snowflake.ml.modeling.decomposition.MiniBatchDictionaryLearning¶

class snowflake.ml.modeling.decomposition.MiniBatchDictionaryLearning(*, n_components=None, alpha=1, n_iter='deprecated', max_iter=None, fit_algorithm='lars', n_jobs=None, batch_size=256, shuffle=True, dict_init=None, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False, transform_max_iter=1000, callback=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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Mini-batch dictionary learning For more details on this class, see sklearn.decomposition.MiniBatchDictionaryLearning

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.

alpha: float, default=1

Sparsity controlling parameter.

n_iter: int, default=1000

Total number of iterations over data batches to perform.

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.

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

The algorithm used:

• ‘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.

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.

batch_size: int, default=256

Number of samples in each mini-batch.

shuffle: bool, default=True

Whether to shuffle the samples before forming batches.

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

Initial value of the dictionary for warm restart scenarios.

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

Algorithm used to transform the data:

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

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

• ‘lasso_cd’: uses the coordinate descent method to compute the Lasso solution (linear_model.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.

verbose: bool or int, 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’.

callback: callable, default=None

A callable that gets invoked at the end of each iteration.

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, 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.

Base class for all transformers.

Methods

fit(dataset: Union[DataFrame, DataFrame]) → MiniBatchDictionaryLearning¶

Fit the model from data in X For more details on this function, see sklearn.decomposition.MiniBatchDictionaryLearning.fit

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:

dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

self

fit_transform(dataset: Union[DataFrame, DataFrame]) → Union[Any, ndarray[Any, dtype[Any]]]¶

Returns:

Transformed dataset.

get_input_cols() → List[str]¶

Input columns getter.

Returns:

Input columns.

get_label_cols() → List[str]¶

Label column getter.

Returns:

Label column(s).

get_output_cols() → List[str]¶

Output columns getter.

Returns:

Output columns.

get_params(deep: bool = True) → Dict[str, Any]¶

Get parameters for this transformer.

Args:

deep: If True, will return the parameters for this transformer and

contained subobjects that are transformers.

Returns:

Parameter names mapped to their values.

get_passthrough_cols() → List[str]¶

Passthrough columns getter.

Returns:

Passthrough column(s).

get_sample_weight_col() → Optional[str]¶

Sample weight column getter.

Returns:

Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) → Dict[str, Any]¶

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Args:

default_sklearn_obj: Sklearn object used to get default parameter values. Necessary when

sklearn_added_keyword_to_version_dict is provided.

sklearn_initial_keywords: Initial keywords in sklearn. sklearn_unused_keywords: Sklearn keywords that are unused in snowml. snowml_only_keywords: snowml only keywords not present in sklearn. sklearn_added_keyword_to_version_dict: Added keywords mapped to the sklearn versions in which they were

added.

sklearn_added_kwarg_value_to_version_dict: Added keyword argument values mapped to the sklearn versions

in which they were added.

sklearn_deprecated_keyword_to_version_dict: Deprecated keywords mapped to the sklearn versions in which

they were deprecated.

sklearn_removed_keyword_to_version_dict: Removed keywords mapped to the sklearn versions in which they

were removed.

Returns:

Sklearn parameter names mapped to their values.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None¶

set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → MiniBatchDictionaryLearning¶

Input columns setter.

Args:

input_cols: A single input column or multiple input columns.

Returns:

self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Label column setter.

Args:

label_cols: A single label column or multiple label columns if multi task learning.

Returns:

self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Output columns setter.

Args:

output_cols: A single output column or multiple output columns.

Returns:

self

set_params(**params: Dict[str, Any]) → None¶

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Args:

**params: Transformer parameter names mapped to their values.

Raises:

SnowflakeMLException: Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Passthrough columns setter.

Args:

passthrough_cols: Column(s) that should not be used or modified by the estimator/transformer.

Estimator/Transformer just passthrough these columns without any modifications.

Returns:

self

set_sample_weight_col(sample_weight_col: Optional[str]) → Base¶

Sample weight column setter.

Args:

sample_weight_col: A single column that represents sample weight.

Returns:

self

to_sklearn() → Any¶

Get sklearn.decomposition.MiniBatchDictionaryLearning object.

transform(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]¶

Encode the data as a sparse combination of the dictionary atoms For more details on this function, see sklearn.decomposition.MiniBatchDictionaryLearning.transform

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:

dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Transformed dataset.

Attributes

model_signatures¶

Returns model signature of current class.

Raises:

exceptions.SnowflakeMLException: If estimator is not fitted, then model signature cannot be inferred

Returns:

Dict[str, ModelSignature]: each method and its input output signature