Skip to main content
Functions related to calculating the local outlier factor of a dataset.

​
local_outlier_factor

Annotates a dataframe with a column containing the local outlier factor for each data record. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named ‘geometry’ will be used. If more than one are present and neither is named ‘geometry’, the column name must be provided. 
def local_outlier_factor(dataframe: DataFrame, k: int, geometry: Optional[str], approximate_knn: bool, handle_ties: bool, use_spheroid, result_column_name: str)

​
Parameters

​
dataframe
DataFrame
required
apache sedona idDataframe containing the point geometries
​
k
int
required
number of nearest neighbors that will be considered for the LOF calculation
​
geometry
Optional[str]
name of the geometry column
​
approximate_knn
bool
required
whether to use approximate KNN. When false will use exact KNN join. Default is False
​
handle_ties
bool
required
whether to handle ties in the k-distance calculation. Default is false
​
use_spheroid
bool
required
whether to use a cartesian or spheroidal distance calculation. Default is false
​
result_column_name
str
required
the name of the column containing the lof for each row. Default is “lof”

​
Returns

A PySpark DataFrame containing the lof for each row.

​
Usage Examples

from local_outlier_factor import *

# Example usage of local_outlier_factor
result = local_outlier_factor(dataframe=example_value, k=example_value, geometry=example_value)