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Implement fastrp endpoint
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‎graphdatascience/procedure_surface/api/fastrp_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, List, Optional, Union
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from pandas import DataFrame
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from pydantic import BaseModel, ConfigDict
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from pydantic.alias_generators import to_camel
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from ...graph.graph_object import Graph
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from .estimation_result import EstimationResult
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class FastRPEndpoints(ABC):
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"""
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Abstract base class defining the API for the FastRP (Fast Random Projection) algorithm.
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FastRP is a node embedding algorithm that creates vector representations of nodes
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by combining random projections and iterative neighbor aggregation.
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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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embedding_dimension: int,
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iteration_weights: Optional[List[float]] = None,
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normalization_strength: Optional[float] = None,
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node_self_influence: Optional[float] = None,
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property_ratio: Optional[float] = None,
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feature_properties: Optional[List[str]] = 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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relationship_weight_property: Optional[str] = None,
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random_seed: Optional[Any] = None,
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) -> FastRPMutateResult:
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"""
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Executes the FastRP algorithm and writes the results back to the graph as a node property.
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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 FastRP embeddings for each node
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embedding_dimension : int
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The dimension of the generated embeddings
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iteration_weights : Optional[List[float]], default=None
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Weights for each iteration. Controls the influence of each iteration on the final embedding
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normalization_strength : Optional[float], default=None
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The normalization strength parameter controls how much the embedding is normalized
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node_self_influence : Optional[float], default=None
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The influence of the node's own features on its embedding
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property_ratio : Optional[float], default=None
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The ratio of node properties to use in the embedding
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feature_properties : Optional[List[str]], default=None
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List of node properties to use as features in the embedding
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relationship_types : Optional[List[str]], default=None
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The relationship 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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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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random_seed : Optional[Any], default=None
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Random seed for reproducible results
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Returns
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-------
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FastRPMutateResult
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Algorithm metrics and statistics
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"""
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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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embedding_dimension: int,
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iteration_weights: Optional[List[float]] = None,
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normalization_strength: Optional[float] = None,
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node_self_influence: Optional[float] = None,
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property_ratio: Optional[float] = None,
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feature_properties: Optional[List[str]] = 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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relationship_weight_property: Optional[str] = None,
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random_seed: Optional[Any] = None,
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) -> FastRPStatsResult:
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"""
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Executes the FastRP algorithm and returns result statistics without writing the result to Neo4j.
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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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embedding_dimension : int
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The dimension of the generated embeddings
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iteration_weights : Optional[List[float]], default=None
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Weights for each iteration. Controls the influence of each iteration on the final embedding
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normalization_strength : Optional[float], default=None
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The normalization strength parameter controls how much the embedding is normalized
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node_self_influence : Optional[float], default=None
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The influence of the node's own features on its embedding
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property_ratio : Optional[float], default=None
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The ratio of node properties to use in the embedding
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feature_properties : Optional[List[str]], default=None
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List of node properties to use as features in the embedding
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relationship_types : Optional[List[str]], default=None
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The relationship 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] = 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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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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random_seed : Optional[Any], default=None
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Random seed for reproducible results
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Returns
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-------
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FastRPStatsResult
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Algorithm statistics
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"""
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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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embedding_dimension: int,
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iteration_weights: Optional[List[float]] = None,
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normalization_strength: Optional[float] = None,
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node_self_influence: Optional[float] = None,
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property_ratio: Optional[float] = None,
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feature_properties: Optional[List[str]] = 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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relationship_weight_property: Optional[str] = None,
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random_seed: Optional[Any] = None,
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) -> DataFrame:
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"""
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Executes the FastRP algorithm and returns the results as a stream.
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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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embedding_dimension : int
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The dimension of the generated embeddings
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iteration_weights : Optional[List[float]], default=None
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Weights for each iteration. Controls the influence of each iteration on the final embedding
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normalization_strength : Optional[float], default=None
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The normalization strength parameter controls how much the embedding is normalized
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node_self_influence : Optional[float], default=None
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The influence of the node's own features on its embedding
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property_ratio : Optional[float], default=None
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The ratio of node properties to use in the embedding
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feature_properties : Optional[List[str]], default=None
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List of node properties to use as features in the embedding
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relationship_types : Optional[List[str]], default=None
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The relationship 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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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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random_seed : Optional[Any], default=None
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Random seed for reproducible results
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Returns
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-------
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DataFrame
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DataFrame with node IDs and their FastRP embeddings
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"""
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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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embedding_dimension: int,
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iteration_weights: Optional[List[float]] = None,
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normalization_strength: Optional[float] = None,
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node_self_influence: Optional[float] = None,
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property_ratio: Optional[float] = None,
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feature_properties: Optional[List[str]] = 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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relationship_weight_property: Optional[str] = None,
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random_seed: Optional[Any] = None,
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write_concurrency: Optional[int] = None,
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) -> FastRPWriteResult:
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"""
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Executes the FastRP algorithm and writes the results to Neo4j.
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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 the FastRP embeddings for each node
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embedding_dimension : int
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The dimension of the generated embeddings
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iteration_weights : Optional[List[float]], default=None
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Weights for each iteration. Controls the influence of each iteration on the final embedding
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normalization_strength : Optional[float], default=None
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The normalization strength parameter controls how much the embedding is normalized
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node_self_influence : Optional[float], default=None
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The influence of the node's own features on its embedding
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property_ratio : Optional[float], default=None
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The ratio of node properties to use in the embedding
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feature_properties : Optional[List[str]], default=None
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List of node properties to use as features in the embedding
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relationship_types : Optional[List[str]], default=None
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The relationship 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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relationship_weight_property : Optional[str], default=None
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The property name that contains weight
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random_seed : Optional[Any], default=None
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Random seed for reproducible results
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write_concurrency : Optional[int], default=None
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The number of concurrent threads used for writing
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Returns
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-------
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FastRPWriteResult
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Algorithm metrics and statistics
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"""
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@abstractmethod
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def estimate(
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self,
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G: Union[Graph, dict[str, Any]],
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embedding_dimension: int,
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iteration_weights: Optional[List[float]] = None,
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normalization_strength: Optional[float] = None,
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node_self_influence: Optional[float] = None,
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property_ratio: Optional[float] = None,
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feature_properties: Optional[List[str]] = 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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concurrency: Optional[Any] = None,
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relationship_weight_property: Optional[str] = None,
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random_seed: Optional[Any] = None,
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) -> EstimationResult:
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"""
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Returns an estimation of the memory consumption for that procedure.
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Parameters
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----------
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G : Union[Graph, dict[str, Any]]
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The graph to run the algorithm on or a dictionary representing the graph.
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embedding_dimension : int
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The dimension of the generated embeddings
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iteration_weights : Optional[List[float]], default=None
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Weights for each iteration. Controls the influence of each iteration on the final embedding
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normalization_strength : Optional[float], default=None
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The normalization strength parameter controls how much the embedding is normalized
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node_self_influence : Optional[float], default=None
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The influence of the node's own features on its embedding
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property_ratio : Optional[float], default=None
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The ratio of node properties to use in the embedding
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feature_properties : Optional[List[str]], default=None
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List of node properties to use as features in the embedding
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relationship_types : Optional[List[str]], default=None
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The relationship 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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concurrency : Optional[Any], default=None
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The number of concurrent threads
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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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random_seed : Optional[Any], default=None
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Random seed for reproducible results
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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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class FastRPMutateResult(BaseModel):
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model_config = ConfigDict(alias_generator=to_camel)
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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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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 self.__dict__[item]
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class FastRPStatsResult(BaseModel):
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model_config = ConfigDict(alias_generator=to_camel)
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pre_processing_millis: int
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compute_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 self.__dict__[item]
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class FastRPWriteResult(BaseModel):
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model_config = ConfigDict(alias_generator=to_camel)
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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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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 self.__dict__[item]

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