snowflake.ml.modeling.decomposition.IncrementalPCA¶

class snowflake.ml.modeling.decomposition.IncrementalPCA(*, n_components=None, whiten=False, copy=True, batch_size=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)¶

Bases: BaseTransformer

Incremental principal components analysis (IPCA) For more details on this class, see sklearn.decomposition.IncrementalPCA

n_components: int, default=None

Number of components to keep. If n_components is None, then n_components is set to min(n_samples, n_features).

whiten: bool, default=False

When True (False by default) the components_ vectors are divided by n_samples times components_ to ensure uncorrelated outputs with unit component-wise variances.

Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometimes improve the predictive accuracy of the downstream estimators by making data respect some hard-wired assumptions.

copy: bool, default=True

If False, X will be overwritten. copy=False can be used to save memory but is unsafe for general use.

batch_size: int, default=None

The number of samples to use for each batch. Only used when calling fit. If batch_size is None, then batch_size is inferred from the data and set to 5 * n_features, to provide a balance between approximation accuracy and memory consumption.

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 with X, using minibatches of size batch_size For more details on this function, see sklearn.decomposition.IncrementalPCA.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.decomposition.IncrementalPCA object.
transform(dataset)	Apply dimensionality reduction to X For more details on this function, see sklearn.decomposition.IncrementalPCA.transform

Attributes

model_signatures

Returns model signature of current class.