snowflake.ml.modeling.decomposition.FastICA

class snowflake.ml.modeling.decomposition.FastICA(*, n_components=None, algorithm='parallel', whiten='unit-variance', fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, whiten_solver='svd', random_state=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

FastICA: a fast algorithm for Independent Component Analysis For more details on this class, see sklearn.decomposition.FastICA

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 components to use. If None is passed, all are used.

algorithm: {‘parallel’, ‘deflation’}, default=’parallel’

Specify which algorithm to use for FastICA.

whiten: str or bool, default=’unit-variance’

Specify the whitening strategy to use.

  • If ‘arbitrary-variance’, a whitening with variance arbitrary is used.

  • If ‘unit-variance’, the whitening matrix is rescaled to ensure that each recovered source has unit variance.

  • If False, the data is already considered to be whitened, and no whitening is performed.

fun: {‘logcosh’, ‘exp’, ‘cube’} or callable, default=’logcosh’

The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. The derivative should be averaged along its last dimension. Example:

def my_g(x):
    return x ** 3, (3 * x ** 2).mean(axis=-1)
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fun_args: dict, default=None

Arguments to send to the functional form. If empty or None and if fun=’logcosh’, fun_args will take value {‘alpha’: 1.0}.

max_iter: int, default=200

Maximum number of iterations during fit.

tol: float, default=1e-4

A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.

w_init: array-like of shape (n_components, n_components), default=None

Initial un-mixing array. If w_init=None, then an array of values drawn from a normal distribution is used.

whiten_solver: {“eigh”, “svd”}, default=”svd”

The solver to use for whitening.

  • “svd” is more stable numerically if the problem is degenerate, and often faster when n_samples <= n_features.

  • “eigh” is generally more memory efficient when n_samples >= n_features, and can be faster when n_samples >= 50 * n_features.

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

Used to initialize w_init when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls. See Glossary.

Methods

fit(dataset)

Fit the model to X For more details on this function, see sklearn.decomposition.FastICA.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.FastICA object.

transform(dataset)

Recover the sources from X (apply the unmixing matrix) For more details on this function, see sklearn.decomposition.FastICA.transform

Attributes

model_signatures

Returns model signature of current class.
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