package sklearn

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package sklearn
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type tag = [
  1. | `IsolationForest
]
type t = [ `BaseBagging | `BaseEnsemble | `BaseEstimator | `IsolationForest | `MetaEstimatorMixin | `Object | `OutlierMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_bagging : t -> [ `BaseBagging ] Obj.t
val as_meta_estimator : t -> [ `MetaEstimatorMixin ] Obj.t
val as_outlier : t -> [ `OutlierMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val as_ensemble : t -> [ `BaseEnsemble ] Obj.t
val create : ?n_estimators:int -> ?max_samples:[ `Auto | `I of int | `F of float ] -> ?contamination:[ `Auto | `F of float ] -> ?max_features:[ `I of int | `F of float ] -> ?bootstrap:bool -> ?n_jobs:int -> ?behaviour:string -> ?random_state:int -> ?verbose:int -> ?warm_start:bool -> unit -> t

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 :ref:`User Guide <isolation_forest>`.

.. versionadded:: 0.18

Parameters ---------- n_estimators : int, default=100 The number of base estimators in the ensemble.

max_samples : 'auto', int or float, 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.shape0` 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 : 'auto' or float, 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.

  • If 'auto', the threshold is determined as in the original paper.
  • If float, the contamination should be in the range 0, 0.5.

.. versionchanged:: 0.22 The default value of ``contamination`` changed from 0.1 to ``'auto'``.

max_features : int or float, 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.shape1` features.

bootstrap : bool, 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, 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 no 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 or RandomState, default=None Controls the pseudo-randomness of the selection of the feature and split values for each branching step and each tree in the forest.

Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`.

verbose : int, default=0 Controls the verbosity of the tree building process.

warm_start : bool, 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_ : int 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.

.. versionadded:: 0.20

estimators_features_ : list of arrays The subset of drawn features for each base estimator.

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 -> [> tag ] Obj.t -> Py.Object.t

Return the index'th estimator in the ensemble.

val iter : [> tag ] Obj.t -> Dict.t Stdlib.Seq.t

Return iterator over estimators in the ensemble.

val decision_function : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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 of 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 : ndarray of 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:[> `ArrayLike ] Np.Obj.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Fit estimator.

Parameters ---------- X : array-like, sparse matrix of 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:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

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

y : Ignored Not used, present for API consistency by convention.

Returns ------- y : ndarray of shape (n_samples,) 1 for inliers, -1 for outliers.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get 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:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Predict if a particular sample is an outlier or not.

Parameters ---------- X : array-like, sparse matrix of 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 : ndarray of 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:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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 of shape (n_samples, n_features) The input samples.

Returns ------- scores : ndarray of shape (n_samples,) The anomaly score of the input samples. The lower, the more abnormal.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val estimators_ : t -> Py.Object.t

Attribute estimators_: get value or raise Not_found if None.

val estimators_opt : t -> Py.Object.t option

Attribute estimators_: get value as an option.

val estimators_samples_ : t -> Np.Numpy.Ndarray.List.t

Attribute estimators_samples_: get value or raise Not_found if None.

val estimators_samples_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute estimators_samples_: get value as an option.

val max_samples_ : t -> int

Attribute max_samples_: get value or raise Not_found if None.

val max_samples_opt : t -> int option

Attribute max_samples_: get value as an option.

val offset_ : t -> float

Attribute offset_: get value or raise Not_found if None.

val offset_opt : t -> float option

Attribute offset_: get value as an option.

val estimators_features_ : t -> Np.Numpy.Ndarray.List.t

Attribute estimators_features_: get value or raise Not_found if None.

val estimators_features_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute estimators_features_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.