Inline default for community endpoints part 2

Author: FlorentinD

graphdatascience/procedure_surface/api/community/clique_counting_endpoints.py

@@ -17,6 +17,7 @@ def mutate(
         self,
         G: GraphV2,
         mutate_property: str,
+        *,
         concurrency: int | None = None,
         job_id: str | None = None,
         log_progress: bool = True,
@@ -65,7 +66,7 @@ def stats(
         log_progress: bool = True,
         node_labels: list[str] = ALL_LABELS,
         relationship_types: list[str] = ALL_TYPES,
-        sudo: bool | None = False,
+        sudo: bool = False,
         username: str | None = None,
     ) -> CliqueCountingStatsResult:
         """
@@ -148,7 +149,7 @@ def write(
         log_progress: bool = True,
         node_labels: list[str] = ALL_LABELS,
         relationship_types: list[str] = ALL_TYPES,
-        sudo: bool | None = False,
+        sudo: bool = False,
         username: str | None = None,
         write_concurrency: int | None = None,
     ) -> CliqueCountingWriteResult:

graphdatascience/procedure_surface/api/community/hdbscan_endpoints.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 from typing import Any
 
-import pandas as pd
+from pandas import DataFrame
 
 from graphdatascience.procedure_surface.api.base_result import BaseResult
 from graphdatascience.procedure_surface.api.catalog.graph_api import GraphV2
@@ -19,14 +19,14 @@ def mutate(
         node_property: str,
         mutate_property: str,
         *,
-        leaf_size: int | None = None,
-        samples: int | None = None,
-        min_cluster_size: int | None = None,
+        leaf_size: int = 1,
+        samples: int = 10,
+        min_cluster_size: int = 5,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
         log_progress: bool = True,
-        sudo: bool | None = None,
+        sudo: bool = False,
         job_id: Any | None = None,
         username: str | None = None,
     ) -> HdbscanMutateResult:
@@ -77,14 +77,14 @@ def stats(
         G: GraphV2,
         node_property: str,
         *,
-        leaf_size: int | None = None,
-        samples: int | None = None,
-        min_cluster_size: int | None = None,
+        leaf_size: int = 1,
+        samples: int = 10,
+        min_cluster_size: int = 5,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
         log_progress: bool = True,
-        sudo: bool | None = None,
+        sudo: bool = False,
         job_id: Any | None = None,
         username: str | None = None,
     ) -> HdbscanStatsResult:
@@ -133,17 +133,17 @@ def stream(
         G: GraphV2,
         node_property: str,
         *,
-        leaf_size: int | None = None,
-        samples: int | None = None,
-        min_cluster_size: int | None = None,
+        leaf_size: int = 1,
+        samples: int = 10,
+        min_cluster_size: int = 5,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
         log_progress: bool = True,
-        sudo: bool | None = None,
+        sudo: bool = False,
         job_id: Any | None = None,
         username: str | None = None,
-    ) -> pd.DataFrame:
+    ) -> DataFrame:
         """
         Runs the HDBSCAN algorithm and returns the cluster ID for each node as a DataFrame.
 
@@ -190,17 +190,17 @@ def write(
         node_property: str,
         write_property: str,
         *,
-        leaf_size: int | None = None,
-        samples: int | None = None,
-        min_cluster_size: int | None = None,
+        leaf_size: int = 1,
+        samples: int = 10,
+        min_cluster_size: int = 5,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
-        write_concurrency: int | None = None,
         concurrency: int | None = None,
         log_progress: bool = True,
-        sudo: bool | None = None,
+        sudo: bool = False,
         job_id: Any | None = None,
         username: str | None = None,
+        write_concurrency: int | None = None,
     ) -> HdbscanWriteResult:
         """
         Runs the HDBSCAN algorithm and writes the cluster ID for each node back to the database.
@@ -245,19 +245,15 @@ def write(
     @abstractmethod
     def estimate(
         self,
-        G: GraphV2,
+        G: GraphV2 | dict[str, Any],
         node_property: str,
         *,
-        leaf_size: int | None = None,
-        samples: int | None = None,
-        min_cluster_size: int | None = None,
+        leaf_size: int = 1,
+        samples: int = 10,
+        min_cluster_size: int = 5,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
         concurrency: int | None = None,
-        log_progress: bool = True,
-        sudo: bool | None = None,
-        job_id: Any | None = None,
-        username: str | None = None,
     ) -> EstimationResult:
         """
         Estimates memory requirements and other statistics for the HDBSCAN algorithm.

graphdatascience/procedure_surface/api/community/kcore_endpoints.py

@@ -21,13 +21,14 @@ def mutate(
         self,
         G: GraphV2,
         mutate_property: str,
-        relationship_types: list[str] = ALL_TYPES,
-        node_labels: list[str] = ALL_LABELS,
-        sudo: bool | None = None,
+        *,
+        concurrency: int | None = None,
+        job_id: str | None = None,
         log_progress: bool = True,
+        node_labels: list[str] = ALL_LABELS,
+        relationship_types: list[str] = ALL_TYPES,
+        sudo: bool = False,
         username: str | None = None,
-        concurrency: Any | None = None,
-        job_id: Any | None = None,
     ) -> KCoreMutateResult:
         """
         Executes the K-Core algorithm and writes the results to the in-memory graph as node properties.
@@ -38,20 +39,20 @@ def mutate(
             The graph to run the algorithm on
         mutate_property : str
             The property name to store the core value for each node
-        relationship_types : list[str]
-            The relationships types used to select relationships for this algorithm run
+        concurrency : int | None, default=None
+            The number of concurrent threads
+        job_id : str | None, default=None
+            An identifier for the job
+        log_progress : bool, default=True
+            Whether to log progress
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
-        sudo : bool
+        relationship_types : list[str]
+            The relationship types used to select relationships for this algorithm run
+        sudo : bool, default=False
             Override memory estimation limits
-        log_progress : bool | None, default=None
-            Whether to log progress
         username : str | None, default=None
             The username to attribute the procedure run to
-        concurrency : Any | None, default=None
-            The number of concurrent threads
-        job_id : Any | None, default=None
-            An identifier for the job
 
         Returns
         -------
@@ -64,13 +65,14 @@ def mutate(
     def stats(
         self,
         G: GraphV2,
-        relationship_types: list[str] = ALL_TYPES,
-        node_labels: list[str] = ALL_LABELS,
-        sudo: bool | None = None,
+        *,
+        concurrency: int | None = None,
+        job_id: str | None = None,
         log_progress: bool = True,
+        node_labels: list[str] = ALL_LABELS,
+        relationship_types: list[str] = ALL_TYPES,
+        sudo: bool = False,
         username: str | None = None,
-        concurrency: Any | None = None,
-        job_id: Any | None = None,
     ) -> KCoreStatsResult:
         """
         Executes the K-Core algorithm and returns statistics.
@@ -79,20 +81,20 @@ def stats(
         ----------
         G : GraphV2
             The graph to run the algorithm on
-        relationship_types : list[str]
-            The relationships types used to select relationships for this algorithm run
+        concurrency : int | None, default=None
+            The number of concurrent threads
+        job_id : str | None, default=None
+            An identifier for the job
+        log_progress : bool, default=True
+            Whether to log progress
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
-        sudo : bool
+        relationship_types : list[str]
+            The relationship types used to select relationships for this algorithm run
+        sudo : bool, default=False
             Override memory estimation limits
-        log_progress : bool | None, default=None
-            Whether to log progress
         username : str | None, default=None
             The username to attribute the procedure run to
-        concurrency : Any | None, default=None
-            The number of concurrent threads
-        job_id : Any | None, default=None
-            An identifier for the job
 
         Returns
         -------
@@ -105,13 +107,14 @@ def stats(
     def stream(
         self,
         G: GraphV2,
-        relationship_types: list[str] = ALL_TYPES,
-        node_labels: list[str] = ALL_LABELS,
-        sudo: bool | None = None,
+        *,
+        concurrency: int | None = None,
+        job_id: str | None = None,
         log_progress: bool = True,
+        node_labels: list[str] = ALL_LABELS,
+        relationship_types: list[str] = ALL_TYPES,
+        sudo: bool = False,
         username: str | None = None,
-        concurrency: Any | None = None,
-        job_id: Any | None = None,
     ) -> DataFrame:
         """
         Executes the K-Core algorithm and returns a stream of results.
@@ -147,14 +150,15 @@ def write(
         self,
         G: GraphV2,
         write_property: str,
+        *,
         relationship_types: list[str] = ALL_TYPES,
         node_labels: list[str] = ALL_LABELS,
-        sudo: bool | None = None,
+        sudo: bool = False,
         log_progress: bool = True,
         username: str | None = None,
-        concurrency: Any | None = None,
-        job_id: Any | None = None,
-        write_concurrency: Any | None = None,
+        concurrency: int | None = None,
+        job_id: str | None = None,
+        write_concurrency: int | None = None,
     ) -> KCoreWriteResult:
         """
         Executes the K-Core algorithm and writes the results to the Neo4j database.
@@ -165,21 +169,21 @@ def write(
             The graph to run the algorithm on
         write_property : str
             The property name to write core values to
-        relationship_types : list[str]
-            The relationships types considered in this algorithm run
+        concurrency : int | None, default=None
+            The number of concurrent threads
+        job_id : str | None, default=None
+            An identifier for the job
+        log_progress : bool, default=True
+            Whether to log progress
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
-        sudo : bool
+        relationship_types : list[str]
+            The relationship types considered in this algorithm run
+        sudo : bool, default=False
             Override memory estimation limits
-        log_progress : bool | None, default=None
-            Whether to log progress
         username : str | None, default=None
             The username to attribute the procedure run to
-        concurrency : Any | None, default=None
-            The number of concurrent threads
-        job_id : Any | None, default=None
-            An identifier for the job
-        write_concurrency : Any | None, default=None
+        write_concurrency : int | None, default=None
             The number of concurrent threads during the write phase
 
         Returns
@@ -193,9 +197,10 @@ def write(
     def estimate(
         self,
         G: GraphV2 | dict[str, Any],
-        relationship_types: list[str] = ALL_TYPES,
+        *,
+        concurrency: int | None = None,
         node_labels: list[str] = ALL_LABELS,
-        concurrency: Any | None = None,
+        relationship_types: list[str] = ALL_TYPES,
     ) -> EstimationResult:
         """
         Estimate the memory consumption of an algorithm run.
@@ -204,12 +209,12 @@ def estimate(
         ----------
         G : GraphV2 | dict[str, Any]
             The graph to run the algorithm on or a dictionary representing the graph.
-        relationship_types : list[str]
-            The relationship types used to select relationships for this algorithm run
+        concurrency : int | None, default=None
+            The number of concurrent threads
         node_labels : list[str]
             The node labels used to select nodes for this algorithm run
-        concurrency : Any | None, default=None
-            The number of concurrent threads
+        relationship_types : list[str]
+            The relationship types used to select relationships for this algorithm run
 
         Returns
         -------