snowflake.ml.modeling.mixture.GaussianMixture

class snowflake.ml.modeling.mixture.GaussianMixture(*, n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

Gaussian Mixture For more details on this class, see sklearn.mixture.GaussianMixture

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

n_components: int, default=1

The number of mixture components.

covariance_type: {‘full’, ‘tied’, ‘diag’, ‘spherical’}, default=’full’

String describing the type of covariance parameters to use. Must be one of:

  • ‘full’: each component has its own general covariance matrix.

  • ‘tied’: all components share the same general covariance matrix.

  • ‘diag’: each component has its own diagonal covariance matrix.

  • ‘spherical’: each component has its own single variance.

tol: float, default=1e-3

The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.

reg_covar: float, default=1e-6

Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.

max_iter: int, default=100

The number of EM iterations to perform.

n_init: int, default=1

The number of initializations to perform. The best results are kept.

init_params: {‘kmeans’, ‘k-means++’, ‘random’, ‘random_from_data’}, default=’kmeans’

The method used to initialize the weights, the means and the precisions. String must be one of:

  • ‘kmeans’: responsibilities are initialized using kmeans.

  • ‘k-means++’: use the k-means++ method to initialize.

  • ‘random’: responsibilities are initialized randomly.

  • ‘random_from_data’: initial means are randomly selected data points.

weights_init: array-like of shape (n_components, ), default=None

The user-provided initial weights. If it is None, weights are initialized using the init_params method.

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

The user-provided initial means, If it is None, means are initialized using the init_params method.

precisions_init: array-like, default=None

The user-provided initial precisions (inverse of the covariance matrices). If it is None, precisions are initialized using the ‘init_params’ method. The shape depends on ‘covariance_type’:

(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
Copy
random_state: int, RandomState instance or None, default=None

Controls the random seed given to the method chosen to initialize the parameters (see init_params). In addition, it controls the generation of random samples from the fitted distribution (see the method sample). Pass an int for reproducible output across multiple function calls. See Glossary.

warm_start: bool, default=False

If ‘warm_start’ is True, the solution of the last fitting is used as initialization for the next call of fit(). This can speed up convergence when fit is called several times on similar problems. In that case, ‘n_init’ is ignored and only a single initialization occurs upon the first call. See the Glossary.

verbose: int, default=0

Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step.

verbose_interval: int, default=10

Number of iteration done before the next print.

Methods

fit(dataset)

Estimate model parameters with the EM algorithm For more details on this function, see sklearn.mixture.GaussianMixture.fit

fit_predict(dataset)

Estimate model parameters using X and predict the labels for X For more details on this function, see sklearn.mixture.GaussianMixture.fit_predict

get_input_cols()

Input columns getter.

get_label_cols()

Label column getter.

get_output_cols()

Output columns getter.

get_params([deep])

Get parameters for this transformer.

get_passthrough_cols()

Passthrough columns getter.

get_sample_weight_col()

Sample weight column getter.

get_sklearn_args([default_sklearn_obj, ...])

Get sklearn keyword arguments.

predict(dataset)

Predict the labels for the data samples in X using trained model For more details on this function, see sklearn.mixture.GaussianMixture.predict

predict_proba(dataset[, output_cols_prefix])

Evaluate the components' density for each sample For more details on this function, see sklearn.mixture.GaussianMixture.predict_proba

score(dataset)

Compute the per-sample average log-likelihood of the given data X For more details on this function, see sklearn.mixture.GaussianMixture.score

set_drop_input_cols([drop_input_cols])

set_input_cols(input_cols)

Input columns setter.

set_label_cols(label_cols)

Label column setter.

set_output_cols(output_cols)

Output columns setter.

set_params(**params)

Set the parameters of this transformer.

set_passthrough_cols(passthrough_cols)

Passthrough columns setter.

set_sample_weight_col(sample_weight_col)

Sample weight column setter.

to_sklearn()

Get sklearn.mixture.GaussianMixture object.

Attributes

model_signatures

Returns model signature of current class.
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