snowflake.ml.modeling.neighbors.LocalOutlierFactor

class snowflake.ml.modeling.neighbors.LocalOutlierFactor(*, n_neighbors=20, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, contamination='auto', novelty=False, 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

Unsupervised Outlier Detection using the Local Outlier Factor (LOF) For more details on this class, see sklearn.neighbors.LocalOutlierFactor

n_neighbors: int, default=20

Number of neighbors to use by default for kneighbors() queries. If n_neighbors is larger than the number of samples provided, all samples will be used.

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 is 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.

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.

p: int, default=2

Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances(). 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_params: dict, default=None

Additional keyword arguments for the metric function.

contamination: ‘auto’ or float, default=’auto’

The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the scores of the samples.

  • if ‘auto’, the threshold is determined as in the original paper,

  • if a float, the contamination should be in the range (0, 0.5].

novelty: bool, default=False

By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=False). Set novelty to True if you want to use LocalOutlierFactor for novelty detection. In this case be aware that you should only use predict, decision_function and score_samples on new unseen data and not on the training set; and note that the results obtained this way may differ from the standard LOF results.

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

decision_function(dataset[, output_cols_prefix])

Shifted opposite of the Local Outlier Factor of X For more details on this function, see sklearn.neighbors.LocalOutlierFactor.decision_function

fit(dataset)

Fit the local outlier factor detector from the training dataset For more details on this function, see sklearn.neighbors.LocalOutlierFactor.fit

predict(dataset)

Predict the labels (1 inlier, -1 outlier) of X according to LOF For more details on this function, see sklearn.neighbors.LocalOutlierFactor.predict

score(dataset)

Method not supported for this class.

set_input_cols(input_cols)

Input columns setter.

to_sklearn()

Get sklearn.neighbors.LocalOutlierFactor object.

Attributes

model_signatures

Returns model signature of current class.
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