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: {βparallelβ, βdeflationβ}, default=βparallelβ
Specify which algorithm to use for FastICA.
- whiten: str or bool, default=βunit-varianceβ
Specify the whitening strategy to use.
If βarbitrary-varianceβ, a whitening with variance arbitrary is used.
If βunit-varianceβ, 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: {βlogcoshβ, βexpβ, βcubeβ} or callable, default=βlogcoshβ
The functional form of the G function used in the approximation to neg-entropy. Could be either βlogcoshβ, βexpβ, or βcubeβ. 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)
- fun_args: dict, default=None
Arguments to send to the functional form. If empty or None and if fun=βlogcoshβ, fun_args will take value {βalphaβ: 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: {βeighβ, βsvdβ}, default=βsvdβ
The solver to use for whitening.
βsvdβ is more stable numerically if the problem is degenerate, and often faster when n_samples <= n_features.
βeighβ 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 estimatorβs predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examplesβ 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.