snowflake.ml.modeling.cluster.KMeans

class snowflake.ml.modeling.cluster.KMeans(*, n_clusters=8, init='k-means++', n_init='auto', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd', 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

K-Means clustering For more details on this class, see sklearn.cluster.KMeans

Parameters:

  • 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]]]) – A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.

  • 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_clusters (int, default=8) –

    The number of clusters to form as well as the number of centroids to generate.

    For an example of how to choose an optimal value for n_clusters refer to sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py.

  • init ({'k-means++', 'random'}, callable or array-like of shape (n_clusters, n_features), default='k-means++') –

    Method for initialization:

    • ’k-means++’: selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them.

    • ’random’: choose n_clusters observations (rows) at random from data for the initial centroids.

    • If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

    • If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.

    For an example of how to use the different init strategy, see the example entitled sphx_glr_auto_examples_cluster_plot_kmeans_digits.py.

  • n_init ('auto' or int, default='auto') –

    Number of times the k-means algorithm is run with different centroid seeds. The final results is the best output of n_init consecutive runs in terms of inertia. Several runs are recommended for sparse high-dimensional problems (see kmeans_sparse_high_dim).

    When n_init=’auto’, the number of runs depends on the value of init: 10 if using init=’random’ or init is a callable; 1 if using init=’k-means++’ or init is an array-like.

  • max_iter (int, default=300) – Maximum number of iterations of the k-means algorithm for a single run.

  • tol (float, default=1e-4) – Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.

  • verbose (int, default=0) – Verbosity mode.

  • random_state (int, RandomState instance or None, default=None) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See Glossary.

  • copy_x (bool, default=True) – When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean. Note that if the original data is not C-contiguous, a copy will be made even if copy_x is False. If the original data is sparse, but not in CSR format, a copy will be made even if copy_x is False.

  • algorithm ({"lloyd", "elkan"}, default="lloyd") – K-means algorithm to use. The classical EM-style algorithm is “lloyd”. The “elkan” variation can be more efficient on some datasets with well-defined clusters, by using the triangle inequality. However it’s more memory intensive due to the allocation of an extra array of shape (n_samples, n_clusters).

Base class for all transformers.

Methods

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

Runs universal logics for all fit implementations.

fit_predict(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'fit_predict_') → Union[DataFrame, DataFrame]

Compute cluster centers and predict cluster index for each sample For more details on this function, see sklearn.cluster.KMeans.fit_predict

Raises:

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

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns :returns: Predicted dataset.

fit_transform(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'fit_transform_') → Union[DataFrame, DataFrame]

Compute clustering and transform X to cluster-distance space For more details on this function, see sklearn.cluster.KMeans.fit_transform

Raises:

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

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns :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 the snowflake-ml parameters for this transformer.

Parameters:

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.

Parameters:

  • 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 closest cluster each sample in X belongs to For more details on this function, see sklearn.cluster.KMeans.predict

Raises:

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

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

Returns:

Transformed dataset.

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

Opposite of the value of X on the K-means objective For more details on this function, see sklearn.cluster.KMeans.score

Raises:

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

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

Returns:

Score.

score_samples(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'score_samples_') → Union[DataFrame, DataFrame]

Method not supported for this class.

Raises:

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

Parameters:

  • 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.

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

Input columns setter.

Parameters:

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.

Parameters:

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.

Parameters:

output_cols – A single output column or multiple output columns.

Returns:

self

set_params(**params: Any) → None

Set the parameters of this transformer.

The method works on simple transformers as well as on sklearn compatible pipelines with nested objects, once the transformer has been fit. Nested objects have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**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.

Parameters:

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.

Parameters:

sample_weight_col – A single column that represents sample weight.

Returns:

self

to_sklearn() → Any

Get sklearn.cluster.KMeans object.

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

Transform X to a cluster-distance space For more details on this function, see sklearn.cluster.KMeans.transform

Raises:

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

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

Returns:

Transformed dataset.

Attributes

model_signatures

Returns model signature of current class.

Raises:

SnowflakeMLException – If estimator is not fitted, then model signature cannot be inferred

Returns:

Dict with each method and its input output signature

Language: English