Neo4j Machine Learning Procedures (WIP)

This project provides procedures and functions to support machine learning applications with Neo4j.

Note	This project requires Neo4j 3.2.x

Thanks a lot to Encog for the great library and to Stardog for the idea.

Installation

Download the jar from the latest release or build it locally
Copy it into your $NEO4J_HOME/plugins directory.
Restart your server.

Built in classification and regression (WIP)

CALL ml.create("model",{types},"output",{config}) YIELD model, state, info

CALL ml.add("model", {inputs}, given)  YIELD model, state, info

CALL ml.train() YIELD model, state, info

CALL ml.predict("model", {inputs}) YIELD value [, confidence]

CALL ml.remove(model) YIELD model, state

Example: IRIS Classification from Encog

CALL ml.create("iris",
  {sepalLength: "float", sepalWidth: "float", petalLength: "float", petalWidth: "float", kind: "class"}, "kind",{});

LOAD CSV FROM "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" AS row
CALL ml.add('iris', {sepalLength: row[0], sepalWidth: row[1], petalLength: row[2], petalWidth: row[3]}, row[4])
YIELD state
WITH collect(distinct state) as states, collect(distinct row[4]) as kinds

CALL ml.train('iris')  YIELD state, info

RETURN state, states, kinds, info;

╒═══════╤════════════╤══════════════════════════════════════════════════╤══════════════════════════════════════════════════════════════════════╕
│"state"│"states"    │"kinds"                                           │"info"                                                                │
╞═══════╪════════════╪══════════════════════════════════════════════════╪══════════════════════════════════════════════════════════════════════╡
│"ready"│["training"]│["Iris-setosa","Iris-versicolor","Iris-virginica"]│{"trainingSets":150,"methodName":"feedforward","normalization":"[Norma│
│       │            │                                                  │lizationHelper:\n[ColumnDefinition:sepalLength(continuous);low=4,30000│
│       │            │                                                  │0,high=7,900000,mean=5,843333,sd=0,825301]\n[ColumnDefinition:petalLen│
│       │            │                                                  │gth(continuous);low=1,000000,high=6,900000,mean=3,758667,sd=1,758529]\│
│       │            │                                                  │n[ColumnDefinition:sepalWidth(continuous);low=2,000000,high=4,400000,m│
│       │            │                                                  │ean=3,054000,sd=0,432147]\n[ColumnDefinition:petalWidth(continuous);lo│
│       │            │                                                  │w=0,100000,high=2,500000,mean=1,198667,sd=0,760613]\n[ColumnDefinition│
│       │            │                                                  │:kind(nominal);[Iris-setosa, Iris-versicolor, Iris-virginica]]\n]","tr│
│       │            │                                                  │ainingError":0.034672103747075696,"selectedMethod":"[BasicNetwork: Lay│
│       │            │                                                  │ers=3]","validationError":0.05766172747088482}                        │
└───────┴────────────┴──────────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────┘

LOAD CSV FROM "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" AS row
WITH state, info, row limit 10

CALL ml.predict("iris", {sepalLength: row[0], sepalWidth: row[1], petalLength: row[2], petalWidth: row[3]})
YIELD value as prediction

RETURN row[4] as correct, prediction, state, row;

╒═════════════╤═════════════╤═══════╤═══════════════════════════════════════╕
│"correct"    │"prediction" │"state"│"row"                                  │
╞═════════════╪═════════════╪═══════╪═══════════════════════════════════════╡
│"Iris-setosa"│"Iris-setosa"│"ready"│["5.1","3.5","1.4","0.2","Iris-setosa"]│
├─────────────┼─────────────┼───────┼───────────────────────────────────────┤
│"Iris-setosa"│"Iris-setosa"│"ready"│["4.9","3.0","1.4","0.2","Iris-setosa"]│
├─────────────┼─────────────┼───────┼───────────────────────────────────────┤
│"Iris-setosa"│"Iris-setosa"│"ready"│["4.7","3.2","1.3","0.2","Iris-setosa"]│
├─────────────┼─────────────┼───────┼───────────────────────────────────────┤
...

CALL ml.remove('iris') YIELD model, state;

Manual neural network operations (TODO)

Procedures

apoc.ml.propagate(network, {inputs}) yield {outputs}
apoc.ml.backprop(network, {output}) yield {network}

Functions

apoc.ml.sigmoid
apoc.ml.sigmoidPrime

Future plans include storing networks from the common machine learning libraries (TensorFlow, Deeplearning4j, Encog etc.) as executable Network structures in Neo4j.

Building it yourself

This project uses maven, to build a jar-file with the procedure in this project, simply package the project with maven:

mvn clean package

This will produce a jar-file,target/neo4j-ml-procedures-*-SNAPSHOT.jar, that can be copied in the $NEO4J_HOME/plugins directory of your Neo4j instance.

License

Apache License V2, see LICENSE

Next Steps

Normalization / Classification for dl4j
Push frameworks to separate modules
Support external frameworks
Store / Load models from graph
Expose simple ML functions / propagation/ backprop on graph structures as procedures and functions
Load PMML
More fine-grained configuration (JSON) for Networks
K-Means, Classification, Regression
Spark ML-Lib

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neo4j-contrib / neo4j-ml-procedures

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