val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_estimators:int ->
?max_samples:[ `Int of int | `Float of float ] ->
?contamination:[ `Auto | `Float of float ] ->
?max_features:[ `Int of int | `Float of float ] ->
?bootstrap:bool ->
?n_jobs:[ `Int of int | `None ] ->
?behaviour:string ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?verbose:int ->
?warm_start:bool ->
unit ->
tIsolation 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 :ref:`User Guide <isolation_forest>`.
.. versionadded:: 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.
0` samples.If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).
contamination : 'auto' or float, optional (default='auto') 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 scores of the samples.
0, 0.5... versionchanged:: 0.22 The default value of ``contamination`` changed from 0.1 to ``'auto'``.
max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator.
1` features.bootstrap : bool, 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 :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
behaviour : str, default='deprecated' This parameter has not effect, is deprecated, and will be removed.
.. versionadded:: 0.20 ``behaviour`` is added in 0.20 for back-compatibility purpose.
.. deprecated:: 0.20 ``behaviour='old'`` is deprecated in 0.20 and will not be possible in 0.22.
.. deprecated:: 0.22 ``behaviour`` parameter is 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.
warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`the Glossary <warm_start>`.
.. versionadded:: 0.21
Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators.
estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag 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_``. ``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.
Notes ----- The implementation is based on an ensemble of ExtraTreeRegressor. The maximum depth of each tree is set to ``ceil(log_2(n))`` where :math:`n` is the number of samples used to build the tree (see (Liu et al., 2008) for more details).
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.
See Also ---------- sklearn.covariance.EllipticEnvelope : An object for detecting outliers in a Gaussian distributed dataset. sklearn.svm.OneClassSVM : Unsupervised Outlier Detection. Estimate the support of a high-dimensional distribution. The implementation is based on libsvm. sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection using Local Outlier Factor (LOF).
Examples -------- >>> from sklearn.ensemble import IsolationForest >>> X = [-1.1], [0.3], [0.5], [100] >>> clf = IsolationForest(random_state=0).fit(X) >>> clf.predict([0.1], [0], [90]) array( 1, 1, -1)
val get_item : index:Py.Object.t -> t -> Py.Object.tReturn the index'th estimator in the ensemble.
val decision_function :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tAverage 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 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 ------- 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.
val fit :
?y:Py.Object.t ->
?sample_weight:Ndarray.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tFit 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.
y : Ignored Not used, present for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted.
Returns ------- self : object Fitted estimator.
val fit_predict : ?y:Py.Object.t -> x:Ndarray.t -> t -> Ndarray.tPerform 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.
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
val predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict 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.
val score_samples :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tOpposite 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 or sparse matrix, shape (n_samples, n_features) The input samples.
Returns ------- scores : array, shape (n_samples,) The anomaly score of the input samples. The lower, the more abnormal.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val estimators_ : t -> Py.Object.tAttribute estimators_: see constructor for documentation
val estimators_samples_ : t -> Py.Object.tAttribute estimators_samples_: see constructor for documentation
val max_samples_ : t -> intAttribute max_samples_: see constructor for documentation
val offset_ : t -> floatAttribute offset_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.