snowflake.ml.modeling.mixture.BayesianGaussianMixture

class snowflake.ml.modeling.mixture.BayesianGaussianMixture(*, n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=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

Variational Bayesian estimation of a Gaussian mixture For more details on this class, see sklearn.mixture.BayesianGaussianMixture

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. Depending on the data and the value of the weight_concentration_prior the model can decide to not use all the components by setting some component weights_ to values very close to zero. The number of effective components is therefore smaller than n_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).
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tol: float, default=1e-3

The convergence threshold. EM iterations will stop when the lower bound average gain on the likelihood (of the training data with respect to the model) 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 result with the highest lower bound value on the likelihood is kept.

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

The method used to initialize the weights, the means and the covariances. 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.

weight_concentration_prior_type: {‘dirichlet_process’, ‘dirichlet_distribution’}, default=’dirichlet_process’

String describing the type of the weight concentration prior.

weight_concentration_prior: float or None, default=None

The dirichlet concentration of each component on the weight distribution (Dirichlet). This is commonly called gamma in the literature. The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the mixture weights simplex. The value of the parameter must be greater than 0. If it is None, it’s set to 1. / n_components.

mean_precision_prior: float or None, default=None

The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around mean_prior. The value of the parameter must be greater than 0. If it is None, it is set to 1.

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

The prior on the mean distribution (Gaussian). If it is None, it is set to the mean of X.

degrees_of_freedom_prior: float or None, default=None

The prior of the number of degrees of freedom on the covariance distributions (Wishart). If it is None, it’s set to n_features.

covariance_prior: float or array-like, default=None

The prior on the covariance distribution (Wishart). If it is None, the emiprical covariance prior is initialized using the covariance of X. The shape depends on covariance_type:

(n_features, n_features) if 'full',
(n_features, n_features) if 'tied',
(n_features)             if 'diag',
float                    if 'spherical'
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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. 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.

Base class for all transformers.

Methods

fit(dataset: Union[DataFrame, DataFrame]) → BayesianGaussianMixture

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

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

self

fit_predict(dataset: Union[DataFrame, DataFrame]) → Union[Any, ndarray[Any, dtype[Any]]]

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

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Predicted dataset.

fit_transform(dataset: Union[DataFrame, DataFrame]) → Union[Any, ndarray[Any, dtype[Any]]]
Returns:

Transformed dataset.

get_input_cols() → List[str]

Input columns getter.

Returns:

Input columns.

get_label_cols() → List[str]

Label column getter.

Returns:

Label column(s).

get_output_cols() → List[str]

Output columns getter.

Returns:

Output columns.

get_params(deep: bool = True) → Dict[str, Any]

Get parameters for this transformer.

Args:
deep: If True, will return the parameters for this transformer and

contained subobjects that are transformers.

Returns:

Parameter names mapped to their values.

get_passthrough_cols() → List[str]

Passthrough columns getter.

Returns:

Passthrough column(s).

get_sample_weight_col() → Optional[str]

Sample weight column getter.

Returns:

Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) → Dict[str, Any]

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Args:
default_sklearn_obj: Sklearn object used to get default parameter values. Necessary when

sklearn_added_keyword_to_version_dict is provided.

sklearn_initial_keywords: Initial keywords in sklearn. sklearn_unused_keywords: Sklearn keywords that are unused in snowml. snowml_only_keywords: snowml only keywords not present in sklearn. sklearn_added_keyword_to_version_dict: Added keywords mapped to the sklearn versions in which they were

added.

sklearn_added_kwarg_value_to_version_dict: Added keyword argument values mapped to the sklearn versions

in which they were added.

sklearn_deprecated_keyword_to_version_dict: Deprecated keywords mapped to the sklearn versions in which

they were deprecated.

sklearn_removed_keyword_to_version_dict: Removed keywords mapped to the sklearn versions in which they

were removed.

Returns:

Sklearn parameter names mapped to their values.

predict(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]

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

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Transformed dataset.

predict_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_proba_') → Union[DataFrame, DataFrame]

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

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns

Returns:

Output dataset with probability of the sample for each class in the model.

score(dataset: Union[DataFrame, DataFrame]) → float

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

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Score.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None
set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → BayesianGaussianMixture

Input columns setter.

Args:

input_cols: A single input column or multiple input columns.

Returns:

self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) → Base

Label column setter.

Args:

label_cols: A single label column or multiple label columns if multi task learning.

Returns:

self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) → Base

Output columns setter.

Args:

output_cols: A single output column or multiple output columns.

Returns:

self

set_params(**params: Dict[str, Any]) → None

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Args:

**params: Transformer parameter names mapped to their values.

Raises:

SnowflakeMLException: Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) → Base

Passthrough columns setter.

Args:
passthrough_cols: Column(s) that should not be used or modified by the estimator/transformer.

Estimator/Transformer just passthrough these columns without any modifications.

Returns:

self

set_sample_weight_col(sample_weight_col: Optional[str]) → Base

Sample weight column setter.

Args:

sample_weight_col: A single column that represents sample weight.

Returns:

self

to_sklearn() → Any

Get sklearn.mixture.BayesianGaussianMixture object.

Attributes

model_signatures

Returns model signature of current class.

Raises:

exceptions.SnowflakeMLException: If estimator is not fitted, then model signature cannot be inferred

Returns:

Dict[str, ModelSignature]: each method and its input output signature

Sprache: Deutsch