snowflake.ml.modeling.decomposition.FactorAnalysis¶

class snowflake.ml.modeling.decomposition.FactorAnalysis(*, n_components=None, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, svd_method='randomized', iterated_power=3, rotation=None, random_state=0, 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

Factor Analysis (FA) For more details on this class, see sklearn.decomposition.FactorAnalysis

n_components: int, default=None

Dimensionality of latent space, the number of components of X that are obtained after transform. If None, n_components is set to the number of features.

tol: float, default=1e-2

Stopping tolerance for log-likelihood increase.

copy: bool, default=True

Whether to make a copy of X. If False, the input X gets overwritten during fitting.

max_iter: int, default=1000

Maximum number of iterations.

noise_variance_init: array-like of shape (n_features,), default=None

The initial guess of the noise variance for each feature. If None, it defaults to np.ones(n_features).

svd_method: {‘lapack’, ‘randomized’}, default=’randomized’

Which SVD method to use. If ‘lapack’ use standard SVD from scipy.linalg, if ‘randomized’ use fast randomized_svd function. Defaults to ‘randomized’. For most applications ‘randomized’ will be sufficiently precise while providing significant speed gains. Accuracy can also be improved by setting higher values for iterated_power. If this is not sufficient, for maximum precision you should choose ‘lapack’.

iterated_power: int, default=3

Number of iterations for the power method. 3 by default. Only used if svd_method equals ‘randomized’.

rotation: {‘varimax’, ‘quartimax’}, default=None

If not None, apply the indicated rotation. Currently, varimax and quartimax are implemented. See “The varimax criterion for analytic rotation in factor analysis” H. F. Kaiser, 1958.

random_state: int or RandomState instance, default=0

Only used when svd_method equals ‘randomized’. 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 FactorAnalysis model to X using SVD based approach For more details on this function, see sklearn.decomposition.FactorAnalysis.fit
score(dataset)	Compute the average log-likelihood of the samples For more details on this function, see sklearn.decomposition.FactorAnalysis.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.decomposition.FactorAnalysis object.
transform(dataset)	Apply dimensionality reduction to X using the model For more details on this function, see sklearn.decomposition.FactorAnalysis.transform

Attributes

model_signatures

Returns model signature of current class.