neo4j-ml-procedures

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= Neo4j Machine Learning Procedures (WIP)

image:https://travis-ci.org/neo4j-contrib/neo4j-ml-procedures.svg?branch=3.1["Build state", link="https://travis-ci.org/neo4j-contrib/neo4j-ml-procedures"]

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 https://github.com/encog/encog-java-core[Encog for the great library^] and to https://www.stardog.com/docs/#_machine_learning[Stardog for the idea^].

== Installation

1. Download the jar from the https://github.com/neo4j-contrib/neo4j-ml-procedures/releases/latest[latest release] or build it locally
2. Copy it into your $NEO4J_HOME/plugins directory.
3. Restart your server.

== Built in classification and regression (WIP)

[source,cypher]

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

[source,cypher]

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

[source,cypher]

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} │ └───────┴────────────┴──────────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────┘

[source,cypher]

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"]│ ├─────────────┼─────────────┼───────┼───────────────────────────────────────┤ ...

[source,cypher]

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

== Manual neural network operations (TODO)

.Procedures [source,cypher]

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

.Functions [source,cypher]

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