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, 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

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’.

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

fit(dataset)	Fit the model from data in X For more details on this function, see sklearn.decomposition.DictionaryLearning.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns 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.