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OnlineStatsChains.jl Documentation

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  • Report any issues found

See the full notice for security considerations and recommended due diligence.

A Julia package for chaining OnlineStats computations in a Directed Acyclic Graph (DAG) structure with automatic value propagation.

Overview

OnlineStatsChains.jl provides a powerful framework for building computational pipelines where OnlineStats are automatically updated as data flows through a directed acyclic graph.

Key Features

  • 📊 DAG Construction: Build computational graphs with automatic cycle detection
  • ⚡ Three Evaluation Strategies:
    • Eager (default): Immediate propagation when fit!() is called
    • Lazy: Deferred computation until value() is requested
    • Partial: Optimized propagation for affected subgraphs only
  • 🔀 Multi-Input Nodes: Support for fan-in and fan-out patterns
  • 📈 Batch & Streaming: Process data element-by-element or in batches
  • 🔧 Type-Safe: Works with any OnlineStat from OnlineStatsBase.jl

Quick Example

using OnlineStatsChains
using OnlineStatsBase

# Create a computational DAG
dag = StatDAG()

# Add nodes
add_node!(dag, :source, Mean())
add_node!(dag, :variance, Variance())

# Connect nodes
connect!(dag, :source, :variance)

# Fit data (propagates automatically)
fit!(dag, :source => [1.0, 2.0, 3.0, 4.0, 5.0])

# Get results
println("Mean: ", value(dag, :source))       # 3.0
println("Variance: ", value(dag, :variance))

Contents

Index