Errors
Most model and data utility methods return Result<T, rustyml::error::Error>.
Use ? to return an error to the caller.
Use match when the caller can correct a specific problem.
Handle a model state error
This program handles prediction before training. It then trains the model and repeats the prediction.
use ndarray::array;
use rustyml::error::{Error, RustymlResult};
use rustyml::machine_learning::LinearRegression;
fn main() -> RustymlResult<()> {
let x = array![[1.0], [2.0]];
let mut model = LinearRegression::new(true);
match model.predict(&x) {
Err(Error::NotFitted(_)) => println!("fit the model before prediction"),
Err(error) => return Err(error),
Ok(_) => println!("prediction succeeded"),
}
model.fit(&x, &array![3.0, 5.0])?;
println!("prediction: {:.1}", model.predict(&array![[3.0]])?[0]);
Ok(())
}Output:
fit the model before prediction
prediction: 7.0
Error is non-exhaustive.
Include a fallback arm when you match its variants.
Correct common errors
| Error | Action |
|---|---|
EmptyInput | Supply a nonempty array. |
DimensionMismatch | Check sample counts and feature counts. |
ShapeMismatch | Check the complete tensor shape. |
InvalidParameter | Check the constructor or method parameters. |
InvalidInput | Check array ranks, label values, and other input requirements. |
NonFinite | Check for NaN, infinity, or numerical overflow. |
NotFitted | Call fit before prediction or transformation. |
NotConverged | Check data scaling and the selected solver settings. |
NeuralNetwork(NnError::NotCompiled(_)) | Call compile before training or evaluation. |
Io | Check the file path, permissions, and file format. |
NnError belongs to rustyml::neural_network.
The neural network feature enables the NeuralNetwork error variant.
Each method documents the errors that it can return.
Metric input checks
Metrics return scores directly. Invalid metric inputs can cause a panic. Check array lengths, label values, and metric requirements before you call them. See Evaluate models for common input requirements.