Operations

Graph analytics operations.

cypher ¤

cypher(bundle: Bundle, *, query: LongStr, save_as: str = 'result')

Run a Cypher query on the graph in the bundle. Save the results as a new DataFrame.

export_to_file ¤

export_to_file(bundle: Bundle, *, table_name: str, filename: str, file_format: FileFormat = csv)

Exports a DataFrame to a file.

PARAMETER	DESCRIPTION
`bundle`	The bundle containing the DataFrame to export. TYPE: `Bundle`
`table_name`	The name of the DataFrame in the bundle to export. TYPE: `str`
`filename`	The name of the file to export to. TYPE: `str`
`file_format`	The format of the file to export to. Defaults to CSV. TYPE: `FileFormat` DEFAULT: `csv`

import_csv ¤

import_csv(*, filename: str, columns: str = '<from file>', separator: str = '<auto>')

Imports a CSV file.

import_file ¤

import_file(*, file_path: str, table_name: str, file_format: FileFormat = csv, **kwargs) -> Bundle

Read the contents of the a file into a Bundle.

PARAMETER	DESCRIPTION
`file_path`	Path to the file to import. TYPE: `str`
`table_name`	Name to use for identifying the table in the bundle. TYPE: `str`
`file_format`	Format of the file. Has to be one of the values in the `FileFormat` enum. TYPE: `FileFormat` DEFAULT: `csv`

RETURNS	DESCRIPTION
`Bundle`	Bundle with a single table with the contents of the file. TYPE: `Bundle`

import_graphml ¤

import_graphml(*, filename: str)

Imports a GraphML file.

import_parquet ¤

import_parquet(*, filename: str)

Imports a Parquet file.

organize ¤

organize(bundles: list[Bundle], *, relations: str = '')

Merge multiple inputs and construct graphs from the tables.

To create a graph, import tables for edges and nodes, and combine them in this operation.

sample_graph ¤

sample_graph(graph: Graph, *, nodes: int = 100)

Takes a (preferably connected) subgraph.

sql ¤

sql(bundle: Bundle, *, query: LongStr, save_as: str = 'result')

Run a SQL query on the DataFrames in the bundle. Save the results as a new DataFrame.

Operations for machine learning.

define_model ¤

define_model(bundle: Bundle, *, model_workspace: str, save_as: str = 'model')

Trains the selected model on the selected dataset. Most training parameters are set in the model definition.

model_inference ¤

model_inference(bundle: Bundle, *, model_name: PyTorchModelName = 'model', input_mapping: ModelInferenceInputMapping, output_mapping: ModelOutputMapping, batch_size: int = 1)

Executes a trained model.

train_model ¤

train_model(bundle: Bundle, *, model_name: PyTorchModelName = 'model', input_mapping: ModelTrainingInputMapping, epochs: int = 1, batch_size: int = 1)

Trains the selected model on the selected dataset. Training parameters specific to the model are set in the model definition, while parameters specific to the hardware environment and dataset are set here.

train_test_split ¤

train_test_split(bundle: Bundle, *, table_name: TableName, test_ratio: float = 0.1, seed=1234)

Splits a dataframe in the bundle into separate "_train" and "_test" dataframes.

Automatically wraps all NetworkX functions as LynxKite operations.