Expand source code
from copy import deepcopy
from typing import List
import numpy as np
from sklearn import datasets
from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
from sklearn.metrics import r2_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, export_text
from imodels.util import checks
from imodels.util.tree import compute_tree_complexity
class HSTree:
def __init__(self, estimator_: BaseEstimator, reg_param: float = 1, shrinkage_scheme_: str = 'node_based'):
"""HSTree (Tree with hierarchical shrinkage applied).
Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest).
It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter).
Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles.
https://arxiv.org/abs/2202.00858
Params
------
estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting)
reg_param: float
Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str
Experimental: Used to experiment with different forms of shrinkage. options are:
(i) node_based shrinks based on number of samples in parent node
(ii) leaf_based only shrinks leaf nodes based on number of leaf samples
(iii) constant shrinks every node by a constant lambda
"""
super().__init__()
self.reg_param = reg_param
# print('est', estimator_)
self.estimator_ = estimator_
self.shrinkage_scheme_ = shrinkage_scheme_
self._init_prediction_task()
if checks.check_is_fitted(self.estimator_):
self._shrink()
def __init__prediction_task(self):
self.prediction_task = 'regression'
def get_params(self, deep=True):
if deep:
return deepcopy({'reg_param': self.reg_param, 'estimator_': self.estimator_,
# 'prediction_task': self.prediction_task,
'shrinkage_scheme_': self.shrinkage_scheme_})
return {'reg_param': self.reg_param, 'estimator_': self.estimator_,
# 'prediction_task': self.prediction_task,
'shrinkage_scheme_': self.shrinkage_scheme_}
def fit(self, *args, **kwargs):
# remove feature_names if it exists (note: only works as keyword-arg)
self.feature_names = kwargs.pop('feature_names', None) # None returned if not passed
self.estimator_.fit(*args, **kwargs)
self._shrink()
self.complexity_ = compute_tree_complexity(self.estimator_.tree_)
def _shrink_tree(self, tree, reg_param, i=0, parent_val=None, parent_num=None, cum_sum=0):
"""Shrink the tree
"""
if reg_param is None:
reg_param = 1.0
left = tree.children_left[i]
right = tree.children_right[i]
is_leaf = left == right
n_samples = tree.n_node_samples[i]
if self.prediction_task == 'regression':
val = tree.value[i][0, 0]
else:
if len(tree.value[i][0]) == 1:
val = tree.value[i][0, 0]
else:
val = tree.value[i][0, 1] / (tree.value[i][0, 0] + tree.value[i][0, 1]) # binary classification
# if root
if parent_val is None and parent_num is None:
if not is_leaf:
self._shrink_tree(tree, reg_param, left,
parent_val=val, parent_num=n_samples, cum_sum=val)
self._shrink_tree(tree, reg_param, right,
parent_val=val, parent_num=n_samples, cum_sum=val)
# if has parent
else:
if self.shrinkage_scheme_ == 'node_based':
val_new = (val - parent_val) / (1 + reg_param / parent_num)
elif self.shrinkage_scheme_ == 'constant':
val_new = (val - parent_val) / (1 + reg_param)
else:
val_new = val
cum_sum += val_new
if is_leaf:
if self.prediction_task == 'regression':
if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant':
tree.value[i, 0, 0] = cum_sum
else:
# tree.value[i, 0, 0] = cum_sum/(1 + reg_param/n_samples)
tree.value[i, 0, 0] = tree.value[0][0, 0] + (val - tree.value[0][0, 0]) / (
1 + reg_param / n_samples)
else:
if len(tree.value[i][0]) == 1:
if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant':
tree.value[i, 0, 0,] = cum_sum
else:
tree.value[i, 0, 0,] = tree.value[0][0, 0] + (val - tree.value[0][0, 0]) / (
1 + reg_param / n_samples)
else:
if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant':
tree.value[i, 0, 1] = cum_sum
tree.value[i, 0, 0] = 1.0 - cum_sum
else:
root_prediction = tree.value[0][0, 1] / (tree.value[0][0, 0] + tree.value[0][0, 1])
tree.value[i, 0, 1] = root_prediction + (val - root_prediction) / (
1 + reg_param / n_samples)
tree.value[i, 0, 0] = 1.0 - tree.value[i, 0, 1]
else:
if self.prediction_task == 'regression':
tree.value[i][0, 0] = parent_val + val_new
else:
if len(tree.value[i][0]) == 1:
tree.value[i][0, 0] = parent_val + val_new
else:
tree.value[i][0, 1] = parent_val + val_new
tree.value[i][0, 0] = 1.0 - parent_val + val_new
self._shrink_tree(tree, reg_param, left,
parent_val=val, parent_num=n_samples, cum_sum=cum_sum)
self._shrink_tree(tree, reg_param, right,
parent_val=val, parent_num=n_samples, cum_sum=cum_sum)
# edit the non-leaf nodes for later visualization (doesn't effect predictions)
# pass # not sure exactly what to put here
return tree
def _shrink(self):
if hasattr(self.estimator_, 'tree_'):
self._shrink_tree(self.estimator_.tree_, self.reg_param)
elif hasattr(self.estimator_, 'estimators_'):
for t in self.estimator_.estimators_:
if isinstance(t, np.ndarray):
assert t.size == 1, 'multiple trees stored under tree_?'
t = t[0]
self._shrink_tree(t.tree_, self.reg_param)
def predict(self, *args, **kwargs):
return self.estimator_.predict(*args, **kwargs)
def predict_proba(self, *args, **kwargs):
if hasattr(self.estimator_, 'predict_proba'):
return self.estimator_.predict_proba(*args, **kwargs)
else:
return NotImplemented
def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
def __str__(self):
s = '> ------------------------------\n'
s += '> Decision Tree with Hierarchical Shrinkage\n'
s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n'
s += '> ------------------------------' + '\n'
if hasattr(self, 'feature_names') and self.feature_names is not None:
return s + export_text(self.estimator_, feature_names=self.feature_names, show_weights=True)
else:
return s + export_text(self.estimator_, show_weights=True)
class HSTreeRegressor(HSTree, RegressorMixin):
def _init_prediction_task(self):
self.prediction_task = 'regression'
class HSTreeClassifier(HSTree, ClassifierMixin):
def _init_prediction_task(self):
self.prediction_task = 'classification'
class HSTreeClassifierCV(HSTreeClassifier):
def __init__(self, estimator_: BaseEstimator = None,
reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
shrinkage_scheme_: str = 'node_based',
max_leaf_nodes: int = 20,
cv: int = 3, scoring=None, *args, **kwargs):
"""Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
------
estimator_
Sklearn estimator (already initialized).
If no estimator_ is passsed, sklearn decision tree is used
max_rules
If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs
Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
"""
if estimator_ is None:
estimator_ = DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
def fit(self, X, y, *args, **kwargs):
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeClassifier(deepcopy(self.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y, *args, **kwargs)
class HSTreeRegressorCV(HSTreeRegressor):
def __init__(self, estimator_: BaseEstimator = None,
reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
shrinkage_scheme_: str = 'node_based',
max_leaf_nodes: int = 20,
cv: int = 3, scoring=None, *args, **kwargs):
"""Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
------
estimator_
Sklearn estimator (already initialized).
If no estimator_ is passsed, sklearn decision tree is used
max_rules
If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs
Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
"""
if estimator_ is None:
estimator_ = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes)
super().__init__(estimator_, reg_param=None)
self.reg_param_list = np.array(reg_param_list)
self.cv = cv
self.scoring = scoring
self.shrinkage_scheme_ = shrinkage_scheme_
# print('estimator', self.estimator_,
# 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_))
# if checks.check_is_fitted(self.estimator_):
# raise Warning('Passed an already fitted estimator,'
# 'but shrinking not applied until fit method is called.')
def fit(self, X, y, *args, **kwargs):
self.scores_ = []
for reg_param in self.reg_param_list:
est = HSTreeRegressor(deepcopy(self.estimator_), reg_param)
cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
self.scores_.append(np.mean(cv_scores))
self.reg_param = self.reg_param_list[np.argmax(self.scores_)]
super().fit(X=X, y=y, *args, **kwargs)
if __name__ == '__main__':
np.random.seed(15)
# X, y = datasets.fetch_california_housing(return_X_y=True) # regression
# X, y = datasets.load_breast_cancer(return_X_y=True) # binary classification
X, y = datasets.load_diabetes(return_X_y=True) # regression
# X = np.random.randn(500, 10)
# y = (X[:, 0] > 0).astype(float) + (X[:, 1] > 1).astype(float)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=10
)
print('X.shape', X.shape)
print('ys', np.unique(y_train))
# m = HSTree(estimator_=DecisionTreeClassifier(), reg_param=0.1)
# m = DecisionTreeClassifier(max_leaf_nodes = 20,random_state=1, max_features=None)
m = DecisionTreeRegressor(random_state=42, max_leaf_nodes=20)
# print('best alpha', m.reg_param)
m.fit(X_train, y_train)
# m.predict_proba(X_train) # just run this
print('score', r2_score(y_test, m.predict(X_test)))
print('running again....')
# x = DecisionTreeRegressor(random_state = 42, ccp_alpha = 0.3)
# x.fit(X_train,y_train)
# m = HSTree(estimator_=DecisionTreeRegressor(random_state=42, max_features=None), reg_param=10)
# m = HSTree(estimator_=DecisionTreeClassifier(random_state=42, max_features=None), reg_param=0)
m = HSTreeClassifierCV(estimator_=DecisionTreeRegressor(max_leaf_nodes=10, random_state=1),
shrinkage_scheme_='node_based',
reg_param_list=[0.1, 1, 2, 5, 10, 25, 50, 100, 500])
# m = ShrunkTreeCV(estimator_=DecisionTreeClassifier())
# m = HSTreeClassifier(estimator_ = GradientBoostingClassifier(random_state = 10),reg_param = 5)
m.fit(X_train, y_train)
print('best alpha', m.reg_param)
# m.predict_proba(X_train) # just run this
# print('score', m.score(X_test, y_test))
print('score', r2_score(y_test, m.predict(X_test)))
Classes
class HSTree (estimator_: sklearn.base.BaseEstimator, reg_param: float = 1, shrinkage_scheme_: str = 'node_based')
-
HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858
Params
estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting)
reg_param: float Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str Experimental: Used to experiment with different forms of shrinkage. options are: (i) node_based shrinks based on number of samples in parent node (ii) leaf_based only shrinks leaf nodes based on number of leaf samples (iii) constant shrinks every node by a constant lambda
Expand source code
class HSTree: def __init__(self, estimator_: BaseEstimator, reg_param: float = 1, shrinkage_scheme_: str = 'node_based'): """HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858 Params ------ estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting) reg_param: float Higher is more regularization (can be arbitrarily large, should not be < 0) shrinkage_scheme: str Experimental: Used to experiment with different forms of shrinkage. options are: (i) node_based shrinks based on number of samples in parent node (ii) leaf_based only shrinks leaf nodes based on number of leaf samples (iii) constant shrinks every node by a constant lambda """ super().__init__() self.reg_param = reg_param # print('est', estimator_) self.estimator_ = estimator_ self.shrinkage_scheme_ = shrinkage_scheme_ self._init_prediction_task() if checks.check_is_fitted(self.estimator_): self._shrink() def __init__prediction_task(self): self.prediction_task = 'regression' def get_params(self, deep=True): if deep: return deepcopy({'reg_param': self.reg_param, 'estimator_': self.estimator_, # 'prediction_task': self.prediction_task, 'shrinkage_scheme_': self.shrinkage_scheme_}) return {'reg_param': self.reg_param, 'estimator_': self.estimator_, # 'prediction_task': self.prediction_task, 'shrinkage_scheme_': self.shrinkage_scheme_} def fit(self, *args, **kwargs): # remove feature_names if it exists (note: only works as keyword-arg) self.feature_names = kwargs.pop('feature_names', None) # None returned if not passed self.estimator_.fit(*args, **kwargs) self._shrink() self.complexity_ = compute_tree_complexity(self.estimator_.tree_) def _shrink_tree(self, tree, reg_param, i=0, parent_val=None, parent_num=None, cum_sum=0): """Shrink the tree """ if reg_param is None: reg_param = 1.0 left = tree.children_left[i] right = tree.children_right[i] is_leaf = left == right n_samples = tree.n_node_samples[i] if self.prediction_task == 'regression': val = tree.value[i][0, 0] else: if len(tree.value[i][0]) == 1: val = tree.value[i][0, 0] else: val = tree.value[i][0, 1] / (tree.value[i][0, 0] + tree.value[i][0, 1]) # binary classification # if root if parent_val is None and parent_num is None: if not is_leaf: self._shrink_tree(tree, reg_param, left, parent_val=val, parent_num=n_samples, cum_sum=val) self._shrink_tree(tree, reg_param, right, parent_val=val, parent_num=n_samples, cum_sum=val) # if has parent else: if self.shrinkage_scheme_ == 'node_based': val_new = (val - parent_val) / (1 + reg_param / parent_num) elif self.shrinkage_scheme_ == 'constant': val_new = (val - parent_val) / (1 + reg_param) else: val_new = val cum_sum += val_new if is_leaf: if self.prediction_task == 'regression': if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant': tree.value[i, 0, 0] = cum_sum else: # tree.value[i, 0, 0] = cum_sum/(1 + reg_param/n_samples) tree.value[i, 0, 0] = tree.value[0][0, 0] + (val - tree.value[0][0, 0]) / ( 1 + reg_param / n_samples) else: if len(tree.value[i][0]) == 1: if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant': tree.value[i, 0, 0,] = cum_sum else: tree.value[i, 0, 0,] = tree.value[0][0, 0] + (val - tree.value[0][0, 0]) / ( 1 + reg_param / n_samples) else: if self.shrinkage_scheme_ == 'node_based' or self.shrinkage_scheme_ == 'constant': tree.value[i, 0, 1] = cum_sum tree.value[i, 0, 0] = 1.0 - cum_sum else: root_prediction = tree.value[0][0, 1] / (tree.value[0][0, 0] + tree.value[0][0, 1]) tree.value[i, 0, 1] = root_prediction + (val - root_prediction) / ( 1 + reg_param / n_samples) tree.value[i, 0, 0] = 1.0 - tree.value[i, 0, 1] else: if self.prediction_task == 'regression': tree.value[i][0, 0] = parent_val + val_new else: if len(tree.value[i][0]) == 1: tree.value[i][0, 0] = parent_val + val_new else: tree.value[i][0, 1] = parent_val + val_new tree.value[i][0, 0] = 1.0 - parent_val + val_new self._shrink_tree(tree, reg_param, left, parent_val=val, parent_num=n_samples, cum_sum=cum_sum) self._shrink_tree(tree, reg_param, right, parent_val=val, parent_num=n_samples, cum_sum=cum_sum) # edit the non-leaf nodes for later visualization (doesn't effect predictions) # pass # not sure exactly what to put here return tree def _shrink(self): if hasattr(self.estimator_, 'tree_'): self._shrink_tree(self.estimator_.tree_, self.reg_param) elif hasattr(self.estimator_, 'estimators_'): for t in self.estimator_.estimators_: if isinstance(t, np.ndarray): assert t.size == 1, 'multiple trees stored under tree_?' t = t[0] self._shrink_tree(t.tree_, self.reg_param) def predict(self, *args, **kwargs): return self.estimator_.predict(*args, **kwargs) def predict_proba(self, *args, **kwargs): if hasattr(self.estimator_, 'predict_proba'): return self.estimator_.predict_proba(*args, **kwargs) else: return NotImplemented def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented def __str__(self): s = '> ------------------------------\n' s += '> Decision Tree with Hierarchical Shrinkage\n' s += '> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n' s += '> ------------------------------' + '\n' if hasattr(self, 'feature_names') and self.feature_names is not None: return s + export_text(self.estimator_, feature_names=self.feature_names, show_weights=True) else: return s + export_text(self.estimator_, show_weights=True)
Subclasses
Methods
def fit(self, *args, **kwargs)
-
Expand source code
def fit(self, *args, **kwargs): # remove feature_names if it exists (note: only works as keyword-arg) self.feature_names = kwargs.pop('feature_names', None) # None returned if not passed self.estimator_.fit(*args, **kwargs) self._shrink() self.complexity_ = compute_tree_complexity(self.estimator_.tree_)
def get_params(self, deep=True)
-
Expand source code
def get_params(self, deep=True): if deep: return deepcopy({'reg_param': self.reg_param, 'estimator_': self.estimator_, # 'prediction_task': self.prediction_task, 'shrinkage_scheme_': self.shrinkage_scheme_}) return {'reg_param': self.reg_param, 'estimator_': self.estimator_, # 'prediction_task': self.prediction_task, 'shrinkage_scheme_': self.shrinkage_scheme_}
def predict(self, *args, **kwargs)
-
Expand source code
def predict(self, *args, **kwargs): return self.estimator_.predict(*args, **kwargs)
def predict_proba(self, *args, **kwargs)
-
Expand source code
def predict_proba(self, *args, **kwargs): if hasattr(self.estimator_, 'predict_proba'): return self.estimator_.predict_proba(*args, **kwargs) else: return NotImplemented
def score(self, *args, **kwargs)
-
Expand source code
def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented
class HSTreeClassifier (estimator_: sklearn.base.BaseEstimator, reg_param: float = 1, shrinkage_scheme_: str = 'node_based')
-
Mixin class for all classifiers in scikit-learn.
HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858
Params
estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting)
reg_param: float Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str Experimental: Used to experiment with different forms of shrinkage. options are: (i) node_based shrinks based on number of samples in parent node (ii) leaf_based only shrinks leaf nodes based on number of leaf samples (iii) constant shrinks every node by a constant lambda
Expand source code
class HSTreeClassifier(HSTree, ClassifierMixin): def _init_prediction_task(self): self.prediction_task = 'classification'
Ancestors
- HSTree
- sklearn.base.ClassifierMixin
Subclasses
class HSTreeClassifierCV (estimator_: sklearn.base.BaseEstimator = None, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs)
-
Mixin class for all classifiers in scikit-learn.
Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
estimator_ Sklearn estimator (already initialized). If no estimator_ is passsed, sklearn decision tree is used
max_rules If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
Expand source code
class HSTreeClassifierCV(HSTreeClassifier): def __init__(self, estimator_: BaseEstimator = None, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs): """Cross-validation is used to select the best regularization parameter for hierarchical shrinkage. Params ------ estimator_ Sklearn estimator (already initialized). If no estimator_ is passsed, sklearn decision tree is used max_rules If estimator is None, then max_leaf_nodes is passed to the default decision tree args, kwargs Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args. """ if estimator_ is None: estimator_ = DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes) super().__init__(estimator_, reg_param=None) self.reg_param_list = np.array(reg_param_list) self.cv = cv self.scoring = scoring self.shrinkage_scheme_ = shrinkage_scheme_ # print('estimator', self.estimator_, # 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_)) # if checks.check_is_fitted(self.estimator_): # raise Warning('Passed an already fitted estimator,' # 'but shrinking not applied until fit method is called.') def fit(self, X, y, *args, **kwargs): self.scores_ = [] for reg_param in self.reg_param_list: est = HSTreeClassifier(deepcopy(self.estimator_), reg_param) cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring) self.scores_.append(np.mean(cv_scores)) self.reg_param = self.reg_param_list[np.argmax(self.scores_)] super().fit(X=X, y=y, *args, **kwargs)
Ancestors
- HSTreeClassifier
- HSTree
- sklearn.base.ClassifierMixin
Methods
def fit(self, X, y, *args, **kwargs)
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Expand source code
def fit(self, X, y, *args, **kwargs): self.scores_ = [] for reg_param in self.reg_param_list: est = HSTreeClassifier(deepcopy(self.estimator_), reg_param) cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring) self.scores_.append(np.mean(cv_scores)) self.reg_param = self.reg_param_list[np.argmax(self.scores_)] super().fit(X=X, y=y, *args, **kwargs)
class HSTreeRegressor (estimator_: sklearn.base.BaseEstimator, reg_param: float = 1, shrinkage_scheme_: str = 'node_based')
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Mixin class for all regression estimators in scikit-learn.
HSTree (Tree with hierarchical shrinkage applied). Hierarchical shinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles. https://arxiv.org/abs/2202.00858
Params
estimator_: sklearn tree or tree ensemble model (e.g. RandomForest or GradientBoosting)
reg_param: float Higher is more regularization (can be arbitrarily large, should not be < 0)
shrinkage_scheme: str Experimental: Used to experiment with different forms of shrinkage. options are: (i) node_based shrinks based on number of samples in parent node (ii) leaf_based only shrinks leaf nodes based on number of leaf samples (iii) constant shrinks every node by a constant lambda
Expand source code
class HSTreeRegressor(HSTree, RegressorMixin): def _init_prediction_task(self): self.prediction_task = 'regression'
Ancestors
- HSTree
- sklearn.base.RegressorMixin
Subclasses
class HSTreeRegressorCV (estimator_: sklearn.base.BaseEstimator = None, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs)
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Mixin class for all regression estimators in scikit-learn.
Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
Params
estimator_ Sklearn estimator (already initialized). If no estimator_ is passsed, sklearn decision tree is used
max_rules If estimator is None, then max_leaf_nodes is passed to the default decision tree
args, kwargs Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
Expand source code
class HSTreeRegressorCV(HSTreeRegressor): def __init__(self, estimator_: BaseEstimator = None, reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500], shrinkage_scheme_: str = 'node_based', max_leaf_nodes: int = 20, cv: int = 3, scoring=None, *args, **kwargs): """Cross-validation is used to select the best regularization parameter for hierarchical shrinkage. Params ------ estimator_ Sklearn estimator (already initialized). If no estimator_ is passsed, sklearn decision tree is used max_rules If estimator is None, then max_leaf_nodes is passed to the default decision tree args, kwargs Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args. """ if estimator_ is None: estimator_ = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes) super().__init__(estimator_, reg_param=None) self.reg_param_list = np.array(reg_param_list) self.cv = cv self.scoring = scoring self.shrinkage_scheme_ = shrinkage_scheme_ # print('estimator', self.estimator_, # 'checks.check_is_fitted(estimator)', checks.check_is_fitted(self.estimator_)) # if checks.check_is_fitted(self.estimator_): # raise Warning('Passed an already fitted estimator,' # 'but shrinking not applied until fit method is called.') def fit(self, X, y, *args, **kwargs): self.scores_ = [] for reg_param in self.reg_param_list: est = HSTreeRegressor(deepcopy(self.estimator_), reg_param) cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring) self.scores_.append(np.mean(cv_scores)) self.reg_param = self.reg_param_list[np.argmax(self.scores_)] super().fit(X=X, y=y, *args, **kwargs)
Ancestors
- HSTreeRegressor
- HSTree
- sklearn.base.RegressorMixin
Methods
def fit(self, X, y, *args, **kwargs)
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Expand source code
def fit(self, X, y, *args, **kwargs): self.scores_ = [] for reg_param in self.reg_param_list: est = HSTreeRegressor(deepcopy(self.estimator_), reg_param) cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring) self.scores_.append(np.mean(cv_scores)) self.reg_param = self.reg_param_list[np.argmax(self.scores_)] super().fit(X=X, y=y, *args, **kwargs)