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snowflake.ml.modeling.decomposition.FastICAΒΆ

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, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)ΒΆ

Bases: BaseTransformer

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

n_components: int, default=None

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

algorithm: {β€˜parallel’, β€˜deflation’}, default=’parallel’

Specify which algorithm to use for FastICA.

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

Specify the whitening strategy to use.

  • If β€˜arbitrary-variance’, a whitening with variance arbitrary is used.

  • If β€˜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: {β€˜logcosh’, β€˜exp’, β€˜cube’} or callable, default=’logcosh’

The functional form of the G function used in the approximation to neg-entropy. Could be either β€˜logcosh’, β€˜exp’, or β€˜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 {β€˜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: {β€œeigh”, β€œsvd”}, default=”svd”

The solver to use for whitening.

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

  • β€œeigh” 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.

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 to X For more details on this function, see sklearn.decomposition.FastICA.fit

score(dataset)

Method not supported for this class.

set_input_cols(input_cols)

Input columns 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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