snowflake.ml.modeling.discriminant_analysis.LinearDiscriminantAnalysis¶

class snowflake.ml.modeling.discriminant_analysis.LinearDiscriminantAnalysis(*, solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Linear Discriminant Analysis For more details on this class, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis

solver: {‘svd’, ‘lsqr’, ‘eigen’}, default=’svd’

Solver to use, possible values:

• ‘svd’: Singular value decomposition (default). Does not compute the covariance matrix, therefore this solver is recommended for data with a large number of features.

• ‘lsqr’: Least squares solution. Can be combined with shrinkage or custom covariance estimator.

• ‘eigen’: Eigenvalue decomposition. Can be combined with shrinkage or custom covariance estimator.

shrinkage: ‘auto’ or float, default=None

Shrinkage parameter, possible values:

• None: no shrinkage (default).

• ‘auto’: automatic shrinkage using the Ledoit-Wolf lemma.

• float between 0 and 1: fixed shrinkage parameter.

This should be left to None if covariance_estimator is used. Note that shrinkage works only with ‘lsqr’ and ‘eigen’ solvers.

priors: array-like of shape (n_classes,), default=None

The class prior probabilities. By default, the class proportions are inferred from the training data.

n_components: int, default=None

Number of components (<= min(n_classes - 1, n_features)) for dimensionality reduction. If None, will be set to min(n_classes - 1, n_features). This parameter only affects the transform method.

store_covariance: bool, default=False

If True, explicitly compute the weighted within-class covariance matrix when solver is ‘svd’. The matrix is always computed and stored for the other solvers.

tol: float, default=1.0e-4

Absolute threshold for a singular value of X to be considered significant, used to estimate the rank of X. Dimensions whose singular values are non-significant are discarded. Only used if solver is ‘svd’.

covariance_estimator: covariance estimator, default=None

If not None, covariance_estimator is used to estimate the covariance matrices instead of relying on the empirical covariance estimator (with potential shrinkage). The object should have a fit method and a covariance_ attribute like the estimators in sklearn.covariance. if None the shrinkage parameter drives the estimate.

This should be left to None if shrinkage is used. Note that covariance_estimator works only with ‘lsqr’ and ‘eigen’ solvers.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

decision_function(dataset[, output_cols_prefix])	Apply decision function to an array of samples For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function
fit(dataset)	Fit the Linear Discriminant Analysis model For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit
predict(dataset)	Predict class labels for samples in X For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict
predict_log_proba(dataset[, output_cols_prefix])	Estimate probability For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba
predict_proba(dataset[, output_cols_prefix])	Estimate probability For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.discriminant_analysis.LinearDiscriminantAnalysis object.
transform(dataset)	Project data to maximize class separation For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform

Attributes

model_signatures

Returns model signature of current class.