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Introduce WccEndpoints
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‎graphdatascience/procedure_surface/api/estimation_result.py‎

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from dataclasses import dataclass
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from typing import Any
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@dataclass(frozen=True, repr=True)
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class EstimationResult:
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node_count: int
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relationship_count: int
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required_memory: str
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tree_view: str
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map_view: dict[str, Any]
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bytes_min: int
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bytes_max: int
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heap_percentage_min: float
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heap_percentage_max: float
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)

‎graphdatascience/procedure_surface/api/wcc_endpoints.py‎

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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Any, List, Optional
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from pandas import DataFrame
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from ...graph.graph_object import Graph
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from .estimation_result import EstimationResult
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class WccEndpoints(ABC):
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"""
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Abstract base class defining the API for the Weakly Connected Components (WCC) algorithm.
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"""
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@abstractmethod
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def mutate(
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self,
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G: Graph,
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mutate_property: str,
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threshold: Optional[float] = None,
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relationship_types: Optional[List[str]] = None,
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node_labels: Optional[List[str]] = None,
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sudo: Optional[bool] = None,
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log_progress: Optional[bool] = None,
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username: Optional[str] = None,
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concurrency: Optional[Any] = None,
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job_id: Optional[Any] = None,
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seed_property: Optional[str] = None,
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consecutive_ids: Optional[bool] = None,
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relationship_weight_property: Optional[str] = None,
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) -> WccMutateResult:
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"""
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Executes the WCC algorithm and writes the results to the in-memory graph as node properties.
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Parameters
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----------
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G : Graph
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The graph to run the algorithm on
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mutate_property : str
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The property name to store the component ID for each node
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threshold : Optional[float], default=None
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The minimum required weight to consider a relationship during traversal
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relationship_types : Optional[List[str]], default=None
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The relationships types used to select relationships for this algorithm run
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node_labels : Optional[List[str]], default=None
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The node labels used to select nodes for this algorithm run
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sudo : Optional[bool], default=None
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Override memory estimation limits
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log_progress : Optional[bool], default=None
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Whether to log progress
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username : Optional[str], default=None
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The username to attribute the procedure run to
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concurrency : Optional[Any], default=None
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The number of concurrent threads
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job_id : Optional[Any], default=None
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An identifier for the job
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seed_property : Optional[str], default=None
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Defines node properties that are used as initial component identifiers
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consecutive_ids : Optional[bool], default=None
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Flag to decide whether component identifiers are mapped into a consecutive id space
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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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Returns
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-------
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WccMutateResult
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Algorithm metrics and statistics
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"""
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pass
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@abstractmethod
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def stats(
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self,
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G: Graph,
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threshold: Optional[float] = None,
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relationship_types: Optional[List[str]] = None,
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node_labels: Optional[List[str]] = None,
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sudo: Optional[bool] = None,
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log_progress: Optional[bool] = None,
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username: Optional[str] = None,
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concurrency: Optional[Any] = None,
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job_id: Optional[Any] = None,
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seed_property: Optional[str] = None,
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consecutive_ids: Optional[bool] = None,
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relationship_weight_property: Optional[str] = None,
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) -> WccStatsResult:
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"""
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Executes the WCC algorithm and returns statistics.
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Parameters
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----------
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G : Graph
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The graph to run the algorithm on
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threshold : Optional[float], default=None
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The minimum required weight to consider a relationship during traversal
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relationship_types : Optional[List[str]], default=None
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The relationships types used to select relationships for this algorithm run
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node_labels : Optional[List[str]], default=None
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The node labels used to select nodes for this algorithm run
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sudo : Optional[bool], default=None
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Override memory estimation limits
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log_progress : Optional[bool], default=None
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Whether to log progress
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username : Optional[str], default=None
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The username to attribute the procedure run to
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concurrency : Optional[Any], default=None
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The number of concurrent threads
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job_id : Optional[Any], default=None
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An identifier for the job
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seed_property : Optional[str], default=None
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Defines node properties that are used as initial component identifiers
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consecutive_ids : Optional[bool], default=None
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Flag to decide whether component identifiers are mapped into a consecutive id space
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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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Returns
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-------
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WccStatsResult
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Algorithm metrics and statistics
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"""
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pass
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@abstractmethod
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def stream(
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self,
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G: Graph,
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min_component_size: Optional[int] = None,
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threshold: Optional[float] = None,
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relationship_types: Optional[List[str]] = None,
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node_labels: Optional[List[str]] = None,
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sudo: Optional[bool] = None,
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log_progress: Optional[bool] = None,
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username: Optional[str] = None,
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concurrency: Optional[Any] = None,
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job_id: Optional[Any] = None,
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seed_property: Optional[str] = None,
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consecutive_ids: Optional[bool] = None,
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relationship_weight_property: Optional[str] = None,
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) -> DataFrame:
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"""
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Executes the WCC algorithm and returns a stream of results.
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Parameters
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----------
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G : Graph
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The graph to run the algorithm on
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min_component_size : Optional[int], default=None
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Don't stream components with fewer nodes than this
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threshold : Optional[float], default=None
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The minimum required weight to consider a relationship during traversal
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relationship_types : Optional[List[str]], default=None
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The relationships types considered in this algorithm run
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node_labels : Optional[List[str]], default=None
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The node labels used to select nodes for this algorithm run
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sudo : Optional[bool], default=None
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Override memory estimation limits
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log_progress : Optional[bool], default=None
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Whether to log progress
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username : Optional[str], default=None
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The username to attribute the procedure run to
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concurrency : Optional[Any], default=None
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The number of concurrent threads
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job_id : Optional[Any], default=None
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An identifier for the job
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seed_property : Optional[str], default=None
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Defines node properties that are used as initial component identifiers
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consecutive_ids : Optional[bool], default=None
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Flag to decide whether component identifiers are mapped into a consecutive id space
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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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Returns
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-------
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DataFrame
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DataFrame with the algorithm results
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"""
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pass
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@abstractmethod
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def write(
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self,
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G: Graph,
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write_property: str,
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min_component_size: Optional[int] = None,
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threshold: Optional[float] = None,
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relationship_types: Optional[List[str]] = None,
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node_labels: Optional[List[str]] = None,
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sudo: Optional[bool] = None,
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log_progress: Optional[bool] = None,
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username: Optional[str] = None,
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concurrency: Optional[Any] = None,
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job_id: Optional[Any] = None,
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seed_property: Optional[str] = None,
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consecutive_ids: Optional[bool] = None,
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relationship_weight_property: Optional[str] = None,
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write_concurrency: Optional[Any] = None,
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) -> WccWriteResult:
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"""
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Executes the WCC algorithm and writes the results to the Neo4j database.
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Parameters
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----------
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G : Graph
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The graph to run the algorithm on
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write_property : str
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The property name to write component IDs to
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min_component_size : Optional[int], default=None
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Don't write components with fewer nodes than this
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threshold : Optional[float], default=None
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The minimum required weight to consider a relationship during traversal
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relationship_types : Optional[List[str]], default=None
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The relationships types considered in this algorithm run
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node_labels : Optional[List[str]], default=None
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The node labels used to select nodes for this algorithm run
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sudo : Optional[bool], default=None
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Override memory estimation limits
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log_progress : Optional[bool], default=None
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Whether to log progress
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username : Optional[str], default=None
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The username to attribute the procedure run to
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concurrency : Optional[Any], default=None
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The number of concurrent threads
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job_id : Optional[Any], default=None
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An identifier for the job
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seed_property : Optional[str], default=None
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Defines node properties that are used as initial component identifiers
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consecutive_ids : Optional[bool], default=None
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Flag to decide whether component identifiers are mapped into a consecutive id space
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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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write_concurrency : Optional[Any], default=None
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The number of concurrent threads during the write phase
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Returns
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-------
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WccWriteResult
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Algorithm metrics and statistics
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"""
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pass
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@abstractmethod
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def estimate(
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self,
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graph_name: Optional[str] = None,
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projection_config: Optional[dict[str, Any]] = None,
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) -> EstimationResult:
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"""
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Estimate the results based on the provided graph and configuration.
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This abstract method is intended to be implemented in a subclass to provide
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specific functionality for estimating outcomes based on a graph name and a
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projection configuration object. The implementation should use the given
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parameters to compute and return an appropriate estimation result.
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Parameters
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----------
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graph_name : Optional[str], optional
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Name of the graph to be used in the estimation
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projection_config : Optional[dict[str, Any]], optional
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Configuration dictionary for the projection.
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Returns
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-------
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EstimationResult
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An object containing the result of the estimation, with relevant details
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as defined by the specific implementation.
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"""
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pass
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@dataclass(frozen=True, repr=True)
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class WccMutateResult:
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component_count: int
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component_distribution: dict[str, Any]
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pre_processing_millis: int
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compute_millis: int
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post_processing_millis: int
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mutate_millis: int
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node_properties_written: int
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configuration: dict[str, Any]
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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@dataclass(frozen=True, repr=True)
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class WccStatsResult:
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component_count: int
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component_distribution: dict[str, Any]
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pre_processing_millis: int
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compute_millis: int
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post_processing_millis: int
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configuration: dict[str, Any]
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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@dataclass(frozen=True, repr=True)
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class WccWriteResult:
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component_count: int
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component_distribution: dict[str, Any]
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pre_processing_millis: int
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compute_millis: int
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write_millis: int
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post_processing_millis: int
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node_properties_written: int
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configuration: dict[str, Any]
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)

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