snowflake.ml.modeling.decomposition.MiniBatchSparsePCA¶

class snowflake.ml.modeling.decomposition.MiniBatchSparsePCA(*, n_components=None, alpha=1, ridge_alpha=0.01, n_iter='deprecated', max_iter=None, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None, tol=0.001, max_no_improvement=10, 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

Mini-batch Sparse Principal Components Analysis For more details on this class, see sklearn.decomposition.MiniBatchSparsePCA

n_components: int, default=None

Number of sparse atoms to extract. If None, then n_components is set to n_features.

alpha: int, default=1

Sparsity controlling parameter. Higher values lead to sparser components.

ridge_alpha: float, default=0.01

Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.

n_iter: int, default=100

Number of iterations to perform for each mini batch.

max_iter: int, default=None

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. If max_iter is not None, n_iter is ignored.

callback: callable, default=None

Callable that gets invoked every five iterations.

batch_size: int, default=3

The number of features to take in each mini batch.

verbose: int or bool, default=False

Controls the verbosity; the higher, the more messages. Defaults to 0.

shuffle: bool, default=True

Whether to shuffle the data before splitting it in batches.

n_jobs: int, 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.

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

Method to be used for optimization. lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.

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

Used for random shuffling when shuffle is set to True, during online dictionary learning. Pass an int for reproducible results across multiple function calls. See Glossary.

tol: float, default=1e-3

Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if max_iter is not None.

To disable early stopping based on changes in the dictionary, set tol to 0.0.

max_no_improvement: int or None, default=10

Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. Used only if max_iter is not None.

To disable convergence detection based on cost function, set max_no_improvement to None.

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.MiniBatchSparsePCA.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.decomposition.MiniBatchSparsePCA object.
transform(dataset)	Least Squares projection of the data onto the sparse components For more details on this function, see sklearn.decomposition.MiniBatchSparsePCA.transform

Attributes

model_signatures

Returns model signature of current class.