snowflake.ml.modeling.svm.NuSVC

class snowflake.ml.modeling.svm.NuSVC(*, nu=0.5, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=- 1, decision_function_shape='ovr', break_ties=False, 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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

Nu-Support Vector Classification For more details on this class, see sklearn.svm.NuSVC (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html)

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.

  • nu (float, default=0.5) – An upper bound on the fraction of margin errors (see User Guide) and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].

  • kernel ({'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, default='rbf') – Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

  • degree (int, default=3) – Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels.

  • gamma ({'scale', 'auto'} or float, default='scale') –

    Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

    • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

    • if ‘auto’, uses 1 / n_features

    • if float, must be non-negative.

  • coef0 (float, default=0.0) – Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

  • shrinking (bool, default=True) – Whether to use the shrinking heuristic. See the User Guide.

  • probability (bool, default=False) – Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide.

  • tol (float, default=1e-3) – Tolerance for stopping criterion.

  • cache_size (float, default=200) – Specify the size of the kernel cache (in MB).

  • class_weight ({dict, 'balanced'}, default=None) – Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies as n_samples / (n_classes * np.bincount(y)).

  • verbose (bool, default=False) – Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

  • max_iter (int, default=-1) – Hard limit on iterations within solver, or -1 for no limit.

  • decision_function_shape ({'ovo', 'ovr'}, default='ovr') – Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification.

  • break_ties (bool, default=False) – If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.

  • random_state (int, RandomState instance or None, default=None) – Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

Base class for all transformers.

Methods

decision_function(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'decision_function_') → Union[DataFrame, DataFrame]

Evaluate the decision function for the samples in X For more details on this function, see sklearn.svm.NuSVC.decision_function (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC.decision_function)

Raises:

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

Parameters:

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

  • output_cols_prefix – str Prefix for the response columns

Returns:

Output dataset with results of the decision function for the samples in input dataset.

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

Runs universal logics for all fit implementations.

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

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]

Perform classification on samples in X For more details on this function, see sklearn.svm.NuSVC.predict (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC.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.

predict_log_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_log_proba_') → Union[DataFrame, DataFrame]

Compute probabilities of possible outcomes for samples in X For more details on this function, see sklearn.svm.NuSVC.predict_proba (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC.predict_proba)

Raises:

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

Parameters:

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

  • output_cols_prefix – str Prefix for the response columns

Returns:

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

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

Compute probabilities of possible outcomes for samples in X For more details on this function, see sklearn.svm.NuSVC.predict_proba (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC.predict_proba)

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.

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

Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.svm.NuSVC.score (https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC.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]]]) → NuSVC

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: 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.

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.svm.NuSVC object.

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

语言: 中文