Module imodels.rule_set.fplasso
Expand source code
from typing import List
from sklearn.base import ClassifierMixin, RegressorMixin
from imodels.rule_set.rule_fit import RuleFit
from imodels.util.convert import itemsets_to_rules
from imodels.util.extract import extract_fpgrowth
class FPLasso(RuleFit):
def __init__(self,
minsupport=0.1,
maxcardinality=2,
disc_strategy='mdlp',
disc_kwargs={},
verbose=False,
n_estimators=100,
tree_size=4,
sample_fract='default',
max_rules=2000,
memory_par=0.01,
tree_generator=None,
lin_trim_quantile=0.025,
lin_standardise=True,
exp_rand_tree_size=True,
include_linear=True,
alpha=None,
random_state=None):
super().__init__(n_estimators,
tree_size,
sample_fract,
max_rules,
memory_par,
tree_generator,
lin_trim_quantile,
lin_standardise,
exp_rand_tree_size,
include_linear,
alpha,
random_state)
self.disc_strategy = disc_strategy
self.disc_kwargs = disc_kwargs
self.minsupport = minsupport
self.maxcardinality = maxcardinality
self.verbose = verbose
def fit(self, X, y=None, feature_names=None, undiscretized_features=[]):
self.undiscretized_features = undiscretized_features
super().fit(X, y, feature_names=feature_names)
return self
def _extract_rules(self, X, y) -> List[str]:
itemsets = extract_fpgrowth(X, y,
feature_names=self.feature_placeholders,
minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
undiscretized_features=self.undiscretized_features,
disc_strategy=self.disc_strategy,
disc_kwargs=self.disc_kwargs,
verbose=self.verbose)[0]
return itemsets_to_rules(itemsets)
class FPLassoRegressor(FPLasso, RegressorMixin):
def _init_prediction_task(self):
self.prediction_task = 'regression'
class FPLassoClassifier(FPLasso, ClassifierMixin):
def _init_prediction_task(self):
self.prediction_task = 'classification'
Classes
class FPLasso (minsupport=0.1, maxcardinality=2, disc_strategy='mdlp', disc_kwargs={}, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None)
-
Rulefit class. Rather than using this class directly, should use RuleFitRegressor or RuleFitClassifier
Parameters
tree_size
:Number
ofterminal nodes in generated trees. If exp_rand_tree_size=True,
- this will be the mean number of terminal nodes.
sample_fract
:fraction
ofrandomly chosen training observations used to produce each tree.
- FP 2004 (Sec. 2)
max_rules
:total number
ofterms included in the final model (both linear and rules)
- approximate total number of candidate rules generated for fitting also is based on this Note that actual number of candidate rules will usually be lower than this due to duplicates.
memory_par
:scale multiplier (shrinkage factor) applied to each new tree when
- sequentially induced. FP 2004 (Sec. 2)
lin_standardise
:If True, the linear terms will be standardised as per Friedman Sec 3.2
- by multiplying the winsorised variable by 0.4/stdev.
lin_trim_quantile
:If lin_standardise is True, this quantile will be used to trim linear
- terms before standardisation.
exp_rand_tree_size
:If True, each boosted tree will have a different maximum number
of- terminal nodes based on an exponential distribution about tree_size. (Friedman Sec 3.3)
include_linear
:Include linear terms as opposed to only rules
- random_state: Integer to initialise random objects and provide repeatability.
tree_generator
:Optional: this object will be used as provided to generate the rules.
- This will override almost all the other properties above. Must be GradientBoostingRegressor or GradientBoostingClassifier, optional (default=None)
Attributes
rule_ensemble
:RuleEnsemble
- The rule ensemble
feature_names
:list
ofstrings
, optional(default=None)
- The names of the features (columns)
Expand source code
class FPLasso(RuleFit): def __init__(self, minsupport=0.1, maxcardinality=2, disc_strategy='mdlp', disc_kwargs={}, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None): super().__init__(n_estimators, tree_size, sample_fract, max_rules, memory_par, tree_generator, lin_trim_quantile, lin_standardise, exp_rand_tree_size, include_linear, alpha, random_state) self.disc_strategy = disc_strategy self.disc_kwargs = disc_kwargs self.minsupport = minsupport self.maxcardinality = maxcardinality self.verbose = verbose def fit(self, X, y=None, feature_names=None, undiscretized_features=[]): self.undiscretized_features = undiscretized_features super().fit(X, y, feature_names=feature_names) return self def _extract_rules(self, X, y) -> List[str]: itemsets = extract_fpgrowth(X, y, feature_names=self.feature_placeholders, minsupport=self.minsupport, maxcardinality=self.maxcardinality, undiscretized_features=self.undiscretized_features, disc_strategy=self.disc_strategy, disc_kwargs=self.disc_kwargs, verbose=self.verbose)[0] return itemsets_to_rules(itemsets)
Ancestors
Subclasses
Inherited members
class FPLassoClassifier (minsupport=0.1, maxcardinality=2, disc_strategy='mdlp', disc_kwargs={}, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None)
-
Rulefit class. Rather than using this class directly, should use RuleFitRegressor or RuleFitClassifier
Parameters
tree_size
:Number
ofterminal nodes in generated trees. If exp_rand_tree_size=True,
- this will be the mean number of terminal nodes.
sample_fract
:fraction
ofrandomly chosen training observations used to produce each tree.
- FP 2004 (Sec. 2)
max_rules
:total number
ofterms included in the final model (both linear and rules)
- approximate total number of candidate rules generated for fitting also is based on this Note that actual number of candidate rules will usually be lower than this due to duplicates.
memory_par
:scale multiplier (shrinkage factor) applied to each new tree when
- sequentially induced. FP 2004 (Sec. 2)
lin_standardise
:If True, the linear terms will be standardised as per Friedman Sec 3.2
- by multiplying the winsorised variable by 0.4/stdev.
lin_trim_quantile
:If lin_standardise is True, this quantile will be used to trim linear
- terms before standardisation.
exp_rand_tree_size
:If True, each boosted tree will have a different maximum number
of- terminal nodes based on an exponential distribution about tree_size. (Friedman Sec 3.3)
include_linear
:Include linear terms as opposed to only rules
- random_state: Integer to initialise random objects and provide repeatability.
tree_generator
:Optional: this object will be used as provided to generate the rules.
- This will override almost all the other properties above. Must be GradientBoostingRegressor or GradientBoostingClassifier, optional (default=None)
Attributes
rule_ensemble
:RuleEnsemble
- The rule ensemble
feature_names
:list
ofstrings
, optional(default=None)
- The names of the features (columns)
Expand source code
class FPLassoClassifier(FPLasso, ClassifierMixin): def _init_prediction_task(self): self.prediction_task = 'classification'
Ancestors
- FPLasso
- RuleFit
- sklearn.base.BaseEstimator
- sklearn.base.TransformerMixin
- RuleSet
- sklearn.base.ClassifierMixin
Inherited members
class FPLassoRegressor (minsupport=0.1, maxcardinality=2, disc_strategy='mdlp', disc_kwargs={}, verbose=False, n_estimators=100, tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, include_linear=True, alpha=None, random_state=None)
-
Rulefit class. Rather than using this class directly, should use RuleFitRegressor or RuleFitClassifier
Parameters
tree_size
:Number
ofterminal nodes in generated trees. If exp_rand_tree_size=True,
- this will be the mean number of terminal nodes.
sample_fract
:fraction
ofrandomly chosen training observations used to produce each tree.
- FP 2004 (Sec. 2)
max_rules
:total number
ofterms included in the final model (both linear and rules)
- approximate total number of candidate rules generated for fitting also is based on this Note that actual number of candidate rules will usually be lower than this due to duplicates.
memory_par
:scale multiplier (shrinkage factor) applied to each new tree when
- sequentially induced. FP 2004 (Sec. 2)
lin_standardise
:If True, the linear terms will be standardised as per Friedman Sec 3.2
- by multiplying the winsorised variable by 0.4/stdev.
lin_trim_quantile
:If lin_standardise is True, this quantile will be used to trim linear
- terms before standardisation.
exp_rand_tree_size
:If True, each boosted tree will have a different maximum number
of- terminal nodes based on an exponential distribution about tree_size. (Friedman Sec 3.3)
include_linear
:Include linear terms as opposed to only rules
- random_state: Integer to initialise random objects and provide repeatability.
tree_generator
:Optional: this object will be used as provided to generate the rules.
- This will override almost all the other properties above. Must be GradientBoostingRegressor or GradientBoostingClassifier, optional (default=None)
Attributes
rule_ensemble
:RuleEnsemble
- The rule ensemble
feature_names
:list
ofstrings
, optional(default=None)
- The names of the features (columns)
Expand source code
class FPLassoRegressor(FPLasso, RegressorMixin): def _init_prediction_task(self): self.prediction_task = 'regression'
Ancestors
- FPLasso
- RuleFit
- sklearn.base.BaseEstimator
- sklearn.base.TransformerMixin
- RuleSet
- sklearn.base.RegressorMixin
Inherited members