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FlorentinDFlorentinD
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Max max_flow to v2 endpoints
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10 files changed

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‎graphdatascience/plugin_v2_endpoints.py‎

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@@ -36,6 +36,7 @@
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from graphdatascience.procedure_surface.api.node_embedding.node2vec_endpoints import Node2VecEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.all_shortest_path_endpoints import AllShortestPathEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.k_spanning_tree_endpoints import KSpanningTreeEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.max_flow_endpoints import MaxFlowEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.prize_steiner_tree_endpoints import PrizeSteinerTreeEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.shortest_path_endpoints import ShortestPathEndpoints
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from graphdatascience.procedure_surface.api.pathfinding.single_source_bellman_ford_endpoints import (
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from graphdatascience.procedure_surface.cypher.pathfinding.k_spanning_tree_cypher_endpoints import (
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KSpanningTreeCypherEndpoints,
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)
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from graphdatascience.procedure_surface.cypher.pathfinding.max_flow_cypher_endpoints import MaxFlowCypherEndpoints
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from graphdatascience.procedure_surface.cypher.pathfinding.prize_steiner_tree_cypher_endpoints import (
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PrizeSteinerTreeCypherEndpoints,
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)
@@ -337,6 +339,13 @@ def louvain(self) -> LouvainEndpoints:
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"""
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return LouvainCypherEndpoints(self._db_client)
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@property
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def max_flow(self) -> MaxFlowEndpoints:
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"""
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Return endpoints for the Max Flow algorithm.
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"""
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return MaxFlowCypherEndpoints(self._db_client)
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@property
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def max_k_cut(self) -> MaxKCutEndpoints:
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"""

‎graphdatascience/procedure_surface/api/pathfinding/max_flow_endpoints.py‎

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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any
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from pandas import DataFrame
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from graphdatascience.procedure_surface.api.base_result import BaseResult
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from graphdatascience.procedure_surface.api.catalog.graph_api import GraphV2
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from graphdatascience.procedure_surface.api.default_values import ALL_LABELS, ALL_TYPES
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from graphdatascience.procedure_surface.api.estimation_result import EstimationResult
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class MaxFlowEndpoints(ABC):
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@abstractmethod
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def mutate(
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self,
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G: GraphV2,
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mutate_property: str,
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mutate_relationship_type: str,
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*,
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capacity_property: str | None = None,
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concurrency: int | None = None,
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job_id: str | None = None,
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log_progress: bool = True,
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node_labels: list[str] = ALL_LABELS,
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relationship_types: list[str] = ALL_TYPES,
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source_nodes: list[int] | None = None,
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sudo: bool = False,
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target_nodes: list[int] | None = None,
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username: str | None = None,
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) -> MaxFlowMutateResult:
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"""
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Runs the Max Flow algorithm and stores the results in the graph catalog.
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Parameters
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----------
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G
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Graph object to use
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mutate_property
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Name of the node property to store the flow results in.
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mutate_relationship_type
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Name of the relationship type to store the flow relationships in.
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capacity_property
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Name of the relationship property containing capacities.
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concurrency
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Number of concurrent threads to use.
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job_id
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Identifier for the computation.
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log_progress
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Display progress logging.
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node_labels
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Filter the graph using the given node labels. Nodes with any of the given labels will be included.
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relationship_types
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Filter the graph using the given relationship types. Relationships with any of the given types will be included.
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source_nodes
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List of source node IDs.
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sudo
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Disable the memory guard.
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target_nodes
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List of target node IDs.
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username
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As an administrator, impersonate a different user for accessing their graphs.
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Returns
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-------
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MaxFlowMutateResult
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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: GraphV2,
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*,
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capacity_property: str | None = None,
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concurrency: int | None = None,
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job_id: str | None = None,
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log_progress: bool = True,
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node_labels: list[str] = ALL_LABELS,
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relationship_types: list[str] = ALL_TYPES,
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source_nodes: list[int] | None = None,
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sudo: bool = False,
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target_nodes: list[int] | None = None,
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username: str | None = None,
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) -> MaxFlowStatsResult:
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"""
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Runs the Max Flow algorithm and returns statistics.
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Parameters
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----------
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G
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Graph object to use
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capacity_property
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Name of the relationship property containing capacities.
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concurrency
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Number of concurrent threads to use.
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job_id
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Identifier for the computation.
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log_progress
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Display progress logging.
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node_labels
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Filter the graph using the given node labels. Nodes with any of the given labels will be included.
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relationship_types
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Filter the graph using the given relationship types. Relationships with any of the given types will be included.
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source_nodes
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List of source node IDs.
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sudo
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Disable the memory guard.
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target_nodes
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List of target node IDs.
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username
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As an administrator, impersonate a different user for accessing their graphs.
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Returns
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-------
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MaxFlowStatsResult
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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: GraphV2,
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*,
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capacity_property: str | None = None,
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concurrency: int | None = None,
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job_id: str | None = None,
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log_progress: bool = True,
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node_labels: list[str] = ALL_LABELS,
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relationship_types: list[str] = ALL_TYPES,
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source_nodes: list[int] | None = None,
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sudo: bool = False,
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target_nodes: list[int] | None = None,
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username: str | None = None,
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) -> DataFrame:
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"""
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Runs the Max Flow algorithm and returns a stream of results.
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Parameters
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----------
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G
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Graph object to use
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capacity_property
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Name of the relationship property containing capacities.
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concurrency
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Number of concurrent threads to use.
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job_id
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Identifier for the computation.
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log_progress
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Display progress logging.
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node_labels
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Filter the graph using the given node labels. Nodes with any of the given labels will be included.
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relationship_types
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Filter the graph using the given relationship types. Relationships with any of the given types will be included.
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source_nodes
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List of source node IDs.
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sudo
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Disable the memory guard.
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target_nodes
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List of target node IDs.
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username
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As an administrator, impersonate a different user for accessing their graphs.
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Returns
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-------
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DataFrame
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DataFrame with the algorithm results containing 'source', 'target', and 'flow' columns
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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: GraphV2,
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write_property: str,
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write_relationship_type: str,
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*,
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capacity_property: str | None = None,
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concurrency: int | None = None,
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job_id: str | None = None,
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log_progress: bool = True,
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node_labels: list[str] = ALL_LABELS,
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relationship_types: list[str] = ALL_TYPES,
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source_nodes: list[int] | None = None,
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sudo: bool = False,
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target_nodes: list[int] | None = None,
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username: str | None = None,
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write_concurrency: int | None = None,
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) -> MaxFlowWriteResult:
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"""
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Runs the Max Flow algorithm and writes the results to the Neo4j database.
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Parameters
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----------
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G
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Graph object to use
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write_property
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Name of the node property to store the flow results in.
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write_relationship_type
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Name of the relationship type to store the flow relationships in.
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capacity_property
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Name of the relationship property containing capacities.
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concurrency
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Number of concurrent threads to use.
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job_id
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Identifier for the computation.
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log_progress
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Display progress logging.
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node_labels
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Filter the graph using the given node labels. Nodes with any of the given labels will be included.
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relationship_types
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Filter the graph using the given relationship types. Relationships with any of the given types will be included.
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source_nodes
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List of source node IDs.
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sudo
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Disable the memory guard.
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target_nodes
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List of target node IDs.
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username
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As an administrator, impersonate a different user for accessing their graphs.
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write_concurrency
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Number of concurrent threads to use for writing.
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Returns
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-------
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MaxFlowWriteResult
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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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G: GraphV2 | dict[str, Any],
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*,
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capacity_property: str | None = None,
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concurrency: int | None = None,
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node_labels: list[str] = ALL_LABELS,
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relationship_types: list[str] = ALL_TYPES,
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source_nodes: list[int] | None = None,
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target_nodes: list[int] | None = None,
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) -> EstimationResult:
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"""
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Estimate the memory consumption of an algorithm run.
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Parameters
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----------
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G
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Graph object to use or a dictionary representing the graph dimensions.
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capacity_property
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Name of the relationship property containing capacities.
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concurrency
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Number of concurrent threads to use.
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node_labels
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Filter the graph using the given node labels. Nodes with any of the given labels will be included.
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relationship_types
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Filter the graph using the given relationship types. Relationships with any of the given types will be included.
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source_nodes
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List of source node IDs.
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target_nodes
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List of target node IDs.
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Returns
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-------
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EstimationResult
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Memory estimation details
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"""
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pass
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class MaxFlowMutateResult(BaseResult):
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total_flow: float
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pre_processing_millis: int
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compute_millis: int
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mutate_millis: int
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relationships_written: int
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configuration: dict[str, Any]
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class MaxFlowStatsResult(BaseResult):
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total_flow: float
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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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class MaxFlowWriteResult(BaseResult):
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total_flow: float
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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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relationships_written: int
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configuration: dict[str, Any]

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