snowflake.ml.modeling.neighbors.RadiusNeighborsClassifier

class snowflake.ml.modeling.neighbors.RadiusNeighborsClassifier(*, radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, 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, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

Classifier implementing a vote among neighbors within a given radius For more details on this class, see sklearn.neighbors.RadiusNeighborsClassifier

radius: float, default=1.0

Range of parameter space to use by default for radius_neighbors() queries.

weights: {‘uniform’, ‘distance’}, callable or None, default=’uniform’

Weight function used in prediction. Possible values:

  • ‘uniform’: uniform weights. All points in each neighborhood are weighted equally.

  • ‘distance’: weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.

  • [callable]: a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

Uniform weights are used by default.

algorithm: {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’

Algorithm used to compute the nearest neighbors:

  • ‘ball_tree’ will use BallTree

  • ‘kd_tree’ will use KDTree

  • ‘brute’ will use a brute-force search.

  • ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit() method.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

leaf_size: int, default=30

Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

p: int, default=2

Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric: str or callable, default=’minkowski’

Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance and the metrics listed in distance_metrics for valid metric values.

If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors.

If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string.

outlier_label: {manual label, ‘most_frequent’}, default=None

Label for outlier samples (samples with no neighbors in given radius).

  • manual label: str or int label (should be the same type as y) or list of manual labels if multi-output is used.

  • ‘most_frequent’: assign the most frequent label of y to outliers.

  • None: when any outlier is detected, ValueError will be raised.

metric_params: dict, default=None

Additional keyword arguments for the metric function.

n_jobs: int, default=None

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

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 and sample-weight_col parameters are considered input columns.

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

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

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 mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

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

Methods

fit(dataset)

Fit the radius neighbors classifier from the training dataset For more details on this function, see sklearn.neighbors.RadiusNeighborsClassifier.fit

predict(dataset)

Predict the class labels for the provided data For more details on this function, see sklearn.neighbors.RadiusNeighborsClassifier.predict

predict_proba(dataset[, output_cols_prefix])

Return probability estimates for the test data X For more details on this function, see sklearn.neighbors.RadiusNeighborsClassifier.predict_proba

score(dataset)

Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.neighbors.RadiusNeighborsClassifier.score

set_input_cols(input_cols)

Input columns setter.

to_sklearn()

Get sklearn.neighbors.RadiusNeighborsClassifier object.

Attributes

model_signatures

Returns model signature of current class.
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