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|
- from copy import deepcopy
- import numpy as np
- from matplotlib import pyplot as plt
- from sklearn import datasets
- from sklearn import tree
- from sklearn.base import BaseEstimator
- from sklearn.linear_model import RidgeCV, RidgeClassifierCV
- from sklearn.model_selection import train_test_split
- from sklearn.tree import plot_tree
- from sklearn.utils import check_X_y
- from imodels.tree.viz_utils import extract_sklearn_tree_from_figs
- 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, split_or_linear='split', n_samples=0):
- """Node class for splitting
- """
- # split or linear
- self.is_root = is_root
- self.idxs = idxs
- self.tree_num = tree_num
- self.split_or_linear = split_or_linear
- self.feature = feature
- self.n_samples = n_samples
- self.impurity_reduction = impurity_reduction
- # different meanings
- self.value = value # for split this is mean, for linear this is weight
- # split-specific (for linear these should all be None)
- self.threshold = threshold
- self.left = left
- self.right = right
- self.left_temp = None
- self.right_temp = None
- def update_values(self, X, y):
- self.value = y.mean()
- if self.threshold is not None:
- right_indicator = np.apply_along_axis(
- lambda x: x[self.feature] > self.threshold, 1, X)
- X_right = X[right_indicator, :]
- X_left = X[~right_indicator, :]
- y_right = y[right_indicator]
- y_left = y[~right_indicator]
- if self.left is not None:
- self.left.update_values(X_left, y_left)
- if self.right is not None:
- self.right.update_values(X_right, y_right)
- def shrink(self, reg_param, cum_sum=0):
- if self.is_root:
- cum_sum = self.value
- if self.left is None: # if leaf node, change prediction
- self.value = cum_sum
- else:
- shrunk_diff = (self.left.value - self.value) / \
- (1 + reg_param / self.n_samples)
- self.left.shrink(reg_param, cum_sum + shrunk_diff)
- shrunk_diff = (self.right.value - self.value) / \
- (1 + reg_param / self.n_samples)
- self.right.shrink(reg_param, cum_sum + shrunk_diff)
- def setattrs(self, **kwargs):
- for k, v in kwargs.items():
- setattr(self, k, v)
- def __str__(self):
- if self.split_or_linear == 'linear':
- if self.is_root:
- return f'X_{self.feature} * {self.value:0.3f} (Tree #{self.tree_num} linear root)'
- else:
- return f'X_{self.feature} * {self.value:0.3f} (linear)'
- else:
- 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 FIGSExt(BaseEstimator):
- """FIGSExt (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 a wide array of 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, posthoc_ridge: bool = False,
- include_linear: bool = False,
- max_features=None, min_impurity_decrease: float = 0.0,
- k1: int = 0, k2: int = 0):
- """
- max_features
- The number of features to consider when looking for the best split
- k1: number of iterations of tree-prediction backfitting to do after making each split
- k2: number of iterations of tree-prediction backfitting to do after the end of the entire
- tree-growing phase
- """
- super().__init__()
- self.max_rules = max_rules
- self.posthoc_ridge = posthoc_ridge
- self.include_linear = include_linear
- self.max_features = max_features
- self.weighted_model_ = None # set if using posthoc_ridge
- self.min_impurity_decrease = min_impurity_decrease
- self.k1 = k1
- self.k2 = k2
- self._init_prediction_task() # decides between regressor and classifier
- def _init_prediction_task(self):
- """
- FIGSExtRegressor and FIGSExtClassifier override this method
- to alter the prediction task. When using this class directly,
- it is equivalent to FIGSExtRegressor
- """
- self.prediction_task = 'regression'
- def _init_decision_function(self):
- """Sets decision function based on prediction_task
- """
- # used by sklearn GridSearchCV, BaggingClassifier
- if self.prediction_task == 'classification':
- def decision_function(x): return self.predict_proba(x)[:, 1]
- elif self.prediction_task == 'regression':
- decision_function = self.predict
- def _construct_node_linear(self, X, y, idxs, tree_num=0, sample_weight=None):
- """This can be made a lot faster
- Assumes there are at least 5 points in node
- Doesn't currently support _sample_weight!
- """
- y_target = y[idxs]
- impurity_orig = np.mean(np.square(y_target)) * idxs.sum()
- # find best linear split
- best_impurity = impurity_orig
- best_linear_coef = None
- best_feature = None
- for feature_num in range(X.shape[1]):
- x = X[idxs, feature_num].reshape(-1, 1)
- m = RidgeCV(fit_intercept=False)
- m.fit(x, y_target)
- impurity = np.min(-m.best_score_) * idxs.sum()
- assert impurity >= 0, 'impurity should not be negative'
- if impurity < best_impurity:
- best_impurity = impurity
- best_linear_coef = m.coef_[0]
- best_feature = feature_num
- impurity_reduction = impurity_orig - best_impurity
- # no good linear fit found
- if impurity_reduction == 0:
- return Node(idxs=idxs, value=np.mean(y_target), tree_num=tree_num,
- feature=None, threshold=None,
- impurity_reduction=-1, split_or_linear='split') # leaf node that just returns its value
- else:
- assert isinstance(best_linear_coef,
- float), 'coef should be a float'
- return Node(idxs=idxs, value=best_linear_coef, tree_num=tree_num,
- feature=best_feature, threshold=None,
- impurity_reduction=impurity_reduction, split_or_linear='linear')
- def _construct_node_with_stump(self, X, y, idxs, tree_num, sample_weight=None, max_features=None):
- # array indices
- SPLIT = 0
- LEFT = 1
- RIGHT = 2
- # fit stump
- stump = tree.DecisionTreeRegressor(
- max_depth=1, max_features=max_features)
- 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=-1, n_samples=n_node_samples)
- # 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, n_samples=n_node_samples)
- # 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.
- """
- if self.prediction_task == 'classification':
- self.classes_, y = np.unique(
- y, return_inverse=True) # deals with str inputs
- 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
- def _update_tree_preds(n_iter):
- for k in range(n_iter):
- for tree_num_, tree_ in enumerate(self.trees_):
- y_residuals_per_tree[tree_num_] = deepcopy(y)
- # subtract predictions of all other trees
- for tree_num_2_ in range(len(self.trees_)):
- if not tree_num_2_ == tree_num_:
- y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[tree_num_2_]
- tree_.update_values(X, y_residuals_per_tree[tree_num_])
- y_predictions_per_tree[tree_num_] = self._predict_tree(self.trees_[
- tree_num_], X)
- # 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, max_features=self.max_features)
- potential_splits = [node_init]
- if self.include_linear and idxs.sum() >= 5:
- node_init_linear = self._construct_node_linear(X=X, y=y, idxs=idxs, tree_num=-1,
- sample_weight=sample_weight)
- potential_splits.append(node_init_linear)
- 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])
- # get node with max impurity_reduction (since it's sorted)
- split_node = potential_splits.pop()
- # 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, split_or_linear=split_node.split_or_linear)
- potential_splits.append(node_new_root)
- # add children to potential splits (note this doesn't currently add linear potential splits)
- if split_node.split_or_linear == 'split':
- # 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)
- # dummy 0 preds for possible new trees
- y_predictions_per_tree[-1] = np.zeros(X.shape[0])
- # 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_2_ in range(len(self.trees_)):
- if not tree_num_2_ == tree_num_:
- y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[tree_num_2_]
- _update_tree_preds(self.k1)
- # 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]
- if potential_split.split_or_linear == 'split':
- # 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,
- max_features=self.max_features)
- # 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,
- )
- elif potential_split.split_or_linear == 'linear':
- assert potential_split.is_root, 'Currently, linear node only supported as root'
- assert potential_split.idxs.sum(
- ) == X.shape[0], 'Currently, linear node only supported as root'
- potential_split_updated = self._construct_node_linear(idxs=potential_split.idxs,
- X=X,
- y=y_target,
- tree_num=potential_split.tree_num,
- sample_weight=sample_weight)
- # don't need to retain anything from before (besides maybe is_root)
- potential_split.setattrs(
- feature=potential_split_updated.feature,
- impurity_reduction=potential_split_updated.impurity_reduction,
- value=potential_split_updated.value,
- )
- # 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
- _update_tree_preds(self.k2)
- # potentially fit linear model on the tree preds
- if self.posthoc_ridge:
- if self.prediction_task == 'regression':
- self.weighted_model_ = RidgeCV(
- alphas=(0.01, 0.1, 0.5, 1.0, 5, 10))
- elif self.prediction_task == 'classification':
- self.weighted_model_ = RidgeClassifierCV(
- alphas=(0.01, 0.1, 0.5, 1.0, 5, 10))
- X_feats = self._extract_tree_predictions(X)
- self.weighted_model_.fit(X_feats, y)
- return self
- def _tree_to_str(self, root: Node, prefix=''):
- if root is None:
- return ''
- elif root.split_or_linear == 'linear':
- return prefix + str(root)
- 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):
- if self.posthoc_ridge and self.weighted_model_: # note, during fitting don't use the weighted moel
- X_feats = self._extract_tree_predictions(X)
- return self.weighted_model_.predict(X_feats)
- 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
- elif self.posthoc_ridge and self.weighted_model_: # note, during fitting don't use the weighted moel
- X_feats = self._extract_tree_predictions(X)
- d = self.weighted_model_.decision_function(
- X_feats) # for 2 classes, this (n_samples,)
- probs = np.exp(d) / (1 + np.exp(d))
- return np.vstack((1 - probs, probs)).transpose()
- else:
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- # constrain to range of probabilities
- preds = np.clip(preds, a_min=0., a_max=1.)
- return np.vstack((1 - preds, preds)).transpose()
- def _extract_tree_predictions(self, X):
- """Extract predictions for all trees
- """
- X_feats = np.zeros((X.shape[0], len(self.trees_)))
- for tree_num_ in range(len(self.trees_)):
- preds_tree = self._predict_tree(self.trees_[tree_num_], X)
- X_feats[:, tree_num_] = preds_tree
- return X_feats
- def _predict_tree(self, root: Node, X):
- """Predict for a single tree
- This can be made way faster
- """
- def _predict_tree_single_point(root: Node, x):
- if root.split_or_linear == 'linear':
- return x[root.feature] * root.value
- elif 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 plot(self, cols=2, feature_names=None, filename=None, label="all",
- impurity=False, tree_number=None, dpi=150, fig_size=None):
- is_single_tree = len(self.trees_) < 2 or tree_number is not None
- n_cols = int(cols)
- n_rows = int(np.ceil(len(self.trees_) / n_cols))
- # if is_single_tree:
- # fig, ax = plt.subplots(1)
- # else:
- # fig, axs = plt.subplots(n_rows, n_cols)
- n_plots = int(len(self.trees_)) if tree_number is None else 1
- fig, axs = plt.subplots(n_plots, dpi=dpi)
- if fig_size is not None:
- fig.set_size_inches(fig_size, fig_size)
- criterion = "squared_error" if self.prediction_task == "regression" else "gini"
- n_classes = 1 if self.prediction_task == 'regression' else 2
- ax_size = int(len(self.trees_)) # n_cols * n_rows
- for i in range(n_plots):
- r = i // n_cols
- c = i % n_cols
- if not is_single_tree:
- # ax = axs[r, c]
- ax = axs[i]
- else:
- ax = axs
- try:
- dt = extract_sklearn_tree_from_figs(
- self, i if tree_number is None else tree_number, n_classes)
- plot_tree(dt, ax=ax, feature_names=feature_names,
- label=label, impurity=impurity)
- except IndexError:
- ax.axis('off')
- continue
- ax.set_title(f"Tree {i}")
- if filename is not None:
- plt.savefig(filename)
- return
- plt.show()
- class FIGSExtRegressor(FIGSExt):
- def _init_prediction_task(self):
- self.prediction_task = 'regression'
- class FIGSExtClassifier(FIGSExt):
- 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 = FIGSExtClassifier(max_rules=50)
- m.fit(X_train, y_train)
- print(m.predict_proba(X_train))
- m.plot(2, tree_number=0)
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