snowflake.ml.modeling.decomposition.DictionaryLearning¶

class snowflake.ml.modeling.decomposition.DictionaryLearning(*, n_components=None, alpha=1, max_iter=1000, tol=1e-08, fit_algorithm='lars', transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, n_jobs=None, code_init=None, dict_init=None, callback=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False, transform_max_iter=1000, 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

Dictionary learning For more details on this class, see sklearn.decomposition.DictionaryLearning

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]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

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.

n_components: int, default=None

Number of dictionary elements to extract. If None, then n_components is set to n_features.

alpha: float, default=1.0

Sparsity controlling parameter.

max_iter: int, default=1000

Maximum number of iterations to perform.

tol: float, default=1e-8

Tolerance for numerical error.

fit_algorithm: {‘lars’, ‘cd’}, default=’lars’

• ‘lars’: uses the least angle regression method to solve the lasso problem (lars_path());

• ‘cd’: uses the coordinate descent method to compute the Lasso solution (Lasso). Lars will be faster if the estimated components are sparse.

transform_algorithm: {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’omp’

Algorithm used to transform the data:

• ‘lars’: uses the least angle regression method (lars_path());

• ‘lasso_lars’: uses Lars to compute the Lasso solution.

• ‘lasso_cd’: uses the coordinate descent method to compute the Lasso solution (Lasso). ‘lasso_lars’ will be faster if the estimated components are sparse.

• ‘omp’: uses orthogonal matching pursuit to estimate the sparse solution.

• ‘threshold’: squashes to zero all coefficients less than alpha from the projection dictionary * X'.

transform_n_nonzero_coefs: int, default=None

Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’. If None, then transform_n_nonzero_coefs=int(n_features / 10).

transform_alpha: float, default=None

If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If None, defaults to alpha.

n_jobs: int or None, default=None

Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

code_init: ndarray of shape (n_samples, n_components), default=None

Initial value for the code, for warm restart. Only used if code_init and dict_init are not None.

dict_init: ndarray of shape (n_components, n_features), default=None

Initial values for the dictionary, for warm restart. Only used if code_init and dict_init are not None.

callback: callable, default=None

Callable that gets invoked every five iterations.

verbose: bool, default=False

To control the verbosity of the procedure.

split_sign: bool, default=False

Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.

random_state: int, RandomState instance or None, default=None

Used for initializing the dictionary when dict_init is not specified, randomly shuffling the data when shuffle is set to True, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See Glossary.

positive_code: bool, default=False

Whether to enforce positivity when finding the code.

positive_dict: bool, default=False

Whether to enforce positivity when finding the dictionary.

transform_max_iter: int, default=1000

Maximum number of iterations to perform if algorithm=’lasso_cd’ or ‘lasso_lars’.

Methods

fit(dataset)	Fit the model from data in X For more details on this function, see sklearn.decomposition.DictionaryLearning.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.
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.decomposition.DictionaryLearning object.
transform(dataset)	Encode the data as a sparse combination of the dictionary atoms For more details on this function, see sklearn.decomposition.DictionaryLearning.transform

Attributes

model_signatures

Returns model signature of current class.