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:orphan:

Wildfire Mamba
==============

Overview
--------

``wildfire_mamba`` models county-day ERA5 sequences by combining selective state-space temporal blocks with a simple spatial graph layer.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Wildfire

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Hidden

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Classification

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Unmapped

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``wildfire_mamba`` models county-day ERA5 sequences by combining selective state-space temporal blocks with a simple spatial graph layer.

The PyHazards implementation targets binary next-day per-county wildfire classification and supports an optional count head for multi-task extensions.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** Not yet mapped.

External References
-------------------

**Paper:** `Mamba: Linear-Time Sequence Modeling with Selective State Spaces <https://arxiv.org/abs/2312.00752>`_

Registry Name
-------------

Primary entrypoint: ``wildfire_mamba``

Supported Tasks
---------------

- Classification

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="wildfire_mamba",
       task="classification",
       in_dim=3,
       num_counties=4,
       past_days=5,
   )

   x = torch.randn(2, 5, 4, 3)
   logits = model(x)
   print(logits.shape)

Notes
-----

- The CI smoke test validates the default binary-classification path on synthetic data.