Handle inline math equations

mkdocstrings_parser/parser.py

@@ -10,7 +10,6 @@
 # Suppress griffe warnings
 logging.getLogger("griffe").setLevel(logging.ERROR)
 
-
 class MkDocstringsParser:
     def __init__(self):
         pass
@@ -136,6 +135,12 @@ def generate_documentation(self, module_path: str, options: Dict[str, Any]) -> s
             markdown_docs = render_object_docs(obj, config)  # type: ignore
 
             markdown_docs = markdown_docs.replace(f"### `{to_replace}.", "### `")
+            # Fix double backslashes in inline/block math equations
+            markdown_docs = re.sub(
+                r"\$([^$]+)\$",
+                lambda m: "$" + m.group(1).replace('\\\\', '\\') + "$",
+                markdown_docs
+            )
 
             return markdown_docs
 

tests/test_hierarchicalforecast.py

@@ -167,7 +167,7 @@ def test_bottomup(setup_parser):
 the first time by Orcutt in 1968.
 The corresponding hierarchical "projection" matrix is defined as:
 $$
-\\\\mathbf{P}_{\\\\text{BU}} = \\[\\\\mathbf{0}_{\\\\mathrm{[b],[a]}};|;\\\\mathbf{I}\\_{\\\\mathrm{[b][b]}}\\]
+\\mathbf{P}_{\\text{BU}} = \[\\mathbf{0}_{\\mathrm{[b],[a]}};|;\\mathbf{I}\_{\\mathrm{[b][b]}}\]
 $$
 
 <details class="references" open markdown="1">
@@ -235,4 +235,109 @@ def test_bottomup(setup_parser):
 Name | Type | Description
 ---- | ---- | -----------
 `dict` | | y_tilde: Reconciliated y_hat using the Bottom Up approach.
-"""
+"""
+
+def test_topdown(setup_parser):
+    fn = """::: hierarchicalforecast.methods.TopDown
+    handler: python
+    options:
+      docstring_style: google
+      members:
+        - fit
+        - predict
+        - fit_predict
+        - sample
+      inherited_members: false
+      heading_level: 3
+      show_root_heading: true
+      show_source: true"""
+    output = setup_parser.process_markdown(fn)
+    assert output == """### `TopDown`
+
+```python
+TopDown(method)
+```
+
+Bases: <code>[HReconciler](#hierarchicalforecast.methods.HReconciler)</code>
+
+Top Down Reconciliation Class.
+
+The Top Down hierarchical reconciliation method, distributes the total aggregate predictions and decomposes
+it down the hierarchy using proportions $\mathbf{p}\_{\mathrm{[b]}}$ that can be actual historical values
+or estimated.
+
+$$\mathbf{P}=\[\mathbf{p}_{\mathrm{[b]}};|;\mathbf{0}_{\mathrm{[b][a,b;-1]}}\]$$
+
+**Parameters:**
+
+Name | Type | Description | Default
+---- | ---- | ----------- | -------
+`method` | <code>[str](#str)</code> | One of `forecast_proportions`, `average_proportions` and `proportion_averages`. | *required*
+
+<details class="references" open markdown="1">
+<summary>References</summary>
+
+- [CW. Gross (1990). "Disaggregation methods to expedite product line forecasting". Journal of Forecasting, 9 , 233-254. doi:10.1002/for.3980090304](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304).
+- [G. Fliedner (1999). "An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation". Computers and Operations Research, 26 , 1133-1149. doi:10.1016/S0305-0548(99)00017-9](<https://doi.org/10.1016/S0305-0548(99)00017-9>).
+
+</details>
+
+#### `TopDown.fit`
+
+```python
+fit(S, y_hat, y_insample, y_hat_insample=None, sigmah=None, intervals_method=None, num_samples=None, seed=None, tags=None, idx_bottom=None)
+```
+
+TopDown Fit Method.
+
+**Parameters:**
+
+Name | Type | Description | Default
+---- | ---- | ----------- | -------
+`S` | | Summing matrix of size (`base`, `bottom`). | *required*
+`y_hat` | | Forecast values of size (`base`, `horizon`). | *required*
+`y_insample` | <code>[ndarray](#numpy.ndarray)</code> | Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method. | *required*
+`y_hat_insample` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Insample forecast values of size (`base`, `insample_size`). Optional for `forecast_proportions` method. | <code>None</code>
+`sigmah` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Estimated standard deviation of the conditional marginal distribution. | <code>None</code>
+`interval_method` | | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`. | *required*
+`num_samples` | <code>[Optional](#typing.Optional)\[[int](#int)\]</code> | Number of samples for probabilistic coherent distribution. | <code>None</code>
+`seed` | <code>[Optional](#typing.Optional)\[[int](#int)\]</code> | Seed for reproducibility. | <code>None</code>
+`tags` | <code>[Optional](#typing.Optional)\[[dict](#dict)\[[str](#str), [ndarray](#numpy.ndarray)\]\]</code> | Each key is a level and each value its `S` indices. | <code>None</code>
+`idx_bottom` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Indices corresponding to the bottom level of `S`, size (`bottom`). | <code>None</code>
+
+**Returns:**
+
+Name | Type | Description
+---- | ---- | -----------
+`self` | | object, fitted reconciler.
+
+#### `TopDown.fit_predict`
+
+```python
+fit_predict(S, y_hat, tags, idx_bottom=None, y_insample=None, y_hat_insample=None, sigmah=None, level=None, intervals_method=None, num_samples=None, seed=None)
+```
+
+Top Down Reconciliation Method.
+
+**Parameters:**
+
+Name | Type | Description | Default
+---- | ---- | ----------- | -------
+`S` | <code>[ndarray](#numpy.ndarray)</code> | Summing matrix of size (`base`, `bottom`). | *required*
+`y_hat` | <code>[ndarray](#numpy.ndarray)</code> | Forecast values of size (`base`, `horizon`). | *required*
+`tags` | <code>[dict](#dict)\[[str](#str), [ndarray](#numpy.ndarray)\]</code> | Each key is a level and each value its `S` indices. | *required*
+`idx_bottom` | <code>[ndarray](#numpy.ndarray)</code> | Indices corresponding to the bottom level of `S`, size (`bottom`). | <code>None</code>
+`y_insample` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method. | <code>None</code>
+`y_hat_insample` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Insample forecast values of size (`base`, `insample_size`). Optional for `forecast_proportions` method. | <code>None</code>
+`sigmah` | <code>[Optional](#typing.Optional)\[[ndarray](#numpy.ndarray)\]</code> | Estimated standard deviation of the conditional marginal distribution. | <code>None</code>
+`level` | <code>[Optional](#typing.Optional)\[[list](#list)\[[int](#int)\]\]</code> | float list 0-100, confidence levels for prediction intervals. | <code>None</code>
+`intervals_method` | <code>[Optional](#typing.Optional)\[[str](#str)\]</code> | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`. | <code>None</code>
+`num_samples` | <code>[Optional](#typing.Optional)\[[int](#int)\]</code> | Number of samples for probabilistic coherent distribution. | <code>None</code>
+`seed` | <code>[Optional](#typing.Optional)\[[int](#int)\]</code> | Seed for reproducibility. | <code>None</code>
+
+**Returns:**
+
+Name | Type | Description
+---- | ---- | -----------
+`y_tilde` | | Reconciliated y_hat using the Top Down approach.
+"""

tests/test_utilsforecast.py

@@ -12,7 +12,7 @@ def test_utilsforecast_rmae(setup_parser):
     assert output == """### `rmae`
 
 ```python
-rmae(df, models, baseline, id_col='unique_id', target_col='y')
+rmae(df, models, baseline, id_col='unique_id', target_col='y', cutoff_col='cutoff')
 ```
 
 Relative Mean Absolute Error (RMAE)
@@ -30,10 +30,11 @@ def test_utilsforecast_rmae(setup_parser):
 `baseline` | <code>[str](#str)</code> | Column that identifies the baseline model predictions. | *required*
 `id_col` | <code>[str](#str)</code> | Column that identifies each serie. Defaults to 'unique_id'. | <code>'unique_id'</code>
 `target_col` | <code>[str](#str)</code> | Column that contains the target. Defaults to 'y'. | <code>'y'</code>
+`cutoff_col` | <code>[str](#str)</code> | Column that identifies the cutoff point for each forecast cross-validation fold. Defaults to 'cutoff'. | <code>'cutoff'</code>
 
 **Returns:**
 
 Type | Description
 ---- | -----------
-<code>[DFType](#utilsforecast.compat.DFType)</code> | pandas or polars DataFrame: dataframe with one row per id and one column per model.
+<code>[IntoDataFrameT](#narwhals.stable.v2.typing.IntoDataFrameT)</code> | pandas or polars DataFrame: dataframe with one row per id and one column per model.
 """