Source code for skrules.skope_rules

import numpy as np
import pandas
import numbers
from warnings import warn
from sklearn.base import BaseEstimator
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import check_classification_targets

from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor

from sklearn.ensemble import BaggingClassifier, BaggingRegressor

from sklearn.externals import six
from sklearn.tree import _tree

INTEGER_TYPES = (numbers.Integral, np.integer)


[docs]class SkopeRules(BaseEstimator): """ An easy-interpretable classifier optimizing simple logical rules. Parameters ---------- feature_names : list of str, optional The names of each feature to be used for returning rules in string format. precision_min : float, optional (default=0.5) The minimal precision of a rule to be selected. recall_min : float, optional (default=0.01) The minimal recall of a rule to be selected. n_estimators : int, optional (default=10) The number of base estimators (rules) to use for prediction. More are built before selection. All are available in the estimators_ attribute. similarity_thres : float, optional (default=0.99) Similarity threshold between rules. Rules too similar (> similarity_thres) are fused. The similarity between two rules is computed according to the formula `# {intersection} / # {union}`. max_samples : int or float, optional (default=.8) The number of samples to draw from X to train each decision tree, from which rules are generated and selected. - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling). max_samples_features : int or float, optional (default=1.0) The number of features to draw from X to train each decision tree, from which rules are generated and selected. - If int, then draw `max_features` features. - If float, then draw `max_features * X.shape[1]` features. bootstrap : boolean, optional (default=False) Whether samples are drawn with replacement. bootstrap_features : boolean, optional (default=False) Whether features are drawn with replacement. max_depth : integer or None, optional (default=3) The maximum depth of the decision trees. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. max_features : int, float, string or None, optional (default="auto") The number of features considered (by each decision tree) when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto"). - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node for each decision tree. - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional - If int, random_state is the seed used by the random number generator. - If RandomState instance, random_state is the random number generator. - If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. Attributes ---------- rules_ : dict of tuples (rule, precision, recall, nb). The collection of `n_estimators` rules used in the ``predict`` method. The rules are generated by fitted sub-estimators (decision trees). Each rule satisfies recall_min and precision_min conditions. The selection is done according to OOB precisions. estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators used to generate candidate rules. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. estimators_features_ : list of arrays The subset of drawn features for each base estimator. max_samples_ : integer The actual number of samples n_features_ : integer The number of features when ``fit`` is performed. classes_ : array, shape (n_classes,) The classes labels. """ def __init__(self, feature_names=None, precision_min=0.5, recall_min=0.01, n_estimators=10, similarity_thres=0.95, max_samples=.8, max_samples_features=1., bootstrap=False, bootstrap_features=False, max_depth=3, max_features=1., min_samples_split=2, n_jobs=1, random_state=None, verbose=0): self.precision_min = precision_min self.recall_min = recall_min self.feature_names = feature_names self.n_estimators = n_estimators self.similarity_thres = similarity_thres self.max_samples = max_samples self.max_samples_features = max_samples_features self.bootstrap = bootstrap self.bootstrap_features = bootstrap_features self.max_depth = max_depth self.max_features = max_features self.min_samples_split = min_samples_split self.n_jobs = n_jobs self.random_state = random_state self.verbose = verbose
[docs] def fit(self, X, y, sample_weight=None): """Fit the model according to the given training data. Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Has to follow the convention 0 for normal data, 1 for anomalies. sample_weight : array-like, shape (n_samples,) optional Array of weights that are assigned to individual samples, typically the amount in case of transactions data. Used to grow regression trees producing further rules to be tested. If not provided, then each sample is given unit weight. Returns ------- self : object Returns self. """ X, y = check_X_y(X, y) check_classification_targets(y) self.n_features_ = X.shape[1] self.classes_ = np.unique(y) n_classes = len(self.classes_) if n_classes < 2: raise ValueError("This method needs samples of at least 2 classes" " in the data, but the data contains only one" " class: %r" % self.classes_[0]) if not set(self.classes_) == set([0, 1]): warn("Found labels %s. This method assumes target class to be" " labeled as 1 and normal data to be labeled as 0. Any label" " different from 0 will be considered as being from the" " target class." % set(self.classes_)) y = (y > 0) # ensure similarity_thres is in (0., 1.]: if not (0. < self.similarity_thres <= 1.): raise ValueError("similarity_thres must be in (0, 1], got %r" % self.similarity_thres) # ensure that max_samples is in [1, n_samples]: n_samples = X.shape[0] if isinstance(self.max_samples, six.string_types): raise ValueError('max_samples (%s) is not supported.' 'Valid choices are: "auto", int or' 'float' % self.max_samples) elif isinstance(self.max_samples, INTEGER_TYPES): if self.max_samples > n_samples: warn("max_samples (%s) is greater than the " "total number of samples (%s). max_samples " "will be set to n_samples for estimation." % (self.max_samples, n_samples)) max_samples = n_samples else: max_samples = self.max_samples else: # float if not (0. < self.max_samples <= 1.): raise ValueError("max_samples must be in (0, 1], got %r" % self.max_samples) max_samples = int(self.max_samples * X.shape[0]) self.max_samples_ = max_samples self.rules_ = {} self.estimators_ = [] self.estimators_samples_ = [] self.estimators_features_ = [] # default columns names of the form ['c0', 'c1', ...]: feature_names_ = (self.feature_names if self.feature_names is not None else ['c' + x for x in np.arange(X.shape[1]).astype(str)]) self.feature_names_ = feature_names_ bagging_clf = BaggingClassifier( base_estimator=DecisionTreeClassifier( max_depth=self.max_depth, max_features=self.max_features, min_samples_split=self.min_samples_split), n_estimators=self.n_estimators, max_samples=self.max_samples_, max_features=self.max_samples_features, bootstrap=self.bootstrap, bootstrap_features=self.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=self.n_jobs, random_state=self.random_state, verbose=self.verbose) bagging_reg = BaggingRegressor( base_estimator=DecisionTreeRegressor( max_depth=self.max_depth, max_features=self.max_features, min_samples_split=self.min_samples_split), n_estimators=self.n_estimators, max_samples=self.max_samples_, max_features=self.max_samples_features, bootstrap=self.bootstrap, bootstrap_features=self.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=self.n_jobs, random_state=self.random_state, verbose=self.verbose) bagging_clf.fit(X, y) # define regression target: if sample_weight is not None: 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 else: y_reg = y # same as an other classification bagging bagging_reg.fit(X, y_reg) self.estimators_ += bagging_clf.estimators_ self.estimators_ += bagging_reg.estimators_ self.estimators_samples_ += bagging_clf.estimators_samples_ self.estimators_samples_ += bagging_reg.estimators_samples_ self.estimators_features_ += bagging_clf.estimators_features_ self.estimators_features_ += bagging_reg.estimators_features_ rules_ = [] for estimator, samples, features in zip(self.estimators_, self.estimators_samples_, self.estimators_features_): # Create mask for OOB samples mask = ~samples if sum(mask) == 0: warn("OOB evaluation not possible: doing it in-bag." " Performance evaluation is likely to be wrong" " (overfitting) and selected rules are likely to" " not perform well! Please use max_samples < 1.") mask = samples rules_from_tree = self._tree_to_rules( estimator, np.array(self.feature_names_)[features]) # XXX todo: idem without dataframe X_oob = pandas.DataFrame((X[mask, :])[:, features], columns=np.array( self.feature_names_)[features]) if X_oob.shape[1] > 1: # otherwise pandas bug (cf. issue #16363) y_oob = y[mask] y_oob = np.array((y_oob != 0)) # Add OOB performances to rules: rules_from_tree = [(r, self._eval_rule_perf(r, X_oob, y_oob)) for r in set(rules_from_tree)] rules_ += rules_from_tree # keep only rules verifying precision_min and recall_min: for rule, score in rules_: if score[0] >= self.precision_min and score[1] >= self.recall_min: if rule in self.rules_: # update the score to the new mean c = self.rules_[rule][2] + 1 b = self.rules_[rule][1] + 1. / c * ( score[1] - self.rules_[rule][1]) a = self.rules_[rule][0] + 1. / c * ( score[0] - self.rules_[rule][0]) self.rules_[rule] = (a, b, c) else: self.rules_[rule] = (score[0], score[1], 1) self.rules_ = sorted(self.rules_.items(), key=lambda x: (x[1][0], x[1][1]), reverse=True) # removing rules which have very similar domains X_ = pandas.DataFrame(X, columns=np.array(self.feature_names_)) omit_these_rules_list = [] perimeter_index_of_all_rules = [] for i in range(len(self.rules_)): current = self.rules_[i] perimeter_index_of_all_rules.append( set(list(X_.query(current[0]).index)) ) index_current = perimeter_index_of_all_rules[i] for j in range(i): if j in omit_these_rules_list: continue # if a rule have already been discarded, # it should not be processed again index_rival = perimeter_index_of_all_rules[j] size_union = len(index_rival.union(index_current)) size_intersection = len( index_rival.intersection(index_current)) if float(size_intersection)/size_union > self.similarity_thres: omit_these_rules_list.append(j) self.rules_ = [self.rules_[i] for i in range( len(self.rules_)) if i not in omit_these_rules_list] return self
[docs] def predict(self, X): """Predict if a particular sample is an outlier or not. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` Returns ------- is_outlier : array, shape (n_samples,) For each observations, tells whether or not (1 or 0) it should be considered as an outlier according to the selected rules. """ return np.array((self.decision_function(X) > 0), dtype=int)
[docs] def decision_function(self, X): """Average anomaly score of X of the base classifiers (rules). The anomaly score of an input sample is computed as the weighted sum of the binary rules outputs, the weight being the respective precision of each rule. Parameters ---------- X : array-like, shape (n_samples, n_features) The training input samples. Returns ------- scores : array, shape (n_samples,) The anomaly score of the input samples. The higher, the more abnormal. Positive scores represent outliers, null scores represent inliers. """ # Check if fit had been called check_is_fitted(self, ['rules_', 'estimators_', 'estimators_samples_', 'max_samples_']) # Input validation X = check_array(X) if X.shape[1] != self.n_features_: raise ValueError("X.shape[1] = %d should be equal to %d, " "the number of features at training time." " Please reshape your data." % (X.shape[1], self.n_features_)) df = pandas.DataFrame(X, columns=self.feature_names_) selected_rules = self.rules_ scores = np.zeros(X.shape[0]) for (r, w) in selected_rules: scores[list(df.query(r).index)] += w[0] return scores
[docs] def rules_vote(self, X): """Score representing a vote of the base classifiers (rules). The score of an input sample is computed as the sum of the binary rules outputs: a score of k means than k rules have voted positively. Parameters ---------- X : array-like, shape (n_samples, n_features) The training input samples. Returns ------- scores : array, shape (n_samples,) The score of the input samples. The higher, the more abnormal. Positive scores represent outliers, null scores represent inliers. """ # Check if fit had been called check_is_fitted(self, ['rules_', 'estimators_', 'estimators_samples_', 'max_samples_']) # Input validation X = check_array(X) if X.shape[1] != self.n_features_: raise ValueError("X.shape[1] = %d should be equal to %d, " "the number of features at training time." " Please reshape your data." % (X.shape[1], self.n_features_)) df = pandas.DataFrame(X, columns=self.feature_names_) selected_rules = self.rules_ scores = np.zeros(X.shape[0]) for (r, _) in selected_rules: scores[list(df.query(r).index)] += 1 return scores
[docs] def score_top_rules(self, X): """Score representing an ordering between the base classifiers (rules). The score is high when the instance is detected by a performing rule. If there are n rules, ordered by increasing OOB precision, a score of k means than the kth rule has voted positively, but not the (k-1) first rules. Parameters ---------- X : array-like, shape (n_samples, n_features) The training input samples. Returns ------- scores : array, shape (n_samples,) The score of the input samples. Positive scores represent outliers, null scores represent inliers. """ # Check if fit had been called check_is_fitted(self, ['rules_', 'estimators_', 'estimators_samples_', 'max_samples_']) # Input validation X = check_array(X) if X.shape[1] != self.n_features_: raise ValueError("X.shape[1] = %d should be equal to %d, " "the number of features at training time." " Please reshape your data." % (X.shape[1], self.n_features_)) df = pandas.DataFrame(X, columns=self.feature_names_) selected_rules = self.rules_ scores = np.zeros(X.shape[0]) for (k, r) in enumerate(list((selected_rules))): scores[list(df.query(r[0]).index)] = np.maximum( len(selected_rules) - k, scores[list(df.query(r[0]).index)]) return scores
[docs] def predict_top_rules(self, X, n_rules): """Predict if a particular sample is an outlier or not, using the n_rules most performing rules. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` n_rules : int The number of rules used for the prediction. If one of the n_rules most performing rules is activated, the prediction is equal to 1. Returns ------- is_outlier : array, shape (n_samples,) For each observations, tells whether or not (1 or 0) it should be considered as an outlier according to the selected rules. """ return np.array((self.score_top_rules(X) > len(self.rules_) - n_rules), dtype=int)
def _tree_to_rules(self, tree, feature_names): """ Return a list of rules from a tree Parameters ---------- tree : Decision Tree Classifier/Regressor feature_names: list of variable names Returns ------- rules : list of rules. """ # XXX todo: check the case where tree is build on subset of features, # ie max_features != None tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!" for i in tree_.feature ] rules = [] def recurse(node, base_name): if tree_.feature[node] != _tree.TREE_UNDEFINED: name = feature_name[node] symbol = '<=' symbol2 = '>' threshold = tree_.threshold[node] text = base_name + ["{} {} {}".format(name, symbol, threshold)] recurse(tree_.children_left[node], text) text = base_name + ["{} {} {}".format(name, symbol2, threshold)] recurse(tree_.children_right[node], text) else: rule = str.join(' and ', base_name) rule = (rule if rule != '' else '=='.join([feature_names[0]] * 2)) # a rule selecting all is set to "c0==c0" rules.append(rule) recurse(0, []) return rules if len(rules) > 0 else 'True' def _eval_rule_perf(self, rule, X, y): detected_index = list(X.query(rule).index) if len(detected_index) <= 1: return (0, 0) y_detected = y[detected_index] true_pos = y_detected[y_detected > 0].sum() if true_pos == 0: return (0, 0) pos = y[y > 0].sum() return y_detected.mean(), float(true_pos) / pos