EllipticEnvelope
An object for detecting outliers in a Gaussian distributed dataset.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new EllipticEnvelope(opts?: object): EllipticEnvelope;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.assume_centered? | boolean | If true, the support of robust location and covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If false, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment. Default Value false |
opts.contamination? | number | The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Range is (0, 0.5]. Default Value 0.1 |
opts.random_state? | number | Determines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.store_precision? | boolean | Specify if the estimated precision is stored. Default Value true |
opts.support_fraction? | number | The proportion of points to be included in the support of the raw MCD estimate. If undefined, the minimum value of support_fraction will be used within the algorithm: \[n\_sample + n\_features + 1\] / 2. Range is (0, 1). |
Returns
Defined in: generated/covariance/EllipticEnvelope.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/covariance/EllipticEnvelope.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/covariance/EllipticEnvelope.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/covariance/EllipticEnvelope.ts:19 (opens in a new tab)
id
string
Defined in: generated/covariance/EllipticEnvelope.ts:16 (opens in a new tab)
opts
any
Defined in: generated/covariance/EllipticEnvelope.ts:17 (opens in a new tab)
Accessors
covariance_
Estimated robust covariance matrix.
Signature
covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EllipticEnvelope.ts:619 (opens in a new tab)
dist_
Mahalanobis distances of the training set (on which fit is called) observations.
Signature
dist_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:808 (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/covariance/EllipticEnvelope.ts:862 (opens in a new tab)
location_
Estimated robust location.
Signature
location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:592 (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/covariance/EllipticEnvelope.ts:835 (opens in a new tab)
offset_
Offset used to define the decision function from the raw scores. We have the relation: decision\_function \= score\_samples \- offset\_. The offset depends on the contamination parameter and is defined in such a way we obtain the expected number of outliers (samples with decision function < 0) in training.
Signature
offset_(): Promise<number>;Returns
Promise<number>
Defined in: generated/covariance/EllipticEnvelope.ts:700 (opens in a new tab)
precision_
Estimated pseudo inverse matrix. (stored only if store_precision is true)
Signature
precision_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EllipticEnvelope.ts:646 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/covariance/EllipticEnvelope.ts:59 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/covariance/EllipticEnvelope.ts:63 (opens in a new tab)
raw_covariance_
The raw robust estimated covariance before correction and re-weighting.
Signature
raw_covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EllipticEnvelope.ts:754 (opens in a new tab)
raw_location_
The raw robust estimated location before correction and re-weighting.
Signature
raw_location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:727 (opens in a new tab)
raw_support_
A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting.
Signature
raw_support_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:781 (opens in a new tab)
support_
A mask of the observations that have been used to compute the robust estimates of location and shape.
Signature
support_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:673 (opens in a new tab)
Methods
correct_covariance()
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Signature
correct_covariance(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.data? | ArrayLike[] | The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. |
Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EllipticEnvelope.ts:139 (opens in a new tab)
decision_function()
Compute the decision function of the given observations.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:177 (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/covariance/EllipticEnvelope.ts:120 (opens in a new tab)
error_norm()
Compute the Mean Squared Error between two covariance estimators.
Signature
error_norm(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.comp_cov? | ArrayLike[] | The covariance to compare with. |
opts.norm? | "frobenius" | "spectral" | The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error (comp\_cov \- self.covariance\_). Default Value 'frobenius' |
opts.scaling? | boolean | If true (default), the squared error norm is divided by n_features. If false, the squared error norm is not rescaled. Default Value true |
opts.squared? | boolean | Whether to compute the squared error norm or the error norm. If true (default), the squared error norm is returned. If false, the error norm is returned. Default Value true |
Returns
Promise<number>
Defined in: generated/covariance/EllipticEnvelope.ts:214 (opens in a new tab)
fit()
Fit the EllipticEnvelope model.
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/covariance/EllipticEnvelope.ts:274 (opens in a new tab)
fit_predict()
Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:316 (opens in a new tab)
get_precision()
Getter for the precision matrix.
Signature
get_precision(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.precision_? | ArrayLike[] | The precision matrix associated to the current covariance object. |
Returns
Promise<any>
Defined in: generated/covariance/EllipticEnvelope.ts:356 (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/covariance/EllipticEnvelope.ts:72 (opens in a new tab)
mahalanobis()
Compute the squared Mahalanobis distances of given observations.
Signature
mahalanobis(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:394 (opens in a new tab)
predict()
Predict labels (1 inlier, -1 outlier) of X according to fitted model.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:429 (opens in a new tab)
reweight_covariance()
Re-weight raw Minimum Covariance Determinant estimates.
Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen].
Signature
reweight_covariance(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.data? | ArrayLike[] | The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:466 (opens in a new tab)
score()
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/covariance/EllipticEnvelope.ts:506 (opens in a new tab)
score_samples()
Compute the negative Mahalanobis distances.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EllipticEnvelope.ts:555 (opens in a new tab)