ML.NET Script Class — Machine Learning in FrameworX Scripts

Create machine learning models that run inside FrameworX using Script Classes with ML.NET. The AI writes the C# code, connects it to live tags, creates output tags for predictions, and configures model persistence — all within the FrameworX scripting engine.

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Architecture

No Format

Input Tags ???-> Script Class (ML.NET) ???-> Output Tags
   ?|              ?|                         ?|
 Live data    Train / Predict           Predictions, scores,
 from UNS     Model persisted to        anomaly flags, forecasts
              solution folder            ??? Alarms / Dashboard

When to Use This Skill

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Solution open with input tags already created (the tags the model will read from)
Tags should have live or simulated data flowing (Value Simulator or real device)
If starting from scratch, apply the New Solution skill first to create tags and a data source

MCP Tools and Tables

Category	Items
Tools	`get_table_schema`, `write_objects`, `get_objects`, `list_elements`, `search_docs`
Tables	`UnsTags`, `ScriptsClasses`, `ScriptsExpressions`, `ScriptsTasks`

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Step 0: Identify the ML Task

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Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

User Goal	Suggested Algorithm
Predictive maintenance — single sensor	Anomaly Detection (Spike)
Predictive maintenance — multiple sensors	Binary Classification
Detect sensor failures / outliers	Anomaly Detection (Spike)
Detect gradual drift or process shift	Anomaly Detection (ChangePoint)
Predict future values	Time-Series Forecasting (SSA)
Energy / consumption modeling	Regression
Quality control pass/fail	Binary Classification
Fault prediction yes/no	Binary Classification
Production / demand forecasting	Time-Series Forecasting (SSA)
Process output from multiple inputs	Regression

Required information before proceeding

Information	Why
Input tag path(s)	The model reads from these tags
ML algorithm	Determines the ML.NET pipeline to generate
Output semantics	What the predictions mean (anomaly score, forecast value, etc.)

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Step 1: Create Output Tags

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?? Confirm the solution is set to Multiplatform before starting the runtime. ML.NET requires .NET 8+ — if the solution was created as Windows (.NET 4.8), training will fail with a System.Math / CpuMath error. Instruct the user:
"Before starting the runtime, please confirm your solution is set to Multiplatform: Solution → Settings → Target Platform = Multiplatform, then Product → Modify."
Only proceed if the user confirms this is already set.
Do NOT start the runtime automatically. Inform the user that all scripts are configured and that they can start the runtime whenever ready. Only call designer_action('start_runtime') if the user explicitly requests it.
Wait for training — the model needs MinTrainingSize data points before predictions begin
Check output tags — verify LastPrediction timestamp is updating
Screenshot — if a dashboard exists, take a screenshot to confirm predictions are flowing

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Common Pitfalls

Mistake	Why It Happens	How to Avoid
Missing ML.NET namespaces	Used `AddMLNetNamespaces: true` (field does not exist — silently ignored) or omitted `NamespaceDeclarations` entirely	Always set `NamespaceDeclarations: "Microsoft.ML;Microsoft.ML.Data;..."` in write_objects. `AddMLNetNamespaces` is not a valid field.
`CS0234` error in ScriptsTasks	Called `Script.Class.<Name>.Predict(...)` without `@` prefix	Always use `@Script.Class.<Name>.Predict(...)` inside ScriptsTasks — the `@` prefix is required for all runtime object references.
Tag reference without `@Tag.`	Confusing with Expression syntax	Always `@Tag.Path/Name.Value` inside Script Classes
Model lost on restart	SaveModel or LoadModel not wired up	Always include `SaveModel()` after `TrainModel()` and wire `LoadModel()` in `ServerStartup`
Training on every call	No guard for already-trained model	Use `modelTrained` boolean flag
Wrong data types	ML.NET expects `float`, tags are `double`	Cast with `(float)` when feeding ML.NET, cast back to `double` when writing tags
Expression ObjectName missing `Tag.` prefix	Confusing tag path vs expression binding	Expression ObjectName needs `Tag.` prefix; the UNS tag itself does not
Non-empty `ObjectName` on void ML `Predict()` call	Expression tries to assign void return to a tag	Leave `ObjectName` empty when the ML class writes to output tags internally inside `RunPrediction()`
Used `TriggerTag` field in Expression	Field does not exist — silently ignored, expression never fires	Use `Trigger` field with the tag path (no `Tag.` prefix)
Class is document object	Partial write replaces entire class	Always read-modify-write for existing classes
`CS0029` bool-to-int on Digital tag write	`Digital` tags are backed by `int`, not `bool` — assigning a C# `bool` expression causes a type error	Never assign a raw `bool` to a Digital tag. Use a ternary: `@Tag.Path/ML/Flag.Value = (condition) ? 1 : 0;` — never `= boolVar;` or `= (bool expression);`
`CS0246` Script Class not found in Tasks	Tasks and Classes compile in the same pass; if the Class isn't ordered first, Tasks that reference it fail	Set `BuildOrder: "1"` on every ML Script Class. Tasks that call it do not need an explicit BuildOrder — they default to a later pass.
`Could not load type 'System.Math' from assembly 'System.Runtime'` at training time	Solution is targeting .NET 4.8 (Windows platform) — ML.NET CpuMath trainers (FastTree, SSA) are incompatible with .NET 4.8	Go to Solution → Settings → Target Platform → Multiplatform, then Product → Modify to rebuild. This is required for all ML.NET solutions.

Decision Guide

Scenario	ML Task	Trigger	Notes
Single sensor, detect outliers/spikes	Anomaly Detection (Spike)	Expression OnChange	Fast, one tag in / flags out
Single sensor, detect gradual drift	Anomaly Detection (ChangePoint)	Expression OnChange	AI picks this variant when user mentions "drift" or "regime change"
Single sensor, predict future values	Forecasting (SSA)	Expression OnChange or Periodic	Outputs forecast + confidence bounds
Multiple sensors → one continuous value	Regression	Task Periodic	Energy prediction, process modeling
Multiple sensors → yes/no	Binary Classification	Task Periodic	Fault prediction, quality pass/fail
User says "predictive maintenance" + single sensor	Anomaly Detection	Expression OnChange	Most common PdM entry point
User says "predictive maintenance" + multiple sensors	Binary Classification	Task Periodic	Predicts failure from combined inputs
User says "quality control"	Binary Classification	Task Periodic	Pass/fail prediction
User says "forecast" or "predict demand"	Forecasting (SSA)	Expression OnChange or Periodic	Time-series based
User says "you decide" + single sensor	Anomaly Detection	Expression OnChange	Safest default for monitoring
User says "you decide" + multiple sensors	Regression	Task Periodic	Most general multi-input approach

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Versions Compared

Old Version 1

New Version Current

Key

ML.NET Script Class — Machine Learning in FrameworX Scripts

When to Use This Skill

MCP Tools and Tables

Step 0: Identify the ML Task

Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

Required information before proceeding

Step 1: Create Output Tags

Common Pitfalls

Decision Guide

Page tree

Page History

Versions Compared

Old Version 1

New Version Current

Key

ML.NET Script Class — Machine Learning in FrameworX Scripts

When to Use This Skill

MCP Tools and Tables

Step 0: Identify the ML Task

Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

Required information before proceeding

Step 1: Create Output Tags

Common Pitfalls

Decision Guide