Module imodels.tree.figs
Expand source code
from copy import deepcopy
import numpy as np
from sklearn import datasets
from sklearn import tree
from sklearn.base import BaseEstimator
from sklearn.model_selection import train_test_split
from sklearn.utils import check_X_y
class Node:
def __init__(self, feature: int = None, threshold: int = None,
value=None, idxs=None, is_root: bool = False, left=None,
impurity_reduction: float = None, tree_num: int = None,
right=None):
"""Node class for splitting
"""
# split or linear
self.is_root = is_root
self.idxs = idxs
self.tree_num = tree_num
self.feature = feature
self.impurity_reduction = impurity_reduction
# different meanings
self.value = value # for split this is mean, for linear this is weight
# split-specific
self.threshold = threshold
self.left = left
self.right = right
self.left_temp = None
self.right_temp = None
def setattrs(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def __str__(self):
if self.is_root:
return f'X_{self.feature} <= {self.threshold:0.3f} (Tree #{self.tree_num} root)'
elif self.left is None and self.right is None:
return f'Val: {self.value[0][0]:0.3f} (leaf)'
else:
return f'X_{self.feature} <= {self.threshold:0.3f} (split)'
def __repr__(self):
return self.__str__()
class FIGS(BaseEstimator):
"""FIGS (sum of trees) classifier.
Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
https://arxiv.org/abs/2201.11931
"""
def __init__(self, max_rules: int = None, min_impurity_decrease: float=0.0):
super().__init__()
self.max_rules = max_rules
self.min_impurity_decrease = min_impurity_decrease
self._init_prediction_task() # decides between regressor and classifier
self._init_decision_function()
def _init_prediction_task(self):
"""
SuperCARTRegressor and SuperCARTClassifier override this method
to alter the prediction task. When using this class directly,
it is equivalent to SuperCARTRegressor
"""
self.prediction_task = 'regression'
def _init_decision_function(self):
"""Sets decision function based on prediction_task
"""
# used by sklearn GrriidSearchCV, BaggingClassifier
if self.prediction_task == 'classification':
decision_function = lambda x: self.predict_proba(x)[:, 1]
elif self.prediction_task == 'regression':
decision_function = self.predict
def construct_node_with_stump(self, X, y, idxs, tree_num, sample_weight=None):
# array indices
SPLIT = 0
LEFT = 1
RIGHT = 2
# fit stump
stump = tree.DecisionTreeRegressor(max_depth=1)
if sample_weight is not None:
sample_weight = sample_weight[idxs]
stump.fit(X[idxs], y[idxs], sample_weight=sample_weight)
# these are all arrays, arr[0] is split node
# note: -2 is dummy
feature = stump.tree_.feature
threshold = stump.tree_.threshold
impurity = stump.tree_.impurity
n_node_samples = stump.tree_.n_node_samples
value = stump.tree_.value
# no split
if len(feature) == 1:
# print('no split found!', idxs.sum(), impurity, feature)
return Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num,
feature=feature[SPLIT], threshold=threshold[SPLIT],
impurity_reduction=None)
# split node
impurity_reduction = (
impurity[SPLIT] -
impurity[LEFT] * n_node_samples[LEFT] / n_node_samples[SPLIT] -
impurity[RIGHT] * n_node_samples[RIGHT] / n_node_samples[SPLIT]
) * idxs.sum()
node_split = Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num,
feature=feature[SPLIT], threshold=threshold[SPLIT],
impurity_reduction=impurity_reduction)
# print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction)
# manage children
idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT]
idxs_left = idxs_split & idxs
idxs_right = ~idxs_split & idxs
node_left = Node(idxs=idxs_left, value=value[LEFT], tree_num=tree_num)
node_right = Node(idxs=idxs_right, value=value[RIGHT], tree_num=tree_num)
node_split.setattrs(left_temp=node_left, right_temp=node_right, )
return node_split
def fit(self, X, y=None, feature_names=None, verbose=False, sample_weight=None):
"""
Params
------
sample_weight: array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
Splits that would create child nodes with net zero or negative weight
are ignored while searching for a split in each node.
"""
X, y = check_X_y(X, y)
y = y.astype(float)
if feature_names is not None:
self.feature_names_ = feature_names
self.trees_ = [] # list of the root nodes of added trees
self.complexity_ = 0 # tracks the number of rules in the model
y_predictions_per_tree = {} # predictions for each tree
y_residuals_per_tree = {} # based on predictions above
# set up initial potential_splits
# everything in potential_splits either is_root (so it can be added directly to self.trees_)
# or it is a child of a root node that has already been added
idxs = np.ones(X.shape[0], dtype=bool)
node_init = self.construct_node_with_stump(X=X, y=y, idxs=idxs, tree_num=-1, sample_weight=sample_weight)
potential_splits = [node_init]
for node in potential_splits:
node.setattrs(is_root=True)
potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction)
# start the greedy fitting algorithm
finished = False
while len(potential_splits) > 0 and not finished:
# print('potential_splits', [str(s) for s in potential_splits])
split_node = potential_splits.pop() # get node with max impurity_reduction (since it's sorted)
# don't split on node
if split_node.impurity_reduction < self.min_impurity_decrease:
finished = True
break
# split on node
if verbose:
print('\nadding ' + str(split_node))
self.complexity_ += 1
# if added a tree root
if split_node.is_root:
# start a new tree
self.trees_.append(split_node)
# update tree_num
for node_ in [split_node, split_node.left_temp, split_node.right_temp]:
if node_ is not None:
node_.tree_num = len(self.trees_) - 1
# add new root potential node
node_new_root = Node(is_root=True, idxs=np.ones(X.shape[0], dtype=bool),
tree_num=-1)
potential_splits.append(node_new_root)
# add children to potential splits
# assign left_temp, right_temp to be proper children
# (basically adds them to tree in predict method)
split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp)
# add children to potential_splits
potential_splits.append(split_node.left)
potential_splits.append(split_node.right)
# update predictions for altered tree
for tree_num_ in range(len(self.trees_)):
y_predictions_per_tree[tree_num_] = self.predict_tree(self.trees_[tree_num_], X)
y_predictions_per_tree[-1] = np.zeros(X.shape[0]) # dummy 0 preds for possible new trees
# update residuals for each tree
# -1 is key for potential new tree
for tree_num_ in list(range(len(self.trees_))) + [-1]:
y_residuals_per_tree[tree_num_] = deepcopy(y)
# subtract predictions of all other trees
for tree_num_other_ in range(len(self.trees_)):
if not tree_num_other_ == tree_num_:
y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[tree_num_other_]
# recompute all impurities + update potential_split children
potential_splits_new = []
for potential_split in potential_splits:
y_target = y_residuals_per_tree[potential_split.tree_num]
# re-calculate the best split
potential_split_updated = self.construct_node_with_stump(X=X,
y=y_target,
idxs=potential_split.idxs,
tree_num=potential_split.tree_num,
sample_weight=sample_weight, )
# need to preserve certain attributes from before (value at this split + is_root)
# value may change because residuals may have changed, but we want it to store the value from before
potential_split.setattrs(
feature=potential_split_updated.feature,
threshold=potential_split_updated.threshold,
impurity_reduction=potential_split_updated.impurity_reduction,
left_temp=potential_split_updated.left_temp,
right_temp=potential_split_updated.right_temp,
)
# this is a valid split
if potential_split.impurity_reduction is not None:
potential_splits_new.append(potential_split)
# sort so largest impurity reduction comes last (should probs make this a heap later)
potential_splits = sorted(potential_splits_new, key=lambda x: x.impurity_reduction)
if verbose:
print(self)
if self.max_rules is not None and self.complexity_ >= self.max_rules:
finished = True
break
return self
def tree_to_str(self, root: Node, prefix=''):
if root is None:
return ''
elif root.threshold is None:
return ''
pprefix = prefix + '\t'
return prefix + str(root) + '\n' + self.tree_to_str(root.left, pprefix) + self.tree_to_str(root.right, pprefix)
def __str__(self):
s = '------------\n' + '\n\t+\n'.join([self.tree_to_str(t) for t in self.trees_])
if hasattr(self, 'feature_names_') and self.feature_names_ is not None:
for i in range(len(self.feature_names_))[::-1]:
s = s.replace(f'X_{i}', self.feature_names_[i])
return s
def predict(self, X):
preds = np.zeros(X.shape[0])
for tree in self.trees_:
preds += self.predict_tree(tree, X)
if self.prediction_task == 'regression':
return preds
elif self.prediction_task == 'classification':
return (preds > 0.5).astype(int)
def predict_proba(self, X):
if self.prediction_task == 'regression':
return NotImplemented
preds = np.zeros(X.shape[0])
for tree in self.trees_:
preds += self.predict_tree(tree, X)
preds = np.clip(preds, a_min=0., a_max=1.) # constrain to range of probabilities
return np.vstack((1 - preds, preds)).transpose()
def predict_tree(self, root: Node, X):
"""Predict for a single tree
"""
def predict_tree_single_point(root: Node, x):
if root.left is None and root.right is None:
return root.value
left = x[root.feature] <= root.threshold
if left:
if root.left is None: # we don't actually have to worry about this case
return root.value
else:
return predict_tree_single_point(root.left, x)
else:
if root.right is None: # we don't actually have to worry about this case
return root.value
else:
return predict_tree_single_point(root.right, x)
preds = np.zeros(X.shape[0])
for i in range(X.shape[0]):
preds[i] = predict_tree_single_point(root, X[i])
return preds
class FIGSRegressor(FIGS):
def _init_prediction_task(self):
self.prediction_task = 'regression'
class FIGSClassifier(FIGS):
def _init_prediction_task(self):
self.prediction_task = 'classification'
if __name__ == '__main__':
np.random.seed(13)
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=42
)
print('X.shape', X.shape)
print('ys', np.unique(y_train), '\n\n')
m = FIGSClassifier(max_rules=5)
m.fit(X_train, y_train)
print(m.predict_proba(X_train))
Classes
class FIGS (max_rules: int = None, min_impurity_decrease: float = 0.0)
-
FIGS (sum of trees) classifier. Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20). https://arxiv.org/abs/2201.11931
Expand source code
class FIGS(BaseEstimator): """FIGS (sum of trees) classifier. Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20). https://arxiv.org/abs/2201.11931 """ def __init__(self, max_rules: int = None, min_impurity_decrease: float=0.0): super().__init__() self.max_rules = max_rules self.min_impurity_decrease = min_impurity_decrease self._init_prediction_task() # decides between regressor and classifier self._init_decision_function() def _init_prediction_task(self): """ SuperCARTRegressor and SuperCARTClassifier override this method to alter the prediction task. When using this class directly, it is equivalent to SuperCARTRegressor """ self.prediction_task = 'regression' def _init_decision_function(self): """Sets decision function based on prediction_task """ # used by sklearn GrriidSearchCV, BaggingClassifier if self.prediction_task == 'classification': decision_function = lambda x: self.predict_proba(x)[:, 1] elif self.prediction_task == 'regression': decision_function = self.predict def construct_node_with_stump(self, X, y, idxs, tree_num, sample_weight=None): # array indices SPLIT = 0 LEFT = 1 RIGHT = 2 # fit stump stump = tree.DecisionTreeRegressor(max_depth=1) if sample_weight is not None: sample_weight = sample_weight[idxs] stump.fit(X[idxs], y[idxs], sample_weight=sample_weight) # these are all arrays, arr[0] is split node # note: -2 is dummy feature = stump.tree_.feature threshold = stump.tree_.threshold impurity = stump.tree_.impurity n_node_samples = stump.tree_.n_node_samples value = stump.tree_.value # no split if len(feature) == 1: # print('no split found!', idxs.sum(), impurity, feature) return Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num, feature=feature[SPLIT], threshold=threshold[SPLIT], impurity_reduction=None) # split node impurity_reduction = ( impurity[SPLIT] - impurity[LEFT] * n_node_samples[LEFT] / n_node_samples[SPLIT] - impurity[RIGHT] * n_node_samples[RIGHT] / n_node_samples[SPLIT] ) * idxs.sum() node_split = Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num, feature=feature[SPLIT], threshold=threshold[SPLIT], impurity_reduction=impurity_reduction) # print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction) # manage children idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT] idxs_left = idxs_split & idxs idxs_right = ~idxs_split & idxs node_left = Node(idxs=idxs_left, value=value[LEFT], tree_num=tree_num) node_right = Node(idxs=idxs_right, value=value[RIGHT], tree_num=tree_num) node_split.setattrs(left_temp=node_left, right_temp=node_right, ) return node_split def fit(self, X, y=None, feature_names=None, verbose=False, sample_weight=None): """ Params ------ sample_weight: array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. """ X, y = check_X_y(X, y) y = y.astype(float) if feature_names is not None: self.feature_names_ = feature_names self.trees_ = [] # list of the root nodes of added trees self.complexity_ = 0 # tracks the number of rules in the model y_predictions_per_tree = {} # predictions for each tree y_residuals_per_tree = {} # based on predictions above # set up initial potential_splits # everything in potential_splits either is_root (so it can be added directly to self.trees_) # or it is a child of a root node that has already been added idxs = np.ones(X.shape[0], dtype=bool) node_init = self.construct_node_with_stump(X=X, y=y, idxs=idxs, tree_num=-1, sample_weight=sample_weight) potential_splits = [node_init] for node in potential_splits: node.setattrs(is_root=True) potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction) # start the greedy fitting algorithm finished = False while len(potential_splits) > 0 and not finished: # print('potential_splits', [str(s) for s in potential_splits]) split_node = potential_splits.pop() # get node with max impurity_reduction (since it's sorted) # don't split on node if split_node.impurity_reduction < self.min_impurity_decrease: finished = True break # split on node if verbose: print('\nadding ' + str(split_node)) self.complexity_ += 1 # if added a tree root if split_node.is_root: # start a new tree self.trees_.append(split_node) # update tree_num for node_ in [split_node, split_node.left_temp, split_node.right_temp]: if node_ is not None: node_.tree_num = len(self.trees_) - 1 # add new root potential node node_new_root = Node(is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1) potential_splits.append(node_new_root) # add children to potential splits # assign left_temp, right_temp to be proper children # (basically adds them to tree in predict method) split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp) # add children to potential_splits potential_splits.append(split_node.left) potential_splits.append(split_node.right) # update predictions for altered tree for tree_num_ in range(len(self.trees_)): y_predictions_per_tree[tree_num_] = self.predict_tree(self.trees_[tree_num_], X) y_predictions_per_tree[-1] = np.zeros(X.shape[0]) # dummy 0 preds for possible new trees # update residuals for each tree # -1 is key for potential new tree for tree_num_ in list(range(len(self.trees_))) + [-1]: y_residuals_per_tree[tree_num_] = deepcopy(y) # subtract predictions of all other trees for tree_num_other_ in range(len(self.trees_)): if not tree_num_other_ == tree_num_: y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[tree_num_other_] # recompute all impurities + update potential_split children potential_splits_new = [] for potential_split in potential_splits: y_target = y_residuals_per_tree[potential_split.tree_num] # re-calculate the best split potential_split_updated = self.construct_node_with_stump(X=X, y=y_target, idxs=potential_split.idxs, tree_num=potential_split.tree_num, sample_weight=sample_weight, ) # need to preserve certain attributes from before (value at this split + is_root) # value may change because residuals may have changed, but we want it to store the value from before potential_split.setattrs( feature=potential_split_updated.feature, threshold=potential_split_updated.threshold, impurity_reduction=potential_split_updated.impurity_reduction, left_temp=potential_split_updated.left_temp, right_temp=potential_split_updated.right_temp, ) # this is a valid split if potential_split.impurity_reduction is not None: potential_splits_new.append(potential_split) # sort so largest impurity reduction comes last (should probs make this a heap later) potential_splits = sorted(potential_splits_new, key=lambda x: x.impurity_reduction) if verbose: print(self) if self.max_rules is not None and self.complexity_ >= self.max_rules: finished = True break return self def tree_to_str(self, root: Node, prefix=''): if root is None: return '' elif root.threshold is None: return '' pprefix = prefix + '\t' return prefix + str(root) + '\n' + self.tree_to_str(root.left, pprefix) + self.tree_to_str(root.right, pprefix) def __str__(self): s = '------------\n' + '\n\t+\n'.join([self.tree_to_str(t) for t in self.trees_]) if hasattr(self, 'feature_names_') and self.feature_names_ is not None: for i in range(len(self.feature_names_))[::-1]: s = s.replace(f'X_{i}', self.feature_names_[i]) return s def predict(self, X): preds = np.zeros(X.shape[0]) for tree in self.trees_: preds += self.predict_tree(tree, X) if self.prediction_task == 'regression': return preds elif self.prediction_task == 'classification': return (preds > 0.5).astype(int) def predict_proba(self, X): if self.prediction_task == 'regression': return NotImplemented preds = np.zeros(X.shape[0]) for tree in self.trees_: preds += self.predict_tree(tree, X) preds = np.clip(preds, a_min=0., a_max=1.) # constrain to range of probabilities return np.vstack((1 - preds, preds)).transpose() def predict_tree(self, root: Node, X): """Predict for a single tree """ def predict_tree_single_point(root: Node, x): if root.left is None and root.right is None: return root.value left = x[root.feature] <= root.threshold if left: if root.left is None: # we don't actually have to worry about this case return root.value else: return predict_tree_single_point(root.left, x) else: if root.right is None: # we don't actually have to worry about this case return root.value else: return predict_tree_single_point(root.right, x) preds = np.zeros(X.shape[0]) for i in range(X.shape[0]): preds[i] = predict_tree_single_point(root, X[i]) return preds
Ancestors
- sklearn.base.BaseEstimator
Subclasses
Methods
def construct_node_with_stump(self, X, y, idxs, tree_num, sample_weight=None)
-
Expand source code
def construct_node_with_stump(self, X, y, idxs, tree_num, sample_weight=None): # array indices SPLIT = 0 LEFT = 1 RIGHT = 2 # fit stump stump = tree.DecisionTreeRegressor(max_depth=1) if sample_weight is not None: sample_weight = sample_weight[idxs] stump.fit(X[idxs], y[idxs], sample_weight=sample_weight) # these are all arrays, arr[0] is split node # note: -2 is dummy feature = stump.tree_.feature threshold = stump.tree_.threshold impurity = stump.tree_.impurity n_node_samples = stump.tree_.n_node_samples value = stump.tree_.value # no split if len(feature) == 1: # print('no split found!', idxs.sum(), impurity, feature) return Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num, feature=feature[SPLIT], threshold=threshold[SPLIT], impurity_reduction=None) # split node impurity_reduction = ( impurity[SPLIT] - impurity[LEFT] * n_node_samples[LEFT] / n_node_samples[SPLIT] - impurity[RIGHT] * n_node_samples[RIGHT] / n_node_samples[SPLIT] ) * idxs.sum() node_split = Node(idxs=idxs, value=value[SPLIT], tree_num=tree_num, feature=feature[SPLIT], threshold=threshold[SPLIT], impurity_reduction=impurity_reduction) # print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction) # manage children idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT] idxs_left = idxs_split & idxs idxs_right = ~idxs_split & idxs node_left = Node(idxs=idxs_left, value=value[LEFT], tree_num=tree_num) node_right = Node(idxs=idxs_right, value=value[RIGHT], tree_num=tree_num) node_split.setattrs(left_temp=node_left, right_temp=node_right, ) return node_split
def fit(self, X, y=None, feature_names=None, verbose=False, sample_weight=None)
-
Params
sample_weight: array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
Expand source code
def fit(self, X, y=None, feature_names=None, verbose=False, sample_weight=None): """ Params ------ sample_weight: array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. """ X, y = check_X_y(X, y) y = y.astype(float) if feature_names is not None: self.feature_names_ = feature_names self.trees_ = [] # list of the root nodes of added trees self.complexity_ = 0 # tracks the number of rules in the model y_predictions_per_tree = {} # predictions for each tree y_residuals_per_tree = {} # based on predictions above # set up initial potential_splits # everything in potential_splits either is_root (so it can be added directly to self.trees_) # or it is a child of a root node that has already been added idxs = np.ones(X.shape[0], dtype=bool) node_init = self.construct_node_with_stump(X=X, y=y, idxs=idxs, tree_num=-1, sample_weight=sample_weight) potential_splits = [node_init] for node in potential_splits: node.setattrs(is_root=True) potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction) # start the greedy fitting algorithm finished = False while len(potential_splits) > 0 and not finished: # print('potential_splits', [str(s) for s in potential_splits]) split_node = potential_splits.pop() # get node with max impurity_reduction (since it's sorted) # don't split on node if split_node.impurity_reduction < self.min_impurity_decrease: finished = True break # split on node if verbose: print('\nadding ' + str(split_node)) self.complexity_ += 1 # if added a tree root if split_node.is_root: # start a new tree self.trees_.append(split_node) # update tree_num for node_ in [split_node, split_node.left_temp, split_node.right_temp]: if node_ is not None: node_.tree_num = len(self.trees_) - 1 # add new root potential node node_new_root = Node(is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1) potential_splits.append(node_new_root) # add children to potential splits # assign left_temp, right_temp to be proper children # (basically adds them to tree in predict method) split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp) # add children to potential_splits potential_splits.append(split_node.left) potential_splits.append(split_node.right) # update predictions for altered tree for tree_num_ in range(len(self.trees_)): y_predictions_per_tree[tree_num_] = self.predict_tree(self.trees_[tree_num_], X) y_predictions_per_tree[-1] = np.zeros(X.shape[0]) # dummy 0 preds for possible new trees # update residuals for each tree # -1 is key for potential new tree for tree_num_ in list(range(len(self.trees_))) + [-1]: y_residuals_per_tree[tree_num_] = deepcopy(y) # subtract predictions of all other trees for tree_num_other_ in range(len(self.trees_)): if not tree_num_other_ == tree_num_: y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[tree_num_other_] # recompute all impurities + update potential_split children potential_splits_new = [] for potential_split in potential_splits: y_target = y_residuals_per_tree[potential_split.tree_num] # re-calculate the best split potential_split_updated = self.construct_node_with_stump(X=X, y=y_target, idxs=potential_split.idxs, tree_num=potential_split.tree_num, sample_weight=sample_weight, ) # need to preserve certain attributes from before (value at this split + is_root) # value may change because residuals may have changed, but we want it to store the value from before potential_split.setattrs( feature=potential_split_updated.feature, threshold=potential_split_updated.threshold, impurity_reduction=potential_split_updated.impurity_reduction, left_temp=potential_split_updated.left_temp, right_temp=potential_split_updated.right_temp, ) # this is a valid split if potential_split.impurity_reduction is not None: potential_splits_new.append(potential_split) # sort so largest impurity reduction comes last (should probs make this a heap later) potential_splits = sorted(potential_splits_new, key=lambda x: x.impurity_reduction) if verbose: print(self) if self.max_rules is not None and self.complexity_ >= self.max_rules: finished = True break return self
def predict(self, X)
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def predict(self, X): preds = np.zeros(X.shape[0]) for tree in self.trees_: preds += self.predict_tree(tree, X) if self.prediction_task == 'regression': return preds elif self.prediction_task == 'classification': return (preds > 0.5).astype(int)
def predict_proba(self, X)
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def predict_proba(self, X): if self.prediction_task == 'regression': return NotImplemented preds = np.zeros(X.shape[0]) for tree in self.trees_: preds += self.predict_tree(tree, X) preds = np.clip(preds, a_min=0., a_max=1.) # constrain to range of probabilities return np.vstack((1 - preds, preds)).transpose()
def predict_tree(self, root: Node, X)
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Predict for a single tree
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def predict_tree(self, root: Node, X): """Predict for a single tree """ def predict_tree_single_point(root: Node, x): if root.left is None and root.right is None: return root.value left = x[root.feature] <= root.threshold if left: if root.left is None: # we don't actually have to worry about this case return root.value else: return predict_tree_single_point(root.left, x) else: if root.right is None: # we don't actually have to worry about this case return root.value else: return predict_tree_single_point(root.right, x) preds = np.zeros(X.shape[0]) for i in range(X.shape[0]): preds[i] = predict_tree_single_point(root, X[i]) return preds
def tree_to_str(self, root: Node, prefix='')
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def tree_to_str(self, root: Node, prefix=''): if root is None: return '' elif root.threshold is None: return '' pprefix = prefix + '\t' return prefix + str(root) + '\n' + self.tree_to_str(root.left, pprefix) + self.tree_to_str(root.right, pprefix)
class FIGSClassifier (max_rules: int = None, min_impurity_decrease: float = 0.0)
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FIGS (sum of trees) classifier. Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20). https://arxiv.org/abs/2201.11931
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class FIGSClassifier(FIGS): def _init_prediction_task(self): self.prediction_task = 'classification'
Ancestors
- FIGS
- sklearn.base.BaseEstimator
Inherited members
class FIGSRegressor (max_rules: int = None, min_impurity_decrease: float = 0.0)
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FIGS (sum of trees) classifier. Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20). https://arxiv.org/abs/2201.11931
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class FIGSRegressor(FIGS): def _init_prediction_task(self): self.prediction_task = 'regression'
Ancestors
- FIGS
- sklearn.base.BaseEstimator
Inherited members
class Node (feature: int = None, threshold: int = None, value=None, idxs=None, is_root: bool = False, left=None, impurity_reduction: float = None, tree_num: int = None, right=None)
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Node class for splitting
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class Node: def __init__(self, feature: int = None, threshold: int = None, value=None, idxs=None, is_root: bool = False, left=None, impurity_reduction: float = None, tree_num: int = None, right=None): """Node class for splitting """ # split or linear self.is_root = is_root self.idxs = idxs self.tree_num = tree_num self.feature = feature self.impurity_reduction = impurity_reduction # different meanings self.value = value # for split this is mean, for linear this is weight # split-specific self.threshold = threshold self.left = left self.right = right self.left_temp = None self.right_temp = None def setattrs(self, **kwargs): for k, v in kwargs.items(): setattr(self, k, v) def __str__(self): if self.is_root: return f'X_{self.feature} <= {self.threshold:0.3f} (Tree #{self.tree_num} root)' elif self.left is None and self.right is None: return f'Val: {self.value[0][0]:0.3f} (leaf)' else: return f'X_{self.feature} <= {self.threshold:0.3f} (split)' def __repr__(self): return self.__str__()
Methods
def setattrs(self, **kwargs)
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def setattrs(self, **kwargs): for k, v in kwargs.items(): setattr(self, k, v)