snowflake.ml.modeling.neural_network.BernoulliRBM¶

class snowflake.ml.modeling.neural_network.BernoulliRBM(*, n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, 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)¶

Bases: BaseTransformer

Bernoulli Restricted Boltzmann Machine (RBM) For more details on this class, see sklearn.neural_network.BernoulliRBM

n_components: int, default=256

Number of binary hidden units.

learning_rate: float, default=0.1

The learning rate for weight updates. It is highly recommended to tune this hyper-parameter. Reasonable values are in the 10**[0., -3.] range.

batch_size: int, default=10

Number of examples per minibatch.

n_iter: int, default=10

Number of iterations/sweeps over the training dataset to perform during training.

verbose: int, default=0

The verbosity level. The default, zero, means silent mode. Range of values is [0, inf].

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

Determines random number generation for:

• Gibbs sampling from visible and hidden layers.

• Initializing components, sampling from layers during fit.

• Corrupting the data when scoring samples.

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 the data X For more details on this function, see sklearn.neural_network.BernoulliRBM.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.neural_network.BernoulliRBM object.
transform(dataset)	Compute the hidden layer activation probabilities, P(h=1|v=X) For more details on this function, see sklearn.neural_network.BernoulliRBM.transform

Attributes

model_signatures

Returns model signature of current class.