Class: 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.
Constructors
new LocalOutlierFactor()
new LocalOutlierFactor(
opts?):LocalOutlierFactor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "auto" | "ball_tree" | "kd_tree" | "brute" | Algorithm used to compute the nearest neighbors: |
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. |
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. |
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 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. |
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 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. |
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. |
opts.p? | number | Parameter for the Minkowski metric from sklearn.metrics.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. |
Returns LocalOutlierFactor
Defined in generated/neighbors/LocalOutlierFactor.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/neighbors/LocalOutlierFactor.ts:21 |
_isInitialized | boolean | false | generated/neighbors/LocalOutlierFactor.ts:20 |
_py | PythonBridge | undefined | generated/neighbors/LocalOutlierFactor.ts:19 |
id | string | undefined | generated/neighbors/LocalOutlierFactor.ts:16 |
opts | any | undefined | generated/neighbors/LocalOutlierFactor.ts:17 |
Accessors
effective_metric_
Get Signature
get effective_metric_():
Promise<string>
The effective metric used for the distance computation.
Returns Promise<string>
Defined in generated/neighbors/LocalOutlierFactor.ts:579
effective_metric_params_
Get Signature
get effective_metric_params_():
Promise<any>
The effective additional keyword arguments for the metric function.
Returns Promise<any>
Defined in generated/neighbors/LocalOutlierFactor.ts:606
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/neighbors/LocalOutlierFactor.ts:660
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/neighbors/LocalOutlierFactor.ts:633
n_neighbors_
Get Signature
get n_neighbors_():
Promise<number>
The actual number of neighbors used for kneighbors queries.
Returns Promise<number>
Defined in generated/neighbors/LocalOutlierFactor.ts:525
n_samples_fit_
Get Signature
get n_samples_fit_():
Promise<number>
It is the number of samples in the fitted data.
Returns Promise<number>
Defined in generated/neighbors/LocalOutlierFactor.ts:687
negative_outlier_factor_
Get Signature
get negative_outlier_factor_():
Promise<ArrayLike>
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.
Returns Promise<ArrayLike>
Defined in generated/neighbors/LocalOutlierFactor.ts:498
offset_
Get Signature
get offset_():
Promise<number>
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.
Returns Promise<number>
Defined in generated/neighbors/LocalOutlierFactor.ts:552
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/neighbors/LocalOutlierFactor.ts:91
Methods
decision_function()
decision_function(
opts):Promise<ArrayLike>
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.
Parameters
| Parameter | 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:168
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/neighbors/LocalOutlierFactor.ts:147
fit()
fit(
opts):Promise<any>
Fit the local outlier factor detector from the training dataset.
Parameters
| Parameter | 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:204
fit_predict()
fit_predict(
opts):Promise<ArrayLike>
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.
Parameters
| Parameter | 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:245
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/neighbors/LocalOutlierFactor.ts:288
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/neighbors/LocalOutlierFactor.ts:104
kneighbors()
kneighbors(
opts):Promise<ArrayLike[]>
Find the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
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. |
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. |
Returns Promise<ArrayLike[]>
Defined in generated/neighbors/LocalOutlierFactor.ts:326
kneighbors_graph()
kneighbors_graph(
opts):Promise<any[]>
Compute the (weighted) graph of k-Neighbors for points in X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
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. |
opts.n_neighbors? | number | Number of neighbors for each sample. The default is the value passed to the constructor. |
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). |
Returns Promise<any[]>
Defined in generated/neighbors/LocalOutlierFactor.ts:372
predict()
predict(
opts):Promise<ArrayLike>
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.
Parameters
| Parameter | 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:422
score_samples()
score_samples(
opts):Promise<ArrayLike>
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.
Parameters
| Parameter | 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>