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, passthrough_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

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

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

A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

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

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

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

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.

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
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
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_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column 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.