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fix: 1938 generate models overview from registry
Signed-off-by: Pinaka07 <anandsingh.as1996@gmail.com>
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5 files changed

+400
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‎docs/source/_static/custom.css‎

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@@ -56,3 +56,81 @@ span.highlighted {
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.highlight > pre > .s2 {
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color: #647db6 !important;
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}
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/* Model overview table styling */
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.model-overview-table {
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width: 100%;
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font-size: 0.9em;
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border-collapse: collapse;
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margin: 20px 0;
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}
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.model-overview-table th {
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background-color: #f0f0f0;
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font-weight: bold;
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padding: 12px;
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text-align: left;
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border-bottom: 2px solid #ddd;
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}
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.model-overview-table td {
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padding: 10px 12px;
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border-bottom: 1px solid #ddd;
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}
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.model-overview-table tr:hover {
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background-color: #f9f9f9;
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}
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.model-overview-table a {
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color: #647db6;
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text-decoration: none;
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}
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.model-overview-table a:hover {
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text-decoration: underline;
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color: #ee4c2c;
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}
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#model-overview-container {
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margin: 20px 0;
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}
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#model-filters {
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margin-bottom: 15px;
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padding: 10px;
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background-color: #f9f9f9;
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border-radius: 4px;
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}
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#model-filters label {
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margin-right: 15px;
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font-weight: 500;
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}
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#model-filters select {
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margin-left: 5px;
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padding: 5px 10px;
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border: 1px solid #ddd;
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border-radius: 4px;
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background-color: white;
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}
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/* DataTables styling overrides */
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#model-table_wrapper {
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margin-top: 20px;
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}
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#model-table_wrapper .dataTables_filter input {
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margin-left: 10px;
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padding: 5px;
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border: 1px solid #ddd;
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border-radius: 4px;
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}
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#model-table_wrapper .dataTables_length select {
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padding: 5px;
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border: 1px solid #ddd;
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border-radius: 4px;
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margin: 0 5px;
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}

‎docs/source/_static/model_overview.js‎

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/**
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* JavaScript for interactive model overview table.
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*
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* This script loads the model overview data from JSON and creates
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* an interactive DataTable with search and filtering capabilities.
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*/
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$(document).ready(function() {
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// Determine the correct path to the JSON file
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// In built HTML, the path should be relative to the current page
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var jsonPath = '_static/model_overview_db.json';
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// Load model data from JSON
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$.getJSON(jsonPath, function(data) {
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// Initialize DataTable
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var table = $('#model-table').DataTable({
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data: data,
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columns: [
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{
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data: 'Model Name',
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title: 'Model Name',
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render: function(data, type, row) {
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// If data is already HTML (from pandas), return as-is
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if (type === 'display' && data && data.includes('<a')) {
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return data;
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}
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return data || row['Model Name'] || '';
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}
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},
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{ data: 'Type', title: 'Type' },
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{ data: 'Covariates', title: 'Covariates' },
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{ data: 'Multiple targets', title: 'Multiple targets' },
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{ data: 'Uncertainty', title: 'Uncertainty' },
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{ data: 'Flexible history', title: 'Flexible history' },
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{ data: 'Cold-start', title: 'Cold-start' },
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{ data: 'Compute', title: 'Compute' }
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],
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pageLength: 25,
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order: [[0, 'asc']],
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responsive: true,
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dom: 'lfrtip',
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language: {
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search: "Search models:",
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lengthMenu: "Show _MENU_ models per page",
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info: "Showing _START_ to _END_ of _TOTAL_ models",
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infoEmpty: "No models found",
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infoFiltered: "(filtered from _MAX_ total models)"
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}
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});
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// Filter by type
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$('#type-filter').on('change', function() {
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var val = $(this).val();
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table.column(1).search(val).draw();
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});
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// Filter by capability
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$('#capability-filter').on('change', function() {
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var val = $(this).val();
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if (val === '') {
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// Clear all capability filters
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table.columns([2, 3, 4, 5, 6]).search('').draw();
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} else {
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// Map capability name to column index
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var capabilityMap = {
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'Covariates': 2,
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'Multiple targets': 3,
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'Uncertainty': 4,
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'Flexible history': 5,
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'Cold-start': 6
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};
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var colIdx = capabilityMap[val];
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if (colIdx !== undefined) {
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// Clear all capability columns first
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table.columns([2, 3, 4, 5, 6]).search('');
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// Then search in the specific column
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table.column(colIdx).search('✓').draw();
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} else {
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// If capability not found, search in all columns
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table.search(val).draw();
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}
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}
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});
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// Clear filters when "All" is selected
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$('#type-filter, #capability-filter').on('change', function() {
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if ($(this).val() === '') {
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if ($(this).attr('id') === 'type-filter') {
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table.column(1).search('').draw();
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}
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}
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});
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}).fail(function(jqXHR, textStatus, errorThrown) {
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// Handle error loading JSON
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console.error('Error loading model overview data:', textStatus, errorThrown);
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$('#model-table').html(
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'<tr><td colspan="8" style="text-align: center; padding: 20px;">' +
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'Error loading model overview data. Please ensure the documentation was built correctly.' +
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'</td></tr>'
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);
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});
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});
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‎docs/source/conf.py‎

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@@ -132,11 +132,152 @@ def get_items(self, names):
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return new_items
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def _make_estimator_overview(app):
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"""Make estimator/model overview table.
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This function generates a dynamic table of all models in pytorch-forecasting
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by querying the registry system. The table is written as HTML and JSON files
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for inclusion in the documentation.
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"""
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try:
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import pandas as pd
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from pytorch_forecasting._registry import all_objects
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# Base classes to exclude from the overview
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BASE_CLASSES = {
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"BaseModel",
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"BaseModelWithCovariates",
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"AutoRegressiveBaseModel",
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"AutoRegressiveBaseModelWithCovariates",
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"_BaseObject",
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"_BasePtForecaster",
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"_BasePtForecasterV2",
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"_BasePtForecaster_Common",
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}
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# Get all objects from registry
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all_objs = all_objects(return_names=True, suppress_import_stdout=True)
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records = []
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for obj_name, obj_class in all_objs:
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# Skip base classes
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if obj_name in BASE_CLASSES:
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continue
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# Skip if it's not a model class (check if it has get_class_tag method)
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if not hasattr(obj_class, "get_class_tag"):
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continue
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try:
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# Get model name from tags or use class name
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model_name = obj_class.get_class_tag("info:name", obj_name)
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# Get authors
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authors = obj_class.get_class_tag("authors", None)
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if authors is None:
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authors = "pytorch-forecasting developers"
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elif isinstance(authors, list):
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authors = ", ".join(authors)
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# Get object type
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object_type = obj_class.get_class_tag("object_type", "model")
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if isinstance(object_type, list):
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object_type = ", ".join(object_type)
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# Get capabilities
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has_exogenous = obj_class.get_class_tag("capability:exogenous", False)
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has_multivariate = obj_class.get_class_tag("capability:multivariate", False)
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has_pred_int = obj_class.get_class_tag("capability:pred_int", False)
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has_flexible_history = obj_class.get_class_tag("capability:flexible_history_length", False)
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has_cold_start = obj_class.get_class_tag("capability:cold_start", False)
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# Get compute requirement
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compute = obj_class.get_class_tag("info:compute", None)
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# Get module path for documentation link
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module_path = obj_class.__module__
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class_name = obj_class.__name__
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# Construct documentation link
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# Convert module path to API documentation path
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api_path = module_path.replace(".", "/")
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doc_link = f"api/{api_path}.html#{module_path}.{class_name}"
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# Create model name with link
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model_name_link = f'<a href="{doc_link}">{model_name}</a>'
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# Build capabilities string
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capabilities = []
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if has_exogenous:
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capabilities.append("Covariates")
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if has_multivariate:
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capabilities.append("Multiple targets")
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if has_pred_int:
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capabilities.append("Uncertainty")
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if has_flexible_history:
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capabilities.append("Flexible history")
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if has_cold_start:
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capabilities.append("Cold-start")
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capabilities_str = ", ".join(capabilities) if capabilities else ""
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records.append({
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"Model Name": model_name_link,
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"Type": object_type,
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"Authors": authors,
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"Covariates": "✓" if has_exogenous else "",
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"Multiple targets": "✓" if has_multivariate else "",
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"Uncertainty": "✓" if has_pred_int else "",
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"Flexible history": "✓" if has_flexible_history else "",
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"Cold-start": "✓" if has_cold_start else "",
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"Compute": str(compute) if compute is not None else "",
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"Capabilities": capabilities_str,
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"Module": module_path,
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})
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except Exception as e:
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# Skip objects that can't be processed
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print(f"Warning: Could not process {obj_name}: {e}")
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continue
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if not records:
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print("Warning: No models found in registry")
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return
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# Create DataFrame
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df = pd.DataFrame(records)
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# Ensure _static directory exists
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static_dir = SOURCE_PATH.joinpath("_static")
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static_dir.mkdir(exist_ok=True)
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# Write HTML table
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html_file = static_dir.joinpath("model_overview_table.html")
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html_content = df[["Model Name", "Type", "Covariates", "Multiple targets",
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"Uncertainty", "Flexible history", "Cold-start", "Compute"]].to_html(
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classes="model-overview-table", index=False, border=0, escape=False
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)
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html_file.write_text(html_content, encoding="utf-8")
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print(f"Generated model overview table: {html_file}")
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# Write JSON database for interactive filtering (optional)
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json_file = static_dir.joinpath("model_overview_db.json")
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df.to_json(json_file, orient="records", indent=2)
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print(f"Generated model overview JSON: {json_file}")
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except ImportError as e:
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print(f"Warning: Could not generate model overview (missing dependency): {e}")
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except Exception as e:
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print(f"Warning: Error generating model overview: {e}")
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def setup(app: Sphinx):
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app.add_css_file("custom.css")
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app.connect("autodoc-skip-member", skip)
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app.add_directive("moduleautosummary", ModuleAutoSummary)
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app.add_js_file("https://buttons.github.io/buttons.js", **{"async": "async"})
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# Connect model overview generator to builder-inited event
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app.connect("builder-inited", _make_estimator_overview)
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# extension configuration

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