lale.lib.autogen.linear_discriminant_analysis module

class lale.lib.autogen.linear_discriminant_analysis.LinearDiscriminantAnalysis(*, solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• solver (‘eigen’, ‘lsqr’, or ‘svd’, default ‘svd’) –

  Solver to use, possible values: - ‘svd’: Singular value decomposition (default)

  See also constraint-1, constraint-2, constraint-3, constraint-4, constraint-4.

• shrinkage (union type, default None) –

  Shrinkage parameter, possible values: - None: no shrinkage (default)

  • ’auto’

  • or float, >0, >=0 for optimizer, <1, <=1 for optimizer, uniform distribution

  • or None

  See also constraint-1, constraint-4.

• priors (None, not for optimizer, default None) – Class priors.

• n_components (union type, default None) –

  Number of components (< n_classes - 1) for dimensionality reduction.

  • integer, >=2 for optimizer, <=’X/items/maxItems’, <=256 for optimizer, uniform distribution

  • or None

• store_covariance (boolean, not for optimizer, default False) –

  Additionally compute class covariance matrix (default False), used only in ‘svd’ solver

  See also constraint-2.

• tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0001) – Threshold used for rank estimation in SVD solver

• covariance_estimator (union type, optional, not for optimizer, default None) –

  type of (covariance estimator). Estimate the covariance matrices instead of relying on the empirical covariance estimator (with potential shrinkage)

  • string, not for optimizer

  • or None

  See also constraint-3, constraint-4.

Notes

constraint-1 : union type

shrinkage, only with ‘lsqr’ and ‘eigen’ solvers

• shrinkage : None

• or solver : ‘lsqr’ or ‘eigen’

constraint-2 : union type

store_covariance, only in ‘svd’ solver

• store_covariance : False

• or solver : ‘svd’

constraint-3 : union type

covariance estimator is not supported with svd solver. Try another solver

• solver : negated type of ‘svd’

• or covariance_estimator : None

constraint-4 : union type

covariance_estimator and shrinkage parameters are not None. Only one of the two can be set.

• solver : ‘svd’ or ‘lsqr’

• or solver : negated type of ‘eigen’

• or covariance_estimator : None

• or shrinkage : None or 0

decision_function(X)

Confidence scores for all classes.

Note: The decision_function method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Samples.

• array of items : Any

• or array of items : array of items : float

Returns

result – Confidence scores per (sample, class) combination

Return type

Any

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training data.

• y (array of items : float) – Target values.

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Samples.

• array of items : Any

• or array of items : array of items : float

Returns

result – Predicted class label per sample.

Return type

array of items : float

predict_proba(X)

Probability estimates for all classes.

Note: The predict_proba method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) – Input data.

Returns

result – Estimated probabilities.

Return type

array of items : array of items : float

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) – Input data.

Returns

result – Transformed data.

Return type

array of items : array of items : float