snowflake.ml.modeling.gaussian_process.GaussianProcessClassifier¶

class snowflake.ml.modeling.gaussian_process.GaussianProcessClassifier(*, kernel=None, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=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

Gaussian process classification (GPC) based on Laplace approximation For more details on this class, see sklearn.gaussian_process.GaussianProcessClassifier

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.
kernel (kernel instance, default=None) – The kernel specifying the covariance function of the GP. If None is passed, the kernel “1.0 * RBF(1.0)” is used as default. Note that the kernel’s hyperparameters are optimized during fitting. Also kernel cannot be a CompoundKernel.

optimizer ('fmin_l_bfgs_b', callable or None, default='fmin_l_bfgs_b') –

Can either be one of the internally supported optimizers for optimizing the kernel’s parameters, specified by a string, or an externally defined optimizer passed as a callable. If a callable is passed, it must have the signature:

def optimizer(obj_func, initial_theta, bounds):
    # * 'obj_func' is the objective function to be maximized, which
    #   takes the hyperparameters theta as parameter and an
    #   optional flag eval_gradient, which determines if the
    #   gradient is returned additionally to the function value
    # * 'initial_theta': the initial value for theta, which can be
    #   used by local optimizers
    # * 'bounds': the bounds on the values of theta
    ....
    # Returned are the best found hyperparameters theta and
    # the corresponding value of the target function.
    return theta_opt, func_min

Copy

Per default, the ‘L-BFGS-B’ algorithm from scipy.optimize.minimize is used. If None is passed, the kernel’s parameters are kept fixed. Available internal optimizers are:

'fmin_l_bfgs_b'

Copy

n_restarts_optimizer (int, default=0) – The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood. The first run of the optimizer is performed from the kernel’s initial parameters, the remaining ones (if any) from thetas sampled log-uniform randomly from the space of allowed theta-values. If greater than 0, all bounds must be finite. Note that n_restarts_optimizer=0 implies that one run is performed.
max_iter_predict (int, default=100) – The maximum number of iterations in Newton’s method for approximating the posterior during predict. Smaller values will reduce computation time at the cost of worse results.
warm_start (bool, default=False) – If warm-starts are enabled, the solution of the last Newton iteration on the Laplace approximation of the posterior mode is used as initialization for the next call of _posterior_mode(). This can speed up convergence when _posterior_mode is called several times on similar problems as in hyperparameter optimization. See the Glossary.
copy_X_train (bool, default=True) – If True, a persistent copy of the training data is stored in the object. Otherwise, just a reference to the training data is stored, which might cause predictions to change if the data is modified externally.
random_state (int, RandomState instance or None, default=None) – Determines random number generation used to initialize the centers. Pass an int for reproducible results across multiple function calls. See Glossary.
multi_class ({'one_vs_rest', 'one_vs_one'}, default='one_vs_rest') – Specifies how multi-class classification problems are handled. Supported are ‘one_vs_rest’ and ‘one_vs_one’. In ‘one_vs_rest’, one binary Gaussian process classifier is fitted for each class, which is trained to separate this class from the rest. In ‘one_vs_one’, one binary Gaussian process classifier is fitted for each pair of classes, which is trained to separate these two classes. The predictions of these binary predictors are combined into multi-class predictions. Note that ‘one_vs_one’ does not support predicting probability estimates.
n_jobs (int, default=None) – The number of jobs to use for the computation: the specified multiclass problems are computed in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Base class for all transformers.

Methods

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 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]¶

Perform classification on an array of test vectors X For more details on this function, see sklearn.gaussian_process.GaussianProcessClassifier.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_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_proba_') → Union[DataFrame, DataFrame]¶

Return probability estimates for the test vector X For more details on this function, see sklearn.gaussian_process.GaussianProcessClassifier.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.gaussian_process.GaussianProcessClassifier.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]]]) → GaussianProcessClassifier¶

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.gaussian_process.GaussianProcessClassifier 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