Struct PyTrendModel 

pub struct PyTrendModel { /* private fields */ }

A Python wrapper for a trend model.

This allows users to implement their own trend models in Python and use them in the MSTL algorithm using MSTL::custom_trend.

The Python class must implement the following methods:

• fit(self, y: np.ndarray) -> None
• predict(self, horizon: int, level: float | None = None) -> augurs.Forecast
• predict_in_sample(self, level: float | None = None) -> augurs.Forecast

Implementations

impl PyTrendModel

pub fn new(model: Py<PyAny>) -> Self

Wrap a trend model implemented in Python into a PyTrendModel.

The returned PyTrendModel can be used in MSTL models using the custom_trend method of the MSTL class.

Trait Implementations

impl Clone for PyTrendModel

fn clone(&self) -> PyTrendModel

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PyTrendModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl IntoPy<Py<PyAny>> for PyTrendModel

fn into_py(self, py: Python<'_>) -> PyObject

Performs the conversion.

impl PyClass for PyTrendModel

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyTrendModel

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

type Layout = PyCell<PyTrendModel>

Layout

type BaseType = PyAny

Base class

type ThreadChecker = ThreadCheckerStub<PyTrendModel>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn doc(py: Python<'_>) -> PyResult<&'static CStr>

Rendered class doc

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl PyClassNewTextSignature<PyTrendModel> for PyClassImplCollector<PyTrendModel>

fn new_text_signature(self) -> Option<&'static str>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a PyTrendModel

type Holder = Option<PyRef<'py, PyTrendModel>>

fn extract(obj: &'py PyAny, holder: &'a mut Self::Holder) -> PyResult<Self>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a mut PyTrendModel

type Holder = Option<PyRefMut<'py, PyTrendModel>>

fn extract(obj: &'py PyAny, holder: &'a mut Self::Holder) -> PyResult<Self>

impl PyMethods<PyTrendModel> for PyClassImplCollector<PyTrendModel>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyTrendModel

const NAME: &'static str = "TrendModel"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

type AsRefTarget = PyCell<PyTrendModel>

Utility type to make Py::as_ref work.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> &PyType

Returns the safe abstraction over the type object.

fn is_type_of(object: &PyAny) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &PyAny) -> bool

Checks if object is an instance of this type.

impl TrendModel for PyTrendModel

fn name(&self) -> Cow<'_, str>

Return the name of the trend model.

fn fit( &mut self, y: &[f64], ) -> Result<(), Box<dyn Error + Send + Sync + 'static>>

Fit the model to the given time series.

fn predict( &self, horizon: usize, level: Option<f64>, ) -> Result<Forecast, Box<dyn Error + Send + Sync + 'static>>

Produce a forecast for the next horizon time points.

fn predict_in_sample( &self, level: Option<f64>, ) -> Result<Forecast, Box<dyn Error + Send + Sync + 'static>>

Produce in-sample predictions.