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Format notebooks with ruff (#768)
* Format notebooks with ruff * Update nbconvert & fix title level * Print notebook diff * Bump dependencies to match CI * Format notebooks using pandoc 3.3.1 brew updated to 3.5 (and only has one version)
1 parent 83e5d02 commit 4ea6f90

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-67
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‎doc/modules/ROOT/pages/tutorials/centrality-algorithms.adoc

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@@ -34,9 +34,11 @@ connection to the database.
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[source, python, role=no-test]
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----
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from graphdatascience import GraphDataScience
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import pandas as pd
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import os
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import pandas as pd
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from graphdatascience import GraphDataScience
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----
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[source, python, role=no-test]
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gds.run_cypher(
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"""
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UNWIND $rels AS rel
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MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})
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UNWIND $rels AS rel
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MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})
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CREATE (source)-[:HAS_FLIGHT_TO]->(target)
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""",
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params={"rels": routes_df.to_dict("records")},

‎doc/modules/ROOT/pages/tutorials/community-detection.adoc

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@@ -34,9 +34,11 @@ connection to the database.
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[source, python, role=no-test]
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----
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from graphdatascience import GraphDataScience
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import pandas as pd
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import os
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import pandas as pd
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from graphdatascience import GraphDataScience
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----
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[source, python, role=no-test]
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gds.run_cypher(
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"""
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UNWIND $rels AS rel
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MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT})
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UNWIND $rels AS rel
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MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT})
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CREATE (source)-[:HYPERLINKED_TO]->(target)
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""",
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params={"rels": relationship_df.to_dict("records")},
@@ -232,7 +234,7 @@ We can also check that the property was written by the below command.
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----
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gds.run_cypher(
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"""
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MATCH (n) WHERE 'louvainCommunityId' IN keys(n)
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MATCH (n) WHERE 'louvainCommunityId' IN keys(n)
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RETURN n.name, n.louvainCommunityId LIMIT 10
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"""
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)

‎doc/modules/ROOT/pages/tutorials/fastrp-and-knn.adoc

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@@ -55,6 +55,7 @@ our GDS client connection to the database.
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[source, python, role=no-test]
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----
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import os
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from graphdatascience import GraphDataScience
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# Get Neo4j DB URI and credentials from environment if applicable

‎doc/modules/ROOT/pages/tutorials/gds-sessions-self-managed.adoc

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@@ -34,6 +34,7 @@ version `+1.12a1+` or later.
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[source, python, role=no-test]
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----
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from datetime import timedelta
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%pip install "graphdatascience>=1.12a1"
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----
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@@ -50,7 +51,8 @@ is the main entry point for managing GDS Sessions.
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[source, python, role=no-test]
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----
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import os
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from graphdatascience.session import GdsSessions, AuraAPICredentials
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from graphdatascience.session import AuraAPICredentials, GdsSessions
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client_id = os.environ["AURA_API_CLIENT_ID"]
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client_secret = os.environ["AURA_API_CLIENT_SECRET"]
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[source, python, role=no-test]
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----
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import os
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from graphdatascience.session import DbmsConnectionInfo, AlgorithmCategory, CloudLocation
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from datetime import timedelta
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from graphdatascience.session import AlgorithmCategory, CloudLocation, DbmsConnectionInfo
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# Identify the Neo4j DBMS
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db_connection = DbmsConnectionInfo(uri=os.environ["NEO4J_URI"], username=os.environ["NEO4J_USER"], password=os.environ["NEO4J_PASSWORD"])
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db_connection = DbmsConnectionInfo(
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uri=os.environ["NEO4J_URI"], username=os.environ["NEO4J_USER"], password=os.environ["NEO4J_PASSWORD"]
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)
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# Specify where to create the GDS session
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cloud_location = CloudLocation(provider="gcp", region="europe-west1")
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‎doc/modules/ROOT/pages/tutorials/gds-sessions.adoc

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@@ -50,7 +50,8 @@ is the main entry point for managing GDS Sessions.
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[source, python, role=no-test]
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----
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import os
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from graphdatascience.session import GdsSessions, AuraAPICredentials
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from graphdatascience.session import AuraAPICredentials, GdsSessions
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client_id = os.environ["AURA_API_CLIENT_ID"]
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client_secret = os.environ["AURA_API_CLIENT_SECRET"]
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----
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import os
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from datetime import timedelta
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from graphdatascience.session import DbmsConnectionInfo, AlgorithmCategory
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from graphdatascience.session import AlgorithmCategory, DbmsConnectionInfo
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# Identify the AuraDB instance
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db_connection = DbmsConnectionInfo(uri=os.environ["AURA_DB_ADDRESS"], username=os.environ["AURA_DB_USER"], password=os.environ["AURA_DB_PW"])
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db_connection = DbmsConnectionInfo(
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uri=os.environ["AURA_DB_ADDRESS"], username=os.environ["AURA_DB_USER"], password=os.environ["AURA_DB_PW"]
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)
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# Create a GDS session!
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memory = sessions.estimate(
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node_count=20,

‎doc/modules/ROOT/pages/tutorials/heterogeneous-node-classification-with-hashgnn.adoc

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----
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# Import our dependencies
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import os
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from graphdatascience import GraphDataScience
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----
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‎doc/modules/ROOT/pages/tutorials/import-sample-export-gnn.adoc

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[source, python, role=no-test]
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----
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import os
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import pandas as pd
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from graphdatascience import GraphDataScience
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import random
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import numpy as np
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import torch
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from torch_geometric.data import Data
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import torch.nn.functional as F
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from torch_geometric.data import Data
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from torch_geometric.nn import GCNConv
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from torch_geometric.transforms import RandomNodeSplit
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import random
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import numpy as np
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from graphdatascience import GraphDataScience
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[source, python, role=no-test]

‎doc/modules/ROOT/pages/tutorials/kge-predict-transe-pyg-train.adoc

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[source, python, role=no-test]
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import collections
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import os
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from graphdatascience import GraphDataScience
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import pandas as pd
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import torch
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import torch.optim as optim
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from torch_geometric.data import Data, download_url
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from torch_geometric.nn import TransE
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import collections
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from tqdm import tqdm
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import pandas as pd
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from graphdatascience import GraphDataScience
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[source, python, role=no-test]

‎doc/modules/ROOT/pages/tutorials/load-data-via-graph-construction.adoc

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@@ -33,6 +33,7 @@ the `+graphdatascience+` package and import the client class.
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[source, python, role=no-test]
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import os
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from graphdatascience import GraphDataScience
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----
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‎doc/modules/ROOT/pages/tutorials/ml-pipelines-node-classification.adoc

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[source, python, role=no-test]
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import os
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from graphdatascience import GraphDataScience
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----
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‎examples/centrality-algorithms.ipynb

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"\n",
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"This notebook will show how you can apply eigenvector centrality, betweenness centrality, degree centrality and closeness centrality on a graph dataset.\n",
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"\n",
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"### Setup\n",
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"## Setup\n",
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"\n",
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"We start by importing our dependencies and setting up our GDS client connection to the database."
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"from graphdatascience import GraphDataScience\n",
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"import os\n",
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"\n",
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"import pandas as pd\n",
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"import os"
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"\n",
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"from graphdatascience import GraphDataScience"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing the dataset\n",
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"## Importing the dataset\n",
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"\n",
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"We import the dataset as a pandas dataframe first. We deal with two files here. The file `reachability-meta.csv.gz` stores the names of the cities and their information while the file `reachability.txt.gz` stores the edges of the graph. An edge exists from city `i` to city `j` if the estimated airline travel time is less than a threshold.\n"
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"\n",
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"gds.run_cypher(\n",
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" \"\"\"\n",
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" UNWIND $rels AS rel \n",
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" MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination}) \n",
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" UNWIND $rels AS rel\n",
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" MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})\n",
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" CREATE (source)-[:HAS_FLIGHT_TO]->(target)\n",
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" \"\"\",\n",
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" params={\"rels\": routes_df.to_dict(\"records\")},\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Eigenvector Centrality\n",
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"## Eigenvector Centrality\n",
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"\n",
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"[Eigenvector centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/eigenvector-centrality/) measures the importance or influence of a node based on its connections to other nodes in the network. A higher eigenvector centrality score suggests that a node is more central and influential within the network.\n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Betweenness Centrality\n",
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"## Betweenness Centrality\n",
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"\n",
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"[Betweenness Centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/betweenness-centrality/) quantifies the importance of a node as a bridge or intermediary in the network. It measures how often a node lies on the shortest path between other pairs of nodes. \n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Degree Centrality\n",
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"## Degree Centrality\n",
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"\n",
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"[Degree Centrality](https://neo4j.com/docs/graph-data-science/current/algorithms/degree-centrality/) measures the number of connections (edges) a node has in the network. \n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Cleanup\n",
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"## Cleanup\n",
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"\n",
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"Before finishing we can clean up the example data from both the GDS in-memory state and the database."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### References\n",
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"## References\n",
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"- For the network:\n",
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"Brendan J. Frey and Delbert Dueck. \"Clustering by passing messages between data points.\" Science 315.5814 (2007): 972-976.\n",
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"\n",

‎examples/community-detection.ipynb

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"\n",
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"The tasks we cover here include performing initial graph preprocessing using Weakly Connected Components and then performing community detection on the largest component using the Louvain algorithm.\n",
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"\n",
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"### Setup\n",
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"## Setup\n",
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"\n",
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"We start by importing our dependencies and setting up our GDS client connection to the database."
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"from graphdatascience import GraphDataScience\n",
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"import os\n",
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"\n",
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"import pandas as pd\n",
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"import os"
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"\n",
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"from graphdatascience import GraphDataScience"
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]
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},
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{
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"id": "48bd8af1",
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"metadata": {},
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"source": [
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"### Importing the dataset\n",
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"## Importing the dataset\n",
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"\n",
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"We import the dataset as a pandas dataframe first. We work with only a subset of the dataset. The sampled data is only till 1st March 2014. "
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"\n",
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"gds.run_cypher(\n",
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" \"\"\"\n",
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" UNWIND $rels AS rel \n",
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" MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT}) \n",
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" UNWIND $rels AS rel\n",
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" MATCH (source:Subreddit {name: rel.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel.TARGET_SUBREDDIT})\n",
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" CREATE (source)-[:HYPERLINKED_TO]->(target)\n",
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" \"\"\",\n",
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" params={\"rels\": relationship_df.to_dict(\"records\")},\n",
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"id": "9c259471",
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"metadata": {},
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"source": [
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"### Weakly Connected Components\n",
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"## Weakly Connected Components\n",
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"\n",
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"A graph dataset need not always be connected. That is, there may not exist a path from every node to \n",
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"every other node in the graph dataset (subgraphs in it may not be connected to each other at all). Hence, we \n",
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"id": "17942d04",
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"metadata": {},
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"source": [
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"### Community Detection using Louvain\n",
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"## Community Detection using Louvain\n",
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"\n",
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"We use the [Louvain](https://neo4j.com/docs/graph-data-science/current/algorithms/louvain/) algorithm to detect communities in our subgraph and assign a `louvainCommunityId` to each community."
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"source": [
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"gds.run_cypher(\n",
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" \"\"\"\n",
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" MATCH (n) WHERE 'louvainCommunityId' IN keys(n) \n",
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" MATCH (n) WHERE 'louvainCommunityId' IN keys(n)\n",
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" RETURN n.name, n.louvainCommunityId LIMIT 10\n",
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" \"\"\"\n",
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")"
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"id": "5ed56f82",
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"metadata": {},
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"source": [
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"### Further ideas\n",
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"## Further ideas\n",
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"\n",
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"* Inspect the produced communities using [Bloom](https://neo4j.com/docs/bloom-user-guide/current/). You can use rule-based styling based on the community property.\n",
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"* Try to tune more parameters of Louvain and see how the communities differ.\n",
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"id": "6e00ed7b",
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"metadata": {},
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"source": [
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"### Cleanup\n",
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"## Cleanup\n",
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"\n",
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"Before finishing we can clean up the example data from both the GDS in-memory state and the database."
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"id": "65dcb952",
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"metadata": {},
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"source": [
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"### References\n",
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"## References\n",
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"\n",
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"Srijan Kumar, William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2018. Community Interaction and Conflict on the Web. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 933–943. https://doi.org/10.1145/3178876.3186141"
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‎examples/fastrp-and-knn.ipynb

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"outputs": [],
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"import os\n",
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"\n",
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"from graphdatascience import GraphDataScience\n",
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"\n",
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"# Get Neo4j DB URI and credentials from environment if applicable\n",

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