vastorbit.machine_learning.vast.neighbors.LocalOutlierFactor¶
- class vastorbit.machine_learning.vast.neighbors.LocalOutlierFactor(name: str = None, overwrite_model: bool = False, n_neighbors: int = 20, p: int = 2)¶
[Beta Version] Creates a
LocalOutlierFactorobject by using the Local Outlier Factor algorithm. Works without creating persistent tables - generates SQL on-demand.- __init__(name: str = None, overwrite_model: bool = False, n_neighbors: int = 20, p: int = 2) None¶
Methods
__init__([name, overwrite_model, n_neighbors, p])contour([nbins, chart])Draws the model's contour plot.
deploySQL([X])Returns the SQL code needed to deploy the model.
drop()Drops the model (clears stored SQL).
export_models(name, path[, kind])Exports machine learning models.
fit(input_relation[, X, key_columns, index, ...])Trains the model by generating and storing the LOF SQL query.
get_attributes([attr_name])Returns the model attributes.
get_match_index(x, col_list[, str_check])Returns the matching index.
Returns the parameters of the model.
get_plotting_lib([class_name, chart, ...])Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
import_models(path[, schema, kind])Imports machine learning models.
plot([max_nb_points, chart])Draws the model.
predict()Returns a VastFrame with the LOF scores.
set_params([parameters])Sets the parameters of the model.
Summarizes the model.
to_binary(path)Exports the model to the VAST Binary format.
to_python([return_proba, ...])Returns the Python function needed for in-memory scoring without using built-in VAST functions.
to_sql([X, return_proba, ...])Returns the SQL code needed to deploy the model without using built-in VAST functions.
Attributes