Update notebooks to GDS library algorithms

README.adoc

@@ -1,6 +1,6 @@
-= Neo4j Graph Algorithms Jupyter Notebooks
+= Neo4j Graph Data Science Jupyter Notebooks
 
-This repository contains Jupyter Notebooks for each of the https://neo4j-contrib.github.io/neo4j-graph-algorithms/[Neo4j graph algorithms^].
+This repository contains Jupyter Notebooks for each of the https://neo4j.com/docs/graph-data-science/current/[Neo4j graph algorithms^].
 
 
 == Path finding
@@ -40,10 +40,10 @@ pip install -r requirements.txt
 
 === Neo4j
 
-We'll also need to have a Neo4j server, with the Graph Algorithms library installed, running locally.
+We'll also need to have a Neo4j server, with the Graph Data Science library installed, running locally.
 The easiest way to do this is to download the Neo4j Desktop from http://neo4j.com/download[neo4j.com/download^].
 
-Once we've done that we can create a project and then install Graph Algorithms from the Plugins section.
+Once we've done that we can create a project and then install Graph Data Science from the Plugins section.
 
 image::images/installation.png[]
 

empty.py

@@ -1,11 +1,11 @@
 import os
 
-from neo4j.v1 import GraphDatabase, basic_auth
+from neo4j import GraphDatabase
 
 host = os.environ.get("NEO4J_HOST", "bolt://localhost")
 user = os.environ.get("NEO4J_USER", "neo4j")
 password = os.environ.get("NEO4J_PASSWORD", "neo")
-driver = GraphDatabase.driver(host, auth=basic_auth(user, password))
+driver = GraphDatabase.driver(host, auth=(user, password))
 
 
 def clear_db():

notebooks/AllPairsShortestPath.ipynb

@@ -17,7 +17,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "from neo4j.v1 import GraphDatabase, basic_auth\n",
+ "from neo4j import GraphDatabase\n",
  "import pandas as pd\n",
  "import os"
  ]
@@ -38,7 +38,7 @@
  "host = os.environ.get(\"NEO4J_HOST\", \"bolt://localhost\") \n",
  "user = os.environ.get(\"NEO4J_USER\", \"neo4j\")\n",
  "password = os.environ.get(\"NEO4J_PASSWORD\", \"neo\")\n",
- "driver = GraphDatabase.driver(host, auth=basic_auth(user, password))"
+ "driver = GraphDatabase.driver(host, auth=(user, password))"
  ]
  },
  {
@@ -92,7 +92,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 7,
  "metadata": {},
  "outputs": [
  {
@@ -126,54 +126,54 @@
  " <th>0</th>\n",
  " <td>A</td>\n",
  " <td>F</td>\n",
- " <td>160.0</td>\n",
+ " <td>100.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>1</th>\n",
- " <td>A</td>\n",
- " <td>E</td>\n",
- " <td>120.0</td>\n",
+ " <td>C</td>\n",
+ " <td>F</td>\n",
+ " <td>90.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>2</th>\n",
  " <td>B</td>\n",
  " <td>F</td>\n",
- " <td>110.0</td>\n",
+ " <td>90.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>3</th>\n",
- " <td>C</td>\n",
- " <td>F</td>\n",
- " <td>110.0</td>\n",
+ " <td>A</td>\n",
+ " <td>E</td>\n",
+ " <td>80.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>4</th>\n",
- " <td>A</td>\n",
- " <td>D</td>\n",
- " <td>90.0</td>\n",
+ " <td>B</td>\n",
+ " <td>E</td>\n",
+ " <td>70.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>5</th>\n",
- " <td>B</td>\n",
+ " <td>C</td>\n",
  " <td>E</td>\n",
  " <td>70.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>6</th>\n",
+ " <td>A</td>\n",
  " <td>D</td>\n",
- " <td>F</td>\n",
- " <td>70.0</td>\n",
+ " <td>50.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>7</th>\n",
- " <td>C</td>\n",
- " <td>E</td>\n",
- " <td>70.0</td>\n",
+ " <td>A</td>\n",
+ " <td>B</td>\n",
+ " <td>50.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>8</th>\n",
- " <td>A</td>\n",
- " <td>B</td>\n",
+ " <td>D</td>\n",
+ " <td>F</td>\n",
  " <td>50.0</td>\n",
  " </tr>\n",
  " <tr>\n",
@@ -188,29 +188,38 @@
  ],
  "text/plain": [
  " source target distance\n",
- "0 A F 160.0\n",
- "1 A E 120.0\n",
- "2 B F 110.0\n",
- "3 C F 110.0\n",
- "4 A D 90.0\n",
- "5 B E 70.0\n",
- "6 D F 70.0\n",
- "7 C E 70.0\n",
- "8 A B 50.0\n",
+ "0 A F 100.0\n",
+ "1 C F  90.0\n",
+ "2 B F  90.0\n",
+ "3 A E  80.0\n",
+ "4 B E 70.0\n",
+ "5 C E 70.0\n",
+ "6 A D 50.0\n",
+ "7 A B 50.0\n",
+ "8 D F 50.0\n",
  "9 A C 50.0"
  ]
  },
- "execution_count": 4,
+ "execution_count": 7,
  "metadata": {},
  "output_type": "execute_result"
  }
  ],
  "source": [
  "streaming_query = \"\"\"\n",
- "CALL algo.allShortestPaths.stream('cost',{nodeQuery:'Loc',defaultValue:1.0})\n",
+ "CALL gds.alpha.allShortestPaths.stream({\n",
+ " nodeProjection:'Loc',\n",
+ " relationshipProjection:{\n",
+ " ALL:{\n",
+ " type:'*',\n",
+ " orientation:'NATURAL',\n",
+ " properties: {cost:{property:'cost', defaultValue:1.0}}\n",
+ " }\n",
+ " },\n",
+ " relationshipWeightProperty:'cost'})\n",
  "YIELD sourceNodeId, targetNodeId, distance\n",
  "WITH sourceNodeId, targetNodeId, distance \n",
- "WHERE algo.isFinite(distance) = true\n",
+ "WHERE gds.util.isFinite(distance) = true\n",
  "\n",
  "MATCH (source:Loc) WHERE id(source) = sourceNodeId\n",
  "MATCH (target:Loc) WHERE id(target) = targetNodeId\n",
@@ -222,8 +231,8 @@
  "\"\"\"\n",
  "\n",
  "with driver.session() as session:\n",
- " result = session.read_transaction(lambda tx: tx.run(streaming_query)) \n",
- " df = pd.DataFrame([r.values() for r in result], columns=result.keys())\n",
+ " result = session.run(streaming_query)\n",
+ " df = pd.DataFrame([dict(row) for row in result])\n",
  "\n",
  "df"
  ]
@@ -233,11 +242,36 @@
  "metadata": {},
  "source": [
  "This query returned the top 10 pairs of nodes that are the furthest away from each other.\n",
- "\"F\" and \"E\" seem to be quite distant from the others."
+ "Node \"F\" seem to be quite distant from the others."
  ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
  }
  ],
- "metadata": {},
+ "metadata": {
+ "kernelspec": {
+ "display_name": "scispacy",
+ "language": "python",
+ "name": "scispacy"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }