_VALUE [ "induction_measure" ],
384 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 384 pruning_measure : Union [ Measures , str ] = DEFAULT_PARAMS_VALUE [ "pruning_measure" ],
385 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 385 voting_measure : Measures = DEFAULT_PARAMS_VALUE [ "voting_measure" ],
386 "complementary_conditions" 386 max_growing : float = DEFAULT_PARAMS_VALUE [ "max_growing" ],
387 ], 387 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
388 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ], 388 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
389 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 389 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
390 ): 390 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
391 """ 391 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
392 Parameters 392 "complementary_conditions"
393 ---------- 393 ],
394 minsupp_all: Tuple[float, float, float, float] 394 mean_based_regression : bool = DEFAULT_PARAMS_VALUE [ "mean_based_regression" ],
395 a minimum positive support of a contrast set (p/P). When multiple values are 395 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
396 specified, a metainduction is performed; Default and recommended sequence 396 ):
397 is: 0.8, 0.5, 0.2, 0.1 397 """
398 max_neg2pos: float 398 Parameters
399 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5 399 ----------
400 max_passes_count: int 400 minsupp_all: Tuple[float, float, float, float]
401 a maximum number of sequential covering passes for a single minsupp-all; 401 a minimum positive support of a contrast set (p/P). When multiple values are
402 Default is 5 402 specified, a metainduction is performed; Default and recommended sequence
403 penalty_strength: float 403 is: 0.8, 0.5, 0.2, 0.1
404 (s) - penalty strength; Default is 0.5 404 max_neg2pos: float
405 penalty_saturation: float 405 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5
406 the value of p_new / P at which penalty reward saturates; Default is 0.2. 406 max_passes_count: int
407 minsupp_new : float = 5.0 407 a maximum number of sequential covering passes for a single minsupp-all;
408 a minimum number (or fraction, if value < 1.0) of previously uncovered 408 Default is 5
409 examples to be covered by a new rule (positive examples for classification 409 penalty_strength: float
410 problems); default: 5, 410 (s) - penalty strength; Default is 0.5
411 induction_measure : :class:`rulekit.params.Measures` = \ 411 penalty_saturation: float
412 :class:`rulekit.params.Measures.Correlation` 412 the value of p_new / P at which penalty reward saturates; Default is 0.2.
413 measure used during induction; default measure is correlation 413 minsupp_new : float = 5.0
414 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \ 414 a minimum number (or fraction, if value < 1.0) of previously uncovered
415 :class:`rulekit.params.Measures.Correlation` 415 examples to be covered by a new rule (positive examples for classification
416 measure used during pruning. Could be user defined (string), for example 416 problems); default: 5,
417 :code:`2 * p / n`; default measure is correlation 417 induction_measure : :class:`rulekit.params.Measures` = \
418 voting_measure : :class:`rulekit.params.Measures` = \ 418 :class:`rulekit.params.Measures.Correlation`
419 :class:`rulekit.params.Measures.Correlation` 419 measure used during induction; default measure is correlation
420 measure used during voting; default measure is correlation 420 pruning_measure : Union[:class:`rulekit.params.Measures`, str] = \
421 max_growing : int = 0.0 421 :class:`rulekit.params.Measures.Correlation`
422 non-negative integer representing maximum number of conditions which can be 422 measure used during pruning. Could be user defined (string), for example
423 added to the rule in the growing phase (use this parameter for large 423 :code:`2 * p / n`; default measure is correlation
424 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 424 voting_measure : :class:`rulekit.params.Measures` = \
425 enable_pruning : bool = True 425 :class:`rulekit.params.Measures.Correlation`
426 enable or disable pruning, default is True. 426 measure used during voting; default measure is correlation
427 ignore_missing : bool = False 427 max_growing : int = 0.0
428 boolean telling whether missing values should be ignored (by default, a 428 non-negative integer representing maximum number of conditions which can be
429 missing value of given attribute is always considered as not fulfilling the 429 added to the rule in the growing phase (use this parameter for large
430 condition build upon that attribute); default: False. 430 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
431 max_uncovered_fraction : float = 0.0 431 enable_pruning : bool = True
432 Floating-point number from [0,1] interval representing maximum fraction of 432 enable or disable pruning, default is True.
433 examples that may remain uncovered by the rule set, default: 0.0. 433 ignore_missing : bool = False
434 select_best_candidate : bool = False 434 boolean telling whether missing values should be ignored (by default, a
435 Flag determining if best candidate should be selected from growing phase; 435 missing value of given attribute is always considered as not fulfilling the
436 default: False. 436 condition build upon that attribute); default: False.
437 complementary_conditions : bool = False 437 max_uncovered_fraction : float = 0.0
438 If enabled, complementary conditions in the form a = !{value} for nominal 438 Floating-point number from [0,1] interval representing maximum fraction of
439 attributes are supported. 439 examples that may remain uncovered by the rule set, default: 0.0.
440 mean_based_regression : bool = True 440 select_best_candidate : bool = False
441 Enable fast induction of mean-based regression rules instead of default 441 Flag determining if best candidate should be selected from growing phase;
442 median-based. 442 default: False.
443 max_rule_count : int = 0 443 complementary_conditions : bool = False
444 Maximum number of rules to be generated; 0 indicates no limit. 444 If enabled, complementary conditions in the form a = !{value} for nominal
445 """ 445 attributes are supported.
446 super () . __init__ ( 446 mean_based_regression : bool = True
447 minsupp_all = minsupp_all , 447 Enable fast induction of mean-based regression rules instead of default
448 max_neg2pos = max_neg2pos , 448 median-based.
449 max_passes_count = max_passes_count , 449 max_rule_count : int = 0
450 penalty_strength = penalty_strength , 450 Maximum number of rules to be generated; 0 indicates no limit.
451 penalty_saturation = penalty_saturation , 451 """
452 minsupp_new = minsupp_new , 452 super () . __init__ (
453 induction_measure = induction_measure , 453 minsupp_all = minsupp_all ,
454 pruning_measure = pruning_measure , 454 max_neg2pos = max_neg2pos ,
455 voting_measure = voting_measure , 455 max_passes_count = max_passes_count ,
456 max_growing = max_growing , 456 penalty_strength = penalty_strength ,
457 enable_pruning = enable_pruning , 457 penalty_saturation = penalty_saturation ,
458 ignore_missing = ignore_missing , 458 minsupp_new = minsupp_new ,
459 max_uncovered_fraction = max_uncovered_fraction , 459 induction_measure = induction_measure ,
460 select_best_candidate = select_best_candidate , 460 pruning_measure = pruning_measure ,
461 complementary_conditions = complementary_conditions , 461 voting_measure = voting_measure ,
462 mean_based_regression = mean_based_regression , 462 max_growing = max_growing ,
463 max_rule_count = max_rule_count , 463 enable_pruning = enable_pruning ,
464 ) 464 ignore_missing = ignore_missing ,
465 self . contrast_attribute : str = None 465 max_uncovered_fraction = max_uncovered_fraction ,
466 self . model : RuleSet [ RegressionRule ] = None 466 select_best_candidate = select_best_candidate ,
467 467 complementary_conditions = complementary_conditions ,
468 def fit ( 468 mean_based_regression = mean_based_regression ,
469 self , values : Data , labels : Data , contrast_attribute : str 469 max_rule_count = max_rule_count ,
470 ) -> ContrastSetRuleRegressor : # pylint: disable=arguments-differ 470 )
471 """Train model on given dataset. 471 self . contrast_attribute : str = None
472 472 self . model : RuleSet [ RegressionRule ] = None
473 Parameters 473
474 ---------- 474 def fit (
475 values : :class:`rulekit.operator.Data` 475 self , values : Data , labels : Data , contrast_attribute : str
476 attributes 476 ) -> ContrastSetRuleRegressor : # pylint: disable=arguments-differ
477 labels : :class:`rulekit.operator.Data` 477 """Train model on given dataset.
478 target values 478
479 contrast_attribute: str 479 Parameters
480 group attribute 480 ----------
481 Returns 481 values : :class:`rulekit.operator.Data`
482 ------- 482 attributes
483 self : ContrastSetRuleRegressor 483 labels : :class:`rulekit.operator.Data`
484 """ 484 target values
485 RuleRegressor . _validate_labels ( self , labels ) # pylint: disable=protected-access 485 contrast_attribute: str
486 super () . fit ( values , labels , contrast_attribute = contrast_attribute ) 486 group attribute
487 self . contrast_attribute = contrast_attribute 487 Returns
488 return self 488 -------
489 489 self : ContrastSetRuleRegressor
490 def predict ( self , values : Data ) -> np . ndarray : 490 """
491 """Perform prediction and returns predicted values. 491 RuleRegressor . _validate_labels ( self , labels ) # pylint: disable=protected-access
492 492 super () . fit ( values , labels , contrast_attribute = contrast_attribute )
493 Parameters 493 self . contrast_attribute = contrast_attribute
494 ---------- 494 return self
495 values : :class:`rulekit.operator.Data` 495
496 attributes 496 def predict ( self , values : Data ) -> np . ndarray :
497 497 """Perform prediction and returns predicted values.
498 Returns 498
499 ------- 499 Parameters
500 result : np.ndarray 500 ----------
501 predicted values 501 values : :class:`rulekit.operator.Data`
502 """ 502 attributes
503 return RuleRegressor . predict ( self , values ) 503
504 504 Returns
505 def score ( self , values : Data , labels : Data ) -> float : 505 -------
506 """Return the coefficient of determination R2 of the prediction 506 result : np.ndarray
507 507 predicted values
508 Parameters 508 """
509 ---------- 509 return RuleRegressor . predict ( self , values )
510 values : :class:`rulekit.operator.Data` 510
511 attributes 511 def score ( self , values : Data , labels : Data ) -> float :
512 labels : :class:`rulekit.operator.Data` 512 """Return the coefficient of determination R2 of the prediction
513 true target values 513
514 514 Parameters
515 Returns 515 ----------
516 ------- 516 values : :class:`rulekit.operator.Data`
517 score : float 517 attributes
518 R2 of self.predict(values) wrt. labels. 518 labels : :class:`rulekit.operator.Data`
519 """ 519 true target values
520 return RuleRegressor . score ( self , values , labels ) 520
521 521 Returns
522 def __getstate__ ( self ) -> dict : 522 -------
523 return { 523 score : float
524 ** BaseOperator . __getstate__ ( self ), 524 R2 of self.predict(values) wrt. labels.
525 ** { 525 """
526 "contrast_attribute" : self . contrast_attribute , 526 return RuleRegressor . score ( self , values , labels )
527 }, 527
528 } 528 def __getstate__ ( self ) -> dict :
529 529 return {
530 def __setstate__ ( self , state : dict ): 530 ** BaseOperator . __getstate__ ( self ),
531 BaseOperator . __setstate__ ( self , state ) 531 ** {
532 self . contrast_attribute = state [ "contrast_attribute" ] 532 "contrast_attribute" : self . contrast_attribute ,
533 533 },
534 def _get_problem_type ( self ) -> ProblemType : 534 }
535 return ProblemType . CONTRAST_REGRESSION 535
+ 536 def __setstate__ ( self , state : dict ):
+ 537 BaseOperator . __setstate__ ( self , state )
+ 538 self . contrast_attribute = state [ "contrast_attribute" ]
+ 539
+ 540 def _get_problem_type ( self ) -> ProblemType :
+ 541 return ProblemType . CONTRAST_REGRESSION
diff --git a/docs/reports/flake8/rulekit.survival.report.html b/docs/reports/flake8/rulekit.survival.report.html
index 0b8389f..5f71c74 100644
--- a/docs/reports/flake8/rulekit.survival.report.html
+++ b/docs/reports/flake8/rulekit.survival.report.html
@@ -64,22 +64,22 @@
Line too long (94 > 88 characters):
-
- 56 self . model . _java_object . getTrainingEstimator () # pylint: disable=protected-access
+ 62 self . model . _java_object . getTrainingEstimator () # pylint: disable=protected-access
Line too long (93 > 88 characters):
-
- 265 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals
+ 271 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals
Line too long (112 > 88 characters):
-
- 557 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access
+ 563 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access
diff --git a/docs/reports/flake8/rulekit.survival.source.html b/docs/reports/flake8/rulekit.survival.source.html
index 84e748d..ef17356 100644
--- a/docs/reports/flake8/rulekit.survival.source.html
+++ b/docs/reports/flake8/rulekit.survival.source.html
@@ -23,745 +23,745 @@
class="code sev- ">2 """ 3 3 from __future__ import annotations
4 from __future__ import annotations 4
5 5 from typing import Optional
6 from typing import Optional , Tuple , Union 6 from typing import Tuple
7 7 from typing import Union
8 import numpy as np 8
9 import pandas as pd 9 import numpy as np
10 from jpype import JClass 10 import pandas as pd
11 from pydantic import BaseModel # pylint: disable=no-name-in-module 11 from jpype import JClass
12 12 from pydantic import BaseModel # pylint: disable=no-name-in-module
13 from rulekit._helpers import ExampleSetFactory , PredictionResultMapper 13
14 from rulekit._operator import BaseOperator , Data , ExpertKnowledgeOperator 14 from rulekit._helpers import ExampleSetFactory
15 from rulekit._problem_types import ProblemType 15 from rulekit._helpers import PredictionResultMapper
16 from rulekit.kaplan_meier import KaplanMeierEstimator 16 from rulekit._operator import BaseOperator
17 from rulekit.params import ( DEFAULT_PARAMS_VALUE , ContrastSetModelParams , 17 from rulekit._operator import Data
18 ExpertModelParams ) 18 from rulekit._operator import ExpertKnowledgeOperator
19 from rulekit.rules import RuleSet , SurvivalRule 19 from rulekit._problem_types import ProblemType
20 20 from rulekit.kaplan_meier import KaplanMeierEstimator
21 _DEFAULT_SURVIVAL_TIME_ATTR : str = "survival_time" 21 from rulekit.params import ContrastSetModelParams
22 22 from rulekit.params import DEFAULT_PARAMS_VALUE
23 23 from rulekit.params import ExpertModelParams
24 class _SurvivalModelsParams ( BaseModel ): 24 from rulekit.rules import RuleSet
25 survival_time_attr : Optional [ str ] 25 from rulekit.rules import SurvivalRule
26 minsupp_new : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "minsupp_new" ] 26
27 max_growing : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "max_growing" ] 27 _DEFAULT_SURVIVAL_TIME_ATTR : str = "survival_time"
28 enable_pruning : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "enable_pruning" ] 28
29 ignore_missing : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "ignore_missing" ] 29
30 max_uncovered_fraction : Optional [ float ] = DEFAULT_PARAMS_VALUE [ 30 class _SurvivalModelsParams ( BaseModel ):
31 "max_uncovered_fraction" 31 survival_time_attr : Optional [ str ]
32 ] 32 minsupp_new : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "minsupp_new" ]
33 select_best_candidate : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 33 max_growing : Optional [ float ] = DEFAULT_PARAMS_VALUE [ "max_growing" ]
34 "select_best_candidate" 34 enable_pruning : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "enable_pruning" ]
35 ] 35 ignore_missing : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ "ignore_missing" ]
36 complementary_conditions : Optional [ bool ] = DEFAULT_PARAMS_VALUE [ 36 max_uncovered_fraction : Optional [ float ] = DEFAULT_PARAMS_VALUE [
37 "complementary_conditions" 37 "max_uncovered_fraction"
38 ]
39 39 select_best_candidate : Optional [ bool ] = DEFAULT_PARAMS_VALUE [
40 40 "select_best_candidate"
41 class _SurvivalExpertModelParams ( _SurvivalModelsParams , ExpertModelParams ): 41 ]
42 pass 42 complementary_conditions : Optional [ bool ] = DEFAULT_PARAMS_VALUE [
43 43 "complementary_conditions"
44 44 ]
45 class _BaseSurvivalRulesModel : 45
46
47 model : RuleSet [ SurvivalRule ] 47 class _SurvivalExpertModelParams ( _SurvivalModelsParams , ExpertModelParams ):
48 48 pass
49 def get_train_set_kaplan_meier ( self ) -> KaplanMeierEstimator : 49
50 """Returns train set KaplanMeier estimator 50
51 51 class _BaseSurvivalRulesModel :
52 Returns: 52
53 KaplanMeierEstimator: estimator 53 model : RuleSet [ SurvivalRule ]
54 """ 54
55 return KaplanMeierEstimator ( 55 def get_train_set_kaplan_meier ( self ) -> KaplanMeierEstimator :
-
-
-
-
- E501
-
- Line too long (94 > 88 characters)
-
- 56 self . model . _java_object . getTrainingEstimator () # pylint: disable=protected-access
+ class="code sev- ">
56 """Returns train set KaplanMeier estimator 57 ) 57
58 58 Returns:
59 59 KaplanMeierEstimator: estimator
60 class SurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ): 60 """
61 """Survival model.""" 61 return KaplanMeierEstimator (
62
+ class="code sev-2 le">
+
+
+
+
+ E501
+
+ Line too long (94 > 88 characters)
+
+ 62 self . model . _java_object . getTrainingEstimator () # pylint: disable=protected-access 63 __params_class__ = _SurvivalModelsParams 63 )
64
65 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments 65
66 self , 66 class SurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ):
67 survival_time_attr : str = None , 67 """Survival model."""
68 minsupp_new : int = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 68
69 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ], 69 __params_class__ = _SurvivalModelsParams
70 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 70
71 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 71 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
72 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 72 self ,
73 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 73 survival_time_attr : str = None ,
74 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 74 minsupp_new : int = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
75 "complementary_conditions" 75 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ],
76 ], 76 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
77 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 77 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
78 ): 78 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
79 """ 79 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
80 Parameters 80 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
81 ---------- 81 "complementary_conditions"
82 survival_time_attr : str 82 ],
83 name of column containing survival time data (use when data passed to model 83 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
84 is padnas dataframe). 84 ):
85 minsupp_new : float = 5.0 85 """
86 a minimum number (or fraction, if value < 1.0) of previously uncovered 86 Parameters
87 examples to be covered by a new rule (positive examples for classification 87 ----------
88 problems); default: 5, 88 survival_time_attr : str
89 max_growing : int = 0.0 89 name of column containing survival time data (use when data passed to model
90 non-negative integer representing maximum number of conditions which can be 90 is padnas dataframe).
91 added to the rule in the growing phase (use this parameter for large 91 minsupp_new : float = 5.0
92 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 92 a minimum number (or fraction, if value < 1.0) of previously uncovered
93 enable_pruning : bool = True 93 examples to be covered by a new rule (positive examples for classification
94 enable or disable pruning, default is True. 94 problems); default: 5,
95 ignore_missing : bool = False 95 max_growing : int = 0.0
96 boolean telling whether missing values should be ignored (by default, a 96 non-negative integer representing maximum number of conditions which can be
97 missing value of given attribute is always considered as not fulfilling the 97 added to the rule in the growing phase (use this parameter for large
98 condition build upon that attribute); default: False. 98 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
99 max_uncovered_fraction : float = 0.0 99 enable_pruning : bool = True
100 Floating-point number from [0,1] interval representing maximum fraction of 100 enable or disable pruning, default is True.
101 examples that may remain uncovered by the rule set, default: 0.0. 101 ignore_missing : bool = False
102 select_best_candidate : bool = False 102 boolean telling whether missing values should be ignored (by default, a
103 Flag determining if best candidate should be selected from growing phase; 103 missing value of given attribute is always considered as not fulfilling the
104 default: False. 104 condition build upon that attribute); default: False.
105 complementary_conditions : bool = False 105 max_uncovered_fraction : float = 0.0
106 If enabled, complementary conditions in the form a = !{value} for nominal 106 Floating-point number from [0,1] interval representing maximum fraction of
107 attributes are supported. 107 examples that may remain uncovered by the rule set, default: 0.0.
108 max_rule_count : int = 0 108 select_best_candidate : bool = False
109 Maximum number of rules to be generated (for classification data sets it 109 Flag determining if best candidate should be selected from growing phase;
110 applies to a single class); 0 indicates no limit. 110 default: False.
111 """ 111 complementary_conditions : bool = False
112 self . _params = None 112 If enabled, complementary conditions in the form a = !{value} for nominal
113 self . _rule_generator = None 113 attributes are supported.
114 self . _configurator = None 114 max_rule_count : int = 0
115 self . _initialize_rulekit () 115 Maximum number of rules to be generated (for classification data sets it
116 self . set_params ( 116 applies to a single class); 0 indicates no limit.
117 survival_time_attr = survival_time_attr , 117 """
118 minsupp_new = minsupp_new , 118 self . _params = None
119 max_growing = max_growing , 119 self . _rule_generator = None
120 enable_pruning = enable_pruning , 120 self . _configurator = None
121 ignore_missing = ignore_missing , 121 self . _initialize_rulekit ()
122 max_uncovered_fraction = max_uncovered_fraction , 122 self . set_params (
123 select_best_candidate = select_best_candidate , 123 survival_time_attr = survival_time_attr ,
124 complementary_conditions = complementary_conditions , 124 minsupp_new = minsupp_new ,
125 max_rule_count = max_rule_count , 125 max_growing = max_growing ,
126 ) 126 enable_pruning = enable_pruning ,
127 self . model : RuleSet [ SurvivalRule ] = None 127 ignore_missing = ignore_missing ,
128 128 max_uncovered_fraction = max_uncovered_fraction ,
129 def set_params ( self , ** kwargs ) -> object : 129 select_best_candidate = select_best_candidate ,
130 """Set models hyperparameters. Parameters are the same as in constructor.""" 130 complementary_conditions = complementary_conditions ,
131 self . survival_time_attr = kwargs . get ( "survival_time_attr" ) 131 max_rule_count = max_rule_count ,
132 return BaseOperator . set_params ( self , ** kwargs ) 132 )
133 133 self . model : RuleSet [ SurvivalRule ] = None
134 @staticmethod 134
135 def _append_survival_time_columns ( 135 def set_params ( self , ** kwargs ) -> object :
136 values , survival_time : Union [ pd . Series , np . ndarray , list ] 136 """Set models hyperparameters. Parameters are the same as in constructor."""
137 ) -> Optional [ str ]: 137 self . survival_time_attr = kwargs . get ( "survival_time_attr" )
138 survival_time_attr : str = _DEFAULT_SURVIVAL_TIME_ATTR 138 return BaseOperator . set_params ( self , ** kwargs )
139 if isinstance ( survival_time , pd . Series ): 139
140 if survival_time . name is None : 140 @staticmethod
141 survival_time . name = survival_time_attr 141 def _append_survival_time_columns (
142 else : 142 values , survival_time : Union [ pd . Series , np . ndarray , list ]
143 survival_time_attr = survival_time . name 143 ) -> Optional [ str ]:
144 values [ survival_time . name ] = survival_time 144 survival_time_attr : str = _DEFAULT_SURVIVAL_TIME_ATTR
145 elif isinstance ( survival_time , np . ndarray ): 145 if isinstance ( survival_time , pd . Series ):
146 np . append ( values , survival_time , axis = 1 ) 146 if survival_time . name is None :
147 elif isinstance ( survival_time , list ): 147 survival_time . name = survival_time_attr
148 for index , row in enumerate ( values ): 148 else :
149 row . append ( survival_time [ index ]) 149 survival_time_attr = survival_time . name
150 else : 150 values [ survival_time . name ] = survival_time
151 raise ValueError ( 151 elif isinstance ( survival_time , np . ndarray ):
152 "Data values must be instance of either pandas DataFrame, numpy array" 152 np . append ( values , survival_time , axis = 1 )
153 " or list" 153 elif isinstance ( survival_time , list ):
154 ) 154 for index , row in enumerate ( values ):
155 return survival_time_attr 155 row . append ( survival_time [ index ])
156 156 else :
157 def _prepare_survival_attribute ( 157 raise ValueError (
158 self , survival_time : Optional [ Data ], values : Data 158 "Data values must be instance of either pandas DataFrame, numpy array"
159 ) -> str : 159 " or list"
160 if self . survival_time_attr is None and survival_time is None : 160 )
161 raise ValueError ( 161 return survival_time_attr
162 'No "survival_time" attribute name was specified. ' 162
163 + "Specify it using method set_params" 163 def _prepare_survival_attribute (
164 ) 164 self , survival_time : Optional [ Data ], values : Data
165 if survival_time is not None : 165 ) -> str :
166 return SurvivalRules . _append_survival_time_columns ( values , survival_time ) 166 if self . survival_time_attr is None and survival_time is None :
167 return self . survival_time_attr 167 raise ValueError (
168 168 'No "survival_time" attribute name was specified. '
169 def fit ( 169 + "Specify it using method set_params"
170 self , values : Data , labels : Data , survival_time : Data = None 170 )
171 ) -> SurvivalRules : # pylint: disable=arguments-differ 171 if survival_time is not None :
172 """Train model on given dataset. 172 return SurvivalRules . _append_survival_time_columns ( values , survival_time )
173 173 return self . survival_time_attr
174 Parameters 174
175 ---------- 175 def fit (
176 values : :class:`rulekit.operator.Data` 176 self , values : Data , labels : Data , survival_time : Data = None
177 attributes 177 ) -> SurvivalRules : # pylint: disable=arguments-differ
178 labels : :class:`rulekit.operator.Data` 178 """Train model on given dataset.
179 survival status 179
180 survival_time: :class:`rulekit.operator.Data` 180 Parameters
181 data about survival time. Could be omitted when *survival_time_attr* 181 ----------
182 parameter was specified. 182 values : :class:`rulekit.operator.Data`
183 183 attributes
184 Returns 184 labels : :class:`rulekit.operator.Data`
185 ------- 185 survival status
186 self : SurvivalRules 186 survival_time: :class:`rulekit.operator.Data`
187 """ 187 data about survival time. Could be omitted when *survival_time_attr*
188 survival_time_attribute = self . _prepare_survival_attribute ( 188 parameter was specified.
189 survival_time , values 189
190 ) 190 Returns
191 super () . fit ( values , labels , survival_time_attribute ) 191 -------
192 return self 192 self : SurvivalRules
193 193 """
194 def predict ( self , values : Data ) -> np . ndarray : 194 survival_time_attribute = self . _prepare_survival_attribute (
195 """Perform prediction and return estimated survival function for each example. 195 survival_time , values
196 196 )
197 Parameters 197 super () . fit ( values , labels , survival_time_attribute )
198 ---------- 198 return self
199 values : :class:`rulekit.operator.Data` 199
200 attributes 200 def predict ( self , values : Data ) -> np . ndarray :
201 201 """Perform prediction and return estimated survival function for each example.
202 Returns 202
203 ------- 203 Parameters
204 result : np.ndarray 204 ----------
205 Each row represent single example from dataset and contains estimated 205 values : :class:`rulekit.operator.Data`
206 survival function for that example. Estimated survival function is returned 206 attributes
207 as a dictionary containing times and corresponding probabilities. 207
208 """ 208 Returns
209 return PredictionResultMapper . map_survival ( super () . predict ( values )) 209 -------
210 210 result : np.ndarray
211 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float : 211 Each row represent single example from dataset and contains estimated
212 """Return the Integrated Brier Score on the given dataset and labels 212 survival function for that example. Estimated survival function is returned
213 (event status indicator). 213 as a dictionary containing times and corresponding probabilities.
214 214 """
215 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared 215 return PredictionResultMapper . map_survival ( super () . predict ( values ))
216 difference between true event status at time T and predicted event status at 216
217 that time; the Integrated Brier score summarizes the prediction error over all 217 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
218 observations and over all times in a test set. 218 """Return the Integrated Brier Score on the given dataset and labels
219 219 (event status indicator).
220 Parameters 220
221 ---------- 221 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared
222 values : :class:`rulekit.operator.Data` 222 difference between true event status at time T and predicted event status at
223 attributes 223 that time; the Integrated Brier score summarizes the prediction error over all
224 labels : :class:`rulekit.operator.Data` 224 observations and over all times in a test set.
225 survival status 225
226 survival_time: :class:`rulekit.operator.Data` 226 Parameters
227 data about survival time. Could be omitted when *survival_time_attr* 227 ----------
228 parameter was specified 228 values : :class:`rulekit.operator.Data`
229 229 attributes
230 Returns 230 labels : :class:`rulekit.operator.Data`
231 ------- 231 survival status
232 score : float 232 survival_time: :class:`rulekit.operator.Data`
233 Integrated Brier Score of self.predict(values) wrt. labels. 233 data about survival time. Could be omitted when *survival_time_attr*
234 """ 234 parameter was specified
235
236 survival_time_attribute = self . _prepare_survival_attribute ( 236 Returns
237 survival_time , values 237 -------
238 ) 238 score : float
239 example_set = ExampleSetFactory ( self . _get_problem_type ()) . make ( 239 Integrated Brier Score of self.predict(values) wrt. labels.
240 values , labels , survival_time_attribute = survival_time_attribute 240 """
241 ) 241
242 242 survival_time_attribute = self . _prepare_survival_attribute (
243 predicted_example_set = ( 243 survival_time , values
244 self . model . _java_object . apply ( # pylint: disable=protected-access 244 )
245 example_set 245 example_set = ExampleSetFactory ( self . _get_problem_type ()) . make (
246 ) 246 values , labels , survival_time_attribute = survival_time_attribute
247 )
class="code sev- ">248
249 IntegratedBrierScore = JClass ( # pylint: disable=invalid-name 249 predicted_example_set = (
250 "adaa.analytics.rules.logic.performance.IntegratedBrierScore" 250 self . model . _java_object . apply ( # pylint: disable=protected-access
251 ) 251 example_set
252 integrated_brier_score = IntegratedBrierScore () 252 )
253 ibs = integrated_brier_score . countExample ( predicted_example_set ) . getValue () 253 )
254 return float ( ibs ) 254
255 255 IntegratedBrierScore = JClass ( # pylint: disable=invalid-name
256 def _get_problem_type ( self ) -> ProblemType : 256 "adaa.analytics.rules.logic.performance.IntegratedBrierScore"
257 return ProblemType . SURVIVAL 257 )
258 258 integrated_brier_score = IntegratedBrierScore ()
259 259 ibs = integrated_brier_score . countExample ( predicted_example_set ) . getValue ()
260 class ExpertSurvivalRules ( ExpertKnowledgeOperator , SurvivalRules ): 260 return float ( ibs )
261 """Expert Survival model.""" 261
262 262 def _get_problem_type ( self ) -> ProblemType :
263 __params_class__ = _SurvivalExpertModelParams 263 return ProblemType . SURVIVAL
264
-
-
-
-
- E501
-
- Line too long (93 > 88 characters)
-
- 265 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals
+ class="code sev- ">
265 266 self , 266 class ExpertSurvivalRules ( ExpertKnowledgeOperator , SurvivalRules ):
267 survival_time_attr : str = None , 267 """Expert Survival model."""
268 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 268
269 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ], 269 __params_class__ = _SurvivalExpertModelParams
270 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 270
271 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
+ class="code sev-2 le">
+
+
+
+
+ E501
+
+ Line too long (93 > 88 characters)
+
+ 271 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments,too-many-locals 272 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 272 self ,
273 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 273 survival_time_attr : str = None ,
274 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 274 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
275 "complementary_conditions" 275 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ],
276 ], 276 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
277 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ], 277 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
278 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ], 278 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
279 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ], 279 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
280 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ], 280 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
281 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [ 281 "complementary_conditions"
282 "preferred_conditions_per_rule" 282 ],
283 ], 283 extend_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "extend_using_preferred" ],
284 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [ 284 extend_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "extend_using_automatic" ],
285 "preferred_attributes_per_rule" 285 induce_using_preferred : bool = DEFAULT_PARAMS_VALUE [ "induce_using_preferred" ],
286 ], 286 induce_using_automatic : bool = DEFAULT_PARAMS_VALUE [ "induce_using_automatic" ],
287 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 287 preferred_conditions_per_rule : int = DEFAULT_PARAMS_VALUE [
288 ): 288 "preferred_conditions_per_rule"
289 """ 289 ],
290 Parameters 290 preferred_attributes_per_rule : int = DEFAULT_PARAMS_VALUE [
291 ---------- 291 "preferred_attributes_per_rule"
292 minsupp_new : float = 5.0 292 ],
293 a minimum number (or fraction, if value < 1.0) of previously uncovered 293 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
294 examples to be covered by a new rule (positive examples for classification 294 ):
295 problems); default: 5, 295 """
296 survival_time_attr : str 296 Parameters
297 name of column containing survival time data (use when data passed to model 297 ----------
298 is pandas dataframe). 298 minsupp_new : float = 5.0
299 max_growing : int = 0.0 299 a minimum number (or fraction, if value < 1.0) of previously uncovered
300 non-negative integer representing maximum number of conditions which can be 300 examples to be covered by a new rule (positive examples for classification
301 added to the rule in the growing phase (use this parameter for large 301 problems); default: 5,
302 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 302 survival_time_attr : str
303 enable_pruning : bool = True 303 name of column containing survival time data (use when data passed to model
304 enable or disable pruning, default is True. 304 is pandas dataframe).
305 ignore_missing : bool = False 305 max_growing : int = 0.0
306 boolean telling whether missing values should be ignored (by default, a 306 non-negative integer representing maximum number of conditions which can be
307 missing value of given attribute is always considered as not fulfilling the 307 added to the rule in the growing phase (use this parameter for large
308 condition build upon that attribute); default: False. 308 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
309 max_uncovered_fraction : float = 0.0 309 enable_pruning : bool = True
310 Floating-point number from [0,1] interval representing maximum fraction of 310 enable or disable pruning, default is True.
311 examples that may remain uncovered by the rule set, default: 0.0. 311 ignore_missing : bool = False
312 select_best_candidate : bool = False 312 boolean telling whether missing values should be ignored (by default, a
313 Flag determining if best candidate should be selected from growing phase; 313 missing value of given attribute is always considered as not fulfilling the
314 default: False. 314 condition build upon that attribute); default: False.
315 complementary_conditions : bool = False 315 max_uncovered_fraction : float = 0.0
316 If enabled, complementary conditions in the form a = !{value} for nominal 316 Floating-point number from [0,1] interval representing maximum fraction of
317 attributes are supported. 317 examples that may remain uncovered by the rule set, default: 0.0.
318 max_rule_count : int = 0 318 select_best_candidate : bool = False
319 Maximum number of rules to be generated (for classification data sets it 319 Flag determining if best candidate should be selected from growing phase;
320 applies to a single class); 0 indicates no limit. 320 default: False.
321 321 complementary_conditions : bool = False
322 extend_using_preferred : bool = False 322 If enabled, complementary conditions in the form a = !{value} for nominal
323 boolean indicating whether initial rules should be extended with a use of 323 attributes are supported.
324 preferred conditions and attributes; default is False 324 max_rule_count : int = 0
325 extend_using_automatic : bool = False 325 Maximum number of rules to be generated (for classification data sets it
326 boolean indicating whether initial rules should be extended with a use of 326 applies to a single class); 0 indicates no limit.
327 automatic conditions and attributes; default is False 327
328 induce_using_preferred : bool = False 328 extend_using_preferred : bool = False
329 boolean indicating whether new rules should be induced with a use of 329 boolean indicating whether initial rules should be extended with a use of
330 preferred conditions and attributes; default is False
331 induce_using_automatic : bool = False 331 extend_using_automatic : bool = False
332 boolean indicating whether new rules should be induced with a use of 332 boolean indicating whether initial rules should be extended with a use of
333 automatic conditions and attributes; default is False
334 preferred_conditions_per_rule : int = None 334 induce_using_preferred : bool = False
335 maximum number of preferred conditions per rule; default: unlimited, 335 boolean indicating whether new rules should be induced with a use of
336 preferred_attributes_per_rule : int = None 336 preferred conditions and attributes; default is False
337 maximum number of preferred attributes per rule; default: unlimited. 337 induce_using_automatic : bool = False
338 """ 338 boolean indicating whether new rules should be induced with a use of
339 self . _params = None 339 automatic conditions and attributes; default is False
340 self . _rule_generator = None 340 preferred_conditions_per_rule : int = None
341 self . _configurator = None 341 maximum number of preferred conditions per rule; default: unlimited,
342 self . _initialize_rulekit () 342 preferred_attributes_per_rule : int = None
343 self . set_params ( 343 maximum number of preferred attributes per rule; default: unlimited.
344 survival_time_attr = survival_time_attr , 344 """
345 minsupp_new = minsupp_new , 345 self . _params = None
346 max_growing = max_growing , 346 self . _rule_generator = None
347 enable_pruning = enable_pruning , 347 self . _configurator = None
348 ignore_missing = ignore_missing , 348 self . _initialize_rulekit ()
349 max_uncovered_fraction = max_uncovered_fraction , 349 self . set_params (
350 select_best_candidate = select_best_candidate , 350 survival_time_attr = survival_time_attr ,
351 extend_using_preferred = extend_using_preferred , 351 minsupp_new = minsupp_new ,
352 extend_using_automatic = extend_using_automatic , 352 max_growing = max_growing ,
353 induce_using_preferred = induce_using_preferred , 353 enable_pruning = enable_pruning ,
354 induce_using_automatic = induce_using_automatic , 354 ignore_missing = ignore_missing ,
355 preferred_conditions_per_rule = preferred_conditions_per_rule , 355 max_uncovered_fraction = max_uncovered_fraction ,
356 preferred_attributes_per_rule = preferred_attributes_per_rule , 356 select_best_candidate = select_best_candidate ,
357 complementary_conditions = complementary_conditions , 357 extend_using_preferred = extend_using_preferred ,
358 max_rule_count = max_rule_count , 358 extend_using_automatic = extend_using_automatic ,
359 ) 359 induce_using_preferred = induce_using_preferred ,
360 self . model : RuleSet [ SurvivalRule ] = None 360 induce_using_automatic = induce_using_automatic ,
361 361 preferred_conditions_per_rule = preferred_conditions_per_rule ,
362 def set_params ( self , ** kwargs ) -> object : # pylint: disable=arguments-differ 362 preferred_attributes_per_rule = preferred_attributes_per_rule ,
363 self . survival_time_attr = kwargs [ "survival_time_attr" ] 363 complementary_conditions = complementary_conditions ,
364 return ExpertKnowledgeOperator . set_params ( self , ** kwargs ) 364 max_rule_count = max_rule_count ,
365 365 )
366 def fit ( # pylint: disable=arguments-differ,too-many-arguments 366 self . model : RuleSet [ SurvivalRule ] = None
367 self , 367
368 values : Data , 368 def set_params ( self , ** kwargs ) -> object : # pylint: disable=arguments-differ
369 labels : Data , 369 self . survival_time_attr = kwargs [ "survival_time_attr" ]
370 survival_time : Data = None , 370 return ExpertKnowledgeOperator . set_params ( self , ** kwargs )
371 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None , 371
372 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 372 def fit ( # pylint: disable=arguments-differ,too-many-arguments
373 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None , 373 self ,
374 ) -> ExpertSurvivalRules : 374 values : Data ,
375 """Train model on given dataset. 375 labels : Data ,
376 376 survival_time : Data = None ,
377 Parameters 377 expert_rules : list [ Union [ str , tuple [ str , str ]]] = None ,
378 ---------- 378 expert_preferred_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
379 values : :class:`rulekit.operator.Data` 379 expert_forbidden_conditions : list [ Union [ str , tuple [ str , str ]]] = None ,
380 attributes 380 ) -> ExpertSurvivalRules :
381 labels : Data 381 """Train model on given dataset.
382 survival status 382
383 survival_time: :class:`rulekit.operator.Data` 383 Parameters
384 data about survival time. Could be omitted when *survival_time_attr* 384 ----------
385 parameter was specified. 385 values : :class:`rulekit.operator.Data`
386 expert_rules : List[Union[str, Tuple[str, str]]] 386 attributes
387 set of initial rules, either passed as a list of strings representing rules 387 labels : Data
388 or as list of tuples where first element is name of the rule and second one 388 survival status
389 is rule string. 389 survival_time: :class:`rulekit.operator.Data`
390 expert_preferred_conditions : List[Union[str, Tuple[str, str]]] 390 data about survival time. Could be omitted when *survival_time_attr*
391 multiset of preferred conditions (used also for specifying preferred 391 parameter was specified.
392 attributes by using special value Any). Either passed as a list of strings 392 expert_rules : List[Union[str, Tuple[str, str]]]
393 representing rules or as list of tuples where first element is name of the 393 set of initial rules, either passed as a list of strings representing rules
394 rule and second one is rule string. 394 or as list of tuples where first element is name of the rule and second one
395 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]] 395 is rule string.
396 set of forbidden conditions (used also for specifying forbidden attributes 396 expert_preferred_conditions : List[Union[str, Tuple[str, str]]]
397 by using special valye Any). Either passed as a list of strings representing 397 multiset of preferred conditions (used also for specifying preferred
398 rules or as list of tuples where first element is name of the rule and 398 attributes by using special value Any). Either passed as a list of strings
399 second one is rule string. 399 representing rules or as list of tuples where first element is name of the
400 400 rule and second one is rule string.
401 Returns 401 expert_forbidden_conditions : List[Union[str, Tuple[str, str]]]
402 ------- 402 set of forbidden conditions (used also for specifying forbidden attributes
403 self : ExpertSurvivalRules 403 by using special valye Any). Either passed as a list of strings representing
404 """ 404 rules or as list of tuples where first element is name of the rule and
405 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( 405 second one is rule string.
406 self , survival_time , values 406
407 ) 407 Returns
408 return ExpertKnowledgeOperator . fit ( 408 -------
409 self , 409 self : ExpertSurvivalRules
410 values = values , 410 """
411 labels = labels , 411 survival_time_attribute = SurvivalRules . _prepare_survival_attribute (
412 survival_time_attribute = survival_time_attribute , 412 self , survival_time , values
413 expert_rules = expert_rules , 413 )
414 expert_preferred_conditions = expert_preferred_conditions , 414 return ExpertKnowledgeOperator . fit (
415 expert_forbidden_conditions = expert_forbidden_conditions , 415 self ,
416 ) 416 values = values ,
417 417 labels = labels ,
418 def predict ( self , values : Data ) -> np . ndarray : 418 survival_time_attribute = survival_time_attribute ,
419 return PredictionResultMapper . map_survival ( 419 expert_rules = expert_rules ,
420 ExpertKnowledgeOperator . predict ( self , values ) 420 expert_preferred_conditions = expert_preferred_conditions ,
421 ) 421 expert_forbidden_conditions = expert_forbidden_conditions ,
422 422 )
423 def _get_problem_type ( self ) -> ProblemType : 423
424 return ProblemType . SURVIVAL 424 def predict ( self , values : Data ) -> np . ndarray :
425 425 return PredictionResultMapper . map_survival (
426 426 ExpertKnowledgeOperator . predict ( self , values )
427 class _SurvivalContrastSetModelParams ( ContrastSetModelParams , _SurvivalModelsParams ): 427 )
428 pass 428
429 429 def _get_problem_type ( self ) -> ProblemType :
430 430 return ProblemType . SURVIVAL
431 class ContrastSetSurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ): 431
432 """Contrast set survival model.""" 432
433 433 class _SurvivalContrastSetModelParams ( ContrastSetModelParams , _SurvivalModelsParams ):
434 __params_class__ = _SurvivalContrastSetModelParams 434 pass
435
436 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments 436
437 self , 437 class ContrastSetSurvivalRules ( BaseOperator , _BaseSurvivalRulesModel ):
438 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [ 438 """Contrast set survival model."""
439 "minsupp_all" 439
440 ], 440 __params_class__ = _SurvivalContrastSetModelParams
441 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ], 441
442 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ], 442 def __init__ ( # pylint: disable=super-init-not-called,too-many-arguments
443 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ], 443 self ,
444 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ], 444 minsupp_all : Tuple [ float , float , float , float ] = DEFAULT_PARAMS_VALUE [
445 survival_time_attr : str = None , 445 "minsupp_all"
446 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ], 446 ],
447 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ], 447 max_neg2pos : float = DEFAULT_PARAMS_VALUE [ "max_neg2pos" ],
448 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ], 448 max_passes_count : int = DEFAULT_PARAMS_VALUE [ "max_passes_count" ],
449 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ], 449 penalty_strength : float = DEFAULT_PARAMS_VALUE [ "penalty_strength" ],
450 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ], 450 penalty_saturation : float = DEFAULT_PARAMS_VALUE [ "penalty_saturation" ],
451 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ], 451 survival_time_attr : str = None ,
452 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [ 452 minsupp_new : float = DEFAULT_PARAMS_VALUE [ "minsupp_new" ],
453 "complementary_conditions" 453 max_growing : int = DEFAULT_PARAMS_VALUE [ "max_growing" ],
454 ], 454 enable_pruning : bool = DEFAULT_PARAMS_VALUE [ "enable_pruning" ],
455 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ], 455 ignore_missing : bool = DEFAULT_PARAMS_VALUE [ "ignore_missing" ],
456 ): 456 max_uncovered_fraction : float = DEFAULT_PARAMS_VALUE [ "max_uncovered_fraction" ],
457 """ 457 select_best_candidate : bool = DEFAULT_PARAMS_VALUE [ "select_best_candidate" ],
458 Parameters 458 complementary_conditions : bool = DEFAULT_PARAMS_VALUE [
459 ---------- 459 "complementary_conditions"
460 minsupp_all: Tuple[float, float, float, float] 460 ],
461 a minimum positive support of a contrast set (p/P). When multiple values are 461 max_rule_count : int = DEFAULT_PARAMS_VALUE [ "max_rule_count" ],
462 specified, a metainduction is performed; Default and recommended sequence 462 ):
463 is: 0.8, 0.5, 0.2, 0.1 463 """
464 max_neg2pos: float 464 Parameters
465 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5 465 ----------
466 max_passes_count: int 466 minsupp_all: Tuple[float, float, float, float]
467 a maximum number of sequential covering passes for a single minsupp-all; 467 a minimum positive support of a contrast set (p/P). When multiple values are
468 Default is 5 468 specified, a metainduction is performed; Default and recommended sequence
469 penalty_strength: float 469 is: 0.8, 0.5, 0.2, 0.1
470 (s) - penalty strength; Default is 0.5 470 max_neg2pos: float
471 penalty_saturation: float 471 a maximum ratio of negative to positive supports (nP/pN); Default is 0.5
472 the value of p_new / P at which penalty reward saturates; Default is 0.2. 472 max_passes_count: int
473 survival_time_attr : str 473 a maximum number of sequential covering passes for a single minsupp-all;
474 name of column containing survival time data (use when data passed to model 474 Default is 5
475 is pandas dataframe). 475 penalty_strength: float
476 minsupp_new : float = 5.0 476 (s) - penalty strength; Default is 0.5
477 a minimum number (or fraction, if value < 1.0) of previously uncovered 477 penalty_saturation: float
478 examples to be covered by a new rule (positive examples for classification 478 the value of p_new / P at which penalty reward saturates; Default is 0.2.
479 problems); default: 5, 479 survival_time_attr : str
480 max_growing : int = 0.0 480 name of column containing survival time data (use when data passed to model
481 non-negative integer representing maximum number of conditions which can be 481 is pandas dataframe).
482 added to the rule in the growing phase (use this parameter for large 482 minsupp_new : float = 5.0
483 datasets if execution time is prohibitive); 0 indicates no limit; default: 0 483 a minimum number (or fraction, if value < 1.0) of previously uncovered
484 enable_pruning : bool = True 484 examples to be covered by a new rule (positive examples for classification
485 enable or disable pruning, default is True. 485 problems); default: 5,
486 ignore_missing : bool = False 486 max_growing : int = 0.0
487 boolean telling whether missing values should be ignored (by default, a 487 non-negative integer representing maximum number of conditions which can be
488 missing value of given attribute is always considered as not fulfilling the 488 added to the rule in the growing phase (use this parameter for large
489 condition build upon that attribute); default: False. 489 datasets if execution time is prohibitive); 0 indicates no limit; default: 0
490 max_uncovered_fraction : float = 0.0 490 enable_pruning : bool = True
491 Floating-point number from [0,1] interval representing maximum fraction of 491 enable or disable pruning, default is True.
492 examples that may remain uncovered by the rule set, default: 0.0. 492 ignore_missing : bool = False
493 select_best_candidate : bool = False 493 boolean telling whether missing values should be ignored (by default, a
494 Flag determining if best candidate should be selected from growing phase; 494 missing value of given attribute is always considered as not fulfilling the
495 default: False. 495 condition build upon that attribute); default: False.
496 complementary_conditions : bool = False 496 max_uncovered_fraction : float = 0.0
497 If enabled, complementary conditions in the form a = !{value} for nominal 497 Floating-point number from [0,1] interval representing maximum fraction of
498 attributes are supported. 498 examples that may remain uncovered by the rule set, default: 0.0.
499 max_rule_count : int = 0 499 select_best_candidate : bool = False
500 Maximum number of rules to be generated (for classification data sets it 500 Flag determining if best candidate should be selected from growing phase;
501 applies to a single class); 0 indicates no limit. 501 default: False.
502 """ 502 complementary_conditions : bool = False
503 self . _params = None 503 If enabled, complementary conditions in the form a = !{value} for nominal
504 self . _rule_generator = None 504 attributes are supported.
505 self . _configurator = None 505 max_rule_count : int = 0
506 self . contrast_attribute : str = None 506 Maximum number of rules to be generated (for classification data sets it
507 self . _initialize_rulekit () 507 applies to a single class); 0 indicates no limit.
508 self . set_params ( 508 """
509 minsupp_all = minsupp_all , 509 self . _params = None
510 max_neg2pos = max_neg2pos , 510 self . _rule_generator = None
511 max_passes_count = max_passes_count , 511 self . _configurator = None
512 penalty_strength = penalty_strength , 512 self . contrast_attribute : str = None
513 penalty_saturation = penalty_saturation , 513 self . _initialize_rulekit ()
514 survival_time_attr = survival_time_attr , 514 self . set_params (
515 minsupp_new = minsupp_new , 515 minsupp_all = minsupp_all ,
516 max_growing = max_growing , 516 max_neg2pos = max_neg2pos ,
517 enable_pruning = enable_pruning , 517 max_passes_count = max_passes_count ,
518 ignore_missing = ignore_missing , 518 penalty_strength = penalty_strength ,
519 max_uncovered_fraction = max_uncovered_fraction , 519 penalty_saturation = penalty_saturation ,
520 select_best_candidate = select_best_candidate , 520 survival_time_attr = survival_time_attr ,
521 complementary_conditions = complementary_conditions , 521 minsupp_new = minsupp_new ,
522 max_rule_count = max_rule_count , 522 max_growing = max_growing ,
523 ) 523 enable_pruning = enable_pruning ,
524 self . model : RuleSet [ SurvivalRule ] = None 524 ignore_missing = ignore_missing ,
525 525 max_uncovered_fraction = max_uncovered_fraction ,
526 def set_params ( self , ** kwargs ) -> object : 526 select_best_candidate = select_best_candidate ,
527 """Set models hyperparameters. Parameters are the same as in constructor.""" 527 complementary_conditions = complementary_conditions ,
528 # params validation 528 max_rule_count = max_rule_count ,
529 self . survival_time_attr = kwargs [ "survival_time_attr" ] 529 )
530 return BaseOperator . set_params ( self , ** kwargs ) 530 self . model : RuleSet [ SurvivalRule ] = None
531
532 def fit ( # pylint: disable=arguments-renamed 532 def set_params ( self , ** kwargs ) -> object :
533 self , 533 """Set models hyperparameters. Parameters are the same as in constructor."""
534 values : Data , 534 # params validation
535 labels : Data , 535 self . survival_time_attr = kwargs [ "survival_time_attr" ]
536 contrast_attribute : str , 536 return BaseOperator . set_params ( self , ** kwargs )
537 survival_time : Data = None , 537
538 ) -> ContrastSetSurvivalRules : 538 def fit ( # pylint: disable=arguments-renamed
539 """Train model on given dataset. 539 self ,
540 540 values : Data ,
541 Parameters 541 labels : Data ,
542 ---------- 542 contrast_attribute : str ,
543 values : :class:`rulekit.operator.Data` 543 survival_time : Data = None ,
544 attributes 544 ) -> ContrastSetSurvivalRules :
545 labels : :class:`rulekit.operator.Data` 545 """Train model on given dataset.
546 survival status 546
547 contrast_attribute: str 547 Parameters
548 group attribute 548 ----------
549 survival_time: :class:`rulekit.operator.Data` 549 values : :class:`rulekit.operator.Data`
550 data about survival time. Could be omitted when *survival_time_attr* 550 attributes
551 parameter was specified. 551 labels : :class:`rulekit.operator.Data`
552 552 survival status
553 Returns 553 contrast_attribute: str
554 ------- 554 group attribute
555 self : ContrastSetSurvivalRules 555 survival_time: :class:`rulekit.operator.Data`
556 """ 556 data about survival time. Could be omitted when *survival_time_attr*
-
-
-
-
- E501
-
- Line too long (112 > 88 characters)
-
- 557 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access
+ class="code sev- ">
557 parameter was specified. 558 self , survival_time , values 558
559 ) 559 Returns
560 super () . fit ( 560 -------
561 values , 561 self : ContrastSetSurvivalRules
562 labels , 562 """
563 survival_time_attribute = survival_time_attribute ,
+ class="code sev-2 le">
+
+
+
+
+ E501
+
+ Line too long (112 > 88 characters)
+
+ 563 survival_time_attribute = SurvivalRules . _prepare_survival_attribute ( # pylint: disable=protected-access 564 contrast_attribute = contrast_attribute , 564 self , survival_time , values
565 )
566 self . contrast_attribute = contrast_attribute 566 super () . fit (
567 return self 567 values ,
568 568 labels ,
569 def predict ( self , values : Data ) -> np . ndarray : 569 survival_time_attribute = survival_time_attribute ,
570 """Perform prediction and return estimated survival function for each example. 570 contrast_attribute = contrast_attribute ,
571 571 )
572 Parameters 572 self . contrast_attribute = contrast_attribute
573 ---------- 573 return self
574 values : :class:`rulekit.operator.Data` 574
575 attributes 575 def predict ( self , values : Data ) -> np . ndarray :
576 576 """Perform prediction and return estimated survival function for each example.
577 Returns 577
578 ------- 578 Parameters
579 result : np.ndarray 579 ----------
580 Each row represent single example from dataset and contains estimated 580 values : :class:`rulekit.operator.Data`
581 survival function for that example. Estimated survival function is returned 581 attributes
582 as a dictionary containing times and corresponding probabilities. 582
583 """ 583 Returns
584 return PredictionResultMapper . map_survival ( super () . predict ( values )) 584 -------
585 585 result : np.ndarray
586 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float : 586 Each row represent single example from dataset and contains estimated
587 """Return the Integrated Brier Score on the given dataset and 587 survival function for that example. Estimated survival function is returned
588 labels(event status indicator). 588 as a dictionary containing times and corresponding probabilities.
589 589 """
590 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared 590 return PredictionResultMapper . map_survival ( super () . predict ( values ))
591 differencebetween true event status at time T and predicted event status at that 591
592 time; the Integrated Brier score summarizes the prediction error over all 592 def score ( self , values : Data , labels : Data , survival_time : Data = None ) -> float :
593 observations and over all times in a test set. 593 """Return the Integrated Brier Score on the given dataset and
594 594 labels(event status indicator).
595 Parameters 595
596 ---------- 596 Integrated Brier Score (IBS) - the Brier score (BS) represents the squared
597 values : :class:`rulekit.operator.Data` 597 differencebetween true event status at time T and predicted event status at that
598 attributes 598 time; the Integrated Brier score summarizes the prediction error over all
599 labels : :class:`rulekit.operator.Data` 599 observations and over all times in a test set.
600 survival status 600
601 survival_time: :class:`rulekit.operator.Data` 601 Parameters
602 data about survival time. Could be omitted when *survival_time_attr* 602 ----------
603 parameter was specified 603 values : :class:`rulekit.operator.Data`
604 604 attributes
605 Returns 605 labels : :class:`rulekit.operator.Data`
606 ------- 606 survival status
607 score : float 607 survival_time: :class:`rulekit.operator.Data`
608 Integrated Brier Score of self.predict(values) wrt. labels. 608 data about survival time. Could be omitted when *survival_time_attr*
609 """ 609 parameter was specified
610 return SurvivalRules . score ( self , values , labels , survival_time = survival_time ) 610
611 611 Returns
612 def _get_problem_type ( self ) -> ProblemType : 612 -------
613 return ProblemType . CONTRAST_SURVIVAL 613 score : float
+ 614 Integrated Brier Score of self.predict(values) wrt. labels.
+ 615 """
+ 616 return SurvivalRules . score ( self , values , labels , survival_time = survival_time )
+ 617
+ 618 def _get_problem_type ( self ) -> ProblemType :
+ 619 return ProblemType . CONTRAST_SURVIVAL
diff --git a/docs/reports/junit/report.html b/docs/reports/junit/report.html
index 3c44db4..0d42d07 100644
--- a/docs/reports/junit/report.html
+++ b/docs/reports/junit/report.html
@@ -260,29 +260,29 @@
tests.test_classifier.TestClassifier
@@ -290,13 +290,13 @@
tests.test_classifier.TestExperClassifier
@@ -304,11 +304,11 @@
tests.test_regression.TestExpertRegressor
@@ -316,13 +316,13 @@
tests.test_regression.TestRegressor
@@ -330,9 +330,9 @@
tests.test_rulekit.TestRuleKitMainClass
@@ -340,19 +340,19 @@
tests.test_serialization.TestModelSerialization
@@ -360,7 +360,7 @@
tests.test_sklearn_metrics.TestMetrics
@@ -368,7 +368,7 @@
tests.test_statistics.TestRuleClassifier
@@ -376,9 +376,11 @@
tests.test_survival.TestExpertSurvivalRules
@@ -386,11 +388,11 @@
tests.test_survival.TestKaplanMeierEstimator
@@ -398,17 +400,17 @@
tests.test_survival.TestSurvivalRules
@@ -509,6 +511,8 @@
+
+
@@ -542,16 +546,16 @@
Test Suite:
-
+
Results
- Duration 30.906 sec
+ Duration 30.032 sec
- Tests 45
+ Tests 46
Failures 0
@@ -566,11 +570,11 @@ tests.test_classifier.TestClassifier
-
+
Test case: test_classification_accuracy_on_iris Outcome: Passed Duration: 0.974 sec Duration: 0.91 sec
@@ -584,11 +588,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_classification_metrics Outcome: Passed Duration: 0.028 sec Duration: 0.027 sec
@@ -602,7 +606,7 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_classification_predict_proba Outcome: Passed tests.test_classifier.TestClassifier
-
+
Test case: test_compare_with_java_results Outcome: Passed Duration: 0.374 sec Duration: 0.394 sec
@@ -638,11 +642,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_fit_and_predict_on_boolean_columns Outcome: Passed Duration: 0.157 sec Duration: 0.155 sec
@@ -656,11 +660,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_fit_on_integer_labels Outcome: Passed Duration: 0.09 sec Duration: 0.027 sec
@@ -674,11 +678,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_getting_examples_coverage Outcome: Passed Duration: 0.017 sec Duration: 0.095 sec
@@ -692,11 +696,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_induction_progress_listener Outcome: Passed Duration: 0.065 sec Duration: 0.064 sec
@@ -710,11 +714,11 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_predict_proba Outcome: Passed Duration: 0.155 sec Duration: 0.073 sec
@@ -728,7 +732,7 @@
tests.test_classifier.TestClassifier
-
+
Test case: test_prediction_on_nominal_values Outcome: Passed tests.test_classifier.TestClassifier
-
+
Test case: test_prediction_results_mapping Outcome: Passed tests.test_classifier.TestClassifier
-
+
Test case: test_score Outcome: Passed Duration: 0.015 sec Duration: 0.136 sec
@@ -789,11 +793,11 @@
tests.test_classifier.TestExperClassifier
-
+
Test case: test_compare_with_java_results Outcome: Passed Duration: 0.795 sec Duration: 0.724 sec
@@ -807,11 +811,11 @@
tests.test_classifier.TestExperClassifier
-
+
Test case: test_left_open_intervals_in_expert_induction Outcome: Passed Duration: 0.073 sec Duration: 0.064 sec
@@ -825,11 +829,11 @@
tests.test_classifier.TestExperClassifier
-
+
Test case: test_predict_proba Outcome: Passed Duration: 0.047 sec Duration: 0.07 sec
@@ -843,11 +847,11 @@
tests.test_classifier.TestExperClassifier
-
+
Test case: test_refining_conditions_for_nominal_attributes Outcome: Passed Duration: 0.534 sec Duration: 0.561 sec
@@ -868,11 +872,11 @@
tests.test_regression.TestExpertRegressor
-
+
Test case: test_compare_with_java_results Outcome: Passed Duration: 1.196 sec Duration: 1.08 sec
@@ -886,7 +890,7 @@
tests.test_regression.TestExpertRegressor
-
+
Test case: test_legacy_expert_rules_format Outcome: Passed tests.test_regression.TestExpertRegressor
-
+
Test case: test_refining_conditions_for_nominal_attributes Outcome: Passed tests.test_regression.TestRegressor
-
+
Test case: test_cholesterol Outcome: Passed Duration: 3.309 sec Duration: 3.302 sec
@@ -947,7 +951,7 @@
tests.test_regression.TestRegressor
-
+
Test case: test_compare_with_java_results Outcome: Passed tests.test_regression.TestRegressor
-
+
Test case: test_fit_and_predict_on_boolean_columns Outcome: Passed Duration: 0.495 sec Duration: 0.58 sec
@@ -983,11 +987,11 @@
tests.test_regression.TestRegressor
-
+
Test case: test_induction_progress_listener Outcome: Passed Duration: 0.03 sec Duration: 0.035 sec
@@ -1008,7 +1012,7 @@
tests.test_rulekit.TestRuleKitMainClass
-
+
Test case: test_loading_rulekit_class Outcome: Passed tests.test_rulekit.TestRuleKitMainClass
-
+
Test case: test_reading_version Outcome: Passed tests.test_serialization.TestModelSerialization
-
+
Test case: test_classifier_serialization Outcome: Passed Duration: 0.085 sec Duration: 0.099 sec
@@ -1069,11 +1073,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_expert_classifier_serialization Outcome: Passed Duration: 0.031 sec Duration: 0.028 sec
@@ -1087,11 +1091,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_expert_regressor_serialization Outcome: Passed Duration: 0.068 sec Duration: 0.081 sec
@@ -1105,11 +1109,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_expert_survival_serialization Outcome: Passed Duration: 0.052 sec Duration: 0.046 sec
@@ -1123,11 +1127,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_multiple_serialization Outcome: Passed Duration: 0.015 sec Duration: 0.014 sec
@@ -1141,11 +1145,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_regressor_serialization Outcome: Passed Duration: 4.265 sec Duration: 3.701 sec
@@ -1159,11 +1163,11 @@
tests.test_serialization.TestModelSerialization
-
+
Test case: test_survival_serialization Outcome: Passed Duration: 0.214 sec Duration: 0.306 sec
@@ -1184,11 +1188,11 @@
tests.test_sklearn_metrics.TestMetrics
-
+
Test case: test_classification_accuracy_on_iris Outcome: Passed Duration: 0.014 sec Duration: 0.022 sec
@@ -1209,11 +1213,11 @@
tests.test_statistics.TestRuleClassifier
-
+
Test case: test_classification_accuracy_on_iris Outcome: Passed Duration: 0.01 sec Duration: 0.013 sec
@@ -1234,11 +1238,11 @@
tests.test_survival.TestExpertSurvivalRules
-
+
Test case: test_compare_with_java_results Outcome: Passed Duration: 1.635 sec Duration: 1.892 sec
@@ -1252,11 +1256,29 @@
tests.test_survival.TestExpertSurvivalRules
-
+
Test case: test_refining_conditions_for_nominal_attributes Outcome: Passed Duration: 0.165 sec Duration: 0.191 sec
+
+
+
+
+
+
+
+
+
+
+
+
+ Test case: test_refining_conditions_for_numerical_attributes Outcome: Passed Duration: 0.12 sec
@@ -1277,11 +1299,11 @@
tests.test_survival.TestKaplanMeierEstimator
-
+
Test case: test_accessing_at_risk_count Outcome: Passed Duration: 1.242 sec Duration: 1.39 sec
@@ -1295,11 +1317,11 @@
tests.test_survival.TestKaplanMeierEstimator
-
+
Test case: test_accessing_events_count Outcome: Passed Duration: 1.034 sec Duration: 1.089 sec
@@ -1313,11 +1335,11 @@
tests.test_survival.TestKaplanMeierEstimator
-
+
Test case: test_accessing_probabilities Outcome: Passed Duration: 0.999 sec Duration: 1.071 sec
@@ -1338,11 +1360,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_compare_with_java_results Outcome: Passed Duration: 1.712 sec Duration: 1.507 sec
@@ -1356,11 +1378,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_fit_and_predict_on_boolean_columns Outcome: Passed Duration: 3.122 sec Duration: 3.02 sec
@@ -1374,11 +1396,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_getting_training_dataset_kaplan_meier_estimator Outcome: Passed Duration: 1.552 sec Duration: 1.386 sec
@@ -1392,11 +1414,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_ibs_calculation Outcome: Passed Duration: 1.723 sec Duration: 1.535 sec
@@ -1410,11 +1432,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_induction_progress_listener Outcome: Passed Duration: 1.065 sec Duration: 0.984 sec
@@ -1428,11 +1450,11 @@
tests.test_survival.TestSurvivalRules
-
+
Test case: test_passing_survival_time_column_to_fit_method Outcome: Passed Duration: 3.172 sec Duration: 2.866 sec
diff --git a/docs/serve/index.html b/docs/serve/index.html
index 7885096..0a38c1c 100644
--- a/docs/serve/index.html
+++ b/docs/serve/index.html
@@ -123,7 +123,14 @@
- v2.1.21.0 (latest)
+ v2.1.21.0
+
+
+
+
+
+
+ v2.1.24.0 (latest)
@@ -137,6 +144,8 @@
+
+
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new file mode 100644
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+++ b/docs/serve/v2.1.24.0/.buildinfo
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+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 12f3af7734ee25ce1367bfd71b5b933c
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diff --git a/docs/serve/v2.1.24.0/_sources/index.rst.txt b/docs/serve/v2.1.24.0/_sources/index.rst.txt
new file mode 100644
index 0000000..555fe10
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/index.rst.txt
@@ -0,0 +1,25 @@
+
+RuleKit
+========================================
+
+This package is python wrapper for `RuleKit
`__ library (a versatile tool for rule learning).
+Based on a sequential covering induction algorithm, it is suitable for:
+
+- classification,
+
+- regression,
+
+- survival problems.
+
+This wrapper aims to give an easy way to integrate RuleKit functionality into python projects and notebooks.
+All package models are writen to be compatible with scikit learn library.
+
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Table of contents:
+
+ Quick start <./rst/quick_start.rst>
+ Whats new <./rst/whats_new/Changes in this version.ipynb>
+ Tutorials <./rst/tutorials.rst>
+ Code documentation <./rst/autodoc.rst>
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc.rst.txt
new file mode 100644
index 0000000..92c55aa
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc.rst.txt
@@ -0,0 +1,17 @@
+
+Code documentation
+========================================
+
+.. automodule:: rulekit.main
+ :members:
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Table of contents:
+
+ Classification <./autodoc/classification.rst>
+ Regression <./autodoc/regression.rst>
+ Survival <./autodoc/survival.rst>
+ Parameters <./autodoc/params.rst>
+ Rules <./autodoc/rules.rst>
+ Statistics <./autodoc/stats.rst>
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/classification.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/classification.rst.txt
new file mode 100644
index 0000000..078705a
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/classification.rst.txt
@@ -0,0 +1,11 @@
+
+Classification
+========================================
+
+.. autoclass:: rulekit.classification.RuleClassifier
+ :members:
+ :inherited-members:
+
+.. autoclass:: rulekit.classification.ExpertRuleClassifier
+ :members:
+ :inherited-members:
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/params.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/params.rst.txt
new file mode 100644
index 0000000..43ee18a
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/params.rst.txt
@@ -0,0 +1,93 @@
+Parameters
+========================================
+
+
+.. data:: rulekit.operator.Data
+
+ alias of type Union[numpy.ndarray, pandas.core.frame.DataFrame, List]
+
+.. automodule:: rulekit.params
+ :exclude-members: Measures
+
+ .. autoclass:: Measures
+ :show-inheritance:
+
+ .. autoattribute:: Accuracy
+
+ .. autoattribute:: BinaryEntropy
+
+ .. autoattribute:: C1
+
+ .. autoattribute:: C2
+
+ .. autoattribute:: CFoil
+
+ .. autoattribute:: CN2Significnce
+
+ .. autoattribute:: Correlation
+
+ .. autoattribute:: Coverage
+
+ .. autoattribute:: FBayesianConfirmation
+
+ .. autoattribute:: FMeasure
+
+ .. autoattribute:: FullCoverage
+
+ .. autoattribute:: GeoRSS
+
+ .. autoattribute:: GMeasure
+
+ .. autoattribute:: InformationGain
+
+ .. autoattribute:: JMeasure
+
+ .. autoattribute:: Kappa
+
+ .. autoattribute:: Klosgen
+
+ .. autoattribute:: Laplace
+
+ .. autoattribute:: Lift
+
+ .. autoattribute:: LogicalSufficiency
+
+ .. autoattribute:: MEstimate
+
+ .. autoattribute:: MutualSupport
+
+ .. autoattribute:: Novelty
+
+ .. autoattribute:: OddsRatio
+
+ .. autoattribute:: OneWaySupport
+
+ .. autoattribute:: PawlakDependencyFactor
+
+ .. autoattribute:: Q2
+
+ .. autoattribute:: Precision
+
+ .. autoattribute:: RelativeRisk
+
+ .. autoattribute:: Ripper
+
+ .. autoattribute:: RuleInterest
+
+ .. autoattribute:: RSS
+
+ .. autoattribute:: SBayesian
+
+ .. autoattribute:: Sensitivity
+
+ .. autoattribute:: Specificity
+
+ .. autoattribute:: TwoWaySupport
+
+ .. autoattribute:: WeightedLaplace
+
+ .. autoattribute:: WeightedRelativeAccuracy
+
+ .. autoattribute:: YAILS
+
+ .. autoattribute:: LogRank
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/regression.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/regression.rst.txt
new file mode 100644
index 0000000..7fac70e
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/regression.rst.txt
@@ -0,0 +1,10 @@
+Regression
+==========
+
+.. autoclass:: rulekit.regression.RuleRegressor
+ :members:
+ :inherited-members:
+
+.. autoclass:: rulekit.regression.ExpertRuleRegressor
+ :members:
+ :inherited-members:
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/rules.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/rules.rst.txt
new file mode 100644
index 0000000..bbfb451
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/rules.rst.txt
@@ -0,0 +1,5 @@
+Rules
+=====
+
+.. automodule:: rulekit.rules
+ :members:
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/stats.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/stats.rst.txt
new file mode 100644
index 0000000..f541dad
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/stats.rst.txt
@@ -0,0 +1,5 @@
+Statistics
+==========
+
+.. automodule:: rulekit.stats
+ :members:
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/autodoc/survival.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/autodoc/survival.rst.txt
new file mode 100644
index 0000000..b7d7d77
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/autodoc/survival.rst.txt
@@ -0,0 +1,10 @@
+Survival
+==========
+
+.. autoclass:: rulekit.survival.SurvivalRules
+ :members:
+ :inherited-members:
+
+.. autoclass:: rulekit.survival.ExpertSurvivalRules
+ :members:
+ :inherited-members:
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/quick_start.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/quick_start.rst.txt
new file mode 100644
index 0000000..b35dd8d
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/quick_start.rst.txt
@@ -0,0 +1,69 @@
+
+Quick start
+========================================
+
+.. warning::
+ This package is a wrapper for Java library, and requires Java Development Kit version 8
+ or later to be installed on the computer. Both Open JDK and Oracle implementations are supported.
+
+ If you don't have JDK installed on your computer you can quickly set it up using
+ :ref:`install-jdk
` package.
+
+ .. code-block:: bash
+
+ pip install install-jdk
+
+ .. code-block:: python
+
+ import jdk
+
+ jdk.install('11', jre=True)
+
+Installation
+-------------
+
+.. code-block:: bash
+
+ pip install rulekit
+
+.. note::
+
+To check if everything was installed correctly call:
+
+.. code-block:: python
+
+ import rulekit
+ rulekit.__version__
+
+It should run without errors and print package version.
+
+Package usage
+--------------------
+
+Now we are finally ready to use rulekit package and its models.
+
+.. code-block:: python
+
+ from sklearn import datasets
+ from rulekit import RuleKit
+ from rulekit.classification import RuleClassifier
+
+ iris = datasets.load_iris()
+ X = iris.data
+ y = iris.target
+
+ classifier = RuleClassifier()
+ classifier.fit(X, y)
+
+ prediction = classifier.predict(X)
+
+ from sklearn.metrics import accuracy_score
+
+ print('Accuracy: ', accuracy_score(y, prediction))
+
+
+As you may noticed, training and usage of rulekit models is the same as in scikit learn. This
+mean you easily can use scikit: metrics, cross-validation, hyper-parameters tuning etc.
+
+
+For more examples head to :doc:`Tutorials <./tutorials>` section.
diff --git a/docs/serve/v2.1.24.0/_sources/rst/tutorials.rst.txt b/docs/serve/v2.1.24.0/_sources/rst/tutorials.rst.txt
new file mode 100644
index 0000000..ab6b2f1
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/tutorials.rst.txt
@@ -0,0 +1,12 @@
+
+Tutorials
+=========
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Table of contents:
+
+ Classification <./tutorials/classification.ipynb>
+ Regression <./tutorials/regression.ipynb>
+ Survival <./tutorials/survival.ipynb>
+ Expert Rules <./tutorials/expert_rules.ipynb>
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_sources/rst/tutorials/classification.ipynb.txt b/docs/serve/v2.1.24.0/_sources/rst/tutorials/classification.ipynb.txt
new file mode 100644
index 0000000..1e89340
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/tutorials/classification.ipynb.txt
@@ -0,0 +1,1592 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook presents example usage of package for solving classification problem on `seismic-bumps` dataset. You can access dataset [here](https://raw.githubusercontent.com/adaa-polsl/RuleKit/master/data/seismic-bumps/seismic-bumps.arff).\n",
+ "\n",
+ "This tutorial will cover topics such as: \n",
+ "- training model \n",
+ "- changing model hyperparameters \n",
+ "- hyperparameters tuning \n",
+ "- calculating metrics for model \n",
+ "- getting RuleKit inbuilt "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Install dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install matplotlib"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summary of the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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+ "25% 0.000000 0.000000 0.000000 0.000000 0.0 0.0 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 0.0 0.0 \n",
+ "75% 1.000000 1.000000 0.000000 0.000000 0.0 0.0 \n",
+ "max 8.000000 7.000000 3.000000 1.000000 0.0 0.0 \n",
+ "\n",
+ " nbumps89 senergy maxenergy class \n",
+ "count 2584.0 2584.000000 2584.000000 2584.000000 \n",
+ "mean 0.0 4975.270898 4278.850619 0.065789 \n",
+ "std 0.0 20450.833222 19357.454882 0.247962 \n",
+ "min 0.0 0.000000 0.000000 0.000000 \n",
+ "25% 0.0 0.000000 0.000000 0.000000 \n",
+ "50% 0.0 0.000000 0.000000 0.000000 \n",
+ "75% 0.0 2600.000000 2000.000000 0.000000 \n",
+ "max 0.0 402000.000000 400000.000000 1.000000 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from rulekit.arff import read_arff\n",
+ "\n",
+ "DATASET_URL: str = (\n",
+ " 'https://raw.githubusercontent.com/'\n",
+ " 'adaa-polsl/RuleKit/refs/heads/master/data/seismic-bumps/'\n",
+ " 'seismic-bumps.arff'\n",
+ ")\n",
+ "\n",
+ "df_full: pd.DataFrame = read_arff(DATASET_URL)\n",
+ "df_full['class'] = df_full['class'].astype(int)\n",
+ "df_full.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Decision class distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "groups = df_full['class'].value_counts()\n",
+ "sizes = [groups[0], groups[1]]\n",
+ "labels = [str(e) for e in groups.index]\n",
+ "\n",
+ "fig1, ax1 = plt.subplots()\n",
+ "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
+ "ax1.axis('equal')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Helper function for calculating metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "from sklearn import metrics\n",
+ "import numpy as np\n",
+ "from rulekit.classification import RuleClassifier\n",
+ "\n",
+ "\n",
+ "X: pd.DataFrame = df_full.drop(['class'], axis=1)\n",
+ "y: pd.Series = df_full['class']\n",
+ "\n",
+ "\n",
+ "def get_prediction_metrics(\n",
+ " measure: str,\n",
+ " y_pred: np.ndarray,\n",
+ " y_true: pd.Series,\n",
+ " classification_metrics: dict\n",
+ ") -> tuple[pd.DataFrame, np.ndarray]:\n",
+ " confusion_matrix: np.ndarray = metrics.confusion_matrix(y_true, y_pred)\n",
+ " tn, fp, fn, tp = confusion_matrix.ravel()\n",
+ " sensitivity: float = tp / (tp + fn)\n",
+ " specificity: float = tn / (tn + fp)\n",
+ " npv: float = tn / (tn + fn)\n",
+ " ppv: float = tp / (tp + fp)\n",
+ "\n",
+ " dictionary = {\n",
+ " 'Measure': measure,\n",
+ " 'Accuracy': metrics.accuracy_score(y_true, y_pred),\n",
+ " 'MAE': metrics.mean_absolute_error(y_true, y_pred),\n",
+ " 'Kappa': metrics.cohen_kappa_score(y_true, y_pred),\n",
+ " 'Balanced accuracy': metrics.balanced_accuracy_score(y_true, y_pred),\n",
+ " 'Logistic loss': metrics.log_loss(y_true, y_pred),\n",
+ " 'Precision': metrics.log_loss(y_true, y_pred),\n",
+ " 'Sensitivity': sensitivity,\n",
+ " 'Specificity': specificity,\n",
+ " 'NPV': npv,\n",
+ " 'PPV': ppv,\n",
+ " 'psep': ppv + npv - 1,\n",
+ " 'Fall-out': fp / (fp + tn),\n",
+ " \"Youden's J statistic\": sensitivity + specificity - 1,\n",
+ " 'Lift': (tp / (tp + fp)) / ((tp + fn) / (tp + tn + fp + fn)),\n",
+ " 'F-measure': 2 * tp / (2 * tp + fp + fn),\n",
+ " 'Fowlkes-Mallows index': metrics.fowlkes_mallows_score(y_true, y_pred),\n",
+ " 'False positive': fp,\n",
+ " 'False negative': fn,\n",
+ " 'True positive': tp,\n",
+ " 'True negative': tn,\n",
+ " 'Rules per example': classification_metrics['rules_per_example'],\n",
+ " 'Voting conflicts': classification_metrics['voting_conflicts'],\n",
+ " 'Geometric mean': math.sqrt(specificity * sensitivity),\n",
+ " 'Geometric mean': math.sqrt(specificity * sensitivity),\n",
+ " }\n",
+ " return pd.DataFrame.from_records([dictionary], index='Measure'), confusion_matrix\n",
+ "\n",
+ "\n",
+ "def get_ruleset_stats(\n",
+ " measure: str,\n",
+ " model: RuleClassifier\n",
+ ") -> pd.DataFrame:\n",
+ " return pd.DataFrame.from_records(\n",
+ " [{'Measure': measure, **model.stats.__dict__}],\n",
+ " index='Measure'\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rule induction on full dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 1.218550 \n",
+ " 1.036229 \n",
+ " 0.111458 \n",
+ " 35 \n",
+ " 4.742857 \n",
+ " 22.142857 \n",
+ " 0.259410 \n",
+ " 0.670793 \n",
+ " 0.322125 \n",
+ " 0.005729 \n",
+ " 0.005879 \n",
+ " 0.010640 \n",
+ " 0.971429 \n",
+ " 0.971429 \n",
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\n",
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\n",
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\n",
+ "
, 'minsupp_new': 5, 'pruning_measure': , 'voting_measure': }\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "from rulekit.params import Measures\n",
+ "\n",
+ "N_SPLITS: int = 3\n",
+ "\n",
+ "# define models and parameters\n",
+ "model = RuleClassifier()\n",
+ "minsupp_new = range(3, 15, 2)\n",
+ "measures_choice = [Measures.C2, Measures.RSS, Measures.WeightedLaplace, Measures.Correlation]\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'minsupp_new': minsupp_new, \n",
+ " 'induction_measure': measures_choice, \n",
+ " 'pruning_measure': measures_choice, \n",
+ " 'voting_measure': measures_choice\n",
+ "}\n",
+ "cv = StratifiedKFold(n_splits=N_SPLITS)\n",
+ "grid_search = GridSearchCV(\n",
+ " estimator=model, \n",
+ " param_grid=grid, \n",
+ " cv=cv, \n",
+ " scoring='balanced_accuracy', \n",
+ " n_jobs=3\n",
+ ")\n",
+ "grid_result = grid_search.fit(X, y)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\"Best BAC: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building model with tuned hyperparameters\n",
+ "\n",
+ "### Split dataset to train and test (80%/20%)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from IPython.display import display\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)\n",
+ "\n",
+ "\n",
+ "clf = RuleClassifier(**grid_result.best_params_)\n",
+ "clf.fit(X_train, y_train)\n",
+ "ruleset: RuleSet[ClassificationRule] = clf.model\n",
+ "ruleset_stats = get_ruleset_stats('Best', ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rules evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "time_total_s 0.173054\n",
+ "time_growing_s 0.120460\n",
+ "time_pruning_s 0.029986\n",
+ "rules_count 29.000000\n",
+ "conditions_per_rule 2.689655\n",
+ "induced_conditions_per_rule 15.310345\n",
+ "avg_rule_coverage 0.491183\n",
+ "avg_rule_precision 0.736226\n",
+ "avg_rule_quality 1.309334\n",
+ "pvalue 0.019831\n",
+ "FDR_pvalue 0.019993\n",
+ "FWER_pvalue 0.024284\n",
+ "fraction_significant 0.931034\n",
+ "fraction_FDR_significant 0.931034\n",
+ "fraction_FWER_significant 0.931034\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Validate model on test dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Accuracy 0.808511\n",
+ "MAE 0.191489\n",
+ "Kappa 0.170010\n",
+ "Balanced accuracy 0.679398\n",
+ "Logistic loss 6.901976\n",
+ "Precision 6.901976\n",
+ "Sensitivity 0.533333\n",
+ "Specificity 0.825462\n",
+ "NPV 0.966346\n",
+ "PPV 0.158416\n",
+ "psep 0.124762\n",
+ "Fall-out 0.174538\n",
+ "Youden's J statistic 0.358795\n",
+ "Lift 2.730033\n",
+ "F-measure 0.244275\n",
+ "Fowlkes-Mallows index 0.809997\n",
+ "False positive 85.000000\n",
+ "False negative 14.000000\n",
+ "True positive 16.000000\n",
+ "True negative 402.000000\n",
+ "Rules per example 14.034816\n",
+ "Voting conflicts 360.000000\n",
+ "Geometric mean 0.663511\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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\n",
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\n",
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\n",
+ " \n",
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+ " normalized_absolute_error \n",
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+ " root_mean_squared_error \n",
+ " root_relative_squared_error \n",
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+ " squared_correlation \n",
+ " \n",
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+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " absolute_error \n",
+ " relative_error \n",
+ " relative_error_lenient \n",
+ " relative_error_strict \n",
+ " normalized_absolute_error \n",
+ " squared_error \n",
+ " root_mean_squared_error \n",
+ " root_relative_squared_error \n",
+ " correlation \n",
+ " squared_correlation \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
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+ " 0.094935 \n",
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+ " 0.142569 \n",
+ " 0.126236 \n",
+ " 0.835385 \n",
+ " 0.697868 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.105350 \n",
+ " 0.091827 \n",
+ " 0.090950 \n",
+ " 0.109321 \n",
+ " 0.726385 \n",
+ " 0.021890 \n",
+ " 0.147951 \n",
+ " 0.119472 \n",
+ " 0.866898 \n",
+ " 0.751512 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 0.128302 \n",
+ " 0.113411 \n",
+ " 0.111947 \n",
+ " 0.134690 \n",
+ " 0.884639 \n",
+ " 0.027270 \n",
+ " 0.165136 \n",
+ " 0.134849 \n",
+ " 0.866442 \n",
+ " 0.750722 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
, 'minsupp_new': 6, 'pruning_measure': , 'voting_measure': }\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "\n",
+ "\n",
+ "# define models and parameters\n",
+ "model = RuleRegressor(mean_based_regression=True)\n",
+ "minsupp_new = range(5, 7)\n",
+ "measures_choice = [Measures.C2, Measures.Correlation, Measures.RSS]\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'minsupp_new': minsupp_new, \n",
+ " 'induction_measure': measures_choice, \n",
+ " 'pruning_measure': measures_choice, \n",
+ " 'voting_measure': measures_choice\n",
+ "}\n",
+ "cv = KFold(n_splits=2)\n",
+ "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=cv, scoring='neg_root_mean_squared_error')\n",
+ "grid_result = grid_search.fit(X_train, y_train)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\"Best RMSE: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prediction using the model selected from the tuning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reg: RuleRegressor = grid_result.best_estimator_\n",
+ "ruleset: RuleSet[RegressionRule] = reg.model\n",
+ "ruleset_stats = get_ruleset_stats('', ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generated rules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IF MM31 = (-inf, 0.23) THEN MM116_pred = {0.40} [0.39,0.41]\n",
+ "IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.40} [0.39,0.42]\n",
+ "IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) THEN MM116_pred = {0.40} [0.38,0.42]\n",
+ "IF MM31 = <0.24, 0.25) AND AS038 = (-inf, 2.45) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) THEN MM116_pred = {0.50} [0.47,0.54]\n",
+ "IF MM116 = (-inf, 0.45) AND MM31 = <0.24, 0.25) AND PG072 = (-inf, 2.05) AND AS038 = (-inf, 2.45) AND PD = <0.50, inf) THEN MM116_pred = {0.41} [0.38,0.44]\n",
+ "IF MM31 = <0.24, 0.25) AND PD = (-inf, 0.50) THEN MM116_pred = {0.51} [0.47,0.54]\n",
+ "IF MM31 = (-inf, 0.26) AND DMM116 = <-0.05, 0.05) THEN MM116_pred = {0.46} [0.36,0.55]\n",
+ "IF MM116 = (-inf, 0.45) THEN MM116_pred = {0.40} [0.37,0.44]\n",
+ "IF MM31 = <0.23, 0.24) AND BA13 = (-inf, 1075.50) AND MM116 = <0.45, inf) THEN MM116_pred = {0.50} [0.48,0.52]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.51} [0.48,0.53]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND MM31 = <0.23, 0.29) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.51} [0.48,0.53]\n",
+ "IF MM116 = <0.35, 0.55) AND MM31 = (-inf, 0.26) AND BA13 = <1077.50, inf) AND DMM116 = (-inf, -0.05) THEN MM116_pred = {0.54} [0.48,0.60]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND AS038 = (-inf, 2.45) AND PD = (-inf, 0.50) AND BA13 = (-inf, 1077.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.50} [0.48,0.53]\n",
+ "IF MM116 = <0.45, 0.55) AND MM31 = <0.28, 0.30) AND DMM116 = <-0.05, 0.05) AND AS038 = <2.25, 2.35) AND PG072 = <1.75, 1.95) AND BA13 = <1075.50, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]\n",
+ "IF MM116 = (-inf, 0.55) AND MM31 = <0.29, 0.30) AND PG072 = (-inf, 1.95) AND BA13 = (-inf, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]\n",
+ "IF MM116 = (-inf, 0.55) THEN MM116_pred = {0.45} [0.39,0.52]\n",
+ "IF MM31 = <0.26, 0.27) AND MM116 = <0.55, 0.65) AND PG072 = <1.75, 1.85) AND AS038 = <2.25, 2.45) AND DMM116 = <-0.05, 0.05) AND PD = (-inf, 0.50) AND BA13 = <1074.50, 1077.50) THEN MM116_pred = {0.60} [NaN,NaN]\n",
+ "IF MM116 = <0.45, 0.65) AND MM31 = <0.23, inf) THEN MM116_pred = {0.55} [0.49,0.61]\n",
+ "IF MM116 = <0.55, 0.75) THEN MM116_pred = {0.67} [0.58,0.77]\n",
+ "IF MM116 = <0.75, 0.85) THEN MM116_pred = {0.83} [0.76,0.90]\n",
+ "IF MM116 = <0.85, inf) THEN MM116_pred = {1.06} [0.88,1.24]\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rule in ruleset.rules:\n",
+ " print(rule)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ruleset evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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187 rows × 37 columns
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+ " \n",
+ " minimum_covered \n",
+ " maximum_uncovered_fraction \n",
+ " ignore_missing \n",
+ " pruning_enabled \n",
+ " max_growing_condition \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.05 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 1.771417 \n",
+ " 0.797513 \n",
+ " 0.902853 \n",
+ " 5 \n",
+ " 3.6 \n",
+ " 65.2 \n",
+ " 0.308021 \n",
+ " 1.0 \n",
+ " 0.999865 \n",
+ " 0.000135 \n",
+ " 0.000147 \n",
+ " 0.000184 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "for i in range(5):\n",
+ " plt.step(\n",
+ " predictions[i][\"times\"],\n",
+ " predictions[i][\"probabilities\"],\n",
+ " label=f'Example {i}'\n",
+ " )\n",
+ "plt.legend(title='Example index:')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot rules Kaplan-Meier's estimators on top of the training dataset estimator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "r1: IF Donorage = (-inf, 45.16) AND Relapse = {0} AND Recipientage = (-inf, 17.45) THEN \n",
+ "r2: IF Donorage = (-inf, 43.63) AND HLAmismatch = {0} AND Relapse = {1} THEN \n",
+ "r3: IF PLTrecovery = (-inf, 266) AND time_to_aGvHD_III_IV = <12.50, inf) AND ANCrecovery = <10.50, 19.50) AND Rbodymass = (-inf, 69) AND Donorage = (-inf, 44.06) AND Recipientage = <4.60, inf) AND CD34kgx10d6 = (-inf, 16.98) THEN \n",
+ "r4: IF Donorage = <37.16, inf) AND Recipientage = <5.15, inf) AND time_to_aGvHD_III_IV = <23.50, inf) AND CD3dCD34 = <0.90, 73.72) THEN \n",
+ "r5: IF Recipientage = <17.85, 18.85) THEN \n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from rulekit.kaplan_meier import KaplanMeierEstimator\n",
+ "\n",
+ "# plot rules kaplan-meier curves\n",
+ "for i, rule in enumerate(ruleset.rules):\n",
+ " rule_label: str = f'r{i + 1}'\n",
+ " rule_km: KaplanMeierEstimator = rule.kaplan_meier_estimator\n",
+ " plt.step(\n",
+ " rule_km.times,\n",
+ " rule_km.probabilities,\n",
+ " label=rule_label\n",
+ " )\n",
+ " print(f'{rule_label}: {rule}')\n",
+ "\n",
+ "# plot whole dataset kaplan-meier curve\n",
+ "train_km: KaplanMeierEstimator = srv.get_train_set_kaplan_meier()\n",
+ "plt.step(\n",
+ " train_km.times,\n",
+ " train_km.probabilities,\n",
+ " label='Training set estimator'\n",
+ ")\n",
+ "\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GkNBc5iBpwmj"
+ },
+ "source": [
+ "### Rules evaluation on full set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "9UjrC8r-p59d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.19651358709002972\n"
+ ]
+ }
+ ],
+ "source": [
+ "integrated_brier_score = srv.score(X, y)\n",
+ "print(f'Integrated Brier Score: {integrated_brier_score}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FpoSoaKdqAGQ"
+ },
+ "source": [
+ "## Stratified K-Folds cross-validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "0nNv6a84qTsq"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from rulekit.exceptions import RuleKitJavaException\n",
+ "\n",
+ "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
+ "\n",
+ "ruleset_stats = pd.DataFrame()\n",
+ "survival_metrics = []\n",
+ "\n",
+ "for train_index, test_index in skf.split(X, y):\n",
+ " X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
+ " y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
+ "\n",
+ " srv = SurvivalRules(\n",
+ " survival_time_attr='survival_time'\n",
+ " )\n",
+ " srv.fit(X_train, y_train)\n",
+ "\n",
+ " ruleset = srv.model\n",
+ "\n",
+ " ibs: float = srv.score(X_test, y_test)\n",
+ "\n",
+ " survival_metrics.append(ibs)\n",
+ " ruleset_stats = pd.concat([ruleset_stats, get_ruleset_stats(ruleset)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MfCOH_f3sICm"
+ },
+ "source": [
+ "Ruleset characteristics (average)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "xzbazr51sRd3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minimum_covered 0.050000\n",
+ "maximum_uncovered_fraction 0.000000\n",
+ "ignore_missing 0.000000\n",
+ "pruning_enabled 1.000000\n",
+ "max_growing_condition 0.000000\n",
+ "time_total_s 0.799019\n",
+ "time_growing_s 0.296248\n",
+ "time_pruning_s 0.477474\n",
+ "rules_count 4.000000\n",
+ "conditions_per_rule 2.581667\n",
+ "induced_conditions_per_rule 59.825000\n",
+ "avg_rule_coverage 0.486613\n",
+ "avg_rule_precision 1.000000\n",
+ "avg_rule_quality 0.995955\n",
+ "pvalue 0.004045\n",
+ "FDR_pvalue 0.004061\n",
+ "FWER_pvalue 0.004104\n",
+ "fraction_significant 0.980000\n",
+ "fraction_FDR_significant 0.980000\n",
+ "fraction_FWER_significant 0.980000\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_SmDJho4sVEO"
+ },
+ "source": [
+ "Rules evaluation on dataset (average)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "Co-fNd9nshWB"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.20178456199764142\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'Integrated Brier Score: {np.mean(survival_metrics)}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d-GdQ-wUtzW9"
+ },
+ "source": [
+ "## Hyperparameters tuning\n",
+ "\n",
+ "This one gonna take a while..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.model_selection import GridSearchCV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def scorer(estimator: SurvivalRules, X: pd.DataFrame, y: pd.Series) -> float:\n",
+ " return -1 * estimator.score(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best Integrated Brier Score: -0.21437408819868886 using {'minsupp_new': 3, 'survival_time_attr': 'survival_time'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# define models and parameters\n",
+ "model = SurvivalRules(survival_time_attr='survival_time')\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'survival_time_attr': ['survival_time'],\n",
+ " 'minsupp_new': range(1, 10),\n",
+ "}\n",
+ "\n",
+ "cv = StratifiedKFold(n_splits=3)\n",
+ "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=cv, scoring=scorer)\n",
+ "grid_result = grid_search.fit(X, y)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\n",
+ " 'Best Integrated Brier Score: '\n",
+ " f'{grid_result.best_score_} using {grid_result.best_params_}'\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building model with tuned hyperparameters\n",
+ "\n",
+ "### Split dataset to train and test (80%/20%)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, stratify=y)\n",
+ "\n",
+ "srv = SurvivalRules(\n",
+ " survival_time_attr='survival_time',\n",
+ " minsupp_new=5\n",
+ ")\n",
+ "srv.fit(X_train, y_train)\n",
+ "ruleset: RuleSet[SurvivalRule] = srv.model\n",
+ "ruleset_stats: pd.DataFrame = get_ruleset_stats(ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rules evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minimum_covered 5.0\n",
+ "maximum_uncovered_fraction 0.0\n",
+ "ignore_missing False\n",
+ "pruning_enabled True\n",
+ "max_growing_condition 0.0\n",
+ "time_total_s 0.594173\n",
+ "time_growing_s 0.244234\n",
+ "time_pruning_s 0.312523\n",
+ "rules_count 4\n",
+ "conditions_per_rule 2.25\n",
+ "induced_conditions_per_rule 55.25\n",
+ "avg_rule_coverage 0.389262\n",
+ "avg_rule_precision 1.0\n",
+ "avg_rule_quality 1.0\n",
+ "pvalue 0.0\n",
+ "FDR_pvalue 0.0\n",
+ "FWER_pvalue 0.0\n",
+ "fraction_significant 1.0\n",
+ "fraction_FDR_significant 1.0\n",
+ "fraction_FWER_significant 1.0\n",
+ "Name: 0, dtype: object"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.iloc[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Validate model on test dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.14054870564224475\n"
+ ]
+ }
+ ],
+ "source": [
+ "integrated_brier_score = srv.score(X_test, y_test)\n",
+ "print(f'Integrated Brier Score: {integrated_brier_score}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictions = srv.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Predicted Kaplan-Meier curves for 5 examples from test set')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for i in range(5):\n",
+ " plt.step(\n",
+ " predictions[i][\"times\"], \n",
+ " predictions[i][\"probabilities\"],\n",
+ " label=f'Example {i}'\n",
+ " )\n",
+ "plt.legend(title='Examples indices:')\n",
+ "plt.title('Predicted Kaplan-Meier curves for 5 examples from test set')"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Raport_przezyciowy.ipynb",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "tutorials_env",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.0"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "62266c16fff41e971c13e9cb2ad3d47e4ef45d0678714c255381eb9fdcbd7032"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs/serve/v2.1.24.0/_sources/rst/whats_new/Changes in this version.ipynb.txt b/docs/serve/v2.1.24.0/_sources/rst/whats_new/Changes in this version.ipynb.txt
new file mode 100644
index 0000000..401e665
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_sources/rst/whats_new/Changes in this version.ipynb.txt
@@ -0,0 +1,76 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# What's new in RuleKit version 2.1.24.0?\n",
+ "\n",
+ "\n",
+ "### 1. Revert breaking changes in expert rules induction for regression and survival \n",
+ "The latest version 2.1.21.0 introduced some groundbreaking changes, which you can read more\n",
+ "about them in the latest [release note](https://github.com/adaa-polsl/RuleKit-python/releases/tag/v2.1.21.0).\n",
+ "Now rules and expert conditions can be defined in both the old and new formats, see example below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# both variants will work the same\n",
+ "expert_rules = [\n",
+ " (\n",
+ " 'rule-0',\n",
+ " 'IF [[CD34kgx10d6 = (-inf, 10.0)]] AND [[extcGvHD = {0}]] THEN survival_status = {NaN}'\n",
+ " ),\n",
+ " (\n",
+ " 'rule-0',\n",
+ " 'IF [[CD34kgx10d6 = (-inf, 10.0)]] AND [[extcGvHD = {0}]] THEN'\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. Upgrade to new version of RuleKit\n",
+ "\n",
+ "In the new version of the Java RuleKit library, many bugs regarding expert induction have been corrected."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Other changes\n",
+ "\n",
+ "* Improve flake8 score\n",
+ "* Add more unit tests."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "tutorials_env",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/serve/v2.1.24.0/_static/_sphinx_javascript_frameworks_compat.js b/docs/serve/v2.1.24.0/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000..8141580
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+ if (!x) {
+ return x
+ }
+ return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+ if (typeof s === 'undefined')
+ s = document.location.search;
+ var parts = s.substr(s.indexOf('?') + 1).split('&');
+ var result = {};
+ for (var i = 0; i < parts.length; i++) {
+ var tmp = parts[i].split('=', 2);
+ var key = jQuery.urldecode(tmp[0]);
+ var value = jQuery.urldecode(tmp[1]);
+ if (key in result)
+ result[key].push(value);
+ else
+ result[key] = [value];
+ }
+ return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+ function highlight(node, addItems) {
+ if (node.nodeType === 3) {
+ var val = node.nodeValue;
+ var pos = val.toLowerCase().indexOf(text);
+ if (pos >= 0 &&
+ !jQuery(node.parentNode).hasClass(className) &&
+ !jQuery(node.parentNode).hasClass("nohighlight")) {
+ var span;
+ var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+ if (isInSVG) {
+ span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+ } else {
+ span = document.createElement("span");
+ span.className = className;
+ }
+ span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+ node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+ document.createTextNode(val.substr(pos + text.length)),
+ node.nextSibling));
+ node.nodeValue = val.substr(0, pos);
+ if (isInSVG) {
+ var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+ var bbox = node.parentElement.getBBox();
+ rect.x.baseVal.value = bbox.x;
+ rect.y.baseVal.value = bbox.y;
+ rect.width.baseVal.value = bbox.width;
+ rect.height.baseVal.value = bbox.height;
+ rect.setAttribute('class', className);
+ addItems.push({
+ "parent": node.parentNode,
+ "target": rect});
+ }
+ }
+ }
+ else if (!jQuery(node).is("button, select, textarea")) {
+ jQuery.each(node.childNodes, function() {
+ highlight(this, addItems);
+ });
+ }
+ }
+ var addItems = [];
+ var result = this.each(function() {
+ highlight(this, addItems);
+ });
+ for (var i = 0; i < addItems.length; ++i) {
+ jQuery(addItems[i].parent).before(addItems[i].target);
+ }
+ return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+ jQuery.uaMatch = function(ua) {
+ ua = ua.toLowerCase();
+
+ var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+ /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+ /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+ /(msie) ([\w.]+)/.exec(ua) ||
+ ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+ [];
+
+ return {
+ browser: match[ 1 ] || "",
+ version: match[ 2 ] || "0"
+ };
+ };
+ jQuery.browser = {};
+ jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/docs/serve/v2.1.24.0/_static/basic.css b/docs/serve/v2.1.24.0/_static/basic.css
new file mode 100644
index 0000000..30fee9d
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+ clear: both;
+}
+
+div.section::after {
+ display: block;
+ content: '';
+ clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+ width: 100%;
+ font-size: 90%;
+}
+
+div.related h3 {
+ display: none;
+}
+
+div.related ul {
+ margin: 0;
+ padding: 0 0 0 10px;
+ list-style: none;
+}
+
+div.related li {
+ display: inline;
+}
+
+div.related li.right {
+ float: right;
+ margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+ padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+ float: left;
+ width: 230px;
+ margin-left: -100%;
+ font-size: 90%;
+ word-wrap: break-word;
+ overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+ list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+ margin-left: 20px;
+ list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+ margin-top: 0;
+ margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+ margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+ border: 1px solid #98dbcc;
+ font-family: sans-serif;
+ font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+ overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+ float: left;
+ width: 80%;
+ padding: 0.25em;
+ box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+ float: left;
+ width: 20%;
+ border-left: none;
+ padding: 0.25em;
+ box-sizing: border-box;
+}
+
+
+img {
+ border: 0;
+ max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+ margin: 10px 0 0 20px;
+ padding: 0;
+}
+
+ul.search li {
+ padding: 5px 0 5px 20px;
+ background-image: url(file.png);
+ background-repeat: no-repeat;
+ background-position: 0 7px;
+}
+
+ul.search li a {
+ font-weight: bold;
+}
+
+ul.search li p.context {
+ color: #888;
+ margin: 2px 0 0 30px;
+ text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+ font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+ width: 90%;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table.contentstable p.biglink {
+ line-height: 150%;
+}
+
+a.biglink {
+ font-size: 1.3em;
+}
+
+span.linkdescr {
+ font-style: italic;
+ padding-top: 5px;
+ font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+ width: 100%;
+}
+
+table.indextable td {
+ text-align: left;
+ vertical-align: top;
+}
+
+table.indextable ul {
+ margin-top: 0;
+ margin-bottom: 0;
+ list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+ padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+ height: 10px;
+}
+
+table.indextable tr.cap {
+ margin-top: 10px;
+ background-color: #f2f2f2;
+}
+
+img.toggler {
+ margin-right: 3px;
+ margin-top: 3px;
+ cursor: pointer;
+}
+
+div.modindex-jumpbox {
+ border-top: 1px solid #ddd;
+ border-bottom: 1px solid #ddd;
+ margin: 1em 0 1em 0;
+ padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+ border-top: 1px solid #ddd;
+ border-bottom: 1px solid #ddd;
+ margin: 1em 0 1em 0;
+ padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+ padding: 2px;
+ border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+ min-width: 360px;
+ max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+ -moz-hyphens: auto;
+ -ms-hyphens: auto;
+ -webkit-hyphens: auto;
+ hyphens: auto;
+}
+
+a.headerlink {
+ visibility: hidden;
+}
+
+a:visited {
+ color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+ visibility: visible;
+}
+
+div.body p.caption {
+ text-align: inherit;
+}
+
+div.body td {
+ text-align: left;
+}
+
+.first {
+ margin-top: 0 !important;
+}
+
+p.rubric {
+ margin-top: 30px;
+ font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+ clear: left;
+ float: left;
+ margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+ clear: right;
+ float: right;
+ margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+.align-left {
+ text-align: left;
+}
+
+.align-center {
+ text-align: center;
+}
+
+.align-default {
+ text-align: center;
+}
+
+.align-right {
+ text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+ margin: 0 0 0.5em 1em;
+ border: 1px solid #ddb;
+ padding: 7px;
+ background-color: #ffe;
+ width: 40%;
+ float: right;
+ clear: right;
+ overflow-x: auto;
+}
+
+p.sidebar-title {
+ font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+ clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+ border: 1px solid #ccc;
+ padding: 7px;
+ margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+ font-size: 1.1em;
+ font-weight: bold;
+ margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ padding: 7px;
+}
+
+div.admonition dt {
+ font-weight: bold;
+}
+
+p.admonition-title {
+ margin: 0px 10px 5px 0px;
+ font-weight: bold;
+}
+
+div.body p.centered {
+ text-align: center;
+ margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+ display: block;
+ content: '';
+ clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ border: 0;
+ border-collapse: collapse;
+}
+
+table.align-center {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table.align-default {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table caption span.caption-number {
+ font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+ padding: 1px 8px 1px 5px;
+ border-top: 0;
+ border-left: 0;
+ border-right: 0;
+ border-bottom: 1px solid #aaa;
+}
+
+th {
+ text-align: left;
+ padding-right: 5px;
+}
+
+table.citation {
+ border-left: solid 1px gray;
+ margin-left: 1px;
+}
+
+table.citation td {
+ border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+ margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+ margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+ margin: 0.5em;
+ padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+ padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+ font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+ border: 0 !important;
+}
+
+.field-list ul {
+ margin: 0;
+ padding-left: 1em;
+}
+
+.field-list p {
+ margin: 0;
+}
+
+.field-name {
+ -moz-hyphens: manual;
+ -ms-hyphens: manual;
+ -webkit-hyphens: manual;
+ hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+ margin: 1em 0;
+}
+
+table.hlist td {
+ vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+ font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+.sig-name {
+ font-size: 1.1em;
+}
+
+code.descname {
+ font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+ background-color: transparent;
+}
+
+.optional {
+ font-size: 1.3em;
+}
+
+.sig-paren {
+ font-size: larger;
+}
+
+.sig-param.n {
+ font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+ font-family: unset;
+}
+
+.sig.c .k, .sig.c .kt,
+.sig.cpp .k, .sig.cpp .kt {
+ color: #0033B3;
+}
+
+.sig.c .m,
+.sig.cpp .m {
+ color: #1750EB;
+}
+
+.sig.c .s, .sig.c .sc,
+.sig.cpp .s, .sig.cpp .sc {
+ color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+ list-style: decimal;
+}
+
+ol.loweralpha {
+ list-style: lower-alpha;
+}
+
+ol.upperalpha {
+ list-style: upper-alpha;
+}
+
+ol.lowerroman {
+ list-style: lower-roman;
+}
+
+ol.upperroman {
+ list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+ margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+ margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+ margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+ margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+ margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+ float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+ padding-right: 0.5em;
+}
+aside.footnote > p {
+ margin-left: 2em;
+}
+div.citation > p {
+ margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+ margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+ content: "";
+ clear: both;
+}
+
+dl.field-list {
+ display: grid;
+ grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+ font-weight: bold;
+ word-break: break-word;
+ padding-left: 0.5em;
+ padding-right: 5px;
+}
+
+dl.field-list > dd {
+ padding-left: 0.5em;
+ margin-top: 0em;
+ margin-left: 0em;
+ margin-bottom: 0em;
+}
+
+dl {
+ margin-bottom: 15px;
+}
+
+dd > :first-child {
+ margin-top: 0px;
+}
+
+dd ul, dd table {
+ margin-bottom: 10px;
+}
+
+dd {
+ margin-top: 3px;
+ margin-bottom: 10px;
+ margin-left: 30px;
+}
+
+.sig dd {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+
+.sig dl {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+ background-color: #fbe54e;
+}
+
+rect.highlighted {
+ fill: #fbe54e;
+}
+
+dl.glossary dt {
+ font-weight: bold;
+ font-size: 1.1em;
+}
+
+.versionmodified {
+ font-style: italic;
+}
+
+.system-message {
+ background-color: #fda;
+ padding: 5px;
+ border: 3px solid red;
+}
+
+.footnote:target {
+ background-color: #ffa;
+}
+
+.line-block {
+ display: block;
+ margin-top: 1em;
+ margin-bottom: 1em;
+}
+
+.line-block .line-block {
+ margin-top: 0;
+ margin-bottom: 0;
+ margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+ font-family: sans-serif;
+}
+
+.accelerator {
+ text-decoration: underline;
+}
+
+.classifier {
+ font-style: oblique;
+}
+
+.classifier:before {
+ font-style: normal;
+ margin: 0 0.5em;
+ content: ":";
+ display: inline-block;
+}
+
+abbr, acronym {
+ border-bottom: dotted 1px;
+ cursor: help;
+}
+
+.translated {
+ background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+ background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+ overflow: auto;
+ overflow-y: hidden; /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+ clear: both;
+}
+
+span.pre {
+ -moz-hyphens: none;
+ -ms-hyphens: none;
+ -webkit-hyphens: none;
+ hyphens: none;
+ white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+ margin: 1em 0;
+}
+
+td.linenos pre {
+ border: 0;
+ background-color: transparent;
+ color: #aaa;
+}
+
+table.highlighttable {
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
+}
+
+table.highlighttable td {
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
+}
+
+div.code-block-caption {
+ margin-top: 1em;
+ padding: 2px 5px;
+ font-size: small;
+}
+
+div.code-block-caption code {
+ background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
+ -webkit-user-select: text; /* Safari fallback only */
+ -webkit-user-select: none; /* Chrome/Safari */
+ -moz-user-select: none; /* Firefox */
+ -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+ padding: 0.1em 0.3em;
+ font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+ margin: 1em 0;
+}
+
+code.xref, a code {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+ background-color: transparent;
+}
+
+.viewcode-link {
+ float: right;
+}
+
+.viewcode-back {
+ float: right;
+ font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+ margin: -1px -10px;
+ padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+ vertical-align: middle;
+}
+
+div.body div.math p {
+ text-align: center;
+}
+
+span.eqno {
+ float: right;
+}
+
+span.eqno a.headerlink {
+ position: absolute;
+ z-index: 1;
+}
+
+div.math:hover a.headerlink {
+ visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+ div.document,
+ div.documentwrapper,
+ div.bodywrapper {
+ margin: 0 !important;
+ width: 100%;
+ }
+
+ div.sphinxsidebar,
+ div.related,
+ div.footer,
+ #top-link {
+ display: none;
+ }
+}
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_static/check-solid.svg b/docs/serve/v2.1.24.0/_static/check-solid.svg
new file mode 100644
index 0000000..92fad4b
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/check-solid.svg
@@ -0,0 +1,4 @@
+
+
diff --git a/docs/serve/v2.1.24.0/_static/clipboard.min.js b/docs/serve/v2.1.24.0/_static/clipboard.min.js
new file mode 100644
index 0000000..54b3c46
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1
+ Short
+ */
+ .o-tooltip--left {
+ position: relative;
+ }
+
+ .o-tooltip--left:after {
+ opacity: 0;
+ visibility: hidden;
+ position: absolute;
+ content: attr(data-tooltip);
+ padding: .2em;
+ font-size: .8em;
+ left: -.2em;
+ background: grey;
+ color: white;
+ white-space: nowrap;
+ z-index: 2;
+ border-radius: 2px;
+ transform: translateX(-102%) translateY(0);
+ transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+ display: block;
+ opacity: 1;
+ visibility: visible;
+ transform: translateX(-100%) translateY(0);
+ transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+ transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+ button.copybtn {
+ display: none;
+ }
+}
diff --git a/docs/serve/v2.1.24.0/_static/copybutton.js b/docs/serve/v2.1.24.0/_static/copybutton.js
new file mode 100644
index 0000000..2ea7ff3
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+ 'en': {
+ 'copy': 'Copy',
+ 'copy_to_clipboard': 'Copy to clipboard',
+ 'copy_success': 'Copied!',
+ 'copy_failure': 'Failed to copy',
+ },
+ 'es' : {
+ 'copy': 'Copiar',
+ 'copy_to_clipboard': 'Copiar al portapapeles',
+ 'copy_success': '¡Copiado!',
+ 'copy_failure': 'Error al copiar',
+ },
+ 'de' : {
+ 'copy': 'Kopieren',
+ 'copy_to_clipboard': 'In die Zwischenablage kopieren',
+ 'copy_success': 'Kopiert!',
+ 'copy_failure': 'Fehler beim Kopieren',
+ },
+ 'fr' : {
+ 'copy': 'Copier',
+ 'copy_to_clipboard': 'Copier dans le presse-papier',
+ 'copy_success': 'Copié !',
+ 'copy_failure': 'Échec de la copie',
+ },
+ 'ru': {
+ 'copy': 'Скопировать',
+ 'copy_to_clipboard': 'Скопировать в буфер',
+ 'copy_success': 'Скопировано!',
+ 'copy_failure': 'Не удалось скопировать',
+ },
+ 'zh-CN': {
+ 'copy': '复制',
+ 'copy_to_clipboard': '复制到剪贴板',
+ 'copy_success': '复制成功!',
+ 'copy_failure': '复制失败',
+ },
+ 'it' : {
+ 'copy': 'Copiare',
+ 'copy_to_clipboard': 'Copiato negli appunti',
+ 'copy_success': 'Copiato!',
+ 'copy_failure': 'Errore durante la copia',
+ }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+ && messages[document.documentElement.lang] !== undefined ) {
+ locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+ doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `
+ ${messages[locale]['copy_success']}
+ `
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+ iconCopy = `
+ ${messages[locale]['copy_to_clipboard']}
+ `
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+ if (document.readyState != 'loading') {
+ cb()
+ } else if (document.addEventListener) {
+ document.addEventListener('DOMContentLoaded', cb)
+ } else {
+ document.attachEvent('onreadystatechange', function() {
+ if (document.readyState == 'complete') cb()
+ })
+ }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+ if (window.getSelection) {
+ window.getSelection().removeAllRanges()
+ } else if (document.selection) {
+ document.selection.empty()
+ }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+ el.setAttribute('data-tooltip', newText)
+ el.classList.add('success')
+ // Remove success a little bit sooner than we change the tooltip
+ // So that we can use CSS to hide the copybutton first
+ setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+ setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+ el.innerHTML = iconCheck;
+ setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+ // If ClipboardJS hasn't loaded, wait a bit and try again. This
+ // happens because we load ClipboardJS asynchronously.
+ if (window.ClipboardJS === undefined) {
+ setTimeout(addCopyButtonToCodeCells, 250)
+ return
+ }
+
+ // Add copybuttons to all of our code cells
+ const COPYBUTTON_SELECTOR = 'div.highlight pre';
+ const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+ codeCells.forEach((codeCell, index) => {
+ const id = codeCellId(index)
+ codeCell.setAttribute('id', id)
+
+ const clipboardButton = id =>
+ `
+ ${iconCopy}
+ `
+ codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+ })
+
+function escapeRegExp(string) {
+ return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+ const clone = target.cloneNode(true); // clone as to not modify the live DOM
+ if (exclude) {
+ // remove excluded nodes
+ clone.querySelectorAll(exclude).forEach(node => node.remove());
+ }
+ return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+ var regexp;
+ var match;
+
+ // Do we check for line continuation characters and "HERE-documents"?
+ var useLineCont = !!lineContinuationChar
+ var useHereDoc = !!hereDocDelim
+
+ // create regexp to capture prompt and remaining line
+ if (isRegexp) {
+ regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+ } else {
+ regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+ }
+
+ const outputLines = [];
+ var promptFound = false;
+ var gotLineCont = false;
+ var gotHereDoc = false;
+ const lineGotPrompt = [];
+ for (const line of textContent.split('\n')) {
+ match = line.match(regexp)
+ if (match || gotLineCont || gotHereDoc) {
+ promptFound = regexp.test(line)
+ lineGotPrompt.push(promptFound)
+ if (removePrompts && promptFound) {
+ outputLines.push(match[2])
+ } else {
+ outputLines.push(line)
+ }
+ gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+ if (line.includes(hereDocDelim) & useHereDoc)
+ gotHereDoc = !gotHereDoc
+ } else if (!onlyCopyPromptLines) {
+ outputLines.push(line)
+ } else if (copyEmptyLines && line.trim() === '') {
+ outputLines.push(line)
+ }
+ }
+
+ // If no lines with the prompt were found then just use original lines
+ if (lineGotPrompt.some(v => v === true)) {
+ textContent = outputLines.join('\n');
+ }
+
+ // Remove a trailing newline to avoid auto-running when pasting
+ if (textContent.endsWith("\n")) {
+ textContent = textContent.slice(0, -1)
+ }
+ return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+ var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+ // get filtered text
+ let exclude = '.linenos';
+
+ let text = filterText(target, exclude);
+ return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+ // Initialize with a callback so we can modify the text before copy
+ const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+ // Update UI with error/success messages
+ clipboard.on('success', event => {
+ clearSelection()
+ temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+ temporarilyChangeIcon(event.trigger)
+ })
+
+ clipboard.on('error', event => {
+ temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+ })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_static/copybutton_funcs.js b/docs/serve/v2.1.24.0/_static/copybutton_funcs.js
new file mode 100644
index 0000000..dbe1aaa
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+ return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+ const clone = target.cloneNode(true); // clone as to not modify the live DOM
+ if (exclude) {
+ // remove excluded nodes
+ clone.querySelectorAll(exclude).forEach(node => node.remove());
+ }
+ return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+ var regexp;
+ var match;
+
+ // Do we check for line continuation characters and "HERE-documents"?
+ var useLineCont = !!lineContinuationChar
+ var useHereDoc = !!hereDocDelim
+
+ // create regexp to capture prompt and remaining line
+ if (isRegexp) {
+ regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+ } else {
+ regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+ }
+
+ const outputLines = [];
+ var promptFound = false;
+ var gotLineCont = false;
+ var gotHereDoc = false;
+ const lineGotPrompt = [];
+ for (const line of textContent.split('\n')) {
+ match = line.match(regexp)
+ if (match || gotLineCont || gotHereDoc) {
+ promptFound = regexp.test(line)
+ lineGotPrompt.push(promptFound)
+ if (removePrompts && promptFound) {
+ outputLines.push(match[2])
+ } else {
+ outputLines.push(line)
+ }
+ gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+ if (line.includes(hereDocDelim) & useHereDoc)
+ gotHereDoc = !gotHereDoc
+ } else if (!onlyCopyPromptLines) {
+ outputLines.push(line)
+ } else if (copyEmptyLines && line.trim() === '') {
+ outputLines.push(line)
+ }
+ }
+
+ // If no lines with the prompt were found then just use original lines
+ if (lineGotPrompt.some(v => v === true)) {
+ textContent = outputLines.join('\n');
+ }
+
+ // Remove a trailing newline to avoid auto-running when pasting
+ if (textContent.endsWith("\n")) {
+ textContent = textContent.slice(0, -1)
+ }
+ return textContent
+}
diff --git a/docs/serve/v2.1.24.0/_static/css/badge_only.css b/docs/serve/v2.1.24.0/_static/css/badge_only.css
new file mode 100644
index 0000000..c718cee
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/css/badge_only.css
@@ -0,0 +1 @@
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
\ No newline at end of file
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diff --git a/docs/serve/v2.1.24.0/_static/css/fonts/fontawesome-webfont.eot b/docs/serve/v2.1.24.0/_static/css/fonts/fontawesome-webfont.eot
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new file mode 100644
index 0000000..855c845
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+
+
+
+
+Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
+ By ,,,
+Copyright Dave Gandy 2016. All rights reserved.
+
+
+
+
+
diff --git a/docs/serve/v2.1.24.0/_static/css/fonts/fontawesome-webfont.ttf b/docs/serve/v2.1.24.0/_static/css/fonts/fontawesome-webfont.ttf
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diff --git a/docs/serve/v2.1.24.0/_static/css/theme.css b/docs/serve/v2.1.24.0/_static/css/theme.css
new file mode 100644
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diff --git a/docs/serve/v2.1.24.0/_static/doctools.js b/docs/serve/v2.1.24.0/_static/doctools.js
new file mode 100644
index 0000000..d06a71d
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+ "TEXTAREA",
+ "INPUT",
+ "SELECT",
+ "BUTTON",
+]);
+
+const _ready = (callback) => {
+ if (document.readyState !== "loading") {
+ callback();
+ } else {
+ document.addEventListener("DOMContentLoaded", callback);
+ }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+ init: () => {
+ Documentation.initDomainIndexTable();
+ Documentation.initOnKeyListeners();
+ },
+
+ /**
+ * i18n support
+ */
+ TRANSLATIONS: {},
+ PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+ LOCALE: "unknown",
+
+ // gettext and ngettext don't access this so that the functions
+ // can safely bound to a different name (_ = Documentation.gettext)
+ gettext: (string) => {
+ const translated = Documentation.TRANSLATIONS[string];
+ switch (typeof translated) {
+ case "undefined":
+ return string; // no translation
+ case "string":
+ return translated; // translation exists
+ default:
+ return translated[0]; // (singular, plural) translation tuple exists
+ }
+ },
+
+ ngettext: (singular, plural, n) => {
+ const translated = Documentation.TRANSLATIONS[singular];
+ if (typeof translated !== "undefined")
+ return translated[Documentation.PLURAL_EXPR(n)];
+ return n === 1 ? singular : plural;
+ },
+
+ addTranslations: (catalog) => {
+ Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+ Documentation.PLURAL_EXPR = new Function(
+ "n",
+ `return (${catalog.plural_expr})`
+ );
+ Documentation.LOCALE = catalog.locale;
+ },
+
+ /**
+ * helper function to focus on search bar
+ */
+ focusSearchBar: () => {
+ document.querySelectorAll("input[name=q]")[0]?.focus();
+ },
+
+ /**
+ * Initialise the domain index toggle buttons
+ */
+ initDomainIndexTable: () => {
+ const toggler = (el) => {
+ const idNumber = el.id.substr(7);
+ const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+ if (el.src.substr(-9) === "minus.png") {
+ el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+ toggledRows.forEach((el) => (el.style.display = "none"));
+ } else {
+ el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+ toggledRows.forEach((el) => (el.style.display = ""));
+ }
+ };
+
+ const togglerElements = document.querySelectorAll("img.toggler");
+ togglerElements.forEach((el) =>
+ el.addEventListener("click", (event) => toggler(event.currentTarget))
+ );
+ togglerElements.forEach((el) => (el.style.display = ""));
+ if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+ },
+
+ initOnKeyListeners: () => {
+ // only install a listener if it is really needed
+ if (
+ !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+ !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+ )
+ return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+ if (!event.shiftKey) {
+ switch (event.key) {
+ case "ArrowLeft":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const prevLink = document.querySelector('link[rel="prev"]');
+ if (prevLink && prevLink.href) {
+ window.location.href = prevLink.href;
+ event.preventDefault();
+ }
+ break;
+ case "ArrowRight":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const nextLink = document.querySelector('link[rel="next"]');
+ if (nextLink && nextLink.href) {
+ window.location.href = nextLink.href;
+ event.preventDefault();
+ }
+ break;
+ }
+ }
+
+ // some keyboard layouts may need Shift to get /
+ switch (event.key) {
+ case "/":
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+ Documentation.focusSearchBar();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/serve/v2.1.24.0/_static/documentation_options.js b/docs/serve/v2.1.24.0/_static/documentation_options.js
new file mode 100644
index 0000000..a948c6e
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+ VERSION: 'f',
+ LANGUAGE: 'en',
+ COLLAPSE_INDEX: false,
+ BUILDER: 'html',
+ FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt',
+ NAVIGATION_WITH_KEYS: false,
+ SHOW_SEARCH_SUMMARY: true,
+ ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_static/file.png b/docs/serve/v2.1.24.0/_static/file.png
new file mode 100644
index 0000000..a858a41
Binary files /dev/null and b/docs/serve/v2.1.24.0/_static/file.png differ
diff --git a/docs/serve/v2.1.24.0/_static/jquery.js b/docs/serve/v2.1.24.0/_static/jquery.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('0
+ var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
+ var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
+ var s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ this.stemWord = function (w) {
+ var stem;
+ var suffix;
+ var firstch;
+ var origword = w;
+
+ if (w.length < 3)
+ return w;
+
+ var re;
+ var re2;
+ var re3;
+ var re4;
+
+ firstch = w.substr(0,1);
+ if (firstch == "y")
+ w = firstch.toUpperCase() + w.substr(1);
+
+ // Step 1a
+ re = /^(.+?)(ss|i)es$/;
+ re2 = /^(.+?)([^s])s$/;
+
+ if (re.test(w))
+ w = w.replace(re,"$1$2");
+ else if (re2.test(w))
+ w = w.replace(re2,"$1$2");
+
+ // Step 1b
+ re = /^(.+?)eed$/;
+ re2 = /^(.+?)(ed|ing)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = new RegExp(mgr0);
+ if (re.test(fp[1])) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = new RegExp(s_v);
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = /(at|bl|iz)$/;
+ re3 = new RegExp("([^aeiouylsz])\\1$");
+ re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re2.test(w))
+ w = w + "e";
+ else if (re3.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ else if (re4.test(w))
+ w = w + "e";
+ }
+ }
+
+ // Step 1c
+ re = /^(.+?)y$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(s_v);
+ if (re.test(stem))
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step2list[suffix];
+ }
+
+ // Step 3
+ re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step3list[suffix];
+ }
+
+ // Step 4
+ re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ re2 = /^(.+?)(s|t)(ion)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ if (re.test(stem))
+ w = stem;
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = new RegExp(mgr1);
+ if (re2.test(stem))
+ w = stem;
+ }
+
+ // Step 5
+ re = /^(.+?)e$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ re2 = new RegExp(meq1);
+ re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+ w = stem;
+ }
+ re = /ll$/;
+ re2 = new RegExp(mgr1);
+ if (re.test(w) && re2.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+ if (firstch == "y")
+ w = firstch.toLowerCase() + w.substr(1);
+ return w;
+ }
+}
+
diff --git a/docs/serve/v2.1.24.0/_static/minus.png b/docs/serve/v2.1.24.0/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/docs/serve/v2.1.24.0/_static/minus.png differ
diff --git a/docs/serve/v2.1.24.0/_static/nbsphinx-broken-thumbnail.svg b/docs/serve/v2.1.24.0/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000..4919ca8
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+
+
+
diff --git a/docs/serve/v2.1.24.0/_static/nbsphinx-code-cells.css b/docs/serve/v2.1.24.0/_static/nbsphinx-code-cells.css
new file mode 100644
index 0000000..a3fb27c
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+ border: none;
+ padding: 0;
+ margin: 0;
+ box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+ margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+ background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+ background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+ color: unset; /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+ line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+ display: -webkit-flex;
+ display: flex;
+ align-items: flex-start;
+ margin: 0;
+ width: 100%;
+}
+@media (max-width: 540px) {
+ div.nbinput.container,
+ div.nboutput.container {
+ flex-direction: column;
+ }
+}
+
+/* input container */
+div.nbinput.container {
+ padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+ padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+ color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+ color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+ width: 4.5ex;
+ padding-top: 5px;
+ position: relative;
+ user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+ position: absolute;
+ right: 0;
+ margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+ div.nbinput.container div.prompt,
+ div.nboutput.container div.prompt {
+ width: unset;
+ text-align: left;
+ padding: 0.4em;
+ }
+ div.nboutput.container div.prompt.empty {
+ padding: 0;
+ }
+
+ div.nbinput.container div.prompt > div,
+ div.nboutput.container div.prompt > div {
+ position: unset;
+ }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+ overflow: hidden;
+ white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+ -webkit-flex: 1;
+ flex: 1;
+ overflow: auto;
+}
+@media (max-width: 540px) {
+ div.nbinput.container div.input_area,
+ div.nboutput.container div.output_area {
+ width: 100%;
+ }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+ border: 1px solid #e0e0e0;
+ border-radius: 2px;
+ /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+ text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+ text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+ background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+ padding: 5px;
+ margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+ overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+ display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+ border: none;
+ border-collapse: collapse;
+ border-spacing: 0;
+ color: black;
+ font-size: 12px;
+ table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+ border-bottom: 1px solid black;
+ vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+ text-align: right;
+ vertical-align: middle;
+ padding: 0.5em 0.5em;
+ line-height: normal;
+ white-space: normal;
+ max-width: none;
+ border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+ font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+ background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+ background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/docs/serve/v2.1.24.0/_static/nbsphinx-gallery.css b/docs/serve/v2.1.24.0/_static/nbsphinx-gallery.css
new file mode 100644
index 0000000..365c27a
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+ display: grid;
+ grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+ gap: 5px;
+ margin-top: 1em;
+ margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+ padding: 5px;
+ border: 1px dotted currentColor;
+ border-radius: 2px;
+ text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+ border-style: solid;
+}
+
+.nbsphinx-gallery img {
+ max-width: 100%;
+ max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+ display: flex;
+ align-items: start;
+ justify-content: center;
+ height: 120px;
+ margin-bottom: 5px;
+}
diff --git a/docs/serve/v2.1.24.0/_static/nbsphinx-no-thumbnail.svg b/docs/serve/v2.1.24.0/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 0000000..9dca758
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+
+
+
diff --git a/docs/serve/v2.1.24.0/_static/plus.png b/docs/serve/v2.1.24.0/_static/plus.png
new file mode 100644
index 0000000..7107cec
Binary files /dev/null and b/docs/serve/v2.1.24.0/_static/plus.png differ
diff --git a/docs/serve/v2.1.24.0/_static/pygments.css b/docs/serve/v2.1.24.0/_static/pygments.css
new file mode 100644
index 0000000..84ab303
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/serve/v2.1.24.0/_static/searchtools.js b/docs/serve/v2.1.24.0/_static/searchtools.js
new file mode 100644
index 0000000..7918c3f
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/searchtools.js
@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [docname, title, anchor, descr, score, filename]
+ // and returns the new score.
+ /*
+ score: result => {
+ const [docname, title, anchor, descr, score, filename] = result
+ return score
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {
+ 0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5, // used to be unimportantResults
+ },
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2,
+ };
+}
+
+const _removeChildren = (element) => {
+ while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+ string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+ const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+ const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+ const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+ const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+ const contentRoot = document.documentElement.dataset.content_root;
+
+ const [docName, title, anchor, descr, score, _filename] = item;
+
+ let listItem = document.createElement("li");
+ let requestUrl;
+ let linkUrl;
+ if (docBuilder === "dirhtml") {
+ // dirhtml builder
+ let dirname = docName + "/";
+ if (dirname.match(/\/index\/$/))
+ dirname = dirname.substring(0, dirname.length - 6);
+ else if (dirname === "index/") dirname = "";
+ requestUrl = contentRoot + dirname;
+ linkUrl = requestUrl;
+ } else {
+ // normal html builders
+ requestUrl = contentRoot + docName + docFileSuffix;
+ linkUrl = docName + docLinkSuffix;
+ }
+ let linkEl = listItem.appendChild(document.createElement("a"));
+ linkEl.href = linkUrl + anchor;
+ linkEl.dataset.score = score;
+ linkEl.innerHTML = title;
+ if (descr) {
+ listItem.appendChild(document.createElement("span")).innerHTML =
+ " (" + descr + ")";
+ // highlight search terms in the description
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ }
+ else if (showSearchSummary)
+ fetch(requestUrl)
+ .then((responseData) => responseData.text())
+ .then((data) => {
+ if (data)
+ listItem.appendChild(
+ Search.makeSearchSummary(data, searchTerms)
+ );
+ // highlight search terms in the summary
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ });
+ Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+ Search.stopPulse();
+ Search.title.innerText = _("Search Results");
+ if (!resultCount)
+ Search.status.innerText = Documentation.gettext(
+ "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+ );
+ else
+ Search.status.innerText = _(
+ `Search finished, found ${resultCount} page(s) matching the search query.`
+ );
+};
+const _displayNextItem = (
+ results,
+ resultCount,
+ searchTerms,
+ highlightTerms,
+) => {
+ // results left, load the summary and display it
+ // this is intended to be dynamic (don't sub resultsCount)
+ if (results.length) {
+ _displayItem(results.pop(), searchTerms, highlightTerms);
+ setTimeout(
+ () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+ 5
+ );
+ }
+ // search finished, update title and status message
+ else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+ var splitQuery = (query) => query
+ .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+ .filter(term => term) // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+ _index: null,
+ _queued_query: null,
+ _pulse_status: -1,
+
+ htmlToText: (htmlString) => {
+ const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+ htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+ const docContent = htmlElement.querySelector('[role="main"]');
+ if (docContent !== undefined) return docContent.textContent;
+ console.warn(
+ "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+ );
+ return "";
+ },
+
+ init: () => {
+ const query = new URLSearchParams(window.location.search).get("q");
+ document
+ .querySelectorAll('input[name="q"]')
+ .forEach((el) => (el.value = query));
+ if (query) Search.performSearch(query);
+ },
+
+ loadIndex: (url) =>
+ (document.body.appendChild(document.createElement("script")).src = url),
+
+ setIndex: (index) => {
+ Search._index = index;
+ if (Search._queued_query !== null) {
+ const query = Search._queued_query;
+ Search._queued_query = null;
+ Search.query(query);
+ }
+ },
+
+ hasIndex: () => Search._index !== null,
+
+ deferQuery: (query) => (Search._queued_query = query),
+
+ stopPulse: () => (Search._pulse_status = -1),
+
+ startPulse: () => {
+ if (Search._pulse_status >= 0) return;
+
+ const pulse = () => {
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ Search.dots.innerText = ".".repeat(Search._pulse_status);
+ if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+ };
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch: (query) => {
+ // create the required interface elements
+ const searchText = document.createElement("h2");
+ searchText.textContent = _("Searching");
+ const searchSummary = document.createElement("p");
+ searchSummary.classList.add("search-summary");
+ searchSummary.innerText = "";
+ const searchList = document.createElement("ul");
+ searchList.classList.add("search");
+
+ const out = document.getElementById("search-results");
+ Search.title = out.appendChild(searchText);
+ Search.dots = Search.title.appendChild(document.createElement("span"));
+ Search.status = out.appendChild(searchSummary);
+ Search.output = out.appendChild(searchList);
+
+ const searchProgress = document.getElementById("search-progress");
+ // Some themes don't use the search progress node
+ if (searchProgress) {
+ searchProgress.innerText = _("Preparing search...");
+ }
+ Search.startPulse();
+
+ // index already loaded, the browser was quick!
+ if (Search.hasIndex()) Search.query(query);
+ else Search.deferQuery(query);
+ },
+
+ /**
+ * execute search (requires search index to be loaded)
+ */
+ query: (query) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+ const allTitles = Search._index.alltitles;
+ const indexEntries = Search._index.indexentries;
+
+ // stem the search terms and add them to the correct list
+ const stemmer = new Stemmer();
+ const searchTerms = new Set();
+ const excludedTerms = new Set();
+ const highlightTerms = new Set();
+ const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+ splitQuery(query.trim()).forEach((queryTerm) => {
+ const queryTermLower = queryTerm.toLowerCase();
+
+ // maybe skip this "word"
+ // stopwords array is from language_data.js
+ if (
+ stopwords.indexOf(queryTermLower) !== -1 ||
+ queryTerm.match(/^\d+$/)
+ )
+ return;
+
+ // stem the word
+ let word = stemmer.stemWord(queryTermLower);
+ // select the correct list
+ if (word[0] === "-") excludedTerms.add(word.substr(1));
+ else {
+ searchTerms.add(word);
+ highlightTerms.add(queryTermLower);
+ }
+ });
+
+ if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
+ localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+ }
+
+ // console.debug("SEARCH: searching for:");
+ // console.info("required: ", [...searchTerms]);
+ // console.info("excluded: ", [...excludedTerms]);
+
+ // array of [docname, title, anchor, descr, score, filename]
+ let results = [];
+ _removeChildren(document.getElementById("search-progress"));
+
+ const queryLower = query.toLowerCase();
+ for (const [title, foundTitles] of Object.entries(allTitles)) {
+ if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+ for (const [file, id] of foundTitles) {
+ let score = Math.round(100 * queryLower.length / title.length)
+ results.push([
+ docNames[file],
+ titles[file] !== title ? `${titles[file]} > ${title}` : title,
+ id !== null ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // search for explicit entries in index directives
+ for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+ if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+ for (const [file, id] of foundEntries) {
+ let score = Math.round(100 * queryLower.length / entry.length)
+ results.push([
+ docNames[file],
+ titles[file],
+ id ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // lookup as object
+ objectTerms.forEach((term) =>
+ results.push(...Search.performObjectSearch(term, objectTerms))
+ );
+
+ // lookup as search terms in fulltext
+ results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+ // let the scorer override scores with a custom scoring function
+ if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+ // now sort the results by score (in opposite order of appearance, since the
+ // display function below uses pop() to retrieve items) and then
+ // alphabetically
+ results.sort((a, b) => {
+ const leftScore = a[4];
+ const rightScore = b[4];
+ if (leftScore === rightScore) {
+ // same score: sort alphabetically
+ const leftTitle = a[1].toLowerCase();
+ const rightTitle = b[1].toLowerCase();
+ if (leftTitle === rightTitle) return 0;
+ return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+ }
+ return leftScore > rightScore ? 1 : -1;
+ });
+
+ // remove duplicate search results
+ // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+ let seen = new Set();
+ results = results.reverse().reduce((acc, result) => {
+ let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+ if (!seen.has(resultStr)) {
+ acc.push(result);
+ seen.add(resultStr);
+ }
+ return acc;
+ }, []);
+
+ results = results.reverse();
+
+ // for debugging
+ //Search.lastresults = results.slice(); // a copy
+ // console.info("search results:", Search.lastresults);
+
+ // print the results
+ _displayNextItem(results, results.length, searchTerms, highlightTerms);
+ },
+
+ /**
+ * search for object names
+ */
+ performObjectSearch: (object, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const objects = Search._index.objects;
+ const objNames = Search._index.objnames;
+ const titles = Search._index.titles;
+
+ const results = [];
+
+ const objectSearchCallback = (prefix, match) => {
+ const name = match[4]
+ const fullname = (prefix ? prefix + "." : "") + name;
+ const fullnameLower = fullname.toLowerCase();
+ if (fullnameLower.indexOf(object) < 0) return;
+
+ let score = 0;
+ const parts = fullnameLower.split(".");
+
+ // check for different match types: exact matches of full name or
+ // "last name" (i.e. last dotted part)
+ if (fullnameLower === object || parts.slice(-1)[0] === object)
+ score += Scorer.objNameMatch;
+ else if (parts.slice(-1)[0].indexOf(object) > -1)
+ score += Scorer.objPartialMatch; // matches in last name
+
+ const objName = objNames[match[1]][2];
+ const title = titles[match[0]];
+
+ // If more than one term searched for, we require other words to be
+ // found in the name/title/description
+ const otherTerms = new Set(objectTerms);
+ otherTerms.delete(object);
+ if (otherTerms.size > 0) {
+ const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+ if (
+ [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+ )
+ return;
+ }
+
+ let anchor = match[3];
+ if (anchor === "") anchor = fullname;
+ else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+ const descr = objName + _(", in ") + title;
+
+ // add custom score for some objects according to scorer
+ if (Scorer.objPrio.hasOwnProperty(match[2]))
+ score += Scorer.objPrio[match[2]];
+ else score += Scorer.objPrioDefault;
+
+ results.push([
+ docNames[match[0]],
+ fullname,
+ "#" + anchor,
+ descr,
+ score,
+ filenames[match[0]],
+ ]);
+ };
+ Object.keys(objects).forEach((prefix) =>
+ objects[prefix].forEach((array) =>
+ objectSearchCallback(prefix, array)
+ )
+ );
+ return results;
+ },
+
+ /**
+ * search for full-text terms in the index
+ */
+ performTermsSearch: (searchTerms, excludedTerms) => {
+ // prepare search
+ const terms = Search._index.terms;
+ const titleTerms = Search._index.titleterms;
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+
+ const scoreMap = new Map();
+ const fileMap = new Map();
+
+ // perform the search on the required terms
+ searchTerms.forEach((word) => {
+ const files = [];
+ const arr = [
+ { files: terms[word], score: Scorer.term },
+ { files: titleTerms[word], score: Scorer.title },
+ ];
+ // add support for partial matches
+ if (word.length > 2) {
+ const escapedWord = _escapeRegExp(word);
+ Object.keys(terms).forEach((term) => {
+ if (term.match(escapedWord) && !terms[word])
+ arr.push({ files: terms[term], score: Scorer.partialTerm });
+ });
+ Object.keys(titleTerms).forEach((term) => {
+ if (term.match(escapedWord) && !titleTerms[word])
+ arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+ });
+ }
+
+ // no match but word was a required one
+ if (arr.every((record) => record.files === undefined)) return;
+
+ // found search word in contents
+ arr.forEach((record) => {
+ if (record.files === undefined) return;
+
+ let recordFiles = record.files;
+ if (recordFiles.length === undefined) recordFiles = [recordFiles];
+ files.push(...recordFiles);
+
+ // set score for the word in each file
+ recordFiles.forEach((file) => {
+ if (!scoreMap.has(file)) scoreMap.set(file, {});
+ scoreMap.get(file)[word] = record.score;
+ });
+ });
+
+ // create the mapping
+ files.forEach((file) => {
+ if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+ fileMap.get(file).push(word);
+ else fileMap.set(file, [word]);
+ });
+ });
+
+ // now check if the files don't contain excluded terms
+ const results = [];
+ for (const [file, wordList] of fileMap) {
+ // check if all requirements are matched
+
+ // as search terms with length < 3 are discarded
+ const filteredTermCount = [...searchTerms].filter(
+ (term) => term.length > 2
+ ).length;
+ if (
+ wordList.length !== searchTerms.size &&
+ wordList.length !== filteredTermCount
+ )
+ continue;
+
+ // ensure that none of the excluded terms is in the search result
+ if (
+ [...excludedTerms].some(
+ (term) =>
+ terms[term] === file ||
+ titleTerms[term] === file ||
+ (terms[term] || []).includes(file) ||
+ (titleTerms[term] || []).includes(file)
+ )
+ )
+ break;
+
+ // select one (max) score for the file.
+ const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+ // add result to the result list
+ results.push([
+ docNames[file],
+ titles[file],
+ "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ return results;
+ },
+
+ /**
+ * helper function to return a node containing the
+ * search summary for a given text. keywords is a list
+ * of stemmed words.
+ */
+ makeSearchSummary: (htmlText, keywords) => {
+ const text = Search.htmlToText(htmlText);
+ if (text === "") return null;
+
+ const textLower = text.toLowerCase();
+ const actualStartPosition = [...keywords]
+ .map((k) => textLower.indexOf(k.toLowerCase()))
+ .filter((i) => i > -1)
+ .slice(-1)[0];
+ const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+ const top = startWithContext === 0 ? "" : "...";
+ const tail = startWithContext + 240 < text.length ? "..." : "";
+
+ let summary = document.createElement("p");
+ summary.classList.add("context");
+ summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+ return summary;
+ },
+};
+
+_ready(Search.init);
diff --git a/docs/serve/v2.1.24.0/_static/sphinx_highlight.js b/docs/serve/v2.1.24.0/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/docs/serve/v2.1.24.0/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+ if (node.nodeType === Node.TEXT_NODE) {
+ const val = node.nodeValue;
+ const parent = node.parentNode;
+ const pos = val.toLowerCase().indexOf(text);
+ if (
+ pos >= 0 &&
+ !parent.classList.contains(className) &&
+ !parent.classList.contains("nohighlight")
+ ) {
+ let span;
+
+ const closestNode = parent.closest("body, svg, foreignObject");
+ const isInSVG = closestNode && closestNode.matches("svg");
+ if (isInSVG) {
+ span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+ } else {
+ span = document.createElement("span");
+ span.classList.add(className);
+ }
+
+ span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+ const rest = document.createTextNode(val.substr(pos + text.length));
+ parent.insertBefore(
+ span,
+ parent.insertBefore(
+ rest,
+ node.nextSibling
+ )
+ );
+ node.nodeValue = val.substr(0, pos);
+ /* There may be more occurrences of search term in this node. So call this
+ * function recursively on the remaining fragment.
+ */
+ _highlight(rest, addItems, text, className);
+
+ if (isInSVG) {
+ const rect = document.createElementNS(
+ "http://www.w3.org/2000/svg",
+ "rect"
+ );
+ const bbox = parent.getBBox();
+ rect.x.baseVal.value = bbox.x;
+ rect.y.baseVal.value = bbox.y;
+ rect.width.baseVal.value = bbox.width;
+ rect.height.baseVal.value = bbox.height;
+ rect.setAttribute("class", className);
+ addItems.push({ parent: parent, target: rect });
+ }
+ }
+ } else if (node.matches && !node.matches("button, select, textarea")) {
+ node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+ }
+};
+const _highlightText = (thisNode, text, className) => {
+ let addItems = [];
+ _highlight(thisNode, addItems, text, className);
+ addItems.forEach((obj) =>
+ obj.parent.insertAdjacentElement("beforebegin", obj.target)
+ );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+ /**
+ * highlight the search words provided in localstorage in the text
+ */
+ highlightSearchWords: () => {
+ if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
+
+ // get and clear terms from localstorage
+ const url = new URL(window.location);
+ const highlight =
+ localStorage.getItem("sphinx_highlight_terms")
+ || url.searchParams.get("highlight")
+ || "";
+ localStorage.removeItem("sphinx_highlight_terms")
+ url.searchParams.delete("highlight");
+ window.history.replaceState({}, "", url);
+
+ // get individual terms from highlight string
+ const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+ if (terms.length === 0) return; // nothing to do
+
+ // There should never be more than one element matching "div.body"
+ const divBody = document.querySelectorAll("div.body");
+ const body = divBody.length ? divBody[0] : document.querySelector("body");
+ window.setTimeout(() => {
+ terms.forEach((term) => _highlightText(body, term, "highlighted"));
+ }, 10);
+
+ const searchBox = document.getElementById("searchbox");
+ if (searchBox === null) return;
+ searchBox.appendChild(
+ document
+ .createRange()
+ .createContextualFragment(
+ '' +
+ '' +
+ _("Hide Search Matches") +
+ "
"
+ )
+ );
+ },
+
+ /**
+ * helper function to hide the search marks again
+ */
+ hideSearchWords: () => {
+ document
+ .querySelectorAll("#searchbox .highlight-link")
+ .forEach((el) => el.remove());
+ document
+ .querySelectorAll("span.highlighted")
+ .forEach((el) => el.classList.remove("highlighted"));
+ localStorage.removeItem("sphinx_highlight_terms")
+ },
+
+ initEscapeListener: () => {
+ // only install a listener if it is really needed
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+ if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+ SphinxHighlight.hideSearchWords();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+_ready(() => {
+ /* Do not call highlightSearchWords() when we are on the search page.
+ * It will highlight words from the *previous* search query.
+ */
+ if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+ SphinxHighlight.initEscapeListener();
+});
diff --git a/docs/serve/v2.1.24.0/genindex.html b/docs/serve/v2.1.24.0/genindex.html
new file mode 100644
index 0000000..b923c12
--- /dev/null
+++ b/docs/serve/v2.1.24.0/genindex.html
@@ -0,0 +1,669 @@
+
+
+
+ Index — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+
Index
+
+
+
A
+ |
B
+ |
C
+ |
D
+ |
E
+ |
F
+ |
G
+ |
I
+ |
J
+ |
K
+ |
L
+ |
M
+ |
N
+ |
O
+ |
P
+ |
Q
+ |
R
+ |
S
+ |
T
+ |
V
+ |
W
+ |
Y
+
+
+
A
+
+
+
B
+
+
+
C
+
+
+
D
+
+
+
E
+
+
+
F
+
+
+
G
+
+
+
I
+
+
+
J
+
+
+
K
+
+
+
L
+
+
+
M
+
+
+
N
+
+
+
O
+
+
+
P
+
+
+
Q
+
+
+
R
+
+
+
S
+
+
+
T
+
+
+
V
+
+
+
W
+
+
+
Y
+
+
+
+
+
+
+
+
+
+
RuleKit — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+RuleKit
+This package is python wrapper for RuleKit library (a versatile tool for rule learning).
+Based on a sequential covering induction algorithm, it is suitable for:
+
+classification,
regression,
survival problems.
+This wrapper aims to give an easy way to integrate RuleKit functionality into python projects and notebooks.
+All package models are writen to be compatible with scikit learn library.
+
+
+
+
+
+
+
+
+
+
Python Module Index — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+ Python Module Index
+
+
+
+
+
+
+
+
+
+
Python Module Index
+
+
+
+
+
+
+
+
+
+
+
+
Code documentation — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Code documentation
+Contains classes for initializing RuleKit Java backend
+
+
+class rulekit.main. RuleKit
+Class used for initializing RuleKit. It starts JVM underhood and setups it
+with jars.
+
+
Note
+
Since version 1.7.0 there is no need to manually initialize RuleKit.
+
You may just skip the RuleKit.init() line. However in certain scenarios when
+you want use a custom RuleKit jar file or modify Java VM parameters, this class
+can be used.
+
+
+version
+version of RuleKit jar used by wrapper (not equal to python package version).
+
+Type:
+str
+
+
+
+
+
+static configure_java_logger ( log_file_path : str verbosity_level : int = 1 )
+Enable Java debug logging. You probably don’t need to use this
+method unless you want too deep dive into the process of rules inductions
+
+or your’re debugging some issues.
+
+
+Parameters:
+
+log_file_path (str ) – Path to the file where logs will be stored
verbosity_level (int , optional ) – Verbosity level.
+Minimum value is 1, maximum value is 2, default value is 1.
+
+
+
+
+
+static get_java_logger_config ( ) → _RuleKitJavaLoggerConfig | None
+Returns the Java logger configuration configured using
+configure_java_logger method
+
+Returns:
+Java logger configuration
+Return type:
+Optional[_RuleKitJavaLoggerConfig]
+
+
+
+
+
+static init ( initial_heap_size : int | None = None max_heap_size : int | None = None rulekit_jar_file_path : str | None = None )
+Initialize package.
+This method configure and starts JVM and load RuleKit jar file.
+
+
Note
+
Since version 1.7.0 it don’t have to be called before using any
+
operator class. However in certain scenarios when you want use a custom RuleKit
+jar file or modify Java VM parameters, this method can be used.
+
+Parameters:
+
+initial_heap_size (int ) – JVM initial heap size in mb
max_heap_size (int ) – JVM max heap size in mb
rulekit_jar_file_path (str ) –
Path to the RuleKit jar file. This parameters.
+.. note:
+You probably don 't need to use this parameter unless you want to use
+your own custom version of RuleKit jar file . Otherwise leave it as it
+is and the package will use the official RuleKit release jar file .
+
+
+Raises:
+Exception – If failed to load RuleKit jar file.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Classification — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Classification
+
+
+class rulekit.classification. RuleClassifier ( minsupp_new : float = 0.05 induction_measure : Measures = Measures.Correlation pruning_measure : Measures | str = Measures.Correlation voting_measure : Measures = Measures.Correlation max_growing : float = 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 control_apriori_precision : bool = True max_rule_count : int = 0 approximate_induction : bool = False approximate_bins_count : int = 100 )
+Classification 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,
induction_measure (rulekit.params.Measures rulekit.params. Measures.Correlation
pruning_measure (Union[rulekit.params.Measures rulekit.params.Measures.Correlation
+measure used during pruning. Could be user defined (string), for example 2 * p / n
+
+voting_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
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 valueof given attribute is always cconsidered 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.
control_apriori_precision (bool = True ) – When inducing classification rules, verify if candidate precision is higher
+than apriori precision of the investigated class.
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.
approximate_induction (bool = False ) – Use an approximate induction heuristic which does not check all possible
+splits; note: this is an experimental feature and currently works only for
+classification data sets, results may change in future;
approximate_bins_count (int = 100 ) – maximum number of bins for an attribute evaluated in the approximate
+induction.
+
+
+
+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 ) → RuleClassifier
+Train model on given dataset.
+
+Parameters:
+
+
+Returns:
+self
+Return type:
+RuleClassifier
+
+
+
+
+
+get_coverage_matrix ( values : ndarray | DataFrame | list ) → ndarray
+Calculates coverage matrix for ruleset.
+
+Parameters:
+values (rulekit.operator.Data
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list return_metrics : bool = False ) → ndarray | tuple [ ndarray , ClassificationPredictionMetrics ]
+Perform prediction and returns predicted labels.
+
+Parameters:
+
+values (rulekit.operator.Data
return_metrics (bool = False ) – Optional flag. If set to True method will calculate some additional model
+metrics. Method will then return tuple instead of just predicted labels.
+Returns:
+result – If return_metrics flag wasn’t set it will return just prediction,
+otherwise a tuple will be returned with first element being prediction and
+second one being metrics.
+Return type:
+Union[np.ndarray, tuple[np.ndarray, rulekit.classification. ClassificationPredictionMetrics
+
+
+
+
+
+predict_proba ( values : ndarray | DataFrame | list return_metrics : bool = False ) → ndarray | tuple [ ndarray , ClassificationPredictionMetrics ]
+Perform prediction and returns class probabilities for each example.
+
+Parameters:
+
+values (rulekit.operator.Data
return_metrics (bool = False ) – Optional flag. If set to True method will calculate some additional model
+metrics. Method will then return tuple instead of just probabilities.
+Returns:
+result – If return_metrics flag wasn’t set it will return just probabilities
+matrix, otherwise a tuple will be returned with first element being
+prediction and second one being metrics.
+Return type:
+Union[np.ndarray, tuple[np.ndarray, rulekit.classification. ClassificationPredictionMetrics
+
+
+
+
+
+score ( values : ndarray | DataFrame | list labels : ndarray | DataFrame | list ) → float
+Return the accuracy on the given test data and labels.
+
+Parameters:
+
+
+Returns:
+score – Accuracy 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.classification. ExpertRuleClassifier ( minsupp_new : float = 0.05 induction_measure : Measures = Measures.Correlation pruning_measure : Measures | str = Measures.Correlation voting_measure : Measures = Measures.Correlation max_growing : float = 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 control_apriori_precision : bool = True max_rule_count : int = 0 approximate_induction : bool = False approximate_bins_count : int = 100 extend_using_preferred : bool = False extend_using_automatic : bool = False induce_using_preferred : bool = False induce_using_automatic : bool = False consider_other_classes : bool = False preferred_conditions_per_rule : int = 2147483647 preferred_attributes_per_rule : int = 2147483647 )
+Classification model using expert knowledge.
+
+Parameters:
+
+minsupp_new (float = 5.0 ) –
fraction (a minimum number ( or ) –
examples (if value < 1.0 ) of previously uncovered ) –
problems ) ; (to be covered by a new rule ( positive examples for classification ) –
default (5 , ) –
induction_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
pruning_measure (Union[rulekit.params.Measures rulekit.params.Measures.Correlation
+measure used during pruning. Could be user defined (string), for example 2 * p / n
+
+voting_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
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.
control_apriori_precision (bool = True ) – When inducing classification rules, verify if candidate precision is higher
+than apriori precision of the investigated class.
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.
approximate_induction (bool = False ) – Use an approximate induction heuristic which does not check all possible
+splits; note: this is an experimental feature and currently works only for
+classification data sets, results may change in future;
approximate_bins_count (int = 100 ) – maximum number of bins for an attribute evaluated in the approximate
+induction.
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
consider_other_classes (bool = False ) – boolean indicating whether automatic induction should be performed for
+classes for which no user’s knowledge has been defined
+(classification only); 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 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 ) → ExpertRuleClassifier
+Train model on given dataset.
+
+Parameters:
+
+values (rulekit.operator.Data
labels (rulekit.operator.Data
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 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.
+Returns:
+self
+Return type:
+ExpertRuleClassifier
+
+
+
+
+
+get_coverage_matrix ( values : ndarray | DataFrame | list ) → ndarray
+Calculates coverage matrix for ruleset.
+
+Parameters:
+values (rulekit.operator.Data
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list return_metrics : bool = False ) → ndarray | tuple [ ndarray , ClassificationPredictionMetrics ]
+Perform prediction and returns predicted labels.
+
+Parameters:
+
+values (rulekit.operator.Data
return_metrics (bool = False ) – Optional flag. If set to True method will calculate some additional model
+metrics. Method will then return tuple instead of just predicted labels.
+Returns:
+result – If return_metrics flag wasn’t set it will return just prediction,
+otherwise a tuple will be returned with first element being prediction and
+second one being metrics.
+Return type:
+Union[np.ndarray, tuple[np.ndarray, rulekit.classification. ClassificationPredictionMetrics
+
+
+
+
+
+predict_proba ( values : ndarray | DataFrame | list return_metrics : bool = False ) → ndarray | tuple [ ndarray , ClassificationPredictionMetrics ]
+Perform prediction and returns class probabilities for each example.
+
+Parameters:
+
+values (rulekit.operator.Data
return_metrics (bool = False ) – Optional flag. If set to True method will calculate some additional model
+metrics. Method will then return tuple instead of just probabilities.
+Returns:
+result – If return_metrics flag wasn’t set it will return just probabilities
+matrix, otherwise a tuple will be returned with first element being
+prediction and second one being metrics.
+Return type:
+Union[np.ndarray, tuple[np.ndarray, rulekit.classification. ClassificationPredictionMetrics
+
+
+
+
+
+score ( values : ndarray | DataFrame | list labels : ndarray | DataFrame | list ) → float
+Return the accuracy on the given test data and labels.
+
+Parameters:
+
+
+Returns:
+score – Accuracy of self.predict(values) wrt. labels.
+Return type:
+float
+
+
+
+
+
+set_params ( ** kwargs ) → object
+Set models hyperparameters. Parameters are the same as in constructor.
+
+
+
+
+
+
+
+
+
+
+
+
+
Parameters — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Parameters
+
+
+rulekit.operator. Data
+alias of type Union[numpy.ndarray, pandas.core.frame.DataFrame, List]
+
+
+Contains constants and classes for specyfing models parameters
+
+
+class rulekit.params. Measures ( value )
+Bases: Enum
+Enum for different measures used during induction, pruning and voting.
+You can ream more about each measure and its implementation
+#41-rule-quality>`_ .
+` https://github.com/adaa-polsl/RuleKit/wiki/4-Quality-and-evaluation
+
+
+Accuracy = 'Accuracy'
+
+
+
+
+BinaryEntropy = 'BinaryEntropy'
+
+
+
+
+C1 = 'C1'
+
+
+
+
+C2 = 'C2'
+
+
+
+
+CFoil = 'CFoil'
+
+
+
+
+CN2Significnce = 'CN2Significnce'
+
+
+
+
+Correlation = 'Correlation'
+
+
+
+
+Coverage = 'Coverage'
+
+
+
+
+FBayesianConfirmation = 'FBayesianConfirmation'
+
+
+
+
+FMeasure = 'FMeasure'
+
+
+
+
+FullCoverage = 'FullCoverage'
+
+
+
+
+
+
+
+
+GMeasure = 'GMeasure'
+
+
+
+
+InformationGain = 'InformationGain'
+
+
+
+
+JMeasure = 'JMeasure'
+
+
+
+
+Kappa = 'Kappa'
+
+
+
+
+Klosgen = 'Klosgen'
+
+
+
+
+Laplace = 'Laplace'
+
+
+
+
+Lift = 'Lift'
+
+
+
+
+LogicalSufficiency = 'LogicalSufficiency'
+
+
+
+
+MEstimate = 'MEstimate'
+
+
+
+
+MutualSupport = 'MutualSupport'
+
+
+
+
+Novelty = 'Novelty'
+
+
+
+
+OddsRatio = 'OddsRatio'
+
+
+
+
+OneWaySupport = 'OneWaySupport'
+
+
+
+
+PawlakDependencyFactor = 'PawlakDependencyFactor'
+
+
+
+
+Q2 = 'Q2'
+
+
+
+
+Precision = 'Precision'
+
+
+
+
+RelativeRisk = 'RelativeRisk'
+
+
+
+
+Ripper = 'Ripper'
+
+
+
+
+RuleInterest = 'RuleInterest'
+
+
+
+
+
+
+
+
+SBayesian = 'SBayesian'
+
+
+
+
+Sensitivity = 'Sensitivity'
+
+
+
+
+Specificity = 'Specificity'
+
+
+
+
+TwoWaySupport = 'TwoWaySupport'
+
+
+
+
+WeightedLaplace = 'WeightedLaplace'
+
+
+
+
+WeightedRelativeAccuracy = 'WeightedRelativeAccuracy'
+
+
+
+
+YAILS = 'YAILS'
+
+
+
+
+LogRank = 'LogRank'
+
+
+
+
+
+
+
+
+
+
+
+
+
Regression — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Regression
+
+
+class rulekit.regression. RuleRegressor ( minsupp_new : float = 0.05 induction_measure : Measures = Measures.Correlation pruning_measure : Measures | str = Measures.Correlation voting_measure : Measures = Measures.Correlation max_growing : float = 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 mean_based_regression : bool = True max_rule_count : int = 0 )
+Regression 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,
induction_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
pruning_measure (Union[rulekit.params.Measures rulekit.params.Measures.Correlation
+measure used during pruning. Could be user defined (string), for example 2 * p / n
+
+voting_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
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.
mean_based_regression (bool = True ) – Enable fast induction of mean-based regression rules instead of default
+median-based.
max_rule_count (int = 0 ) – Maximum number of rules to be generated; 0 indicates no limit. Default: 0
+
+
+
+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 ) → RuleRegressor
+Train model on given dataset.
+
+Parameters:
+
+
+Returns:
+self
+Return type:
+RuleRegressor
+
+
+
+
+
+get_coverage_matrix ( values : ndarray | DataFrame | list ) → ndarray
+Calculates coverage matrix for ruleset.
+
+Parameters:
+values (rulekit.operator.Data
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list ) → ndarray
+Perform prediction and returns predicted values.
+
+Parameters:
+values (rulekit.operator.Data
+Returns:
+result – predicted values
+Return type:
+np.ndarray
+
+
+
+
+
+score ( values : ndarray | DataFrame | list labels : ndarray | DataFrame | list ) → float
+Return the coefficient of determination R2 of the prediction
+
+Parameters:
+
+
+Returns:
+score – R2 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.regression. ExpertRuleRegressor ( minsupp_new : float = 0.05 induction_measure : Measures = Measures.Correlation pruning_measure : Measures | str = Measures.Correlation voting_measure : Measures = Measures.Correlation max_growing : float = 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 mean_based_regression : bool = True max_rule_count : int = 0 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 )
+Expert Regression 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,
induction_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
pruning_measure (Union[rulekit.params.Measures rulekit.params.Measures.Correlation
+measure used during pruning. Could be user defined (string), for example 2 * p / n
+
+voting_measure (rulekit.params.Measures rulekit.params.Measures.Correlation
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.
mean_based_regression (bool = True ) – Enable fast induction of mean-based regression rules instead of default
+median-based.
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 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 ) → ExpertRuleRegressor
+Train model on given dataset.
+
+Parameters:
+
+values (rulekit.operator.Data
labels (rulekit.operator.Data
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:
+ExpertRuleRegressor
+
+
+
+
+
+get_coverage_matrix ( values : ndarray | DataFrame | list ) → ndarray
+Calculates coverage matrix for ruleset.
+
+Parameters:
+values (rulekit.operator.Data
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list ) → ndarray
+Perform prediction and returns predicted values.
+
+Parameters:
+values (rulekit.operator.Data
+Returns:
+result – predicted values
+Return type:
+np.ndarray
+
+
+
+
+
+score ( values : ndarray | DataFrame | list labels : ndarray | DataFrame | list ) → float
+Return the coefficient of determination R2 of the prediction
+
+Parameters:
+
+
+Returns:
+score – R2 of self.predict(values) wrt. labels.
+Return type:
+float
+
+
+
+
+
+set_params ( ** kwargs ) → object
+Set models hyperparameters. Parameters are the same as in constructor.
+
+
+
+
+
+
+
+
+
+
+
+
+
Rules — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Rules
+Contains classes representing rules and rulesets.
+
+
+class rulekit.rules. BaseRule ( java_object )
+Base class representing single rule.
+
+
+
+
+get_covering_information ( ) → dict
+Returns information about rule covering
+
+Returns:
+covering_data – Dictionary containing covering information.
+Return type:
+dict
+
+
+
+
+
+print_stats ( )
+Prints rule statistics as formatted text.
+
+
+
+
+property pvalue : float
+Rule significance.
+
+
+
+
+property stats : RuleStatistics
+Rule statistics.
+
+
+
+
+property weight : float
+Rule weight
+
+
+
+
+property weighted_N : float
+Number of negatives in the training set (accounting weights).
+
+
+
+
+property weighted_P : float
+Number of positives in the training set (accounting weights).
+
+
+
+
+property weighted_n : float
+Number of negatives covered by the rule (accounting weights).
+
+
+
+
+property weighted_p : float
+Number of positives covered by the rule (accounting weights).
+
+
+
+
+
+
+class rulekit.rules. ClassificationRule ( java_object )
+Class representing classification rule
+
+
+
+
+property decision_class : str
+Decision class of the rule
+
+
+
+
+
+
+class rulekit.rules. InductionParameters ( java_object )
+Induction parameters.
+
+
+property induction_measure : Measures | str
+Returns:
+Union[Measures, str]: Measure used for induction
+
+
+
+
+property pruning_measure : Measures | str
+Returns:
+Union[Measures, str]: Measure used for pruning
+
+
+
+
+property voting_measure : Measures | str
+Returns:
+Union[Measures, str]: Measure used for voting
+
+
+
+
+
+
+class rulekit.rules. RegressionRule ( java_object )
+Class representing regression rule
+
+
+
+
+property conclusion_value : float
+Value from the rule’s conclusion
+
+
+
+
+
+
+class rulekit.rules. RuleSet ( java_object )
+Class representing ruleset.
+
+
+
+
+calculate_avg_rule_coverage ( ) → float
+
+Returns:
+count – Average rule coverage.
+Return type:
+float
+
+
+
+
+
+calculate_avg_rule_precision ( ) → float
+
+Returns:
+count – Average rule precision.
+Return type:
+float
+
+
+
+
+
+calculate_avg_rule_quality ( ) → float
+
+Returns:
+count – Average rule quality.
+Return type:
+float
+
+
+
+
+
+calculate_conditions_count ( ) → float
+
+Returns:
+count – Number of conditions.
+Return type:
+float
+
+
+
+
+
+calculate_induced_conditions_count ( ) → float
+
+Returns:
+count – Number of induced conditions.
+Return type:
+float
+
+
+
+
+
+calculate_significance ( alpha : float ) → dict
+
+Parameters:
+alpha (float ) –
+Returns:
+count – Significance of the rule set.
+Return type:
+float
+
+
+
+
+
+calculate_significance_fdr ( alpha : float ) → dict
+
+Returns:
+count – Significance of the rule set with false discovery rate correction.
+Dictionary contains two fields: fraction (fraction of rules significant
+at assumed level) and p (average p-value of all rules).
+Return type:
+dict
+
+
+
+
+
+calculate_significance_fwer ( alpha : float ) → dict
+
+Returns:
+count – Significance of the rule set with familiy-wise error rate correction.
+Dictionary contains two fields: fraction (fraction of rules significant
+at assumed level) and p (average p-value of all rules).
+Return type:
+dict
+
+
+
+
+
+property growing_time : float
+Time of growing in seconds
+
+
+
+
+property is_voting : bool
+Value indicating whether rules are voting.
+
+
+
+
+property parameters : object
+Parameters used during rule set induction.
+
+
+
+
+property pruning_time : float
+Time of pruning in seconds
+
+
+
+
+property rules : list [ T ]
+List of rules objects.
+
+
+
+
+property stats : RuleSetStatistics
+Rule set statistics.
+
+
+
+
+property total_time : float
+Time of constructing the rule set in seconds
+
+
+
+
+
+
+class rulekit.rules. SurvivalRule ( java_object )
+Class representing survival rule
+
+
+
+
+property kaplan_meier_estimator : KaplanMeierEstimator
+Kaplan-Meier estimator from the rule concslusion
+
+
+
+
+
+
+
+
+
+
+
+
+
Statistics — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Statistics
+Contains classes describing rules and rulesets statistics and metrics
+
+
+class rulekit.stats. RuleSetStatistics ( ruleset )
+Statistics for ruleset.
+
+
+SIGNIFICANCE_LEVEL
+Significance level, default value is 0.05
+
+Type:
+float
+
+
+
+
+time_total_sfloat Time of constructing the rule set in seconds.
+time_growing_sfloat Time of growing in seconds.
+time_pruning_sfloat Time of pruning in seconds.
+rules_countint Number of rules in ruleset.
+conditions_per_rulefloat Average number of conditions per rule.
+induced_conditions_per_rulefloat Average number of induced conditions.
+avg_rule_coveragefloat Average rule coverage.
+avg_rule_precisionfloat Average rule precision.
+avg_rule_qualityfloat Average rule quality.
+pvaluefloat rule set significance.
+FDR_pvaluefloat Significance of the rule set with false discovery rate correction.
+FWER_pvaluefloat Significance of the rule set with familiy-wise error rate correction.
+fraction_significantfloat Fraction of rules significant at assumed level
+fraction_FDR_significantfloat Fraction of rules significant, set with false discovery rate correction, at assumed level.
+fraction_FWER_significantfloat Fraction of rules significant, set with familiy-wise error rate correction, at assumed level.
+
+
+
+
+
+class rulekit.stats. RuleStatistics ( rule )
+Statistics for single rule.
+
+
+p
+Number of positives covered by the rule (accounting weights).
+
+Type:
+float
+
+
+
+
+
+n
+Number of negatives covered by the rule (accounting weights).
+
+Type:
+float
+
+
+
+
+
+P
+Number of positives in the training set (accounting weights).
+
+Type:
+float
+
+
+
+
+
+N
+Number of negatives in the training set (accounting weights).
+
+Type:
+float
+
+
+
+
+
+weight
+Rule weight.
+
+Type:
+float
+
+
+
+
+
+pvalue
+Rule significance.
+
+Type:
+float
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Survival — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+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
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+get_train_set_kaplan_meier ( ) → KaplanMeierEstimator
+Returns train set KaplanMeier estimator
+
+Returns:
+estimator
+Return type:
+KaplanMeierEstimator
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list ) → ndarray
+Perform prediction and return estimated survival function for each example.
+
+Parameters:
+values (rulekit.operator.Data
+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
labels (Data ) – survival status
survival_time (rulekit.operator.Data 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
+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
+Returns:
+hyperparameters – Dictionary containing model hyperparameters.
+Return type:
+np.ndarray
+
+
+
+
+
+get_train_set_kaplan_meier ( ) → KaplanMeierEstimator
+Returns train set KaplanMeier estimator
+
+Returns:
+estimator
+Return type:
+KaplanMeierEstimator
+
+
+
+
+
+predict ( values : ndarray | DataFrame | list ) → ndarray
+Perform prediction and return estimated survival function for each example.
+
+Parameters:
+values (rulekit.operator.Data
+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.
+
+
+
+
+
+
+
+
+
+
+
+
+
Quick start — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Quick start
+
+
Warning
+
This package is a wrapper for Java library, and requires Java Development Kit version 8
+or later to be installed on the computer. Both Open JDK and Oracle implementations are supported.
+
If you don’t have JDK installed on your computer you can quickly set it up using
+install-jdk package.
+
install install-jdk
+
+
+
import jdk
+
+jdk . install ( '11' , jre = True )
+
+
+
+Installation
+
+To check if everything was installed correctly call:
+import rulekit
+rulekit . __version__
+
+
It should run without errors and print package version.
+
+
+Package usage
+Now we are finally ready to use rulekit package and its models.
+from sklearn import datasets
+from rulekit import RuleKit
+from rulekit.classification import RuleClassifier
+
+iris = datasets . load_iris ()
+X = iris . data
+y = iris . target
+
+classifier = RuleClassifier ()
+classifier . fit ( X , y )
+
+prediction = classifier . predict ( X )
+
+from sklearn.metrics import accuracy_score
+
+print ( 'Accuracy: ' , accuracy_score ( y , prediction ))
+
+
As you may noticed, training and usage of rulekit models is the same as in scikit learn. This
+mean you easily can use scikit: metrics, cross-validation, hyper-parameters tuning etc.
+For more examples head to Tutorials
+
+
+
+
+
+
+
+
+
+
Tutorials — rulekit v2.1.24.0 f documentation
+ Classification — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Classification
+This notebook presents example usage of package for solving classification problem on seismic-bumps here .
+
+
This tutorial will cover topics such as:
+
- training model
+
- changing model hyperparameters
+
- hyperparameters tuning
+
- calculating metrics for model
+
- getting RuleKit inbuilt
+
+Install dependencies
+
+
+
+Summary of the dataset
+
+
+
+
+
+
+
+
+
+ genergy
+ gimpuls
+ goenergy
+ goimpuls
+ nbumps
+ nbumps2
+ nbumps3
+ nbumps4
+ nbumps5
+ nbumps6
+ nbumps7
+ nbumps89
+ senergy
+ maxenergy
+ class
+
+
+
+
+ count
+ 2.584000e+03
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.000000
+ 2584.0
+ 2584.0
+ 2584.0
+ 2584.000000
+ 2584.000000
+ 2584.000000
+
+
+ mean
+ 9.024252e+04
+ 538.579334
+ 12.375774
+ 4.508901
+ 0.859520
+ 0.393576
+ 0.392802
+ 0.067724
+ 0.004644
+ 0.0
+ 0.0
+ 0.0
+ 4975.270898
+ 4278.850619
+ 0.065789
+
+
+ std
+ 2.292005e+05
+ 562.652536
+ 80.319051
+ 63.166556
+ 1.364616
+ 0.783772
+ 0.769710
+ 0.279059
+ 0.068001
+ 0.0
+ 0.0
+ 0.0
+ 20450.833222
+ 19357.454882
+ 0.247962
+
+
+ min
+ 1.000000e+02
+ 2.000000
+ -96.000000
+ -96.000000
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+ 0.000000
+ 0.000000
+ 0.000000
+ 0.0
+ 0.0
+ 0.0
+ 0.000000
+ 0.000000
+ 0.000000
+
+
+ 25%
+ 1.166000e+04
+ 190.000000
+ -37.000000
+ -36.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.0
+ 0.0
+ 0.0
+ 0.000000
+ 0.000000
+ 0.000000
+
+
+ 50%
+ 2.548500e+04
+ 379.000000
+ -6.000000
+ -6.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.000000
+ 0.0
+ 0.0
+ 0.0
+ 0.000000
+ 0.000000
+ 0.000000
+
+
+ 75%
+ 5.283250e+04
+ 669.000000
+ 38.000000
+ 30.250000
+ 1.000000
+ 1.000000
+ 1.000000
+ 0.000000
+ 0.000000
+ 0.0
+ 0.0
+ 0.0
+ 2600.000000
+ 2000.000000
+ 0.000000
+
+
+ max
+ 2.595650e+06
+ 4518.000000
+ 1245.000000
+ 838.000000
+ 9.000000
+ 8.000000
+ 7.000000
+ 3.000000
+ 1.000000
+ 0.0
+ 0.0
+ 0.0
+ 402000.000000
+ 400000.000000
+ 1.000000
+
+
+
+
+
+Decision class distribution
+
+
+
+
+
+
+
+
+
+
+Helper function for calculating metrics
+
+
+
+Rule induction on full dataset
+
+
+
+
+
+
+
+
+
+
+ time_total_s
+ time_growing_s
+ time_pruning_s
+ rules_count
+ conditions_per_rule
+ induced_conditions_per_rule
+ avg_rule_coverage
+ avg_rule_precision
+ avg_rule_quality
+ pvalue
+ FDR_pvalue
+ FWER_pvalue
+ fraction_significant
+ fraction_FDR_significant
+ fraction_FWER_significant
+
+
+ Measure
+
+
+
+
+ C2
+ 1.218550
+ 1.036229
+ 0.111458
+ 35
+ 4.742857
+ 22.142857
+ 0.259410
+ 0.670793
+ 0.322125
+ 0.005729
+ 0.005879
+ 0.010640
+ 0.971429
+ 0.971429
+ 0.885714
+
+
+ Correlation
+ 0.471475
+ 0.339709
+ 0.123223
+ 21
+ 5.142857
+ 51.666667
+ 0.306612
+ 0.469157
+ 0.201772
+ 0.016841
+ 0.017345
+ 0.026655
+ 0.904762
+ 0.904762
+ 0.857143
+
+
+ RSS
+ 1.514044
+ 1.327209
+ 0.171176
+ 14
+ 3.714286
+ 64.428571
+ 0.473795
+ 0.484564
+ 0.253249
+ 0.041892
+ 0.044068
+ 0.068063
+ 0.785714
+ 0.785714
+ 0.714286
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Accuracy
+ MAE
+ Kappa
+ Balanced accuracy
+ Logistic loss
+ Precision
+ Sensitivity
+ Specificity
+ NPV
+ PPV
+ ...
+ Lift
+ F-measure
+ Fowlkes-Mallows index
+ False positive
+ False negative
+ True positive
+ True negative
+ Rules per example
+ Voting conflicts
+ Geometric mean
+
+
+ Measure
+
+
+
+
+ C2
+ 0.932663
+ 0.067337
+ 0.433613
+ 0.709693
+ 2.427088
+ 2.427088
+ 0.452941
+ 0.966446
+ 0.961665
+ 0.487342
+ ...
+ 7.407595
+ 0.469512
+ 0.928703
+ 81
+ 93
+ 77
+ 2333
+ 9.079334
+ 2146.0
+ 0.661622
+
+
+ Correlation
+ 0.827399
+ 0.172601
+ 0.246689
+ 0.729909
+ 6.221157
+ 6.221157
+ 0.617647
+ 0.842171
+ 0.969018
+ 0.216049
+ ...
+ 3.283951
+ 0.320122
+ 0.823757
+ 381
+ 65
+ 105
+ 2033
+ 6.438854
+ 1993.0
+ 0.721224
+
+
+ RSS
+ 0.788313
+ 0.211687
+ 0.207458
+ 0.725394
+ 7.629984
+ 7.629984
+ 0.652941
+ 0.797846
+ 0.970277
+ 0.185309
+ ...
+ 2.816694
+ 0.288687
+ 0.789800
+ 488
+ 59
+ 111
+ 1926
+ 6.633127
+ 2085.0
+ 0.721766
+
+
+
+
3 rows × 23 columns
+
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+
+
+
+
+ 0
+ 2333
+ 81
+
+
+ 1
+ 93
+ 77
+
+
+
+
+
+
+
+
+
+Confusion matrix - Correlation
+
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+
+
+
+
+ 0
+ 2033
+ 381
+
+
+ 1
+ 65
+ 105
+
+
+
+
+
+
+
+
+
+Confusion matrix - RSS
+
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+
+
+
+
+ 0
+ 1926
+ 488
+
+
+ 1
+ 59
+ 111
+
+
+
+
+
+C2 Measure generated rules
+
+
+
+
+
+
+IF gimpuls = (-inf, 32.50) THEN class = {0}
+IF nbumps = (-inf, 0.50) AND goenergy = <-84.50, inf) AND goimpuls = (-inf, -0.50) AND genergy = (-inf, 13675) THEN class = {0}
+IF genergy = (-inf, 17640) AND nbumps = (-inf, 0.50) AND goenergy = <-84.50, inf) THEN class = {0}
+IF genergy = <1625, 17640) AND maxenergy = (-inf, 3500) AND gimpuls = (-inf, 772.50) THEN class = {0}
+IF shift = {N} AND nbumps = (-inf, 0.50) AND goenergy = <-73.50, inf) THEN class = {0}
+IF shift = {N} AND senergy = (-inf, 6150) AND genergy = <1865, inf) AND goimpuls = (-inf, 230.50) THEN class = {0}
+IF senergy = (-inf, 550) AND gimpuls = (-inf, 380.50) AND goimpuls = (-inf, 96.50) AND goenergy = (-inf, 118) THEN class = {0}
+IF senergy = (-inf, 550) AND genergy = (-inf, 31790) AND goenergy = <-84.50, 114.50) THEN class = {0}
+IF senergy = (-inf, 550) AND goenergy = <-84.50, 87.50) AND gimpuls = (-inf, 1342.50) AND goimpuls = (-inf, 96) THEN class = {0}
+IF senergy = (-inf, 550) AND goimpuls = (-inf, 233.50) THEN class = {0}
+IF genergy = <1865, 28515) AND goenergy = (-inf, 105.50) AND nbumps = (-inf, 4.50) THEN class = {0}
+IF nbumps = <0.50, 1.50) AND gimpuls = (-inf, 1210) AND goimpuls = (-inf, 233.50) AND goenergy = <-72.50, inf) AND genergy = <12550, inf) THEN class = {0}
+IF gimpuls = (-inf, 514.50) AND nbumps = (-inf, 6.50) AND goimpuls = (-inf, 96.50) AND goenergy = <-84.50, inf) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 1832.50) AND goimpuls = (-inf, 312) THEN class = {0}
+IF nbumps3 = (-inf, 2.50) AND nbumps = (-inf, 6.50) AND goenergy = <-88.50, inf) THEN class = {0}
+IF genergy = (-inf, 748755) AND goimpuls = (-inf, 95.50) AND maxenergy = (-inf, 55000) AND gimpuls = (-inf, 3096) THEN class = {0}
+IF nbumps3 = <3.50, inf) AND gimpuls = <364, 1459.50) AND senergy = <10150, inf) THEN class = {1}
+IF gimpuls = <2208.50, inf) AND genergy = <513615, 1005720) AND nbumps2 = <0.50, inf) AND nbumps = (-inf, 3.50) THEN class = {1}
+IF gimpuls = <1328, inf) AND goenergy = (-inf, -29.50) AND goimpuls = <-31.50, -14.50) AND nbumps4 = (-inf, 1.50) THEN class = {1}
+IF gimpuls = <1328, 2109) AND goimpuls = (-inf, -5.50) AND senergy = <350, 36350) AND genergy = <159155, 586025) AND nbumps2 = (-inf, 3.50) AND goenergy = <-41.50, inf) THEN class = {1}
+IF nbumps3 = <0.50, 1.50) AND gimpuls = <1408, 1959) AND goimpuls = <-20.50, 13.50) AND senergy = (-inf, 54950) THEN class = {1}
+IF senergy = <750, 38250) AND genergy = <254130, 1133675) AND goenergy = <-16.50, inf) AND gimpuls = <1438.50, inf) AND goimpuls = <-5, inf) THEN class = {1}
+IF nbumps = <4.50, inf) AND nbumps3 = <1.50, 4.50) AND gimpuls = <203.50, inf) AND senergy = <4300, 131700) AND goenergy = <-41.50, inf) THEN class = {1}
+IF nbumps = <2.50, 4.50) AND gimpuls = <740.50, inf) AND genergy = <38935, 127440) AND senergy = (-inf, 14750) AND goimpuls = (-inf, 68.50) THEN class = {1}
+IF nbumps = <2.50, inf) AND gimpuls = <379, 1742) AND senergy = (-inf, 31100) AND genergy = (-inf, 211170) AND goenergy = (-inf, 123.50) AND goimpuls = (-inf, 19.50) THEN class = {1}
+IF gimpuls = <1139.50, inf) AND goimpuls = <-46, 116.50) AND senergy = (-inf, 38250) AND genergy = <46580, 1877915) AND goenergy = (-inf, 183) AND shift = {W} AND nbumps3 = (-inf, 1.50) AND nbumps2 = (-inf, 2.50) THEN class = {1}
+IF nbumps = <1.50, 3.50) AND gimpuls = <521.50, 2344.50) AND nbumps2 = <0.50, inf) AND genergy = <34605, 656965) AND goenergy = (-inf, 137) AND goimpuls = <-39, 41.50) AND maxenergy = <450, inf) THEN class = {1}
+IF nbumps = <1.50, 3.50) AND nbumps2 = <0.50, inf) AND genergy = <18870, inf) AND nbumps3 = (-inf, 1.50) AND gimpuls = <160, inf) AND senergy = <550, inf) AND goimpuls = <-62.50, 8.50) AND goenergy = (-inf, -1.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND gimpuls = <95, 1603.50) AND goenergy = (-inf, 131) AND goimpuls = <-70.50, 119) AND nbumps2 = (-inf, 4.50) AND nbumps3 = <0.50, inf) AND genergy = (-inf, 614380) AND maxenergy = (-inf, 25000) AND senergy = <2250, inf) THEN class = {1}
+IF goenergy = <-59.50, 186) AND genergy = <12415, 129940) AND gimpuls = <121.50, 793) AND senergy = <150, 1350) AND ghazard = {a} AND goimpuls = <-53.50, inf) THEN class = {1}
+IF genergy = <42215, 94300) AND gimpuls = <133.50, 813.50) AND ghazard = {a} AND goenergy = <-74.50, 160) AND senergy = (-inf, 11100) AND nbumps = (-inf, 3.50) AND nbumps3 = (-inf, 0.50) THEN class = {1}
+IF gimpuls = <537.50, 796) AND shift = {W} AND genergy = <17635, 36470) AND goimpuls = <-36.50, inf) AND goenergy = <-37.50, inf) AND nbumps = (-inf, 0.50) THEN class = {1}
+IF genergy = <18800, 52205) AND shift = {W} AND ghazard = {a} AND goimpuls = <-28.50, inf) AND goenergy = (-inf, 181) AND gimpuls = <380.50, 524.50) AND nbumps = (-inf, 0.50) THEN class = {1}
+IF gimpuls = <184.50, inf) AND goenergy = <-55.50, 128.50) AND genergy = <7265, inf) AND goimpuls = <-60.50, 37.50) AND nbumps = (-inf, 7.50) AND nbumps2 = (-inf, 4.50) AND maxenergy = (-inf, 25000) AND senergy = (-inf, 31350) THEN class = {1}
+IF gimpuls = <32.50, inf) AND goimpuls = <-74.50, inf) AND ghazard = {a} AND genergy = <1510, inf) AND goenergy = <-89.50, 124.50) THEN class = {1}
+
+
+
+Correlation Measure generated rules
+
+
+
+
+
+
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1252.50) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 1331) AND goimpuls = (-inf, 312) AND nbumps5 = (-inf, 0.50) THEN class = {0}
+IF nbumps = (-inf, 2.50) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps3 = (-inf, 2.50) AND nbumps = (-inf, 7) THEN class = {0}
+IF nbumps4 = (-inf, 2.50) THEN class = {0}
+IF nbumps2 = <0.50, 2.50) AND maxenergy = <1500, inf) AND senergy = (-inf, 36050) AND nbumps3 = <0.50, 4.50) AND goimpuls = <-34, 95) AND genergy = (-inf, 662435) AND gimpuls = <994.50, 1959) THEN class = {1}
+IF nbumps2 = <0.50, inf) AND maxenergy = <1500, inf) AND goimpuls = <-55, 95) AND nbumps = (-inf, 6.50) AND genergy = <61250, 662435) AND goenergy = (-inf, 96) AND nbumps3 = <0.50, inf) AND gimpuls = <712, 2257.50) AND senergy = (-inf, 31100) THEN class = {1}
+IF nbumps2 = <0.50, inf) AND genergy = <58310, 934630) AND goenergy = (-inf, 186) AND senergy = (-inf, 40650) AND maxenergy = <1500, inf) AND gimpuls = <538.50, inf) AND goimpuls = <-55, inf) THEN class = {1}
+IF nbumps = <1.50, inf) AND nbumps2 = <0.50, inf) AND gimpuls = <521.50, 2374) AND genergy = <58310, 799855) AND senergy = <650, 36050) AND goimpuls = <-71, 58.50) AND ghazard = {a} THEN class = {1}
+IF nbumps = <1.50, 6.50) AND nbumps2 = <0.50, inf) AND gimpuls = <521.50, 2374) AND genergy = <34360, inf) AND maxenergy = <350, inf) AND goimpuls = <-55, 95) AND senergy = <550, inf) AND nbumps4 = (-inf, 1.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND gimpuls = <306, inf) AND genergy = <28325, inf) AND goimpuls = (-inf, 19.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND nbumps2 = <0.50, inf) AND gimpuls = <153.50, 321) AND genergy = <14295, 36250) AND goimpuls = <-60.50, inf) AND senergy = (-inf, 40500) AND nbumps3 = (-inf, 3.50) THEN class = {1}
+IF genergy = <96260, 1062020) AND goimpuls = <-29, inf) AND senergy = <850, 7500) AND nbumps3 = (-inf, 1.50) AND gimpuls = <1404, 2965.50) AND nbumps = (-inf, 3.50) AND goenergy = (-inf, 69.50) THEN class = {1}
+IF gimpuls = <1253.50, inf) AND goenergy = <-50.50, 131.50) AND genergy = <46580, 1789250) AND nbumps = (-inf, 7.50) AND shift = {W} AND goimpuls = <-60.50, 118) AND senergy = (-inf, 95850) AND ghazard = {a} THEN class = {1}
+IF senergy = <550, inf) AND shift = {W} AND genergy = <10495, inf) AND gimpuls = <160, inf) AND goenergy = (-inf, 126) THEN class = {1}
+IF senergy = <350, inf) AND goimpuls = <-74.50, inf) AND gimpuls = <32.50, inf) AND goenergy = <-78.50, inf) AND maxenergy = <250, inf) THEN class = {1}
+IF genergy = <43150, inf) AND gimpuls = <133.50, inf) AND goenergy = (-inf, 176.50) THEN class = {1}
+IF shift = {W} AND genergy = <31760, 49585) AND gimpuls = <362.50, 771) AND goimpuls = <-27.50, inf) AND goenergy = <-3.50, inf) AND maxenergy = (-inf, 650) THEN class = {1}
+IF shift = {W} AND genergy = <20485, 43280) AND gimpuls = <380.50, 796) AND goimpuls = <-37, 142.50) AND goenergy = <-37.50, 181) AND nbumps = (-inf, 0.50) THEN class = {1}
+IF gimpuls = <177.50, inf) AND genergy = <7265, inf) AND goimpuls = (-inf, 241.50) AND goenergy = (-inf, 124.50) THEN class = {1}
+IF gimpuls = <54.50, 90) AND genergy = <1510, 4905) AND goimpuls = <-72.50, 28.50) AND seismoacoustic = {a} THEN class = {1}
+
+
+
+
+
+Stratified K-Folds cross-validation
+
+
+
+
+
+
+
+C:\Users\cezar\AppData\Local\Temp\ipykernel_13196\4002598548.py:22: RuntimeWarning: invalid value encountered in scalar divide
+ ppv: float = tp / (tp + fp)
+C:\Users\cezar\AppData\Local\Temp\ipykernel_13196\4002598548.py:39: RuntimeWarning: invalid value encountered in scalar divide
+ 'Lift': (tp / (tp + fp)) / ((tp + fn) / (tp + tn + fp + fn)),
+C:\Users\cezar\AppData\Local\Temp\ipykernel_13196\4002598548.py:22: RuntimeWarning: invalid value encountered in scalar divide
+ ppv: float = tp / (tp + fp)
+C:\Users\cezar\AppData\Local\Temp\ipykernel_13196\4002598548.py:39: RuntimeWarning: invalid value encountered in scalar divide
+ 'Lift': (tp / (tp + fp)) / ((tp + fn) / (tp + tn + fp + fn)),
+
+
Rules characteristics
+
+
+
+
+
+
+time_total_s 0.292132
+time_growing_s 0.227262
+time_pruning_s 0.047455
+rules_count 34.200000
+conditions_per_rule 4.720378
+induced_conditions_per_rule 21.124536
+avg_rule_coverage 0.239541
+avg_rule_precision 0.690010
+avg_rule_quality 0.337021
+pvalue 0.014757
+FDR_pvalue 0.015234
+FWER_pvalue 0.030014
+fraction_significant 0.909792
+fraction_FDR_significant 0.909792
+fraction_FWER_significant 0.872710
+dtype: float64
+
+
Rules evaluation (average)
+
+
+
+
+
+
+Accuracy 0.855063
+MAE 0.144937
+Kappa 0.107556
+Balanced accuracy 0.567020
+Logistic loss 5.224070
+Precision 5.224070
+Sensitivity 0.235294
+Specificity 0.898747
+NPV 0.945594
+PPV 0.495355
+psep 0.443788
+Fall-out 0.101253
+Youden's J statistic 0.134041
+Lift 7.520255
+F-measure 0.145015
+Fowlkes-Mallows index 0.870605
+False positive 24.500000
+False negative 13.000000
+True positive 4.000000
+True negative 216.900000
+Rules per example 7.871479
+Voting conflicts 179.000000
+Geometric mean 0.344832
+dtype: float64
+
+
Confusion matrix (average)
+
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+
+
+
+
+ 0
+ 216.9
+ 24.5
+
+
+ 1
+ 13.0
+ 4.0
+
+
+
+
+
+
+Hyperparameters tuning
+This one gonna take a while…
+
+
+
+
+
+
+Best BAC: 0.626780 using {'induction_measure': <Measures.WeightedLaplace: 'WeightedLaplace'>, 'minsupp_new': 5, 'pruning_measure': <Measures.RSS: 'RSS'>, 'voting_measure': <Measures.WeightedLaplace: 'WeightedLaplace'>}
+
+
+
+Building model with tuned hyperparameters
+
+Split dataset to train and test (80%/20%).
+
+Rules evaluation
+
+
+
+
+
+
+time_total_s 0.173054
+time_growing_s 0.120460
+time_pruning_s 0.029986
+rules_count 29.000000
+conditions_per_rule 2.689655
+induced_conditions_per_rule 15.310345
+avg_rule_coverage 0.491183
+avg_rule_precision 0.736226
+avg_rule_quality 1.309334
+pvalue 0.019831
+FDR_pvalue 0.019993
+FWER_pvalue 0.024284
+fraction_significant 0.931034
+fraction_FDR_significant 0.931034
+fraction_FWER_significant 0.931034
+dtype: float64
+
+
+
+Validate model on test dataset
+
+
+
+
+
+
+Accuracy 0.808511
+MAE 0.191489
+Kappa 0.170010
+Balanced accuracy 0.679398
+Logistic loss 6.901976
+Precision 6.901976
+Sensitivity 0.533333
+Specificity 0.825462
+NPV 0.966346
+PPV 0.158416
+psep 0.124762
+Fall-out 0.174538
+Youden's J statistic 0.358795
+Lift 2.730033
+F-measure 0.244275
+Fowlkes-Mallows index 0.809997
+False positive 85.000000
+False negative 14.000000
+True positive 16.000000
+True negative 402.000000
+Rules per example 14.034816
+Voting conflicts 360.000000
+Geometric mean 0.663511
+dtype: float64
+
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+
+
+
+
+ 0
+ 402
+ 85
+
+
+ 1
+ 14
+ 16
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " genergy \n",
+ " gimpuls \n",
+ " goenergy \n",
+ " goimpuls \n",
+ " nbumps \n",
+ " nbumps2 \n",
+ " nbumps3 \n",
+ " nbumps4 \n",
+ " nbumps5 \n",
+ " nbumps6 \n",
+ " nbumps7 \n",
+ " nbumps89 \n",
+ " senergy \n",
+ " maxenergy \n",
+ " class \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 2.584000e+03 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.0 \n",
+ " 2584.0 \n",
+ " 2584.0 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " 2584.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 9.024252e+04 \n",
+ " 538.579334 \n",
+ " 12.375774 \n",
+ " 4.508901 \n",
+ " 0.859520 \n",
+ " 0.393576 \n",
+ " 0.392802 \n",
+ " 0.067724 \n",
+ " 0.004644 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 4975.270898 \n",
+ " 4278.850619 \n",
+ " 0.065789 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 2.292005e+05 \n",
+ " 562.652536 \n",
+ " 80.319051 \n",
+ " 63.166556 \n",
+ " 1.364616 \n",
+ " 0.783772 \n",
+ " 0.769710 \n",
+ " 0.279059 \n",
+ " 0.068001 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 20450.833222 \n",
+ " 19357.454882 \n",
+ " 0.247962 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 1.000000e+02 \n",
+ " 2.000000 \n",
+ " -96.000000 \n",
+ " -96.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 1.166000e+04 \n",
+ " 190.000000 \n",
+ " -37.000000 \n",
+ " -36.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 2.548500e+04 \n",
+ " 379.000000 \n",
+ " -6.000000 \n",
+ " -6.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 5.283250e+04 \n",
+ " 669.000000 \n",
+ " 38.000000 \n",
+ " 30.250000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 0.000000 \n",
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+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 2600.000000 \n",
+ " 2000.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 2.595650e+06 \n",
+ " 4518.000000 \n",
+ " 1245.000000 \n",
+ " 838.000000 \n",
+ " 9.000000 \n",
+ " 8.000000 \n",
+ " 7.000000 \n",
+ " 3.000000 \n",
+ " 1.000000 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 402000.000000 \n",
+ " 400000.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "groups = df_full['class'].value_counts()\n",
+ "sizes = [groups[0], groups[1]]\n",
+ "labels = [str(e) for e in groups.index]\n",
+ "\n",
+ "fig1, ax1 = plt.subplots()\n",
+ "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
+ "ax1.axis('equal')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Helper function for calculating metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "from sklearn import metrics\n",
+ "import numpy as np\n",
+ "from rulekit.classification import RuleClassifier\n",
+ "\n",
+ "\n",
+ "X: pd.DataFrame = df_full.drop(['class'], axis=1)\n",
+ "y: pd.Series = df_full['class']\n",
+ "\n",
+ "\n",
+ "def get_prediction_metrics(\n",
+ " measure: str,\n",
+ " y_pred: np.ndarray,\n",
+ " y_true: pd.Series,\n",
+ " classification_metrics: dict\n",
+ ") -> tuple[pd.DataFrame, np.ndarray]:\n",
+ " confusion_matrix: np.ndarray = metrics.confusion_matrix(y_true, y_pred)\n",
+ " tn, fp, fn, tp = confusion_matrix.ravel()\n",
+ " sensitivity: float = tp / (tp + fn)\n",
+ " specificity: float = tn / (tn + fp)\n",
+ " npv: float = tn / (tn + fn)\n",
+ " ppv: float = tp / (tp + fp)\n",
+ "\n",
+ " dictionary = {\n",
+ " 'Measure': measure,\n",
+ " 'Accuracy': metrics.accuracy_score(y_true, y_pred),\n",
+ " 'MAE': metrics.mean_absolute_error(y_true, y_pred),\n",
+ " 'Kappa': metrics.cohen_kappa_score(y_true, y_pred),\n",
+ " 'Balanced accuracy': metrics.balanced_accuracy_score(y_true, y_pred),\n",
+ " 'Logistic loss': metrics.log_loss(y_true, y_pred),\n",
+ " 'Precision': metrics.log_loss(y_true, y_pred),\n",
+ " 'Sensitivity': sensitivity,\n",
+ " 'Specificity': specificity,\n",
+ " 'NPV': npv,\n",
+ " 'PPV': ppv,\n",
+ " 'psep': ppv + npv - 1,\n",
+ " 'Fall-out': fp / (fp + tn),\n",
+ " \"Youden's J statistic\": sensitivity + specificity - 1,\n",
+ " 'Lift': (tp / (tp + fp)) / ((tp + fn) / (tp + tn + fp + fn)),\n",
+ " 'F-measure': 2 * tp / (2 * tp + fp + fn),\n",
+ " 'Fowlkes-Mallows index': metrics.fowlkes_mallows_score(y_true, y_pred),\n",
+ " 'False positive': fp,\n",
+ " 'False negative': fn,\n",
+ " 'True positive': tp,\n",
+ " 'True negative': tn,\n",
+ " 'Rules per example': classification_metrics['rules_per_example'],\n",
+ " 'Voting conflicts': classification_metrics['voting_conflicts'],\n",
+ " 'Geometric mean': math.sqrt(specificity * sensitivity),\n",
+ " 'Geometric mean': math.sqrt(specificity * sensitivity),\n",
+ " }\n",
+ " return pd.DataFrame.from_records([dictionary], index='Measure'), confusion_matrix\n",
+ "\n",
+ "\n",
+ "def get_ruleset_stats(\n",
+ " measure: str,\n",
+ " model: RuleClassifier\n",
+ ") -> pd.DataFrame:\n",
+ " return pd.DataFrame.from_records(\n",
+ " [{'Measure': measure, **model.stats.__dict__}],\n",
+ " index='Measure'\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rule induction on full dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 1.218550 \n",
+ " 1.036229 \n",
+ " 0.111458 \n",
+ " 35 \n",
+ " 4.742857 \n",
+ " 22.142857 \n",
+ " 0.259410 \n",
+ " 0.670793 \n",
+ " 0.322125 \n",
+ " 0.005729 \n",
+ " 0.005879 \n",
+ " 0.010640 \n",
+ " 0.971429 \n",
+ " 0.971429 \n",
+ " 0.885714 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.471475 \n",
+ " 0.339709 \n",
+ " 0.123223 \n",
+ " 21 \n",
+ " 5.142857 \n",
+ " 51.666667 \n",
+ " 0.306612 \n",
+ " 0.469157 \n",
+ " 0.201772 \n",
+ " 0.016841 \n",
+ " 0.017345 \n",
+ " 0.026655 \n",
+ " 0.904762 \n",
+ " 0.904762 \n",
+ " 0.857143 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 1.514044 \n",
+ " 1.327209 \n",
+ " 0.171176 \n",
+ " 14 \n",
+ " 3.714286 \n",
+ " 64.428571 \n",
+ " 0.473795 \n",
+ " 0.484564 \n",
+ " 0.253249 \n",
+ " 0.041892 \n",
+ " 0.044068 \n",
+ " 0.068063 \n",
+ " 0.785714 \n",
+ " 0.785714 \n",
+ " 0.714286 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Accuracy \n",
+ " MAE \n",
+ " Kappa \n",
+ " Balanced accuracy \n",
+ " Logistic loss \n",
+ " Precision \n",
+ " Sensitivity \n",
+ " Specificity \n",
+ " NPV \n",
+ " PPV \n",
+ " ... \n",
+ " Lift \n",
+ " F-measure \n",
+ " Fowlkes-Mallows index \n",
+ " False positive \n",
+ " False negative \n",
+ " True positive \n",
+ " True negative \n",
+ " Rules per example \n",
+ " Voting conflicts \n",
+ " Geometric mean \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 0.932663 \n",
+ " 0.067337 \n",
+ " 0.433613 \n",
+ " 0.709693 \n",
+ " 2.427088 \n",
+ " 2.427088 \n",
+ " 0.452941 \n",
+ " 0.966446 \n",
+ " 0.961665 \n",
+ " 0.487342 \n",
+ " ... \n",
+ " 7.407595 \n",
+ " 0.469512 \n",
+ " 0.928703 \n",
+ " 81 \n",
+ " 93 \n",
+ " 77 \n",
+ " 2333 \n",
+ " 9.079334 \n",
+ " 2146.0 \n",
+ " 0.661622 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.827399 \n",
+ " 0.172601 \n",
+ " 0.246689 \n",
+ " 0.729909 \n",
+ " 6.221157 \n",
+ " 6.221157 \n",
+ " 0.617647 \n",
+ " 0.842171 \n",
+ " 0.969018 \n",
+ " 0.216049 \n",
+ " ... \n",
+ " 3.283951 \n",
+ " 0.320122 \n",
+ " 0.823757 \n",
+ " 381 \n",
+ " 65 \n",
+ " 105 \n",
+ " 2033 \n",
+ " 6.438854 \n",
+ " 1993.0 \n",
+ " 0.721224 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 0.788313 \n",
+ " 0.211687 \n",
+ " 0.207458 \n",
+ " 0.725394 \n",
+ " 7.629984 \n",
+ " 7.629984 \n",
+ " 0.652941 \n",
+ " 0.797846 \n",
+ " 0.970277 \n",
+ " 0.185309 \n",
+ " ... \n",
+ " 2.816694 \n",
+ " 0.288687 \n",
+ " 0.789800 \n",
+ " 488 \n",
+ " 59 \n",
+ " 111 \n",
+ " 1926 \n",
+ " 6.633127 \n",
+ " 2085.0 \n",
+ " 0.721766 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
3 rows × 23 columns
\n",
+ "
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+ "\n",
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+ " 4.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
, 'minsupp_new': 5, 'pruning_measure': , 'voting_measure': }\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "from rulekit.params import Measures\n",
+ "\n",
+ "N_SPLITS: int = 3\n",
+ "\n",
+ "# define models and parameters\n",
+ "model = RuleClassifier()\n",
+ "minsupp_new = range(3, 15, 2)\n",
+ "measures_choice = [Measures.C2, Measures.RSS, Measures.WeightedLaplace, Measures.Correlation]\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'minsupp_new': minsupp_new, \n",
+ " 'induction_measure': measures_choice, \n",
+ " 'pruning_measure': measures_choice, \n",
+ " 'voting_measure': measures_choice\n",
+ "}\n",
+ "cv = StratifiedKFold(n_splits=N_SPLITS)\n",
+ "grid_search = GridSearchCV(\n",
+ " estimator=model, \n",
+ " param_grid=grid, \n",
+ " cv=cv, \n",
+ " scoring='balanced_accuracy', \n",
+ " n_jobs=3\n",
+ ")\n",
+ "grid_result = grid_search.fit(X, y)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\"Best BAC: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building model with tuned hyperparameters\n",
+ "\n",
+ "### Split dataset to train and test (80%/20%)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from IPython.display import display\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)\n",
+ "\n",
+ "\n",
+ "clf = RuleClassifier(**grid_result.best_params_)\n",
+ "clf.fit(X_train, y_train)\n",
+ "ruleset: RuleSet[ClassificationRule] = clf.model\n",
+ "ruleset_stats = get_ruleset_stats('Best', ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rules evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "time_total_s 0.173054\n",
+ "time_growing_s 0.120460\n",
+ "time_pruning_s 0.029986\n",
+ "rules_count 29.000000\n",
+ "conditions_per_rule 2.689655\n",
+ "induced_conditions_per_rule 15.310345\n",
+ "avg_rule_coverage 0.491183\n",
+ "avg_rule_precision 0.736226\n",
+ "avg_rule_quality 1.309334\n",
+ "pvalue 0.019831\n",
+ "FDR_pvalue 0.019993\n",
+ "FWER_pvalue 0.024284\n",
+ "fraction_significant 0.931034\n",
+ "fraction_FDR_significant 0.931034\n",
+ "fraction_FWER_significant 0.931034\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Validate model on test dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Accuracy 0.808511\n",
+ "MAE 0.191489\n",
+ "Kappa 0.170010\n",
+ "Balanced accuracy 0.679398\n",
+ "Logistic loss 6.901976\n",
+ "Precision 6.901976\n",
+ "Sensitivity 0.533333\n",
+ "Specificity 0.825462\n",
+ "NPV 0.966346\n",
+ "PPV 0.158416\n",
+ "psep 0.124762\n",
+ "Fall-out 0.174538\n",
+ "Youden's J statistic 0.358795\n",
+ "Lift 2.730033\n",
+ "F-measure 0.244275\n",
+ "Fowlkes-Mallows index 0.809997\n",
+ "False positive 85.000000\n",
+ "False negative 14.000000\n",
+ "True positive 16.000000\n",
+ "True negative 402.000000\n",
+ "Rules per example 14.034816\n",
+ "Voting conflicts 360.000000\n",
+ "Geometric mean 0.663511\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
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\n",
+ "
Expert Rules — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Expert Rules
+
+
This notebook presents example usage of user-guided rule induction which follows the scheme introduced by the
GuideR algorithm (Sikora et al, 2019).
+
Each problem (classification, regression, survival) in addition to the basic class has an expert class, i.e. RuleClassifier and ExpertRuleClassifier. Expert classes allow you to define set of initial rules, preferred conditions and forbidden conditions.
+
This tutorial will show you how to define rules and conditions
+
+Classification
+
+
+Define expert knowledge
+
+
+
+Rule induction
+
+
+
+
+
+
+
+IF [[gimpuls = <-inf, 750)]] AND [seismic = {a}] AND nbumps = (-inf, 1.50) AND nbumps4 = (-inf, 0.50) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1252.50) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1342.50) AND goimpuls = (-inf, 312) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1427.50) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1653.50) AND genergy = (-inf, 1006585) AND goimpuls = (-inf, 312) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 1752) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND gimpuls = (-inf, 2733) AND goimpuls = (-inf, 312) THEN class = {0}
+IF nbumps = (-inf, 1.50) AND genergy = <634250, inf) AND gimpuls = <2965, inf) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 1331) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 1655.50) AND genergy = (-inf, 386010) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 1686) AND nbumps2 = (-inf, 1.50) AND goimpuls = (-inf, 312) AND nbumps5 = (-inf, 0.50) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND genergy = (-inf, 386010) AND gimpuls = (-inf, 2892) AND goimpuls = (-inf, 312) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 2068.50) AND goimpuls = (-inf, 312) AND nbumps2 = (-inf, 1.50) AND genergy = (-inf, 1004565) THEN class = {0}
+IF nbumps = (-inf, 2.50) AND gimpuls = (-inf, 2184.50) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps3 = (-inf, 1.50) AND nbumps2 = (-inf, 2.50) THEN class = {0}
+IF nbumps = (-inf, 3.50) AND goimpuls = (-inf, 96.50) AND gimpuls = (-inf, 901) AND senergy = (-inf, 3850) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps3 = (-inf, 1.50) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps2 = (-inf, 1.50) AND senergy = (-inf, 9600) AND nbumps3 = (-inf, 2.50) AND goimpuls = (-inf, 312) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps2 = (-inf, 1.50) AND nbumps3 = (-inf, 2.50) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND senergy = (-inf, 8100) AND nbumps2 = (-inf, 2.50) AND goimpuls = (-inf, 312) THEN class = {0}
+IF maxenergy = (-inf, 5500) AND gimpuls = (-inf, 901) AND goenergy = <-40.50, 68.50) AND ghazard = {a} AND goimpuls = <-39.50, inf) AND senergy = <1150, inf) AND nbumps2 = <1.50, inf) THEN class = {0}
+IF nbumps2 = (-inf, 1.50) AND nbumps3 = (-inf, 3.50) AND nbumps = (-inf, 6.50) AND gimpuls = (-inf, 695.50) AND goimpuls = <-54.50, inf) AND goenergy = <-48.50, inf) AND genergy = <10915, inf) AND maxenergy = <2500, inf) AND senergy = <3950, inf) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps = (-inf, 4.50) AND nbumps2 = (-inf, 2.50) THEN class = {0}
+IF gimpuls = (-inf, 1253.50) AND nbumps3 = (-inf, 2.50) AND nbumps = (-inf, 5.50) THEN class = {0}
+IF nbumps3 = (-inf, 3.50) AND nbumps4 = (-inf, 2.50) AND maxenergy = (-inf, 75000) AND genergy = (-inf, 378500) AND gimpuls = (-inf, 901) THEN class = {0}
+IF senergy = (-inf, 85450) AND goimpuls = (-inf, 312) AND gimpuls = (-inf, 1139.50) THEN class = {0}
+IF nbumps2 = (-inf, 0.50) AND nbumps3 = (-inf, 2.50) AND goimpuls = <-35.50, inf) AND nbumps = <1.50, inf) AND gimpuls = <1150.50, inf) THEN class = {0}
+IF senergy = (-inf, 5750) AND genergy = (-inf, 508210) AND goenergy = <-18.50, inf) AND nbumps2 = <1.50, inf) AND gimpuls = <927, inf) THEN class = {0}
+IF senergy = (-inf, 5750) THEN class = {0}
+IF nbumps3 = (-inf, 2.50) AND nbumps2 = (-inf, 2.50) AND gimpuls = (-inf, 2489.50) AND genergy = (-inf, 318735) THEN class = {0}
+IF nbumps2 = (-inf, 1.50) AND goenergy = <-36.50, inf) AND goimpuls = (-inf, 6.50) AND genergy = <392530, inf) AND senergy = <6750, inf) THEN class = {0}
+IF nbumps = (-inf, 4.50) AND nbumps2 = (-inf, 2.50) AND gimpuls = (-inf, 3881.50) THEN class = {0}
+IF [[gimpuls = <750, inf)]] AND nbumps2 = <0.50, inf) AND genergy = <61250, 662435) AND maxenergy = <1500, inf) AND nbumps = (-inf, 7.50) AND nbumps3 = <0.50, inf) AND seismoacoustic = {a} AND goenergy = (-inf, 11) AND senergy = (-inf, 31200) THEN class = {1}
+IF [gimpuls = <1253.50, inf)] AND genergy = <96260, 673155) AND seismic = {b} AND maxenergy = (-inf, 7500) AND goenergy = <-40.50, 87) AND seismoacoustic = {a} AND nbumps = (-inf, 3.50) AND senergy = (-inf, 10000) THEN class = {1}
+IF nbumps2 = <0.50, inf) AND maxenergy = <1500, inf) AND gimpuls = <538.50, 1959) AND nbumps = (-inf, 6.50) AND senergy = (-inf, 36050) AND genergy = <61250, 662435) AND goenergy = (-inf, 96) AND nbumps3 = <0.50, 4.50) AND goimpuls = <-34, 95) THEN class = {1}
+IF nbumps2 = <0.50, inf) AND genergy = <58310, 934630) AND goenergy = (-inf, 186) AND senergy = (-inf, 40650) AND maxenergy = <1500, inf) AND gimpuls = <538.50, inf) AND goimpuls = <-55, inf) THEN class = {1}
+IF nbumps = <1.50, 4.50) AND nbumps2 = <0.50, 3.50) AND gimpuls = <521.50, inf) AND genergy = <58310, 799855) AND nbumps4 = (-inf, 1.50) AND senergy = <850, inf) AND goimpuls = <-39, 64.50) AND nbumps3 = (-inf, 2.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND nbumps2 = <0.50, 4.50) AND gimpuls = <521.50, inf) AND genergy = <34360, 1161025) AND goenergy = (-inf, 186) AND nbumps3 = (-inf, 6) AND maxenergy = <450, 45000) THEN class = {1}
+IF nbumps = <1.50, inf) AND nbumps2 = <0.50, inf) AND genergy = <34880, inf) AND gimpuls = <281.50, inf) AND goenergy = (-inf, 135.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND nbumps2 = <0.50, inf) AND gimpuls = <153.50, 498) AND genergy = <18870, 33010) AND senergy = (-inf, 40500) AND goenergy = (-inf, 106.50) AND nbumps3 = (-inf, 1.50) THEN class = {1}
+IF nbumps = <1.50, inf) AND goenergy = (-inf, 131) AND gimpuls = <176, inf) THEN class = {1}
+IF gimpuls = <1253.50, inf) AND goenergy = (-inf, 131.50) AND genergy = <54930, 1062020) AND shift = {W} AND goimpuls = <-60.50, 109) AND senergy = (-inf, 36050) AND nbumps2 = (-inf, 2.50) THEN class = {1}
+IF nbumps2 = <0.50, inf) AND gimpuls = <98.50, inf) AND goimpuls = <-70.50, inf) AND maxenergy = <550, inf) THEN class = {1}
+IF goimpuls = <-74.50, inf) AND gimpuls = <32.50, inf) AND goenergy = <-78.50, inf) AND senergy = <850, inf) THEN class = {1}
+IF genergy = <48545, inf) AND gimpuls = <131, inf) AND goenergy = (-inf, 176.50) THEN class = {1}
+IF shift = {W} AND genergy = <32795, 49585) AND gimpuls = <396, 1445.50) AND goimpuls = <-19, inf) AND senergy = (-inf, 350) AND goenergy = <-4, inf) THEN class = {1}
+IF genergy = <16805, 32020) AND gimpuls = <537.50, 796) AND goimpuls = <-36.50, inf) AND goenergy = <-37.50, inf) AND senergy = (-inf, 250) THEN class = {1}
+IF shift = {W} AND genergy = <19670, 40735) AND gimpuls = <240, 470.50) AND goenergy = <-37.50, 181) AND goimpuls = <-42.50, inf) THEN class = {1}
+IF gimpuls = <54.50, inf) AND senergy = (-inf, 115450) AND goimpuls = <-74.50, inf) AND genergy = <1510, inf) THEN class = {1}
+
+
+
+
+Regression
+
+Load dataset
+
+
+
+
+
+
+
+
+
+
+ MM31
+ MM116
+ AS038
+ PG072
+ PD
+ BA13
+ DMM116
+
+
+
+
+ 0
+ 0.46
+ 1.3
+ 2.4
+ 2.0
+ 1.0
+ 1076.0
+ 0.0
+
+
+ 1
+ 0.46
+ 1.3
+ 2.2
+ 1.9
+ 1.0
+ 1076.0
+ 0.0
+
+
+ 2
+ 0.49
+ 1.3
+ 2.2
+ 1.9
+ 1.0
+ 1076.0
+ 0.0
+
+
+ 3
+ 0.50
+ 1.3
+ 2.3
+ 1.9
+ 1.0
+ 1076.0
+ 0.0
+
+
+ 4
+ 0.54
+ 1.3
+ 2.3
+ 1.9
+ 1.0
+ 1076.0
+ 0.0
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 13363
+ 0.64
+ 1.2
+ 2.4
+ 1.8
+ 1.0
+ 1077.0
+ 0.0
+
+
+ 13364
+ 0.59
+ 1.2
+ 2.4
+ 1.8
+ 1.0
+ 1077.0
+ 0.0
+
+
+ 13365
+ 0.60
+ 1.1
+ 2.2
+ 1.8
+ 1.0
+ 1077.0
+ -0.1
+
+
+ 13366
+ 0.64
+ 1.1
+ 2.2
+ 1.8
+ 1.0
+ 1077.0
+ 0.0
+
+
+ 13367
+ 0.65
+ 1.2
+ 2.2
+ 1.7
+ 0.0
+ 1077.0
+ 0.1
+
+
+
+
13368 rows × 7 columns
+
+
+
+Define rules and conditions
+
+
+
+Rule induction
+
+
+
+
+
+
+
+IF [PD = <0.50, inf)] AND PG072 = (-inf, 2.05) THEN MM116_pred = {1.01} [0.77,1.25]
+IF [PD = <0.50, inf)] THEN MM116_pred = {1.01} [0.77,1.25]
+IF MM31 = (-inf, 0.23) THEN MM116_pred = {0.40} [0.39,0.41]
+IF MM116 = (-inf, 0.45) AND MM31 = <0.18, 0.24) THEN MM116_pred = {0.40} [0.38,0.42]
+IF MM31 = (-inf, 0.25) THEN MM116_pred = {0.44} [0.37,0.51]
+IF MM116 = <0.25, inf) AND AS038 = <2, 2.45) AND PD = (-inf, 0.50) AND PG072 = (-inf, 1.95) AND BA13 = (-inf, 1075.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.71} [0.50,0.93]
+IF MM116 = (-inf, 0.25) AND BA13 = (-inf, 1075.50) AND MM31 = <0.19, inf) AND AS038 = <2.35, 2.45) AND PG072 = <1.75, 1.95) AND PD = (-inf, 0.50) THEN MM116_pred = {0.25} [0.20,0.30]
+IF MM116 = (-inf, 0.45) AND BA13 = (-inf, 1077.50) AND MM31 = <0.18, inf) THEN MM116_pred = {0.40} [0.37,0.43]
+IF MM116 = (-inf, 0.55) AND MM31 = (-inf, 0.32) THEN MM116_pred = {0.45} [0.39,0.51]
+IF MM116 = <0.45, 0.65) THEN MM116_pred = {0.55} [0.49,0.61]
+IF MM31 = <0.18, 0.27) AND MM116 = (-inf, 0.75) THEN MM116_pred = {0.46} [0.39,0.53]
+IF MM116 = <0.45, 0.85) AND MM31 = <0.25, inf) THEN MM116_pred = {0.70} [0.56,0.84]
+IF MM116 = <0.75, inf) THEN MM116_pred = {1.01} [0.82,1.19]
+
+
+
+
+Survival
+
+
+Define rules and conditions
+
+
+
+Rule induction
+
+
+
+
+
+
+
+IF [[CD34kgx10d6 = (-inf, 10)]] AND [[extcGvHD = {0}]] THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND PLTrecovery = <500142.50, inf) THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND Recipientage = <17.85, inf) AND RecipientRh = {1} THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND PLTrecovery = <26, inf) AND Recipientage = <14.30, inf) AND Relapse = {0} THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND PLTrecovery = <26, inf) AND Recipientage = <12, 18.85) AND Gendermatch = {0} AND Donorage = (-inf, 40.64) THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND CD3dCD34 = (-inf, 10.97) AND Donorage = (-inf, 49.19) AND PLTrecovery = (-inf, 500142.50) AND Txpostrelapse = {0} AND extcGvHD = {1} THEN
+IF [CD34kgx10d6 = <11.86, inf)] AND Relapse = {0} THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND RecipientRh = {1} AND CD3dCD34 = <6.64, inf) THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND Recipientageint = {2} AND CD3dCD34 = <0.94, inf) AND Donorage = <36.03, inf) THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND PLTrecovery = <22.50, inf) THEN
+IF [CD34kgx10d6 = <11.86, inf)] THEN
+IF [CD34kgx10d6 = (-inf, 11.86)] AND CD3dCD34 = <0.89, inf) AND Rbodymass = <36.50, inf) AND Recipientage = <9.20, inf) AND IIIV = {1} AND PLTrecovery = (-inf, 22.50) AND Stemcellsource = {1} THEN
+
+
+
+
+
+
+
+
+
+
+
+
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MM31 \n",
+ " MM116 \n",
+ " AS038 \n",
+ " PG072 \n",
+ " PD \n",
+ " BA13 \n",
+ " DMM116 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.46 \n",
+ " 1.3 \n",
+ " 2.4 \n",
+ " 2.0 \n",
+ " 1.0 \n",
+ " 1076.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.46 \n",
+ " 1.3 \n",
+ " 2.2 \n",
+ " 1.9 \n",
+ " 1.0 \n",
+ " 1076.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.49 \n",
+ " 1.3 \n",
+ " 2.2 \n",
+ " 1.9 \n",
+ " 1.0 \n",
+ " 1076.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.50 \n",
+ " 1.3 \n",
+ " 2.3 \n",
+ " 1.9 \n",
+ " 1.0 \n",
+ " 1076.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.54 \n",
+ " 1.3 \n",
+ " 2.3 \n",
+ " 1.9 \n",
+ " 1.0 \n",
+ " 1076.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 13363 \n",
+ " 0.64 \n",
+ " 1.2 \n",
+ " 2.4 \n",
+ " 1.8 \n",
+ " 1.0 \n",
+ " 1077.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 13364 \n",
+ " 0.59 \n",
+ " 1.2 \n",
+ " 2.4 \n",
+ " 1.8 \n",
+ " 1.0 \n",
+ " 1077.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 13365 \n",
+ " 0.60 \n",
+ " 1.1 \n",
+ " 2.2 \n",
+ " 1.8 \n",
+ " 1.0 \n",
+ " 1077.0 \n",
+ " -0.1 \n",
+ " \n",
+ " \n",
+ " 13366 \n",
+ " 0.64 \n",
+ " 1.1 \n",
+ " 2.2 \n",
+ " 1.8 \n",
+ " 1.0 \n",
+ " 1077.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 13367 \n",
+ " 0.65 \n",
+ " 1.2 \n",
+ " 2.2 \n",
+ " 1.7 \n",
+ " 0.0 \n",
+ " 1077.0 \n",
+ " 0.1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
13368 rows × 7 columns
\n",
+ "
Regression — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Regression
+This notebook presents example usage of package for solving regression problem on methane here and test dataset here
+
+
This tutorial will cover topics such as:
+
- training model
+
- changing model hyperparameters
+
- hyperparameters tuning
+
- calculating metrics for model
+
- getting RuleKit inbuilt
+
+Summary of the dataset
+
+
+Train file
+
+
+
+
+
+
+Train file overview:
+Name: methane-train
+Objects number: 13368; Attributes number: 8
+Basic attribute statistics:
+
+
+
+
+
+
+
+
+
+ MM31
+ MM116
+ AS038
+ PG072
+ PD
+ BA13
+ DMM116
+ MM116_pred
+
+
+
+
+ count
+ 13368.000000
+ 13368.000000
+ 13368.000000
+ 13368.000000
+ 13368.000000
+ 13368.000000
+ 13368.000000
+ 13368.00000
+
+
+ mean
+ 0.363960
+ 0.775007
+ 2.294734
+ 1.835600
+ 0.308573
+ 1073.443372
+ -0.000007
+ 0.79825
+
+
+ std
+ 0.117105
+ 0.269366
+ 0.142504
+ 0.106681
+ 0.461922
+ 3.162811
+ 0.043566
+ 0.28649
+
+
+ min
+ 0.170000
+ 0.200000
+ 1.400000
+ 1.100000
+ 0.000000
+ 1067.000000
+ -1.800000
+ 0.20000
+
+
+ 25%
+ 0.260000
+ 0.500000
+ 2.300000
+ 1.800000
+ 0.000000
+ 1070.000000
+ 0.000000
+ 0.50000
+
+
+ 50%
+ 0.360000
+ 0.800000
+ 2.300000
+ 1.800000
+ 0.000000
+ 1075.000000
+ 0.000000
+ 0.80000
+
+
+ 75%
+ 0.450000
+ 1.000000
+ 2.400000
+ 1.900000
+ 1.000000
+ 1076.000000
+ 0.000000
+ 1.00000
+
+
+ max
+ 0.820000
+ 2.200000
+ 2.700000
+ 2.600000
+ 1.000000
+ 1078.000000
+ 0.800000
+ 2.20000
+
+
+
+
+
+
+Test file
+
+
+
+
+
+
+
+Test file overview:
+Name: methane-test
+Objects number: 5728; Attributes number: 8
+Basic attribute statistics:
+
+
+
+
+
+
+
+
+
+ MM31
+ MM116
+ AS038
+ PG072
+ PD
+ BA13
+ DMM116
+ MM116_pred
+
+
+
+
+ count
+ 5728.000000
+ 5728.000000
+ 5728.000000
+ 5728.000000
+ 5728.000000
+ 5728.000000
+ 5728.000000
+ 5728.000000
+
+
+ mean
+ 0.556652
+ 1.006913
+ 2.236627
+ 1.819239
+ 0.538408
+ 1072.691690
+ -0.000017
+ 1.042458
+
+
+ std
+ 0.114682
+ 0.167983
+ 0.104913
+ 0.078865
+ 0.498566
+ 2.799559
+ 0.046849
+ 0.171393
+
+
+ min
+ 0.350000
+ 0.500000
+ 1.800000
+ 1.600000
+ 0.000000
+ 1067.000000
+ -0.400000
+ 0.600000
+
+
+ 25%
+ 0.460000
+ 0.900000
+ 2.200000
+ 1.800000
+ 0.000000
+ 1071.000000
+ 0.000000
+ 0.900000
+
+
+ 50%
+ 0.550000
+ 1.000000
+ 2.200000
+ 1.800000
+ 1.000000
+ 1073.000000
+ 0.000000
+ 1.000000
+
+
+ 75%
+ 0.640000
+ 1.100000
+ 2.300000
+ 1.900000
+ 1.000000
+ 1075.000000
+ 0.000000
+ 1.200000
+
+
+ max
+ 0.980000
+ 1.600000
+ 2.700000
+ 2.100000
+ 1.000000
+ 1078.000000
+ 0.300000
+ 1.600000
+
+
+
+
+
+
+
+Helper function for calculating metrics
+
+
+
+Rule induction on training dataset
+
+
+
+
+
+
+
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+
+
+
+
+
+
+
+
+
+
+ minimum_covered
+ maximum_uncovered_fraction
+ ignore_missing
+ pruning_enabled
+ max_growing_condition
+ time_total_s
+ time_growing_s
+ time_pruning_s
+ rules_count
+ conditions_per_rule
+ induced_conditions_per_rule
+ avg_rule_coverage
+ avg_rule_precision
+ avg_rule_quality
+ pvalue
+ FDR_pvalue
+ FWER_pvalue
+ fraction_significant
+ fraction_FDR_significant
+ fraction_FWER_significant
+
+
+ Measure
+
+
+
+
+ C2
+ 0.05
+ 0.0
+ False
+ True
+ 0.0
+ 27.670397
+ 1.978997
+ 25.637769
+ 11
+ 3.272727
+ 33.727273
+ 0.345683
+ 0.874767
+ 0.732356
+ 1.612636e-177
+ 1.612636e-177
+ 1.612636e-177
+ 1.0
+ 1.0
+ 1.0
+
+
+ Correlation
+ 0.05
+ 0.0
+ False
+ True
+ 0.0
+ 17.739791
+ 0.836199
+ 16.871719
+ 7
+ 2.714286
+ 35.285714
+ 0.334990
+ 0.862965
+ 0.800819
+ 3.046280e-37
+ 3.046280e-37
+ 3.046280e-37
+ 1.0
+ 1.0
+ 1.0
+
+
+ RSS
+ 0.05
+ 0.0
+ False
+ True
+ 0.0
+ 34.929544
+ 1.020750
+ 33.894867
+ 6
+ 2.333333
+ 38.166667
+ 0.417440
+ 0.855115
+ 0.786208
+ 6.242568e-40
+ 6.242568e-40
+ 6.242568e-40
+ 1.0
+ 1.0
+ 1.0
+
+
+
+
+
+
+
+
+
+
+
+
+
+ absolute_error
+ relative_error
+ relative_error_lenient
+ relative_error_strict
+ normalized_absolute_error
+ squared_error
+ root_mean_squared_error
+ root_relative_squared_error
+ correlation
+ squared_correlation
+
+
+ Measure
+
+
+
+
+ C2
+ 0.089929
+ 0.114526
+ 0.101069
+ 0.125935
+ 0.382694
+ 0.019753
+ 0.140547
+ 0.167429
+ 0.937881
+ 0.879620
+
+
+ Correlation
+ 0.088561
+ 0.112319
+ 0.099635
+ 0.125846
+ 0.376872
+ 0.020912
+ 0.144609
+ 0.184988
+ 0.941044
+ 0.885563
+
+
+ RSS
+ 0.092552
+ 0.111375
+ 0.102026
+ 0.124544
+ 0.393860
+ 0.020544
+ 0.143331
+ 0.153866
+ 0.945779
+ 0.894498
+
+
+
+
+
+C2 Measure generated rules
+
+
+
+
+
+
+IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.40} [0.39,0.42]
+IF MM116 = (-inf, 0.55) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.45} [0.39,0.52]
+IF MM31 = <0.19, 0.30) AND MM116 = (-inf, 0.95) AND AS038 = (-inf, 2.45) AND PG072 = <1.55, inf) AND DMM116 = (-inf, 0.15) THEN MM116_pred = {0.50} [0.38,0.61]
+IF MM116 = <1.05, 1.35) AND MM31 = <0.28, inf) THEN MM116_pred = {1.19} [1.08,1.31]
+IF MM116 = <0.95, 1.25) AND DMM116 = (-inf, 0.40) THEN MM116_pred = {1.11} [0.99,1.22]
+IF MM116 = <0.85, 1.15) AND DMM116 = <-0.35, 0.25) THEN MM116_pred = {1.00} [0.89,1.12]
+IF MM31 = (-inf, 0.34) AND MM116 = (-inf, 0.85) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.53} [0.39,0.66]
+IF MM31 = <0.18, 0.37) AND AS038 = <2.15, 2.55) AND DMM116 = <-0.15, 0.05) AND MM116 = <0.25, 0.85) AND PG072 = <1.55, inf) AND BA13 = <1070.50, inf) THEN MM116_pred = {0.55} [0.40,0.70]
+IF MM116 = <0.75, 1.05) AND DMM116 = <-0.15, 0.15) AND PG072 = (-inf, 2.05) AND MM31 = (-inf, 0.53) AND BA13 = (-inf, 1073.50) THEN MM116_pred = {0.91} [0.80,1.02]
+IF MM116 = <0.65, 1.45) AND MM31 = (-inf, 0.67) AND DMM116 = <-0.35, 0.25) AND PG072 = (-inf, 2.35) THEN MM116_pred = {0.96} [0.78,1.14]
+IF MM31 = <0.28, 0.76) AND PG072 = (-inf, 2.35) THEN MM116_pred = {0.93} [0.70,1.16]
+
+
+
+Correlation Measure generated rules
+
+
+
+
+
+
+IF MM116 = (-inf, 0.45) AND MM31 = <0.18, 0.24) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.40} [0.38,0.42]
+IF MM116 = (-inf, 0.55) AND MM31 = (-inf, 0.32) THEN MM116_pred = {0.45} [0.39,0.51]
+IF MM31 = <0.18, 0.31) AND MM116 = (-inf, 0.85) AND AS038 = (-inf, 2.55) AND PG072 = <1.55, inf) AND DMM116 = <-0.30, 0.15) THEN MM116_pred = {0.50} [0.39,0.60]
+IF MM116 = <1.05, 1.35) THEN MM116_pred = {1.19} [1.08,1.31]
+IF MM116 = <0.85, 1.15) AND DMM116 = <-0.35, inf) THEN MM116_pred = {1.00} [0.89,1.12]
+IF MM116 = <0.45, 0.85) AND DMM116 = <-0.15, inf) AND PG072 = <1.55, inf) AND MM31 = <0.31, inf) THEN MM116_pred = {0.77} [0.66,0.88]
+IF MM31 = <0.23, inf) AND PG072 = (-inf, 2.35) THEN MM116_pred = {0.85} [0.59,1.11]
+
+
+
+
+
+Evaluation on a test set
+
+
+
+
+
+
+
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+
+
+
+
+
+
+
+
+
+
+ absolute_error
+ relative_error
+ relative_error_lenient
+ relative_error_strict
+ normalized_absolute_error
+ squared_error
+ root_mean_squared_error
+ root_relative_squared_error
+ correlation
+ squared_correlation
+
+
+ Measure
+
+
+
+
+ C2
+ 0.107227
+ 0.100574
+ 0.094935
+ 0.112747
+ 0.739328
+ 0.020326
+ 0.142569
+ 0.126236
+ 0.835385
+ 0.697868
+
+
+ Correlation
+ 0.105350
+ 0.091827
+ 0.090950
+ 0.109321
+ 0.726385
+ 0.021890
+ 0.147951
+ 0.119472
+ 0.866898
+ 0.751512
+
+
+ RSS
+ 0.128302
+ 0.113411
+ 0.111947
+ 0.134690
+ 0.884639
+ 0.027270
+ 0.165136
+ 0.134849
+ 0.866442
+ 0.750722
+
+
+
+
+
+
+Hyperparameters tuning
+This one gonna take a while…
+
+
+
+
+
+
+Best RMSE: -0.191976 using {'induction_measure': <Measures.RSS: 'RSS'>, 'minsupp_new': 6, 'pruning_measure': <Measures.C2: 'C2'>, 'voting_measure': <Measures.C2: 'C2'>}
+
+
+
+Prediction using the model selected from the tuning
+
+Generated rules
+
+
+
+
+
+
+IF MM31 = (-inf, 0.23) THEN MM116_pred = {0.40} [0.39,0.41]
+IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.40} [0.39,0.42]
+IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) THEN MM116_pred = {0.40} [0.38,0.42]
+IF MM31 = <0.24, 0.25) AND AS038 = (-inf, 2.45) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) THEN MM116_pred = {0.50} [0.47,0.54]
+IF MM116 = (-inf, 0.45) AND MM31 = <0.24, 0.25) AND PG072 = (-inf, 2.05) AND AS038 = (-inf, 2.45) AND PD = <0.50, inf) THEN MM116_pred = {0.41} [0.38,0.44]
+IF MM31 = <0.24, 0.25) AND PD = (-inf, 0.50) THEN MM116_pred = {0.51} [0.47,0.54]
+IF MM31 = (-inf, 0.26) AND DMM116 = <-0.05, 0.05) THEN MM116_pred = {0.46} [0.36,0.55]
+IF MM116 = (-inf, 0.45) THEN MM116_pred = {0.40} [0.37,0.44]
+IF MM31 = <0.23, 0.24) AND BA13 = (-inf, 1075.50) AND MM116 = <0.45, inf) THEN MM116_pred = {0.50} [0.48,0.52]
+IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.51} [0.48,0.53]
+IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND MM31 = <0.23, 0.29) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.51} [0.48,0.53]
+IF MM116 = <0.35, 0.55) AND MM31 = (-inf, 0.26) AND BA13 = <1077.50, inf) AND DMM116 = (-inf, -0.05) THEN MM116_pred = {0.54} [0.48,0.60]
+IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND AS038 = (-inf, 2.45) AND PD = (-inf, 0.50) AND BA13 = (-inf, 1077.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.50} [0.48,0.53]
+IF MM116 = <0.45, 0.55) AND MM31 = <0.28, 0.30) AND DMM116 = <-0.05, 0.05) AND AS038 = <2.25, 2.35) AND PG072 = <1.75, 1.95) AND BA13 = <1075.50, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]
+IF MM116 = (-inf, 0.55) AND MM31 = <0.29, 0.30) AND PG072 = (-inf, 1.95) AND BA13 = (-inf, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]
+IF MM116 = (-inf, 0.55) THEN MM116_pred = {0.45} [0.39,0.52]
+IF MM31 = <0.26, 0.27) AND MM116 = <0.55, 0.65) AND PG072 = <1.75, 1.85) AND AS038 = <2.25, 2.45) AND DMM116 = <-0.05, 0.05) AND PD = (-inf, 0.50) AND BA13 = <1074.50, 1077.50) THEN MM116_pred = {0.60} [NaN,NaN]
+IF MM116 = <0.45, 0.65) AND MM31 = <0.23, inf) THEN MM116_pred = {0.55} [0.49,0.61]
+IF MM116 = <0.55, 0.75) THEN MM116_pred = {0.67} [0.58,0.77]
+IF MM116 = <0.75, 0.85) THEN MM116_pred = {0.83} [0.76,0.90]
+IF MM116 = <0.85, inf) THEN MM116_pred = {1.06} [0.88,1.24]
+
+
Ruleset evaluation
+
+
+
+
+
+
+
+
+
+
+ minimum_covered
+ maximum_uncovered_fraction
+ ignore_missing
+ pruning_enabled
+ max_growing_condition
+ time_total_s
+ time_growing_s
+ time_pruning_s
+ rules_count
+ conditions_per_rule
+ induced_conditions_per_rule
+ avg_rule_coverage
+ avg_rule_precision
+ avg_rule_quality
+ pvalue
+ FDR_pvalue
+ FWER_pvalue
+ fraction_significant
+ fraction_FDR_significant
+ fraction_FWER_significant
+
+
+ Measure
+
+
+
+
+ 6.0
+ 0.0
+ False
+ True
+ 0.0
+ 12.151842
+ 1.240354
+ 10.883363
+ 21
+ 3.190476
+ 29.809524
+ 0.116152
+ 0.849723
+ NaN
+ NaN
+ NaN
+ NaN
+ 0.952381
+ 0.952381
+ 0.952381
+
+
+
+
+
+Validate model on test dataset
+
+
+
+
+
+
+c:\Users\cezar\OneDrive\Pulpit\EMAG\GIT\PythonRulekit\tutorials_env\Lib\site-packages\sklearn\metrics\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.
+ warnings.warn(
+
+
+
+
+
+
+absolute_error 0.111355
+relative_error 0.103524
+relative_error_lenient 0.097884
+relative_error_strict 0.114888
+normalized_absolute_error 0.767792
+squared_error 0.019642
+root_mean_squared_error 0.140148
+root_relative_squared_error 0.125609
+correlation 0.801204
+squared_correlation 0.641927
+Name: , dtype: float64
+
+
+
+
+
+
+
+
+
+
+
+
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MM31 \n",
+ " MM116 \n",
+ " AS038 \n",
+ " PG072 \n",
+ " PD \n",
+ " BA13 \n",
+ " DMM116 \n",
+ " MM116_pred \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.000000 \n",
+ " 13368.00000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 0.363960 \n",
+ " 0.775007 \n",
+ " 2.294734 \n",
+ " 1.835600 \n",
+ " 0.308573 \n",
+ " 1073.443372 \n",
+ " -0.000007 \n",
+ " 0.79825 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 0.117105 \n",
+ " 0.269366 \n",
+ " 0.142504 \n",
+ " 0.106681 \n",
+ " 0.461922 \n",
+ " 3.162811 \n",
+ " 0.043566 \n",
+ " 0.28649 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.170000 \n",
+ " 0.200000 \n",
+ " 1.400000 \n",
+ " 1.100000 \n",
+ " 0.000000 \n",
+ " 1067.000000 \n",
+ " -1.800000 \n",
+ " 0.20000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 0.260000 \n",
+ " 0.500000 \n",
+ " 2.300000 \n",
+ " 1.800000 \n",
+ " 0.000000 \n",
+ " 1070.000000 \n",
+ " 0.000000 \n",
+ " 0.50000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 0.360000 \n",
+ " 0.800000 \n",
+ " 2.300000 \n",
+ " 1.800000 \n",
+ " 0.000000 \n",
+ " 1075.000000 \n",
+ " 0.000000 \n",
+ " 0.80000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 0.450000 \n",
+ " 1.000000 \n",
+ " 2.400000 \n",
+ " 1.900000 \n",
+ " 1.000000 \n",
+ " 1076.000000 \n",
+ " 0.000000 \n",
+ " 1.00000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 0.820000 \n",
+ " 2.200000 \n",
+ " 2.700000 \n",
+ " 2.600000 \n",
+ " 1.000000 \n",
+ " 1078.000000 \n",
+ " 0.800000 \n",
+ " 2.20000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MM31 \n",
+ " MM116 \n",
+ " AS038 \n",
+ " PG072 \n",
+ " PD \n",
+ " BA13 \n",
+ " DMM116 \n",
+ " MM116_pred \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " 5728.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 0.556652 \n",
+ " 1.006913 \n",
+ " 2.236627 \n",
+ " 1.819239 \n",
+ " 0.538408 \n",
+ " 1072.691690 \n",
+ " -0.000017 \n",
+ " 1.042458 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 0.114682 \n",
+ " 0.167983 \n",
+ " 0.104913 \n",
+ " 0.078865 \n",
+ " 0.498566 \n",
+ " 2.799559 \n",
+ " 0.046849 \n",
+ " 0.171393 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.350000 \n",
+ " 0.500000 \n",
+ " 1.800000 \n",
+ " 1.600000 \n",
+ " 0.000000 \n",
+ " 1067.000000 \n",
+ " -0.400000 \n",
+ " 0.600000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 0.460000 \n",
+ " 0.900000 \n",
+ " 2.200000 \n",
+ " 1.800000 \n",
+ " 0.000000 \n",
+ " 1071.000000 \n",
+ " 0.000000 \n",
+ " 0.900000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 0.550000 \n",
+ " 1.000000 \n",
+ " 2.200000 \n",
+ " 1.800000 \n",
+ " 1.000000 \n",
+ " 1073.000000 \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 0.640000 \n",
+ " 1.100000 \n",
+ " 2.300000 \n",
+ " 1.900000 \n",
+ " 1.000000 \n",
+ " 1075.000000 \n",
+ " 0.000000 \n",
+ " 1.200000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 0.980000 \n",
+ " 1.600000 \n",
+ " 2.700000 \n",
+ " 2.100000 \n",
+ " 1.000000 \n",
+ " 1078.000000 \n",
+ " 0.300000 \n",
+ " 1.600000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " minimum_covered \n",
+ " maximum_uncovered_fraction \n",
+ " ignore_missing \n",
+ " pruning_enabled \n",
+ " max_growing_condition \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 0.05 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 27.670397 \n",
+ " 1.978997 \n",
+ " 25.637769 \n",
+ " 11 \n",
+ " 3.272727 \n",
+ " 33.727273 \n",
+ " 0.345683 \n",
+ " 0.874767 \n",
+ " 0.732356 \n",
+ " 1.612636e-177 \n",
+ " 1.612636e-177 \n",
+ " 1.612636e-177 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.05 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 17.739791 \n",
+ " 0.836199 \n",
+ " 16.871719 \n",
+ " 7 \n",
+ " 2.714286 \n",
+ " 35.285714 \n",
+ " 0.334990 \n",
+ " 0.862965 \n",
+ " 0.800819 \n",
+ " 3.046280e-37 \n",
+ " 3.046280e-37 \n",
+ " 3.046280e-37 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 0.05 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 34.929544 \n",
+ " 1.020750 \n",
+ " 33.894867 \n",
+ " 6 \n",
+ " 2.333333 \n",
+ " 38.166667 \n",
+ " 0.417440 \n",
+ " 0.855115 \n",
+ " 0.786208 \n",
+ " 6.242568e-40 \n",
+ " 6.242568e-40 \n",
+ " 6.242568e-40 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " absolute_error \n",
+ " relative_error \n",
+ " relative_error_lenient \n",
+ " relative_error_strict \n",
+ " normalized_absolute_error \n",
+ " squared_error \n",
+ " root_mean_squared_error \n",
+ " root_relative_squared_error \n",
+ " correlation \n",
+ " squared_correlation \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 0.089929 \n",
+ " 0.114526 \n",
+ " 0.101069 \n",
+ " 0.125935 \n",
+ " 0.382694 \n",
+ " 0.019753 \n",
+ " 0.140547 \n",
+ " 0.167429 \n",
+ " 0.937881 \n",
+ " 0.879620 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.088561 \n",
+ " 0.112319 \n",
+ " 0.099635 \n",
+ " 0.125846 \n",
+ " 0.376872 \n",
+ " 0.020912 \n",
+ " 0.144609 \n",
+ " 0.184988 \n",
+ " 0.941044 \n",
+ " 0.885563 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 0.092552 \n",
+ " 0.111375 \n",
+ " 0.102026 \n",
+ " 0.124544 \n",
+ " 0.393860 \n",
+ " 0.020544 \n",
+ " 0.143331 \n",
+ " 0.153866 \n",
+ " 0.945779 \n",
+ " 0.894498 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " absolute_error \n",
+ " relative_error \n",
+ " relative_error_lenient \n",
+ " relative_error_strict \n",
+ " normalized_absolute_error \n",
+ " squared_error \n",
+ " root_mean_squared_error \n",
+ " root_relative_squared_error \n",
+ " correlation \n",
+ " squared_correlation \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " C2 \n",
+ " 0.107227 \n",
+ " 0.100574 \n",
+ " 0.094935 \n",
+ " 0.112747 \n",
+ " 0.739328 \n",
+ " 0.020326 \n",
+ " 0.142569 \n",
+ " 0.126236 \n",
+ " 0.835385 \n",
+ " 0.697868 \n",
+ " \n",
+ " \n",
+ " Correlation \n",
+ " 0.105350 \n",
+ " 0.091827 \n",
+ " 0.090950 \n",
+ " 0.109321 \n",
+ " 0.726385 \n",
+ " 0.021890 \n",
+ " 0.147951 \n",
+ " 0.119472 \n",
+ " 0.866898 \n",
+ " 0.751512 \n",
+ " \n",
+ " \n",
+ " RSS \n",
+ " 0.128302 \n",
+ " 0.113411 \n",
+ " 0.111947 \n",
+ " 0.134690 \n",
+ " 0.884639 \n",
+ " 0.027270 \n",
+ " 0.165136 \n",
+ " 0.134849 \n",
+ " 0.866442 \n",
+ " 0.750722 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
, 'minsupp_new': 6, 'pruning_measure': , 'voting_measure': }\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "\n",
+ "\n",
+ "# define models and parameters\n",
+ "model = RuleRegressor(mean_based_regression=True)\n",
+ "minsupp_new = range(5, 7)\n",
+ "measures_choice = [Measures.C2, Measures.Correlation, Measures.RSS]\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'minsupp_new': minsupp_new, \n",
+ " 'induction_measure': measures_choice, \n",
+ " 'pruning_measure': measures_choice, \n",
+ " 'voting_measure': measures_choice\n",
+ "}\n",
+ "cv = KFold(n_splits=2)\n",
+ "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=cv, scoring='neg_root_mean_squared_error')\n",
+ "grid_result = grid_search.fit(X_train, y_train)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\"Best RMSE: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prediction using the model selected from the tuning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reg: RuleRegressor = grid_result.best_estimator_\n",
+ "ruleset: RuleSet[RegressionRule] = reg.model\n",
+ "ruleset_stats = get_ruleset_stats('', ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generated rules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IF MM31 = (-inf, 0.23) THEN MM116_pred = {0.40} [0.39,0.41]\n",
+ "IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.40} [0.39,0.42]\n",
+ "IF MM116 = <0.35, 0.45) AND MM31 = (-inf, 0.24) THEN MM116_pred = {0.40} [0.38,0.42]\n",
+ "IF MM31 = <0.24, 0.25) AND AS038 = (-inf, 2.45) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) THEN MM116_pred = {0.50} [0.47,0.54]\n",
+ "IF MM116 = (-inf, 0.45) AND MM31 = <0.24, 0.25) AND PG072 = (-inf, 2.05) AND AS038 = (-inf, 2.45) AND PD = <0.50, inf) THEN MM116_pred = {0.41} [0.38,0.44]\n",
+ "IF MM31 = <0.24, 0.25) AND PD = (-inf, 0.50) THEN MM116_pred = {0.51} [0.47,0.54]\n",
+ "IF MM31 = (-inf, 0.26) AND DMM116 = <-0.05, 0.05) THEN MM116_pred = {0.46} [0.36,0.55]\n",
+ "IF MM116 = (-inf, 0.45) THEN MM116_pred = {0.40} [0.37,0.44]\n",
+ "IF MM31 = <0.23, 0.24) AND BA13 = (-inf, 1075.50) AND MM116 = <0.45, inf) THEN MM116_pred = {0.50} [0.48,0.52]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND DMM116 = <-0.05, inf) AND PD = (-inf, 0.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.51} [0.48,0.53]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND MM31 = <0.23, 0.29) AND DMM116 = <-0.05, inf) THEN MM116_pred = {0.51} [0.48,0.53]\n",
+ "IF MM116 = <0.35, 0.55) AND MM31 = (-inf, 0.26) AND BA13 = <1077.50, inf) AND DMM116 = (-inf, -0.05) THEN MM116_pred = {0.54} [0.48,0.60]\n",
+ "IF MM116 = <0.45, 0.55) AND PG072 = <1.65, inf) AND AS038 = (-inf, 2.45) AND PD = (-inf, 0.50) AND BA13 = (-inf, 1077.50) AND MM31 = <0.23, inf) THEN MM116_pred = {0.50} [0.48,0.53]\n",
+ "IF MM116 = <0.45, 0.55) AND MM31 = <0.28, 0.30) AND DMM116 = <-0.05, 0.05) AND AS038 = <2.25, 2.35) AND PG072 = <1.75, 1.95) AND BA13 = <1075.50, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]\n",
+ "IF MM116 = (-inf, 0.55) AND MM31 = <0.29, 0.30) AND PG072 = (-inf, 1.95) AND BA13 = (-inf, 1076.50) AND PD = <0.50, inf) THEN MM116_pred = {0.55} [0.50,0.60]\n",
+ "IF MM116 = (-inf, 0.55) THEN MM116_pred = {0.45} [0.39,0.52]\n",
+ "IF MM31 = <0.26, 0.27) AND MM116 = <0.55, 0.65) AND PG072 = <1.75, 1.85) AND AS038 = <2.25, 2.45) AND DMM116 = <-0.05, 0.05) AND PD = (-inf, 0.50) AND BA13 = <1074.50, 1077.50) THEN MM116_pred = {0.60} [NaN,NaN]\n",
+ "IF MM116 = <0.45, 0.65) AND MM31 = <0.23, inf) THEN MM116_pred = {0.55} [0.49,0.61]\n",
+ "IF MM116 = <0.55, 0.75) THEN MM116_pred = {0.67} [0.58,0.77]\n",
+ "IF MM116 = <0.75, 0.85) THEN MM116_pred = {0.83} [0.76,0.90]\n",
+ "IF MM116 = <0.85, inf) THEN MM116_pred = {1.06} [0.88,1.24]\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rule in ruleset.rules:\n",
+ " print(rule)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ruleset evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " minimum_covered \n",
+ " maximum_uncovered_fraction \n",
+ " ignore_missing \n",
+ " pruning_enabled \n",
+ " max_growing_condition \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " Measure \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 6.0 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 12.151842 \n",
+ " 1.240354 \n",
+ " 10.883363 \n",
+ " 21 \n",
+ " 3.190476 \n",
+ " 29.809524 \n",
+ " 0.116152 \n",
+ " 0.849723 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.952381 \n",
+ " 0.952381 \n",
+ " 0.952381 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Survival analysis — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+Survival analysis
+This notebook presents example usage of package for solving survival problem on bmt here
+
+
This tutorial will cover topics such as:
+
- training model
+
- changing model hyperparameters
+
- hyperparameters tuning
+
- calculating metrics for model
+
+Install dependencies
+
+
+
+Summary of the dataset
+
+
+
+
+
+
+
+
+
+
+ Recipientgender
+ Stemcellsource
+ Donorage
+ Donorage35
+ IIIV
+ Gendermatch
+ DonorABO
+ RecipientABO
+ RecipientRh
+ ABOmatch
+ ...
+ extcGvHD
+ CD34kgx10d6
+ CD3dCD34
+ CD3dkgx10d8
+ Rbodymass
+ ANCrecovery
+ PLTrecovery
+ time_to_aGvHD_III_IV
+ survival_time
+ survival_status
+
+
+
+
+ 0
+ 1
+ 1
+ 22.830137
+ 0
+ 1
+ 0
+ 1
+ 1
+ 1
+ 0
+ ...
+ 1
+ 7.20
+ 1.338760
+ 5.38
+ 35.0
+ 19.0
+ 51.0
+ 32.0
+ 999.0
+ 0
+
+
+ 1
+ 1
+ 0
+ 23.342466
+ 0
+ 1
+ 0
+ -1
+ -1
+ 1
+ 0
+ ...
+ 1
+ 4.50
+ 11.078295
+ 0.41
+ 20.6
+ 16.0
+ 37.0
+ 1000000.0
+ 163.0
+ 1
+
+
+ 2
+ 1
+ 0
+ 26.394521
+ 0
+ 1
+ 0
+ -1
+ -1
+ 1
+ 0
+ ...
+ 1
+ 7.94
+ 19.013230
+ 0.42
+ 23.4
+ 23.0
+ 20.0
+ 1000000.0
+ 435.0
+ 1
+
+
+ 3
+ 0
+ 0
+ 39.684932
+ 1
+ 1
+ 0
+ 1
+ 2
+ 1
+ 1
+ ...
+ None
+ 4.25
+ 29.481647
+ 0.14
+ 50.0
+ 23.0
+ 29.0
+ 19.0
+ 53.0
+ 1
+
+
+ 4
+ 0
+ 1
+ 33.358904
+ 0
+ 0
+ 0
+ 1
+ 2
+ 0
+ 1
+ ...
+ 1
+ 51.85
+ 3.972255
+ 13.05
+ 9.0
+ 14.0
+ 14.0
+ 1000000.0
+ 2043.0
+ 0
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 182
+ 1
+ 1
+ 37.575342
+ 1
+ 1
+ 0
+ 1
+ 1
+ 0
+ 0
+ ...
+ 1
+ 11.08
+ 2.522750
+ 4.39
+ 44.0
+ 15.0
+ 22.0
+ 16.0
+ 385.0
+ 1
+
+
+ 183
+ 0
+ 1
+ 22.895890
+ 0
+ 0
+ 0
+ 1
+ 0
+ 1
+ 1
+ ...
+ 1
+ 4.64
+ 1.038858
+ 4.47
+ 44.5
+ 12.0
+ 30.0
+ 1000000.0
+ 634.0
+ 1
+
+
+ 184
+ 0
+ 1
+ 27.347945
+ 0
+ 1
+ 0
+ 1
+ -1
+ 1
+ 1
+ ...
+ 1
+ 7.73
+ 1.635559
+ 4.73
+ 33.0
+ 16.0
+ 16.0
+ 1000000.0
+ 1895.0
+ 0
+
+
+ 185
+ 1
+ 1
+ 27.780822
+ 0
+ 1
+ 0
+ 1
+ 0
+ 1
+ 1
+ ...
+ 0
+ 15.41
+ 8.077770
+ 1.91
+ 24.0
+ 13.0
+ 14.0
+ 54.0
+ 382.0
+ 1
+
+
+ 186
+ 1
+ 1
+ 55.553425
+ 1
+ 1
+ 0
+ 1
+ 2
+ 1
+ 1
+ ...
+ 1
+ 9.91
+ 0.948135
+ 10.45
+ 37.0
+ 18.0
+ 20.0
+ 1000000.0
+ 1109.0
+ 0
+
+
+
+
187 rows × 37 columns
+
+
+
+
+
+
+Dataset overview:
+Name: bmt
+Objects number: 187; Attributes number: 37
+Basic attribute statistics:
+
+
+
+
+
+
+
+
+
+ Donorage
+ Recipientage
+ CD34kgx10d6
+ CD3dCD34
+ CD3dkgx10d8
+ Rbodymass
+ ANCrecovery
+ PLTrecovery
+ time_to_aGvHD_III_IV
+ survival_time
+
+
+
+
+ count
+ 187.000000
+ 187.000000
+ 187.000000
+ 182.000000
+ 182.000000
+ 185.000000
+ 187.000000
+ 187.000000
+ 187.000000
+ 187.000000
+
+
+ mean
+ 33.472068
+ 9.931551
+ 11.891781
+ 5.385096
+ 4.745714
+ 35.801081
+ 26752.866310
+ 90937.919786
+ 775408.042781
+ 938.743316
+
+
+ std
+ 8.271826
+ 5.305639
+ 9.914386
+ 9.598716
+ 3.859128
+ 19.650922
+ 161747.200525
+ 288242.407688
+ 418425.252689
+ 849.589495
+
+
+ min
+ 18.646575
+ 0.600000
+ 0.790000
+ 0.204132
+ 0.040000
+ 6.000000
+ 9.000000
+ 9.000000
+ 10.000000
+ 6.000000
+
+
+ 25%
+ 27.039726
+ 5.050000
+ 5.350000
+ 1.786683
+ 1.687500
+ 19.000000
+ 13.000000
+ 16.000000
+ 1000000.000000
+ 168.500000
+
+
+ 50%
+ 33.550685
+ 9.600000
+ 9.720000
+ 2.734462
+ 4.325000
+ 33.000000
+ 15.000000
+ 21.000000
+ 1000000.000000
+ 676.000000
+
+
+ 75%
+ 40.117809
+ 14.050000
+ 15.415000
+ 5.823565
+ 6.785000
+ 50.600000
+ 17.000000
+ 37.000000
+ 1000000.000000
+ 1604.000000
+
+
+ max
+ 55.553425
+ 20.200000
+ 57.780000
+ 99.560970
+ 20.020000
+ 103.400000
+ 1000000.000000
+ 1000000.000000
+ 1000000.000000
+ 3364.000000
+
+
+
+
+
+
+Helper function for creating ruleset characteristics dataframe
+
+
+
+Rule induction on full dataset
+
+
+
+
+
+
+
+
+
+
+
+ minimum_covered
+ maximum_uncovered_fraction
+ ignore_missing
+ pruning_enabled
+ max_growing_condition
+ time_total_s
+ time_growing_s
+ time_pruning_s
+ rules_count
+ conditions_per_rule
+ induced_conditions_per_rule
+ avg_rule_coverage
+ avg_rule_precision
+ avg_rule_quality
+ pvalue
+ FDR_pvalue
+ FWER_pvalue
+ fraction_significant
+ fraction_FDR_significant
+ fraction_FWER_significant
+
+
+
+
+ 0
+ 0.05
+ 0.0
+ False
+ True
+ 0.0
+ 1.771417
+ 0.797513
+ 0.902853
+ 5
+ 3.6
+ 65.2
+ 0.308021
+ 1.0
+ 0.999865
+ 0.000135
+ 0.000147
+ 0.000184
+ 1.0
+ 1.0
+ 1.0
+
+
+
+
+
+
+Plot predicted estimators for the first five examples
+
+
+
+
+
+<matplotlib.legend.Legend at 0x289deb3a180>
+
+
+
+
+
+
+
+
+
+Plot rules Kaplan-Meier’s estimators on top of the training dataset estimator
+
+
+
+
+
+
+r1: IF Donorage = (-inf, 45.16) AND Relapse = {0} AND Recipientage = (-inf, 17.45) THEN
+r2: IF Donorage = (-inf, 43.63) AND HLAmismatch = {0} AND Relapse = {1} THEN
+r3: IF PLTrecovery = (-inf, 266) AND time_to_aGvHD_III_IV = <12.50, inf) AND ANCrecovery = <10.50, 19.50) AND Rbodymass = (-inf, 69) AND Donorage = (-inf, 44.06) AND Recipientage = <4.60, inf) AND CD34kgx10d6 = (-inf, 16.98) THEN
+r4: IF Donorage = <37.16, inf) AND Recipientage = <5.15, inf) AND time_to_aGvHD_III_IV = <23.50, inf) AND CD3dCD34 = <0.90, 73.72) THEN
+r5: IF Recipientage = <17.85, 18.85) THEN
+
+
+
+
+
+
+
+
+Rules evaluation on full set
+
+
+
+
+
+
+Integrated Brier Score: 0.19651358709002972
+
+
+
+
+Stratified K-Folds cross-validation
+
+Ruleset characteristics (average)
+
+
+
+
+
+
+minimum_covered 0.050000
+maximum_uncovered_fraction 0.000000
+ignore_missing 0.000000
+pruning_enabled 1.000000
+max_growing_condition 0.000000
+time_total_s 0.799019
+time_growing_s 0.296248
+time_pruning_s 0.477474
+rules_count 4.000000
+conditions_per_rule 2.581667
+induced_conditions_per_rule 59.825000
+avg_rule_coverage 0.486613
+avg_rule_precision 1.000000
+avg_rule_quality 0.995955
+pvalue 0.004045
+FDR_pvalue 0.004061
+FWER_pvalue 0.004104
+fraction_significant 0.980000
+fraction_FDR_significant 0.980000
+fraction_FWER_significant 0.980000
+dtype: float64
+
+
Rules evaluation on dataset (average)
+
+
+
+
+
+
+Integrated Brier Score: 0.20178456199764142
+
+
+
+Hyperparameters tuning
+This one gonna take a while…
+
+
+
+
+
+
+
+
+Best Integrated Brier Score: -0.21437408819868886 using {'minsupp_new': 3, 'survival_time_attr': 'survival_time'}
+
+
+
+Building model with tuned hyperparameters
+
+Split dataset to train and test (80%/20%)
+
+Rules evaluation
+
+
+
+
+
+
+minimum_covered 5.0
+maximum_uncovered_fraction 0.0
+ignore_missing False
+pruning_enabled True
+max_growing_condition 0.0
+time_total_s 0.594173
+time_growing_s 0.244234
+time_pruning_s 0.312523
+rules_count 4
+conditions_per_rule 2.25
+induced_conditions_per_rule 55.25
+avg_rule_coverage 0.389262
+avg_rule_precision 1.0
+avg_rule_quality 1.0
+pvalue 0.0
+FDR_pvalue 0.0
+FWER_pvalue 0.0
+fraction_significant 1.0
+fraction_FDR_significant 1.0
+fraction_FWER_significant 1.0
+Name: 0, dtype: object
+
+
+
+Validate model on test dataset
+
+
+
+
+
+
+Integrated Brier Score: 0.14054870564224475
+
+
+
+
+
+Text(0.5, 1.0, 'Predicted Kaplan-Meier curves for 5 examples from test set')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Recipientgender \n",
+ " Stemcellsource \n",
+ " Donorage \n",
+ " Donorage35 \n",
+ " IIIV \n",
+ " Gendermatch \n",
+ " DonorABO \n",
+ " RecipientABO \n",
+ " RecipientRh \n",
+ " ABOmatch \n",
+ " ... \n",
+ " extcGvHD \n",
+ " CD34kgx10d6 \n",
+ " CD3dCD34 \n",
+ " CD3dkgx10d8 \n",
+ " Rbodymass \n",
+ " ANCrecovery \n",
+ " PLTrecovery \n",
+ " time_to_aGvHD_III_IV \n",
+ " survival_time \n",
+ " survival_status \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 22.830137 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " ... \n",
+ " 1 \n",
+ " 7.20 \n",
+ " 1.338760 \n",
+ " 5.38 \n",
+ " 35.0 \n",
+ " 19.0 \n",
+ " 51.0 \n",
+ " 32.0 \n",
+ " 999.0 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 23.342466 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " -1 \n",
+ " -1 \n",
+ " 1 \n",
+ " 0 \n",
+ " ... \n",
+ " 1 \n",
+ " 4.50 \n",
+ " 11.078295 \n",
+ " 0.41 \n",
+ " 20.6 \n",
+ " 16.0 \n",
+ " 37.0 \n",
+ " 1000000.0 \n",
+ " 163.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1 \n",
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+ " 26.394521 \n",
+ " 0 \n",
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+ " 0 \n",
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+ " ... \n",
+ " 1 \n",
+ " 7.94 \n",
+ " 19.013230 \n",
+ " 0.42 \n",
+ " 23.4 \n",
+ " 23.0 \n",
+ " 20.0 \n",
+ " 1000000.0 \n",
+ " 435.0 \n",
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+ " \n",
+ " \n",
+ " 3 \n",
+ " 0 \n",
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+ " 39.684932 \n",
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+ " 0 \n",
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+ " 2 \n",
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+ " 1 \n",
+ " ... \n",
+ " None \n",
+ " 4.25 \n",
+ " 29.481647 \n",
+ " 0.14 \n",
+ " 50.0 \n",
+ " 23.0 \n",
+ " 29.0 \n",
+ " 19.0 \n",
+ " 53.0 \n",
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+ " \n",
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+ " ... \n",
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+ " 51.85 \n",
+ " 3.972255 \n",
+ " 13.05 \n",
+ " 9.0 \n",
+ " 14.0 \n",
+ " 14.0 \n",
+ " 1000000.0 \n",
+ " 2043.0 \n",
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+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 182 \n",
+ " 1 \n",
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+ " 37.575342 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 1 \n",
+ " 11.08 \n",
+ " 2.522750 \n",
+ " 4.39 \n",
+ " 44.0 \n",
+ " 15.0 \n",
+ " 22.0 \n",
+ " 16.0 \n",
+ " 385.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 183 \n",
+ " 0 \n",
+ " 1 \n",
+ " 22.895890 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " ... \n",
+ " 1 \n",
+ " 4.64 \n",
+ " 1.038858 \n",
+ " 4.47 \n",
+ " 44.5 \n",
+ " 12.0 \n",
+ " 30.0 \n",
+ " 1000000.0 \n",
+ " 634.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 184 \n",
+ " 0 \n",
+ " 1 \n",
+ " 27.347945 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " -1 \n",
+ " 1 \n",
+ " 1 \n",
+ " ... \n",
+ " 1 \n",
+ " 7.73 \n",
+ " 1.635559 \n",
+ " 4.73 \n",
+ " 33.0 \n",
+ " 16.0 \n",
+ " 16.0 \n",
+ " 1000000.0 \n",
+ " 1895.0 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 185 \n",
+ " 1 \n",
+ " 1 \n",
+ " 27.780822 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " ... \n",
+ " 0 \n",
+ " 15.41 \n",
+ " 8.077770 \n",
+ " 1.91 \n",
+ " 24.0 \n",
+ " 13.0 \n",
+ " 14.0 \n",
+ " 54.0 \n",
+ " 382.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 186 \n",
+ " 1 \n",
+ " 1 \n",
+ " 55.553425 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1 \n",
+ " ... \n",
+ " 1 \n",
+ " 9.91 \n",
+ " 0.948135 \n",
+ " 10.45 \n",
+ " 37.0 \n",
+ " 18.0 \n",
+ " 20.0 \n",
+ " 1000000.0 \n",
+ " 1109.0 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
187 rows × 37 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Donorage \n",
+ " Recipientage \n",
+ " CD34kgx10d6 \n",
+ " CD3dCD34 \n",
+ " CD3dkgx10d8 \n",
+ " Rbodymass \n",
+ " ANCrecovery \n",
+ " PLTrecovery \n",
+ " time_to_aGvHD_III_IV \n",
+ " survival_time \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 187.000000 \n",
+ " 187.000000 \n",
+ " 187.000000 \n",
+ " 182.000000 \n",
+ " 182.000000 \n",
+ " 185.000000 \n",
+ " 187.000000 \n",
+ " 187.000000 \n",
+ " 187.000000 \n",
+ " 187.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 33.472068 \n",
+ " 9.931551 \n",
+ " 11.891781 \n",
+ " 5.385096 \n",
+ " 4.745714 \n",
+ " 35.801081 \n",
+ " 26752.866310 \n",
+ " 90937.919786 \n",
+ " 775408.042781 \n",
+ " 938.743316 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 8.271826 \n",
+ " 5.305639 \n",
+ " 9.914386 \n",
+ " 9.598716 \n",
+ " 3.859128 \n",
+ " 19.650922 \n",
+ " 161747.200525 \n",
+ " 288242.407688 \n",
+ " 418425.252689 \n",
+ " 849.589495 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 18.646575 \n",
+ " 0.600000 \n",
+ " 0.790000 \n",
+ " 0.204132 \n",
+ " 0.040000 \n",
+ " 6.000000 \n",
+ " 9.000000 \n",
+ " 9.000000 \n",
+ " 10.000000 \n",
+ " 6.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 27.039726 \n",
+ " 5.050000 \n",
+ " 5.350000 \n",
+ " 1.786683 \n",
+ " 1.687500 \n",
+ " 19.000000 \n",
+ " 13.000000 \n",
+ " 16.000000 \n",
+ " 1000000.000000 \n",
+ " 168.500000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 33.550685 \n",
+ " 9.600000 \n",
+ " 9.720000 \n",
+ " 2.734462 \n",
+ " 4.325000 \n",
+ " 33.000000 \n",
+ " 15.000000 \n",
+ " 21.000000 \n",
+ " 1000000.000000 \n",
+ " 676.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 40.117809 \n",
+ " 14.050000 \n",
+ " 15.415000 \n",
+ " 5.823565 \n",
+ " 6.785000 \n",
+ " 50.600000 \n",
+ " 17.000000 \n",
+ " 37.000000 \n",
+ " 1000000.000000 \n",
+ " 1604.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 55.553425 \n",
+ " 20.200000 \n",
+ " 57.780000 \n",
+ " 99.560970 \n",
+ " 20.020000 \n",
+ " 103.400000 \n",
+ " 1000000.000000 \n",
+ " 1000000.000000 \n",
+ " 1000000.000000 \n",
+ " 3364.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " minimum_covered \n",
+ " maximum_uncovered_fraction \n",
+ " ignore_missing \n",
+ " pruning_enabled \n",
+ " max_growing_condition \n",
+ " time_total_s \n",
+ " time_growing_s \n",
+ " time_pruning_s \n",
+ " rules_count \n",
+ " conditions_per_rule \n",
+ " induced_conditions_per_rule \n",
+ " avg_rule_coverage \n",
+ " avg_rule_precision \n",
+ " avg_rule_quality \n",
+ " pvalue \n",
+ " FDR_pvalue \n",
+ " FWER_pvalue \n",
+ " fraction_significant \n",
+ " fraction_FDR_significant \n",
+ " fraction_FWER_significant \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.05 \n",
+ " 0.0 \n",
+ " False \n",
+ " True \n",
+ " 0.0 \n",
+ " 1.771417 \n",
+ " 0.797513 \n",
+ " 0.902853 \n",
+ " 5 \n",
+ " 3.6 \n",
+ " 65.2 \n",
+ " 0.308021 \n",
+ " 1.0 \n",
+ " 0.999865 \n",
+ " 0.000135 \n",
+ " 0.000147 \n",
+ " 0.000184 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "for i in range(5):\n",
+ " plt.step(\n",
+ " predictions[i][\"times\"],\n",
+ " predictions[i][\"probabilities\"],\n",
+ " label=f'Example {i}'\n",
+ " )\n",
+ "plt.legend(title='Example index:')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot rules Kaplan-Meier's estimators on top of the training dataset estimator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "r1: IF Donorage = (-inf, 45.16) AND Relapse = {0} AND Recipientage = (-inf, 17.45) THEN \n",
+ "r2: IF Donorage = (-inf, 43.63) AND HLAmismatch = {0} AND Relapse = {1} THEN \n",
+ "r3: IF PLTrecovery = (-inf, 266) AND time_to_aGvHD_III_IV = <12.50, inf) AND ANCrecovery = <10.50, 19.50) AND Rbodymass = (-inf, 69) AND Donorage = (-inf, 44.06) AND Recipientage = <4.60, inf) AND CD34kgx10d6 = (-inf, 16.98) THEN \n",
+ "r4: IF Donorage = <37.16, inf) AND Recipientage = <5.15, inf) AND time_to_aGvHD_III_IV = <23.50, inf) AND CD3dCD34 = <0.90, 73.72) THEN \n",
+ "r5: IF Recipientage = <17.85, 18.85) THEN \n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from rulekit.kaplan_meier import KaplanMeierEstimator\n",
+ "\n",
+ "# plot rules kaplan-meier curves\n",
+ "for i, rule in enumerate(ruleset.rules):\n",
+ " rule_label: str = f'r{i + 1}'\n",
+ " rule_km: KaplanMeierEstimator = rule.kaplan_meier_estimator\n",
+ " plt.step(\n",
+ " rule_km.times,\n",
+ " rule_km.probabilities,\n",
+ " label=rule_label\n",
+ " )\n",
+ " print(f'{rule_label}: {rule}')\n",
+ "\n",
+ "# plot whole dataset kaplan-meier curve\n",
+ "train_km: KaplanMeierEstimator = srv.get_train_set_kaplan_meier()\n",
+ "plt.step(\n",
+ " train_km.times,\n",
+ " train_km.probabilities,\n",
+ " label='Training set estimator'\n",
+ ")\n",
+ "\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GkNBc5iBpwmj"
+ },
+ "source": [
+ "### Rules evaluation on full set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "9UjrC8r-p59d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.19651358709002972\n"
+ ]
+ }
+ ],
+ "source": [
+ "integrated_brier_score = srv.score(X, y)\n",
+ "print(f'Integrated Brier Score: {integrated_brier_score}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FpoSoaKdqAGQ"
+ },
+ "source": [
+ "## Stratified K-Folds cross-validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "0nNv6a84qTsq"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from rulekit.exceptions import RuleKitJavaException\n",
+ "\n",
+ "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
+ "\n",
+ "ruleset_stats = pd.DataFrame()\n",
+ "survival_metrics = []\n",
+ "\n",
+ "for train_index, test_index in skf.split(X, y):\n",
+ " X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
+ " y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
+ "\n",
+ " srv = SurvivalRules(\n",
+ " survival_time_attr='survival_time'\n",
+ " )\n",
+ " srv.fit(X_train, y_train)\n",
+ "\n",
+ " ruleset = srv.model\n",
+ "\n",
+ " ibs: float = srv.score(X_test, y_test)\n",
+ "\n",
+ " survival_metrics.append(ibs)\n",
+ " ruleset_stats = pd.concat([ruleset_stats, get_ruleset_stats(ruleset)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MfCOH_f3sICm"
+ },
+ "source": [
+ "Ruleset characteristics (average)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "xzbazr51sRd3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minimum_covered 0.050000\n",
+ "maximum_uncovered_fraction 0.000000\n",
+ "ignore_missing 0.000000\n",
+ "pruning_enabled 1.000000\n",
+ "max_growing_condition 0.000000\n",
+ "time_total_s 0.799019\n",
+ "time_growing_s 0.296248\n",
+ "time_pruning_s 0.477474\n",
+ "rules_count 4.000000\n",
+ "conditions_per_rule 2.581667\n",
+ "induced_conditions_per_rule 59.825000\n",
+ "avg_rule_coverage 0.486613\n",
+ "avg_rule_precision 1.000000\n",
+ "avg_rule_quality 0.995955\n",
+ "pvalue 0.004045\n",
+ "FDR_pvalue 0.004061\n",
+ "FWER_pvalue 0.004104\n",
+ "fraction_significant 0.980000\n",
+ "fraction_FDR_significant 0.980000\n",
+ "fraction_FWER_significant 0.980000\n",
+ "dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_SmDJho4sVEO"
+ },
+ "source": [
+ "Rules evaluation on dataset (average)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "Co-fNd9nshWB"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.20178456199764142\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'Integrated Brier Score: {np.mean(survival_metrics)}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d-GdQ-wUtzW9"
+ },
+ "source": [
+ "## Hyperparameters tuning\n",
+ "\n",
+ "This one gonna take a while..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.model_selection import GridSearchCV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def scorer(estimator: SurvivalRules, X: pd.DataFrame, y: pd.Series) -> float:\n",
+ " return -1 * estimator.score(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best Integrated Brier Score: -0.21437408819868886 using {'minsupp_new': 3, 'survival_time_attr': 'survival_time'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# define models and parameters\n",
+ "model = SurvivalRules(survival_time_attr='survival_time')\n",
+ "\n",
+ "# define grid search\n",
+ "grid = {\n",
+ " 'survival_time_attr': ['survival_time'],\n",
+ " 'minsupp_new': range(1, 10),\n",
+ "}\n",
+ "\n",
+ "cv = StratifiedKFold(n_splits=3)\n",
+ "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=cv, scoring=scorer)\n",
+ "grid_result = grid_search.fit(X, y)\n",
+ "\n",
+ "# summarize results\n",
+ "print(\n",
+ " 'Best Integrated Brier Score: '\n",
+ " f'{grid_result.best_score_} using {grid_result.best_params_}'\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building model with tuned hyperparameters\n",
+ "\n",
+ "### Split dataset to train and test (80%/20%)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, stratify=y)\n",
+ "\n",
+ "srv = SurvivalRules(\n",
+ " survival_time_attr='survival_time',\n",
+ " minsupp_new=5\n",
+ ")\n",
+ "srv.fit(X_train, y_train)\n",
+ "ruleset: RuleSet[SurvivalRule] = srv.model\n",
+ "ruleset_stats: pd.DataFrame = get_ruleset_stats(ruleset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rules evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minimum_covered 5.0\n",
+ "maximum_uncovered_fraction 0.0\n",
+ "ignore_missing False\n",
+ "pruning_enabled True\n",
+ "max_growing_condition 0.0\n",
+ "time_total_s 0.594173\n",
+ "time_growing_s 0.244234\n",
+ "time_pruning_s 0.312523\n",
+ "rules_count 4\n",
+ "conditions_per_rule 2.25\n",
+ "induced_conditions_per_rule 55.25\n",
+ "avg_rule_coverage 0.389262\n",
+ "avg_rule_precision 1.0\n",
+ "avg_rule_quality 1.0\n",
+ "pvalue 0.0\n",
+ "FDR_pvalue 0.0\n",
+ "FWER_pvalue 0.0\n",
+ "fraction_significant 1.0\n",
+ "fraction_FDR_significant 1.0\n",
+ "fraction_FWER_significant 1.0\n",
+ "Name: 0, dtype: object"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(ruleset_stats.iloc[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Validate model on test dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Integrated Brier Score: 0.14054870564224475\n"
+ ]
+ }
+ ],
+ "source": [
+ "integrated_brier_score = srv.score(X_test, y_test)\n",
+ "print(f'Integrated Brier Score: {integrated_brier_score}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictions = srv.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Predicted Kaplan-Meier curves for 5 examples from test set')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for i in range(5):\n",
+ " plt.step(\n",
+ " predictions[i][\"times\"], \n",
+ " predictions[i][\"probabilities\"],\n",
+ " label=f'Example {i}'\n",
+ " )\n",
+ "plt.legend(title='Examples indices:')\n",
+ "plt.title('Predicted Kaplan-Meier curves for 5 examples from test set')"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Raport_przezyciowy.ipynb",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "tutorials_env",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.0"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "62266c16fff41e971c13e9cb2ad3d47e4ef45d0678714c255381eb9fdcbd7032"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs/serve/v2.1.24.0/rst/whats_new/Changes in this version.html b/docs/serve/v2.1.24.0/rst/whats_new/Changes in this version.html
new file mode 100644
index 0000000..54d47cb
--- /dev/null
+++ b/docs/serve/v2.1.24.0/rst/whats_new/Changes in this version.html
@@ -0,0 +1,159 @@
+
+
+
+ What’s new in RuleKit version 2.1.24.0? — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+What’s new in RuleKit version 2.1.24.0?
+
+1. Revert breaking changes in expert rules induction for regression and survival
+The latest version 2.1.21.0 introduced some groundbreaking changes, which you can read more about them in the latest release note . Now rules and expert conditions can be defined in both the old and new formats, see example below.
+
+
+
+2. Upgrade to new version of RuleKit
+In the new version of the Java RuleKit library, many bugs regarding expert induction have been corrected.
+
+
+Other changes
+
+Improve flake8 score
Add more unit tests.
+
+
+
+
+
+
+
+
+
+
Search — rulekit v2.1.24.0 f documentation
+
+
+
+
+
+
+ rulekit v2.1.24.0
+
+
+
+
+
+
+
+
+
+
+
+ Please activate JavaScript to enable the search functionality.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "from rulekit.arff import read_arff\n",
- "from rulekit.survival import SurvivalRules\n",
- "from rulekit.rules import RuleSet, SurvivalRule\n",
- "from rulekit.kaplan_meier import KaplanMeierEstimator # this is a new class\n",
- "\n",
- "DATASET_URL: str = (\n",
- " 'https://raw.githubusercontent.com/'\n",
- " 'adaa-polsl/RuleKit/master/data/bmt/'\n",
- " 'bmt.arff'\n",
- ")\n",
- "df: pd.DataFrame = read_arff(DATASET_URL)\n",
- "X, y = df.drop('survival_status', axis=1), df['survival_status']\n",
- "\n",
- "surv = SurvivalRules(survival_time_attr='survival_time')\n",
- "surv.fit(X, y)\n",
- "\n",
- "ruleset: RuleSet[SurvivalRule] = reg.model\n",
- "rule: SurvivalRule = ruleset.rules[0]\n",
- "\n",
- "# you can now easily access Kaplan-Meier estimator of the rules\n",
- "rule_estimator: KaplanMeierEstimator = rule.kaplan_meier_estimator\n",
- "plt.step(\n",
- " rule_estimator.times, \n",
- " rule_estimator.probabilities,\n",
- " label='First rule'\n",
- ")\n",
- "# you can also access training dataset Kaplan-Meier estimator easily\n",
- "train_dataset_estimator: KaplanMeierEstimator = surv.get_train_set_kaplan_meier()\n",
- "plt.step(\n",
- " train_dataset_estimator.times, \n",
- " train_dataset_estimator.probabilities,\n",
- " label='Training dataset'\n",
- ")\n",
- "plt.legend(title='Kaplan-Meier curves:')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4. Changes in expert rules induction for regression and survival `❗BREAKING CHANGES`\n",
- "\n",
- "> Note that those changes will likely be reverted on the next version and are caused by a known bug in the original RuleKit library. Fixing it is beyond the scope of this package, which is merely a wrapper for it. \n",
- "\n",
- "Since this version, there has been a change in the way expert rules and conditions for regression and survival problems are communicated. All you have to do is remove conclusion part of those rules (everything after **THEN**).\n",
- "\n",
- "Expert rules before:"
+ "### 1. Revert breaking changes in expert rules induction for regression and survival \n",
+ "The latest version 2.1.21.0 introduced some groundbreaking changes, which you can read more\n",
+ "about them in the latest [release note](https://github.com/adaa-polsl/RuleKit-python/releases/tag/v2.1.21.0).\n",
+ "Now rules and expert conditions can be defined in both the old and new formats, see example below."
]
},
{
@@ -261,23 +19,16 @@
"metadata": {},
"outputs": [],
"source": [
+ "# both variants will work the same\n",
"expert_rules = [\n",
" (\n",
" 'rule-0',\n",
" 'IF [[CD34kgx10d6 = (-inf, 10.0)]] AND [[extcGvHD = {0}]] THEN survival_status = {NaN}'\n",
- " )\n",
- "]\n",
- "\n",
- "expert_preferred_conditions = [\n",
+ " ),\n",
" (\n",
- " 'attr-preferred-0',\n",
- " 'inf: IF [CD34kgx10d6 = Any] THEN survival_status = {NaN}'\n",
- " )\n",
- "]\n",
- "\n",
- "\n",
- "expert_forbidden_conditions = [\n",
- " ('attr-forbidden-0', 'IF [ANCrecovery = Any] THEN survival_status = {NaN}')\n",
+ " 'rule-0',\n",
+ " 'IF [[CD34kgx10d6 = (-inf, 10.0)]] AND [[extcGvHD = {0}]] THEN'\n",
+ " ),\n",
"]"
]
},
@@ -285,33 +36,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "And now:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "expert_rules = [\n",
- " (\n",
- " 'rule-0',\n",
- " 'IF [[CD34kgx10d6 = (-inf, 10.0)]] AND [[extcGvHD = {0}]] THEN'\n",
- " )\n",
- "]\n",
+ "### 2. Upgrade to new version of RuleKit\n",
"\n",
- "expert_preferred_conditions = [\n",
- " (\n",
- " 'attr-preferred-0',\n",
- " 'inf: IF [CD34kgx10d6 = Any] THEN'\n",
- " )\n",
- "]\n",
- "\n",
- "\n",
- "expert_forbidden_conditions = [\n",
- " ('attr-forbidden-0', 'IF [ANCrecovery = Any] THEN')\n",
- "]"
+ "In the new version of the Java RuleKit library, many bugs regarding expert induction have been corrected."
]
},
{
@@ -320,10 +47,8 @@
"source": [
"### Other changes\n",
"\n",
- "* Fix expert rules parsing.\n",
- "* Conditions printed in the order they had been added to the rule.\n",
- "* Fixed bug when using `sklearn.base.clone` function with RuleKit model classes.\n",
- "* Update tutorials in the documentation."
+ "* Improve flake8 score\n",
+ "* Add more unit tests."
]
}
],
diff --git a/rulekit/survival.py b/rulekit/survival.py
index 07b73c6..8f64d67 100644
--- a/rulekit/survival.py
+++ b/rulekit/survival.py
@@ -42,6 +42,7 @@ class _SurvivalModelsParams(BaseModel):
complementary_conditions: Optional[bool] = DEFAULT_PARAMS_VALUE[
"complementary_conditions"
]
+ max_rule_count: int = DEFAULT_PARAMS_VALUE["max_rule_count"]
class _SurvivalExpertModelParams(_SurvivalModelsParams, ExpertModelParams):
diff --git a/tests/test_survival.py b/tests/test_survival.py
index a9c6a4d..bda5f44 100644
--- a/tests/test_survival.py
+++ b/tests/test_survival.py
@@ -185,6 +185,23 @@ def test_getting_training_dataset_kaplan_meier_estimator(self):
"Estimator should contain probabilities for each unique time from the dataset",
)
+ def test_max_rule_count(self):
+ MAX_RULE_COUNT = 3
+ df: pd.DataFrame = read_arff(
+ os.path.join(dir_path, "resources", "data", "bmt-train-0.arff")
+ )
+ X, y = df.drop("survival_status", axis=1), df["survival_status"]
+ clf = survival.SurvivalRules(
+ survival_time_attr="survival_time",
+ max_rule_count=MAX_RULE_COUNT,
+ )
+ clf.fit(X, y)
+ self.assertLessEqual(
+ len(clf.model.rules),
+ MAX_RULE_COUNT,
+ f"Ruleset should contain no more than {MAX_RULE_COUNT} rules according to max_rule_count parameter",
+ )
+
class TestExpertSurvivalRules(unittest.TestCase):