Skoperules aims at learning logical, interpretable rules for "scoping" a target class, i.e. detecting with high precision instances of this class.
Skoperules is a trade off between the interpretability of a Decision Tree
and the modelization power of a Random Forest. Code adapted with only minor changes
from here. Full credit to
the authors. You can access the original project and docs here <http://skoperules.readthedocs.io/en/latest/>
_
Example
from sklearn.datasets import load_boston
from sklearn.metrics import precision_recall_curve
from matplotlib import pyplot as plt
from skrules import SkopeRulesClassifier
dataset = load_boston()
clf = SkopeRulesClassifier(max_depth_duplication=None,
n_estimators=30,
precision_min=0.2,
recall_min=0.01,
feature_names=dataset.feature_names)
X, y = dataset.data, dataset.target > 25
X_train, y_train = X[:len(y)//2], y[:len(y)//2]
X_test, y_test = X[len(y)//2:], y[len(y)//2:]
clf.fit(X_train, y_train)
y_score = clf._score_top_rules(X_test) # Get a risk score for each test example
precision, recall, _ = precision_recall_curve(y_test, y_score)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision Recall curve')
plt.show()
Links With Existing Literature
The main advantage of decision rules is that they are offering interpretable models. The problem of generating such rules has been widely considered in machine learning, see e.g. RuleFit [1], Slipper [2], LRI [3], MLRules[4].
A decision rule is a logical expression of the form "IF conditions THEN response". In a binary classification setting, if an instance satisfies conditions of the rule, then it is assigned to one of the two classes. If this instance does not satisfy conditions, it remains unassigned.
1) In [2, 3, 4], rules induction is done by considering each single decision rule as a base classifier in an ensemble, which is built by greedily minimizing some loss function.
2) In [1], rules are extracted from an ensemble of trees; a weighted combination of these rules is then built by solving a L1regularized optimization problem over the weights as described in [5].
In this package, we use the second approach. Rules are extracted from tree ensemble, which allow us to take advantage of existing fast algorithms (such as bagged decision trees, or gradient boosting) to produce such tree ensemble. Too similar or duplicated rules are then removed, based on a similarity threshold of their supports..
The main goal of this package is to provide rules verifying precision and recall conditions. It still implement a score
(decision_function
) method, but which does not solve the L1regularized optimization problem as in [1]. Instead,
weights are simply proportional to the OOB associated precision of the rule.
This package also offers convenient methods to compute predictions with the k most precise rules (cf _score_top_rules() and _predict_top_rules() functions).
[1] Friedman and Popescu, Predictive learning via rule ensembles,Technical Report, 2005.
[2] Cohen and Singer, A simple, fast, and effective rule learner, National Conference on Artificial Intelligence, 1999.
[3] Weiss and Indurkhya, Lightweight rule induction, ICML, 2000.
[4] Dembczyński, Kotłowski and Słowiński, Maximum Likelihood Rule Ensembles, ICML, 2008.
[5] Friedman and Popescu, Gradient directed regularization, Technical Report, 2004.
Expand source code
'''
Skoperules aims at learning logical, interpretable rules for "scoping" a target
class, i.e. detecting with high precision instances of this class.
Skoperules is a trade off between the interpretability of a Decision Tree
and the modelization power of a Random Forest. Code adapted with only minor changes
from [here](https://github.com/scikitlearncontrib/skoperules). Full credit to
the authors. You can access the original project and docs `here <http://skoperules.readthedocs.io/en/latest/>`_
Example

from sklearn.datasets import load_boston
from sklearn.metrics import precision_recall_curve
from matplotlib import pyplot as plt
from skrules import SkopeRulesClassifier
dataset = load_boston()
clf = SkopeRulesClassifier(max_depth_duplication=None,
n_estimators=30,
precision_min=0.2,
recall_min=0.01,
feature_names=dataset.feature_names)
X, y = dataset.data, dataset.target > 25
X_train, y_train = X[:len(y)//2], y[:len(y)//2]
X_test, y_test = X[len(y)//2:], y[len(y)//2:]
clf.fit(X_train, y_train)
y_score = clf._score_top_rules(X_test) # Get a risk score for each test example
precision, recall, _ = precision_recall_curve(y_test, y_score)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision Recall curve')
plt.show()
Links with existing literature

The main advantage of decision rules is that they are offering interpretable models. The problem of generating such
rules has been widely considered in machine learning, see e.g. RuleFit [1], Slipper [2], LRI [3], MLRules[4].
A decision rule is a logical expression of the form "IF conditions THEN response". In a binary classification setting,
if an instance satisfies conditions of the rule, then it is assigned to one of the two classes. If this instance does
not satisfy conditions, it remains unassigned.
1) In [2, 3, 4], rules induction is done by considering each single decision rule as a base classifier in an ensemble,
which is built by greedily minimizing some loss function.
2) In [1], rules are extracted from an ensemble of trees; a weighted combination of these rules is then built by solving
a L1regularized optimization problem over the weights as described in [5].
In this package, we use the second approach. Rules are extracted from tree ensemble, which allow us to take advantage of
existing fast algorithms (such as bagged decision trees, or gradient boosting) to produce such tree ensemble. Too
similar or duplicated rules are then removed, based on a similarity threshold of their supports..
The main goal of this package is to provide rules verifying precision and recall conditions. It still implement a score
(`decision_function`) method, but which does not solve the L1regularized optimization problem as in [1]. Instead,
weights are simply proportional to the OOB associated precision of the rule.
This package also offers convenient methods to compute predictions with the k most precise rules (cf _score_top_rules()
and _predict_top_rules() functions).
[1] Friedman and Popescu, Predictive learning via rule ensembles,Technical Report, 2005.
[2] Cohen and Singer, A simple, fast, and effective rule learner, National Conference on Artificial Intelligence, 1999.
[3] Weiss and Indurkhya, Lightweight rule induction, ICML, 2000.
[4] Dembczyński, Kotłowski and Słowiński, Maximum Likelihood Rule Ensembles, ICML, 2008.
[5] Friedman and Popescu, Gradient directed regularization, Technical Report, 2004.
'''
import numbers
from warnings import warn
from typing import List, Tuple
import numpy as np
import pandas
import six
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.multiclass import check_classification_targets, unique_labels
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from imodels.rule_set.rule_set import RuleSet
from imodels.util.rule import replace_feature_name, get_feature_dict, Rule
from imodels.util.extract import extract_skope
from imodels.util.score import score_precision_recall
from imodels.util.prune import prune_mins, deduplicate
INTEGER_TYPES = (numbers.Integral, np.integer)
BASE_FEATURE_NAME = "feature_"
class SkopeRulesClassifier(BaseEstimator, RuleSet, ClassifierMixin):
"""An easyinterpretable 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.
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 List 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.
If an iterable is passed, you will train n_estimators
for each tree depth. It allows you to create and compare
rules of different length.
max_depth_duplication : integer, optional (default=None)
The maximum depth of the decision tree for rule deduplication,
if None then no deduplication occurs.
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 subestimators (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 subestimators used to generate candidate
rules.
estimators_samples_ : list of arrays
The subset of drawn samples (i.e., the inbag 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,
precision_min=0.5,
recall_min=0.01,
n_estimators=10,
max_samples=.8,
max_samples_features=.8,
bootstrap=False,
bootstrap_features=False,
max_depth=3,
max_depth_duplication=None,
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.n_estimators = n_estimators
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_depth_duplication = max_depth_duplication
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
def fit(self, X, y, feature_names=None, sample_weight=None):
"""Fit the model according to the given training data.
Parameters

X : arraylike, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : arraylike, shape (n_samples,)
Target vector relative to X. Has to follow the convention 0 for
normal data, 1 for anomalies.
sample_weight : arraylike, 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.sample_weight = sample_weight
self.classes_ = unique_labels(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 isinstance(self.max_depth_duplication, int) and self.max_depth_duplication is not None:
raise ValueError("max_depth_duplication should be an integer")
if not set(self.classes_) == {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 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.feature_dict_ = get_feature_dict(X.shape[1], feature_names)
self.feature_placeholders = np.array(list(self.feature_dict_.keys()))
self.feature_names = np.array(list(self.feature_dict_.values()))
extracted_rules, self.estimators_samples_, self.estimators_features_ = self._extract_rules(X, y)
scored_rules = self._score_rules(X, y, extracted_rules)
self.rules_ = self._prune_rules(scored_rules)
self.rules_without_feature_names_ = self.rules_
self.rules_ = [
replace_feature_name(rule, self.feature_dict_) for rule in self.rules_
]
self.complexity_ = self._get_complexity()
return self
def predict(self, X) > np.ndarray:
"""Predict if a particular sample is an outlier or not.
Parameters

X : arraylike, 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.
"""
X = check_array(X)
return np.argmax(self.predict_proba(X), axis=1)
def predict_proba(self, X) > np.ndarray:
'''Predict probability of a particular sample being an outlier or not
'''
X = check_array(X)
weight_sum = np.sum([w[0] for (r, w) in self.rules_without_feature_names_])
if weight_sum == 0:
return np.vstack((np.ones(X.shape[0]), np.zeros(X.shape[0]))).transpose()
y = self._eval_weighted_rule_sum(X) / weight_sum
return np.vstack((1  y, y)).transpose()
def _rules_vote(self, X) > np.ndarray:
"""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 : arraylike, 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_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_placeholders)
selected_rules = self.rules_without_feature_names_
scores = np.zeros(X.shape[0])
for (r, _) in selected_rules:
scores[list(df.query(r).index)] += 1
return scores
def _score_top_rules(self, X) > np.ndarray:
"""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 (k1) first
rules.
Parameters

X : arraylike, 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_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_placeholders)
selected_rules = self.rules_without_feature_names_
scores = np.zeros(X.shape[0])
for (k, r) in enumerate(list((selected_rules))):
scores[list(df.query(r.rule).index)] = np.maximum(
len(selected_rules)  k,
scores[list(df.query(r.rule).index)])
return scores
def _predict_top_rules(self, X, n_rules) > np.ndarray:
"""Predict if a particular sample is an outlier or not,
using the n_rules most performing rules.
Parameters

X : arraylike, 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 _extract_rules(self, X, y) > Tuple[List[str], List[np.array], List[np.array]]:
return extract_skope(X, y,
feature_names=self.feature_placeholders,
sample_weight=self.sample_weight,
n_estimators=self.n_estimators,
max_samples=self.max_samples_,
max_samples_features=self.max_samples_features,
bootstrap=self.bootstrap,
bootstrap_features=self.bootstrap_features,
max_depths=self.max_depth,
max_features=self.max_features,
min_samples_split=self.min_samples_split,
n_jobs=self.n_jobs,
random_state=self.random_state,
verbose=self.verbose)
def _score_rules(self, X, y, rules) > List[Rule]:
return score_precision_recall(X, y, rules, self.estimators_samples_, self.estimators_features_, self.feature_placeholders)
def _prune_rules(self, rules) > List[Rule]:
return deduplicate(
prune_mins(rules, self.precision_min, self.recall_min),
self.max_depth_duplication
)
Classes
class SkopeRulesClassifier (precision_min=0.5, recall_min=0.01, n_estimators=10, max_samples=0.8, max_samples_features=0.8, bootstrap=False, bootstrap_features=False, max_depth=3, max_depth_duplication=None, max_features=1.0, min_samples_split=2, n_jobs=1, random_state=None, verbose=0)

An easyinterpretable classifier optimizing simple logical rules.
Parameters
feature_names
:list
ofstr
, 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.
max_samples
:int
orfloat
, 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 drawmax_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
orfloat
, 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 drawmax_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
orList
orNone
, 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. If an iterable is passed, you will train n_estimators for each tree depth. It allows you to create and compare rules of different length.
max_depth_duplication
:integer
, optional(default=None)
 The maximum depth of the decision tree for rule deduplication, if None then no deduplication occurs.
max_features
:int, float, string
orNone
, 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 andint(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.  If int, then consider
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, thenmin_samples_split
is a percentage andceil(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
andpredict
. If 1, then the number of jobs is set to the number of cores. random_state
:int, RandomState instance
orNone
, 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 thepredict
method. The rules are generated by fitted subestimators (decision trees). Each rule satisfies recall_min and precision_min conditions. The selection is done according to OOB precisions.estimators_
:list
ofDecisionTreeClassifier
 The collection of fitted subestimators used to generate candidate rules.
estimators_samples_
:list
ofarrays
 The subset of drawn samples (i.e., the inbag samples) for each base estimator.
estimators_features_
:list
ofarrays
 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.
Expand source code
class SkopeRulesClassifier(BaseEstimator, RuleSet, ClassifierMixin): """An easyinterpretable 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. 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 List 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. If an iterable is passed, you will train n_estimators for each tree depth. It allows you to create and compare rules of different length. max_depth_duplication : integer, optional (default=None) The maximum depth of the decision tree for rule deduplication, if None then no deduplication occurs. 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 subestimators (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 subestimators used to generate candidate rules. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the inbag 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, precision_min=0.5, recall_min=0.01, n_estimators=10, max_samples=.8, max_samples_features=.8, bootstrap=False, bootstrap_features=False, max_depth=3, max_depth_duplication=None, 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.n_estimators = n_estimators 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_depth_duplication = max_depth_duplication 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 def fit(self, X, y, feature_names=None, sample_weight=None): """Fit the model according to the given training data. Parameters  X : arraylike, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : arraylike, shape (n_samples,) Target vector relative to X. Has to follow the convention 0 for normal data, 1 for anomalies. sample_weight : arraylike, 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.sample_weight = sample_weight self.classes_ = unique_labels(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 isinstance(self.max_depth_duplication, int) and self.max_depth_duplication is not None: raise ValueError("max_depth_duplication should be an integer") if not set(self.classes_) == {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 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.feature_dict_ = get_feature_dict(X.shape[1], feature_names) self.feature_placeholders = np.array(list(self.feature_dict_.keys())) self.feature_names = np.array(list(self.feature_dict_.values())) extracted_rules, self.estimators_samples_, self.estimators_features_ = self._extract_rules(X, y) scored_rules = self._score_rules(X, y, extracted_rules) self.rules_ = self._prune_rules(scored_rules) self.rules_without_feature_names_ = self.rules_ self.rules_ = [ replace_feature_name(rule, self.feature_dict_) for rule in self.rules_ ] self.complexity_ = self._get_complexity() return self def predict(self, X) > np.ndarray: """Predict if a particular sample is an outlier or not. Parameters  X : arraylike, 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. """ X = check_array(X) return np.argmax(self.predict_proba(X), axis=1) def predict_proba(self, X) > np.ndarray: '''Predict probability of a particular sample being an outlier or not ''' X = check_array(X) weight_sum = np.sum([w[0] for (r, w) in self.rules_without_feature_names_]) if weight_sum == 0: return np.vstack((np.ones(X.shape[0]), np.zeros(X.shape[0]))).transpose() y = self._eval_weighted_rule_sum(X) / weight_sum return np.vstack((1  y, y)).transpose() def _rules_vote(self, X) > np.ndarray: """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 : arraylike, 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_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_placeholders) selected_rules = self.rules_without_feature_names_ scores = np.zeros(X.shape[0]) for (r, _) in selected_rules: scores[list(df.query(r).index)] += 1 return scores def _score_top_rules(self, X) > np.ndarray: """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 (k1) first rules. Parameters  X : arraylike, 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_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_placeholders) selected_rules = self.rules_without_feature_names_ scores = np.zeros(X.shape[0]) for (k, r) in enumerate(list((selected_rules))): scores[list(df.query(r.rule).index)] = np.maximum( len(selected_rules)  k, scores[list(df.query(r.rule).index)]) return scores def _predict_top_rules(self, X, n_rules) > np.ndarray: """Predict if a particular sample is an outlier or not, using the n_rules most performing rules. Parameters  X : arraylike, 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 _extract_rules(self, X, y) > Tuple[List[str], List[np.array], List[np.array]]: return extract_skope(X, y, feature_names=self.feature_placeholders, sample_weight=self.sample_weight, n_estimators=self.n_estimators, max_samples=self.max_samples_, max_samples_features=self.max_samples_features, bootstrap=self.bootstrap, bootstrap_features=self.bootstrap_features, max_depths=self.max_depth, max_features=self.max_features, min_samples_split=self.min_samples_split, n_jobs=self.n_jobs, random_state=self.random_state, verbose=self.verbose) def _score_rules(self, X, y, rules) > List[Rule]: return score_precision_recall(X, y, rules, self.estimators_samples_, self.estimators_features_, self.feature_placeholders) def _prune_rules(self, rules) > List[Rule]: return deduplicate( prune_mins(rules, self.precision_min, self.recall_min), self.max_depth_duplication )
Ancestors
 sklearn.base.BaseEstimator
 RuleSet
 sklearn.base.ClassifierMixin
Subclasses
Methods
def fit(self, X, y, feature_names=None, sample_weight=None)

Fit the model according to the given training data.
Parameters
X
:arraylike, shape (n_samples, n_features)
 Training vector, where n_samples is the number of samples and n_features is the number of features.
y
:arraylike, shape (n_samples,)
 Target vector relative to X. Has to follow the convention 0 for normal data, 1 for anomalies.
sample_weight
:arraylike, 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.
Expand source code
def fit(self, X, y, feature_names=None, sample_weight=None): """Fit the model according to the given training data. Parameters  X : arraylike, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : arraylike, shape (n_samples,) Target vector relative to X. Has to follow the convention 0 for normal data, 1 for anomalies. sample_weight : arraylike, 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.sample_weight = sample_weight self.classes_ = unique_labels(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 isinstance(self.max_depth_duplication, int) and self.max_depth_duplication is not None: raise ValueError("max_depth_duplication should be an integer") if not set(self.classes_) == {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 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.feature_dict_ = get_feature_dict(X.shape[1], feature_names) self.feature_placeholders = np.array(list(self.feature_dict_.keys())) self.feature_names = np.array(list(self.feature_dict_.values())) extracted_rules, self.estimators_samples_, self.estimators_features_ = self._extract_rules(X, y) scored_rules = self._score_rules(X, y, extracted_rules) self.rules_ = self._prune_rules(scored_rules) self.rules_without_feature_names_ = self.rules_ self.rules_ = [ replace_feature_name(rule, self.feature_dict_) for rule in self.rules_ ] self.complexity_ = self._get_complexity() return self
def predict(self, X) ‑> numpy.ndarray

Predict if a particular sample is an outlier or not.
Parameters
X
:arraylike, 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.
Expand source code
def predict(self, X) > np.ndarray: """Predict if a particular sample is an outlier or not. Parameters  X : arraylike, 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. """ X = check_array(X) return np.argmax(self.predict_proba(X), axis=1)
def predict_proba(self, X) ‑> numpy.ndarray

Predict probability of a particular sample being an outlier or not
Expand source code
def predict_proba(self, X) > np.ndarray: '''Predict probability of a particular sample being an outlier or not ''' X = check_array(X) weight_sum = np.sum([w[0] for (r, w) in self.rules_without_feature_names_]) if weight_sum == 0: return np.vstack((np.ones(X.shape[0]), np.zeros(X.shape[0]))).transpose() y = self._eval_weighted_rule_sum(X) / weight_sum return np.vstack((1  y, y)).transpose()