Class: EllipticEnvelope
An object for detecting outliers in a Gaussian distributed dataset.
Read more in the User Guide.
Constructors
new EllipticEnvelope()
new EllipticEnvelope(
opts?):EllipticEnvelope
Parameters
| Parameter | 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. |
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]. |
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. |
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_samples + n_features + 1) / 2 \* n_samples. Range is (0, 1). |
Returns EllipticEnvelope
Defined in generated/covariance/EllipticEnvelope.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/covariance/EllipticEnvelope.ts:21 |
_isInitialized | boolean | false | generated/covariance/EllipticEnvelope.ts:20 |
_py | PythonBridge | undefined | generated/covariance/EllipticEnvelope.ts:19 |
id | string | undefined | generated/covariance/EllipticEnvelope.ts:16 |
opts | any | undefined | generated/covariance/EllipticEnvelope.ts:17 |
Accessors
covariance_
Get Signature
get covariance_():
Promise<ArrayLike[]>
Estimated robust covariance matrix.
Returns Promise<ArrayLike[]>
Defined in generated/covariance/EllipticEnvelope.ts:673
dist_
Get Signature
get dist_():
Promise<ArrayLike>
Mahalanobis distances of the training set (on which fit is called) observations.
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:862
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/covariance/EllipticEnvelope.ts:916
location_
Get Signature
get location_():
Promise<ArrayLike>
Estimated robust location.
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:646
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/covariance/EllipticEnvelope.ts:889
offset_
Get Signature
get offset_():
Promise<number>
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.
Returns Promise<number>
Defined in generated/covariance/EllipticEnvelope.ts:754
precision_
Get Signature
get precision_():
Promise<ArrayLike[]>
Estimated pseudo inverse matrix. (stored only if store_precision is true)
Returns Promise<ArrayLike[]>
Defined in generated/covariance/EllipticEnvelope.ts:700
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/covariance/EllipticEnvelope.ts:59
raw_covariance_
Get Signature
get raw_covariance_():
Promise<ArrayLike[]>
The raw robust estimated covariance before correction and re-weighting.
Returns Promise<ArrayLike[]>
Defined in generated/covariance/EllipticEnvelope.ts:808
raw_location_
Get Signature
get raw_location_():
Promise<ArrayLike>
The raw robust estimated location before correction and re-weighting.
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:781
raw_support_
Get Signature
get raw_support_():
Promise<ArrayLike>
A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting.
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:835
support_
Get Signature
get support_():
Promise<ArrayLike>
A mask of the observations that have been used to compute the robust estimates of location and shape.
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:727
Methods
correct_covariance()
correct_covariance(
opts):Promise<ArrayLike[]>
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Parameters
| Parameter | 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:132
decision_function()
decision_function(
opts):Promise<ArrayLike>
Compute the decision function of the given observations.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.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/covariance/EllipticEnvelope.ts:113
error_norm()
error_norm(
opts):Promise<number>
Compute the Mean Squared Error between two covariance estimators.
Parameters
| Parameter | 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_). |
opts.scaling? | boolean | If true (default), the squared error norm is divided by n_features. If false, the squared error norm is not rescaled. |
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. |
Returns Promise<number>
Defined in generated/covariance/EllipticEnvelope.ts:204
fit()
fit(
opts):Promise<any>
Fit the EllipticEnvelope model.
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/covariance/EllipticEnvelope.ts:259
fit_predict()
fit_predict(
opts):Promise<ArrayLike>
Perform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.kwargs? | any | Arguments to be passed to fit. |
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:300
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/covariance/EllipticEnvelope.ts:346
get_precision()
get_precision(
opts):Promise<any>
Getter for the precision matrix.
Parameters
| Parameter | 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:382
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/covariance/EllipticEnvelope.ts:72
mahalanobis()
mahalanobis(
opts):Promise<ArrayLike>
Compute the squared Mahalanobis distances of given observations.
Parameters
| Parameter | 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:418
predict()
predict(
opts):Promise<ArrayLike>
Predict labels (1 inlier, -1 outlier) of X according to fitted model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:452
reweight_covariance()
reweight_covariance(
opts):Promise<ArrayLike>
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].
Parameters
| Parameter | 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:488
score()
score(
opts):Promise<number>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike | True labels for X. |
Returns Promise<number>
Defined in generated/covariance/EllipticEnvelope.ts:526
score_samples()
score_samples(
opts):Promise<ArrayLike>
Compute the negative Mahalanobis distances.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns Promise<ArrayLike>
Defined in generated/covariance/EllipticEnvelope.ts:570
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/covariance/EllipticEnvelope.ts:610