sklearn.ensemble.IsolationForest

class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples='auto', contamination='legacy', max_features=1.0, bootstrap=False, n_jobs=None, behaviour='old', random_state=None, verbose=0)[source]

Isolation Forest Algorithm

Return the anomaly score of each sample using the IsolationForest algorithm

The IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.

This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.

Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.

Read more in the User Guide.

New in version 0.18.

Parameters:
n_estimators : int, optional (default=100)

The number of base estimators in the ensemble.

max_samples : int or float, optional (default=”auto”)
The number of samples to draw from X to train each base estimator.
  • If int, then draw max_samples samples.
  • If float, then draw max_samples * X.shape[0] samples.
  • If “auto”, then max_samples=min(256, n_samples).

If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).

contamination : float in (0., 0.5), optional (default=0.1)

The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function. If ‘auto’, the decision function threshold is determined as in the original paper.

Changed in version 0.20: The default value of contamination will change from 0.1 in 0.20 to 'auto' in 0.22.

max_features : int or float, optional (default=1.0)

The number of features to draw from X to train each base estimator.

  • If int, then draw max_features features.
  • If float, then draw max_features * X.shape[1] features.
bootstrap : boolean, optional (default=False)

If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.

n_jobs : int or None, optional (default=None)

The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

behaviour : str, default=’old’

Behaviour of the decision_function which can be either ‘old’ or ‘new’. Passing behaviour='new' makes the decision_function change to match other anomaly detection algorithm API which will be the default behaviour in the future. As explained in details in the offset_ attribute documentation, the decision_function becomes dependent on the contamination parameter, in such a way that 0 becomes its natural threshold to detect outliers.

New in version 0.20: behaviour is added in 0.20 for back-compatibility purpose.

Deprecated since version 0.20: behaviour='old' is deprecated in 0.20 and will not be possible in 0.22.

Deprecated since version 0.22: behaviour parameter will be deprecated in 0.22 and removed in 0.24.

random_state : int, RandomState instance or None, optional (default=None)

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:
estimators_ : list of DecisionTreeClassifier

The collection of fitted sub-estimators.

estimators_samples_ : list of arrays

The subset of drawn samples for each base estimator.

max_samples_ : integer

The actual number of samples

offset_ : float

Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. Assuming behaviour == ‘new’, offset_ is defined as follows. When the contamination parameter is set to “auto”, the offset is equal to -0.5 as the scores of inliers are close to 0 and the scores of outliers are close to -1. When a contamination parameter different than “auto” is provided, the offset is defined in such a way we obtain the expected number of outliers (samples with decision function < 0) in training. Assuming the behaviour parameter is set to ‘old’, we always have offset_ = -0.5, making the decision function independent from the contamination parameter.

References

[1]Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation forest.” Data Mining, 2008. ICDM‘08. Eighth IEEE International Conference on.
[2]Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation-based anomaly detection.” ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.

Methods

decision_function(X) Average anomaly score of X of the base classifiers.
fit(X[, y, sample_weight]) Fit estimator.
fit_predict(X[, y]) Performs fit on X and returns labels for X.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict if a particular sample is an outlier or not.
score_samples(X) Opposite of the anomaly score defined in the original paper.
set_params(**params) Set the parameters of this estimator.
__init__(n_estimators=100, max_samples='auto', contamination='legacy', max_features=1.0, bootstrap=False, n_jobs=None, behaviour='old', random_state=None, verbose=0)[source]
decision_function(X)[source]

Average anomaly score of X of the base classifiers.

The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest.

The measure of normality of an observation given a tree is the depth of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of a n_left samples isolation tree is added.

Parameters:
X : {array-like, sparse matrix}, shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:
scores : array, shape (n_samples,)

The anomaly score of the input samples. The lower, the more abnormal. Negative scores represent outliers, positive scores represent inliers.
estimators_samples_

The subset of drawn samples for each base estimator.

Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.

Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.

fit(X, y=None, sample_weight=None)[source]

Fit estimator.

Parameters:
X : array-like or sparse matrix, shape (n_samples, n_features)

The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc_matrix for maximum efficiency.

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted.

y : Ignored

not used, present for API consistency by convention.
Returns:
self : object
fit_predict(X, y=None)[source]

Performs fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:
X : ndarray, shape (n_samples, n_features)

Input data.

y : Ignored

not used, present for API consistency by convention.
Returns:
y : ndarray, shape (n_samples,)

1 for inliers, -1 for outliers.
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping of string to any

Parameter names mapped to their values.
predict(X)[source]

Predict if a particular sample is an outlier or not.

Parameters:
X : array-like or sparse matrix, shape (n_samples, n_features)

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
Returns:
is_inlier : array, shape (n_samples,)

For each observation, tells whether or not (+1 or -1) it should be considered as an inlier according to the fitted model.
score_samples(X)[source]

Opposite of the anomaly score defined in the original paper.

The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest.

The measure of normality of an observation given a tree is the depth of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of a n_left samples isolation tree is added.

Parameters:
X : {array-like, sparse matrix}, shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:
scores : array, shape (n_samples,)

The anomaly score of the input samples. The lower, the more abnormal.
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
self