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- from typing import Iterable, Tuple, List
- import numpy as np
- import pandas as pd
- from mlxtend import frequent_patterns as mlx
- from sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor, RandomForestRegressor, \
- GradientBoostingClassifier, RandomForestClassifier
- from sklearn.tree import DecisionTreeRegressor
- from sklearn.utils.validation import check_array
- import inspect
- from imodels.util import rule, convert
- def extract_fpgrowth(X,
- minsupport=0.1,
- maxcardinality=2,
- verbose=False) -> List[Tuple]:
- itemsets_df = mlx.fpgrowth(
- X, min_support=minsupport, max_len=maxcardinality)
- itemsets_indices = [tuple(s[1]) for s in itemsets_df.values]
- itemsets = [np.array(X.columns)[list(inds)] for inds in itemsets_indices]
- itemsets = list(map(tuple, itemsets))
- if verbose:
- print(len(itemsets), 'rules mined')
- return itemsets
- def extract_rulefit(X, y, feature_names,
- n_estimators=10,
- tree_size=4,
- memory_par=0.01,
- tree_generator=None,
- exp_rand_tree_size=True,
- random_state=None) -> List[str]:
- if tree_generator is None:
- sample_fract_ = min(0.5, (100 + 6 * np.sqrt(X.shape[0])) / X.shape[0])
- tree_generator = GradientBoostingRegressor(n_estimators=n_estimators,
- max_leaf_nodes=tree_size,
- learning_rate=memory_par,
- subsample=sample_fract_,
- random_state=random_state,
- max_depth=100)
- if type(tree_generator) not in [GradientBoostingClassifier, GradientBoostingRegressor,
- RandomForestRegressor, RandomForestClassifier]:
- raise ValueError(
- "RuleFit only works with GradientBoostingClassifier(), GradientBoostingRegressor(), "
- "RandomForestRegressor() or RandomForestClassifier()")
- # fit tree generator
- if not exp_rand_tree_size: # simply fit with constant tree size
- tree_generator.fit(X, y)
- else: # randomise tree size as per Friedman 2005 Sec 3.3
- np.random.seed(random_state)
- tree_sizes = np.random.exponential(
- scale=tree_size - 2, size=n_estimators)
- tree_sizes = np.asarray([2 + np.floor(tree_sizes[i_])
- for i_ in np.arange(len(tree_sizes))], dtype=int)
- tree_generator.set_params(warm_start=True)
- curr_est_ = 0
- for i_size in np.arange(len(tree_sizes)):
- size = tree_sizes[i_size]
- tree_generator.set_params(n_estimators=curr_est_ + 1)
- tree_generator.set_params(max_leaf_nodes=size)
- random_state_add = random_state if random_state else 0
- tree_generator.set_params(
- random_state=i_size + random_state_add) # warm_state=True seems to reset random_state, such that the trees are highly correlated, unless we manually change the random_sate here.
- tree_generator.fit(np.copy(X, order='C'), np.copy(y, order='C'))
- curr_est_ = curr_est_ + 1
- tree_generator.set_params(warm_start=False)
- if isinstance(tree_generator, RandomForestRegressor) or isinstance(tree_generator, RandomForestClassifier):
- estimators_ = [[x] for x in tree_generator.estimators_]
- else:
- estimators_ = tree_generator.estimators_
- seen_rules = set()
- extracted_rules = []
- for estimator in estimators_:
- for rule_value_pair in convert.tree_to_rules(estimator[0], np.array(feature_names), prediction_values=True):
- rule_obj = rule.Rule(rule_value_pair[0])
- if rule_obj not in seen_rules:
- extracted_rules.append(rule_value_pair)
- seen_rules.add(rule_obj)
- extracted_rules = sorted(extracted_rules, key=lambda x: x[1])
- extracted_rules = list(map(lambda x: x[0], extracted_rules))
- return extracted_rules
- def extract_skope(X, y, feature_names,
- sample_weight=None,
- n_estimators=10,
- max_samples=.8,
- max_samples_features=1.,
- bootstrap=False,
- bootstrap_features=False,
- max_depths=[3],
- max_features=1.,
- min_samples_split=2,
- n_jobs=1,
- random_state=None,
- verbose=0) -> Tuple[List[str], List[np.array], List[np.array]]:
- ensembles = []
- if not isinstance(max_depths, Iterable):
- max_depths = [max_depths]
- for max_depth in max_depths:
- # pass different key based on sklearn version
- estimator = DecisionTreeRegressor(
- max_depth=max_depth,
- max_features=max_features,
- min_samples_split=min_samples_split,
- )
- init_signature = inspect.signature(BaggingRegressor.__init__)
- estimator_key = 'estimator' if 'estimator' in init_signature.parameters.keys(
- ) else 'base_estimator'
- kwargs = {
- estimator_key: estimator,
- }
- bagging_clf = BaggingRegressor(
- n_estimators=n_estimators,
- max_samples=max_samples,
- max_features=max_samples_features,
- bootstrap=bootstrap,
- bootstrap_features=bootstrap_features,
- # oob_score=... XXX may be added
- # if selection on tree perf needed.
- # warm_start=... XXX may be added to increase computation perf.
- n_jobs=n_jobs,
- random_state=random_state,
- verbose=verbose,
- **kwargs
- )
- ensembles.append(bagging_clf)
- y_reg = y
- if sample_weight is not None:
- sample_weight = check_array(sample_weight, ensure_2d=False)
- weights = sample_weight - sample_weight.min()
- contamination = float(sum(y)) / len(y)
- y_reg = (
- pow(weights, 0.5) * 0.5 / contamination * (y > 0) -
- pow((weights).mean(), 0.5) * (y == 0)
- )
- y_reg = 1. / (1 + np.exp(-y_reg)) # sigmoid
- for e in ensembles[:len(ensembles) // 2]:
- e.fit(X, y)
- for e in ensembles[len(ensembles) // 2:]:
- e.fit(X, y_reg)
- estimators_, estimators_samples_, estimators_features_ = [], [], []
- for ensemble in ensembles:
- estimators_ += ensemble.estimators_
- estimators_samples_ += ensemble.estimators_samples_
- estimators_features_ += ensemble.estimators_features_
- extracted_rules = []
- for estimator, features in zip(estimators_, estimators_features_):
- extracted_rules.append(convert.tree_to_rules(
- estimator, np.array(feature_names)[features]))
- return extracted_rules, estimators_samples_, estimators_features_
- def extract_marginal_curves(clf, X, max_evals=100):
- """Uses predict_proba to compute marginal curves.
- Assumes clf is a classifier with a predict_proba method and that classifier is additive across features
- For GAM, this returns the shape functions
- Params
- ------
- clf : classifier
- A classifier with a predict_proba method
- X : array-like
- The data to compute the marginal curves on (used to calculate unique feature vals)
- max_evals : int
- The maximum number of evaluations to make for each feature
- Returns
- -------
- feature_vals_list : list of arrays
- The values of each feature for which the shape function is evaluated.
- shape_function_vals_list : list of arrays
- The shape function evaluated at each value of the corresponding feature.
- """
- p = X.shape[1]
- dummy_input = np.zeros((1, p))
- base = clf.predict_proba(dummy_input)[:, 1][0]
- feature_vals_list = []
- shape_function_vals_list = []
- for feat_num in range(p):
- feature_vals = sorted(np.unique(X[:, feat_num]))
- while len(feature_vals) > max_evals:
- feature_vals = feature_vals[::2]
- dummy_input = np.zeros((len(feature_vals), p))
- dummy_input[:, feat_num] = feature_vals
- shape_function_vals = clf.predict_proba(dummy_input)[:, 1] - base
- feature_vals_list.append(feature_vals)
- shape_function_vals_list.append(shape_function_vals.tolist())
- return feature_vals_list, shape_function_vals_list
- if __name__ == '__main__':
- init_signature = inspect.signature(BaggingRegressor.__init__)
- print('estimator' in init_signature.parameters.keys())
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