ML
The
ML module facilitates creation of Automated Machine Learning models to embed into Templates.Example
// use a DictType stream
const training_stream = Stream(
"My Training Stream",
DictType(
StringType,
StructType({
qty: FloatType,
price: FloatType,
})
)
);
const prediction_stream = Stream(
"My Prediction Stream",
DictType(
StringType,
StructType({
price: FloatType,
})
)
);
// create a GP to predict demand based on price
const demand = new MLModelBuilder("Demand")
.feature("price", FloatType)
.output(FloatType)
.trainFromStream({
output_name: "qty",
input: training_stream,
});
// directly predict the model outputs from features
const ml_prediction = new MLPredictionBuilder("ML Prediction")
.model(demand)
.predict(prediction_stream);
// or alternatively use the model to predict in a process
// create the sales process and add the ml function
// which is evaluated in each sale
const sales = new ProcessBuilder("sales")
// the ml function can be added
.ml(demand)
// the sale needs a price
.value("price", FloatType)
// the ml can be evaluated
.let(
"qty",
(props, _resources, mls) => mls.demand(Struct({ price: props.price }))
)
.mapFromValue({ date: new Date(0), price: 10 });
Classes
- FloatMLBuilder
- MLExamplesEvaluatorBuilder
- MLModelBuilder
- MLModelEvaluatorBuilder
- MLPredictionBuilder
Other
FloatMLModel
Ƭ FloatMLModel: Object
The machine learning model applicable to
FloatType outputsType declaration
Name | Type |
---|---|
noise | FloatMLModelNoise |
type | FloatMLModelType |
FloatMLModelNoise
Ƭ FloatMLModelNoise: "none"
| "gaussian"
The noise modelled in the
FloatMLModel output.FloatMLModelType
Ƭ FloatMLModelType: "boosted_trees"
| "constant"
| "gaussian_process"
| "linear"
| "neural_network"
The machine learning model types that can be used for a
FloatMLModel.MLModelType
Ƭ MLModelType:
FloatMLModelType
| StringMLModelType
The machine learning model types that can be used for a MLModel.
MLPredictionConfiguration
Ƭ MLPredictionConfiguration<T
>: T
extends
FloatType
? { max
: EastFunction
<FloatType
> ; min
: EastFunction
<FloatType
> } : Record
<string
, never
>
Extra configuration that can modify ML evaluation
Type parameters
Name | Type |
---|---|
T | extends EastType = EastType |
StringMLModel
Ƭ StringMLModel: Object
The machine learning model applicable to
StringType outputsType declaration
Name | Type |
---|---|
noise | StringMLModelNoise |
type | StringMLModelType |
StringMLModelNoise
Ƭ StringMLModelNoise: "maximum_likelihood"
| "weighted"
The noise modelled in the
StringMLModel output.StringMLModelType
Ƭ StringMLModelType: "boosted_trees_string"
| "neural_network_string"
The machine learning model types that can be used for a
StringMLModel.