LocalOutlierFactor
Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
The anomaly score of each sample is called the Local Outlier Factor. It measures the local deviation of the density of a given sample with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood. More precisely, locality is given by k-nearest neighbors, whose distance is used to estimate the local density. By comparing the local density of a sample to the local densities of its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LocalOutlierFactor(opts?: object): LocalOutlierFactor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "auto" | "ball_tree" | "kd_tree" | "brute" | Algorithm used to compute the nearest neighbors: Default Value 'auto' |
opts.contamination? | number | "auto" | The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the scores of the samples. Default Value 'auto' |
opts.leaf_size? | number | Leaf is size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. Default Value 30 |
opts.metric? | string | Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance (opens in a new tab) and the metrics listed in distance\_metrics for valid metric values. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string. Default Value 'minkowski' |
opts.metric_params? | any | Additional keyword arguments for the metric function. |
opts.n_jobs? | number | The number of parallel jobs to run for neighbors search. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.n_neighbors? | number | Number of neighbors to use by default for kneighbors queries. If n_neighbors is larger than the number of samples provided, all samples will be used. Default Value 20 |
opts.novelty? | boolean | By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=false). Set novelty to true if you want to use LocalOutlierFactor for novelty detection. In this case be aware that you should only use predict, decision_function and score_samples on new unseen data and not on the training set; and note that the results obtained this way may differ from the standard LOF results. Default Value false |
opts.p? | number | Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise\_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. Default Value 2 |
Returns
Defined in: generated/neighbors/LocalOutlierFactor.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/neighbors/LocalOutlierFactor.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/neighbors/LocalOutlierFactor.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/neighbors/LocalOutlierFactor.ts:19 (opens in a new tab)
id
string
Defined in: generated/neighbors/LocalOutlierFactor.ts:16 (opens in a new tab)
opts
any
Defined in: generated/neighbors/LocalOutlierFactor.ts:17 (opens in a new tab)
Accessors
effective_metric_
The effective metric used for the distance computation.
Signature
effective_metric_(): Promise<string>;Returns
Promise<string>
Defined in: generated/neighbors/LocalOutlierFactor.ts:565 (opens in a new tab)
effective_metric_params_
The effective additional keyword arguments for the metric function.
Signature
effective_metric_params_(): Promise<any>;Returns
Promise<any>
Defined in: generated/neighbors/LocalOutlierFactor.ts:592 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:646 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/LocalOutlierFactor.ts:619 (opens in a new tab)
n_neighbors_
The actual number of neighbors used for kneighbors queries.
Signature
n_neighbors_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/LocalOutlierFactor.ts:511 (opens in a new tab)
n_samples_fit_
It is the number of samples in the fitted data.
Signature
n_samples_fit_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/LocalOutlierFactor.ts:673 (opens in a new tab)
negative_outlier_factor_
The opposite LOF of the training samples. The higher, the more normal. Inliers tend to have a LOF score close to 1 (negative\_outlier\_factor\_ close to -1), while outliers tend to have a larger LOF score.
The local outlier factor (LOF) of a sample captures its supposed ‘degree of abnormality’. It is the average of the ratio of the local reachability density of a sample and those of its k-nearest neighbors.
Signature
negative_outlier_factor_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:484 (opens in a new tab)
offset_
Offset used to obtain binary labels from the raw scores. Observations having a negative_outlier_factor smaller than offset\_ are detected as abnormal. The offset is set to -1.5 (inliers score around -1), except when a contamination parameter different than “auto” is provided. In that case, the offset is defined in such a way we obtain the expected number of outliers in training.
Signature
offset_(): Promise<number>;Returns
Promise<number>
Defined in: generated/neighbors/LocalOutlierFactor.ts:538 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/neighbors/LocalOutlierFactor.ts:91 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/neighbors/LocalOutlierFactor.ts:95 (opens in a new tab)
Methods
decision_function()
Shifted opposite of the Local Outlier Factor of X.
Bigger is better, i.e. large values correspond to inliers.
Only available for novelty detection (when novelty is set to true). The shift offset allows a zero threshold for being an outlier. The argument X is supposed to contain new data: if X contains a point from training, it considers the later in its own neighborhood. Also, the samples in X are not considered in the neighborhood of any point.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The query sample or samples to compute the Local Outlier Factor w.r.t. the training samples. |
Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:179 (opens in a new tab)
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/neighbors/LocalOutlierFactor.ts:158 (opens in a new tab)
fit()
Fit the local outlier factor detector from the training dataset.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/neighbors/LocalOutlierFactor.ts:217 (opens in a new tab)
fit_predict()
Fit the model to the training set X and return the labels.
Not available for novelty detection (when novelty is set to true). Label is 1 for an inlier and -1 for an outlier according to the LOF score and the contamination parameter.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The query sample or samples to compute the Local Outlier Factor w.r.t. the training samples. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:259 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/neighbors/LocalOutlierFactor.ts:104 (opens in a new tab)
kneighbors()
Find the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Signature
kneighbors(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. |
opts.n_neighbors? | number | Number of neighbors required for each sample. The default is the value passed to the constructor. |
opts.return_distance? | boolean | Whether or not to return the distances. Default Value true |
Returns
Promise<ArrayLike[]>
Defined in: generated/neighbors/LocalOutlierFactor.ts:303 (opens in a new tab)
kneighbors_graph()
Compute the (weighted) graph of k-Neighbors for points in X.
Signature
kneighbors_graph(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. For metric='precomputed' the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features). |
opts.mode? | "connectivity" | "distance" | Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class. Default Value 'connectivity' |
opts.n_neighbors? | number | Number of neighbors for each sample. The default is the value passed to the constructor. |
Returns
Promise<any[]>
Defined in: generated/neighbors/LocalOutlierFactor.ts:352 (opens in a new tab)
predict()
Predict the labels (1 inlier, -1 outlier) of X according to LOF.
Only available for novelty detection (when novelty is set to true). This method allows to generalize prediction to new observations (not in the training set). Note that the result of clf.fit(X) then clf.predict(X) with novelty=True may differ from the result obtained by clf.fit\_predict(X) with novelty=False.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The query sample or samples to compute the Local Outlier Factor w.r.t. the training samples. |
Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:406 (opens in a new tab)
score_samples()
Opposite of the Local Outlier Factor of X.
It is the opposite as bigger is better, i.e. large values correspond to inliers.
Only available for novelty detection (when novelty is set to true). The argument X is supposed to contain new data: if X contains a point from training, it considers the later in its own neighborhood. Also, the samples in X are not considered in the neighborhood of any point. Because of this, the scores obtained via score\_samples may differ from the standard LOF scores. The standard LOF scores for the training data is available via the negative\_outlier\_factor\_ attribute.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The query sample or samples to compute the Local Outlier Factor w.r.t. the training samples. |
Returns
Promise<ArrayLike>
Defined in: generated/neighbors/LocalOutlierFactor.ts:445 (opens in a new tab)