[feat] bump min python and keep lockfile minimal

pyproject.toml

@@ -7,7 +7,7 @@ name = "mkdocstrings-parser"
 version = "0.0.1"
 description = "A simple parser for mkdocstrings signature blocks"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 dependencies = [
     "griffe2md",
     "pip-licenses>=4.5.1",
@@ -24,7 +24,7 @@ nixtlaverse = [
     "coreforecast",
     "utilsforecast",
     "datasetsforecast",
-    "mlforecast[dask]",
+    "mlforecast",
     "hierarchicalforecast",
     "neuralforecast",
     "statsforecast",

tests/test_coreforecast.py

@@ -28,6 +28,7 @@ def test_regular_fn(setup_parser):
 Name | Type | Description
 ---- | ---- | -----------
 `int` | <code>[int](#int)</code> | Optimal number of differences.
+
 """
 
 def test_fn_w_decorator(setup_parser):
@@ -53,7 +54,7 @@ def test_fn_w_decorator(setup_parser):
 
 Type | Description
 ---- | -----------
-| np.ndarray: Array with the expanding statistic
+ | np.ndarray: Array with the expanding statistic
 
 **Examples:**
 
@@ -67,6 +68,7 @@ def test_fn_w_decorator(setup_parser):
 >>> expanding_mean(x, skipna=True)
 array([1., 1.5, 1.5, 2.33..., 3.0])
 ```
+
 """
 
 def test_inherited_fn(setup_parser):
@@ -89,7 +91,7 @@ def test_inherited_fn(setup_parser):
 Lag(lag)
 ```
 
-Bases: <code>[\_BaseLagTransform](#coreforecast.lag_transforms._BaseLagTransform)</code>
+Bases: <code>[_BaseLagTransform](#coreforecast.lag_transforms._BaseLagTransform)</code>
 
 Simple lag operator
 
@@ -122,4 +124,5 @@ def test_inherited_fn(setup_parser):
 ```python
 update(ga)
 ```
+
 """

tests/test_datasetsforecast.py

@@ -53,6 +53,7 @@ def test_yearly_dataclass(setup_parser):
 ```python
 source_url: str = 'https://zenodo.org/api/records/4656222/files/m3_yearly_dataset.zip/content'
 ```
+
 """
 
 @pytest.mark.datasets
@@ -77,4 +78,5 @@ def test_download_file(setup_parser):
 `decompress` | <code>[bool](#bool)</code> | Whether to decompress downloaded file. Default False. | <code>False</code>
 `filename` | <code>[str](#str)</code> | Override filename for the downloaded file. If None, the filename is derived from the URL. | <code>None</code>
 `max_retries` | <code>[int](#int)</code> | Maximum number of retry attempts on transient errors. | <code>3</code>
+
 """

tests/test_hierarchicalforecast.py

@@ -16,7 +16,7 @@ def test_make_future_dataframe(setup_parser):
 Name | Type | Description | Default
 ---- | ---- | ----------- | -------
 `df` | <code>[Frame](#narwhals.typing.Frame)</code> | Dataframe with ids, times and values for the exogenous regressors. | *required*
-`freq` | <code>[Union](#Union)\[[str](#str), [int](#int)\]</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer. | *required*
+`freq` | <code>[Union](#Union)[[str](#str), [int](#int)]</code> | Frequency of the data. Must be a valid pandas or polars offset alias, or an integer. | *required*
 `h` | <code>[int](#int)</code> | Forecast horizon. | *required*
 `id_col` | <code>[str](#str)</code> | Column that identifies each serie. Default is 'unique_id'. | <code>'unique_id'</code>
 `time_col` | <code>[str](#str)</code> | Column that identifies each timestep, its values can be timestamps or integers. Default is 'ds'. | <code>'ds'</code>
@@ -26,6 +26,7 @@ def test_make_future_dataframe(setup_parser):
 Name | Type | Description
 ---- | ---- | -----------
 `FrameT` | <code>[FrameT](#narwhals.typing.FrameT)</code> | DataFrame with future values.
+
 """
 
 def test_evaluate(setup_parser):
@@ -60,6 +61,7 @@ def test_evaluate(setup_parser):
 Type | Description
 ---- | -----------
 <code>[FrameT](#narwhals.typing.FrameT)</code> | pandas, polars DataFrame: Metrics with one row per (id, metric) combination and one column per model. If `agg_fn` is not `None`, there is only one row per metric.
+
 """
 
 @pytest.mark.skip(reason="waiting for new HF release")
@@ -172,16 +174,14 @@ def test_bottomup(setup_parser):
 
 The most basic hierarchical reconciliation is performed using an Bottom-Up strategy. It was proposed for
 the first time by Orcutt in 1968.
-The corresponding hierarchical "projection" matrix is defined as:
-
+The corresponding hierarchical \"projection\" matrix is defined as:
 ```math
 \mathbf{P}_{\\text{BU}} = [\mathbf{0}_{\mathrm{[b],[a]}}\;|\;\mathbf{I}_{\mathrm{[b][b]}}]
 ```
 
 References:
-
 - [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). "Data aggregation and
-  information loss". The American Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).
+information loss". The American Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).
 
 #### `BottomUp.fit`
 
@@ -197,13 +197,13 @@ def test_bottomup(setup_parser):
 ---- | ---- | ----------- | -------
 `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*
-`y_insample` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | In-sample values of size (`base`, `horizon`). Default is None. | <code>None</code>
-`y_hat_insample` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | In-sample forecast values of size (`base`, `horizon`). Default is None. | <code>None</code>
-`sigmah` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | Estimated standard deviation of the conditional marginal distribution. Default is None. | <code>None</code>
-`intervals_method` | <code>[Optional](#Optional)\[[str](#str)\]</code> | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`, `conformal`. Default is None. | <code>None</code>
-`num_samples` | <code>[Optional](#Optional)\[[int](#int)\]</code> | Number of samples for probabilistic coherent distribution. Default is None. | <code>None</code>
-`seed` | <code>[Optional](#Optional)\[[int](#int)\]</code> | Seed for reproducibility. Default is None. | <code>None</code>
-`tags` | <code>[Optional](#Optional)\[[dict](#dict)\[[str](#str), [ndarray](#numpy.ndarray)\]\]</code> | Tags for hierarchical structure. Default is None. | <code>None</code>
+`y_insample` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | In-sample values of size (`base`, `horizon`). Default is None. | <code>None</code>
+`y_hat_insample` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | In-sample forecast values of size (`base`, `horizon`). Default is None. | <code>None</code>
+`sigmah` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | Estimated standard deviation of the conditional marginal distribution. Default is None. | <code>None</code>
+`intervals_method` | <code>[Optional](#Optional)[[str](#str)]</code> | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`, `conformal`. Default is None. | <code>None</code>
+`num_samples` | <code>[Optional](#Optional)[[int](#int)]</code> | Number of samples for probabilistic coherent distribution. Default is None. | <code>None</code>
+`seed` | <code>[Optional](#Optional)[[int](#int)]</code> | Seed for reproducibility. Default is None. | <code>None</code>
+`tags` | <code>[Optional](#Optional)[[dict](#dict)[[str](#str), [ndarray](#numpy.ndarray)]]</code> | Tags for hierarchical structure. Default is None. | <code>None</code>
 
 **Returns:**
 
@@ -225,18 +225,19 @@ def test_bottomup(setup_parser):
 ---- | ---- | ----------- | -------
 `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*
-`y_insample` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | In-sample values of size (`base`, `insample_size`). Default is None. | <code>None</code>
-`y_hat_insample` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | In-sample forecast values of size (`base`, `insample_size`). Default is None. | <code>None</code>
-`sigmah` | <code>[Optional](#Optional)\[[ndarray](#numpy.ndarray)\]</code> | Estimated standard deviation of the conditional marginal distribution. Default is None. | <code>None</code>
-`level` | <code>[Optional](#Optional)\[[list](#list)\[[int](#int)\]\]</code> | float list 0-100, confidence levels for prediction intervals. Default is None. | <code>None</code>
-`intervals_method` | <code>[Optional](#Optional)\[[str](#str)\]</code> | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`, `conformal`. Default is None. | <code>None</code>
-`num_samples` | <code>[Optional](#Optional)\[[int](#int)\]</code> | Number of samples for probabilistic coherent distribution. Default is None. | <code>None</code>
-`seed` | <code>[Optional](#Optional)\[[int](#int)\]</code> | Seed for reproducibility. Default is None. | <code>None</code>
-`tags` | <code>[Optional](#Optional)\[[dict](#dict)\[[str](#str), [ndarray](#numpy.ndarray)\]\]</code> | Tags for hierarchical structure. Default is None. | <code>None</code>
+`y_insample` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | In-sample values of size (`base`, `insample_size`). Default is None. | <code>None</code>
+`y_hat_insample` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | In-sample forecast values of size (`base`, `insample_size`). Default is None. | <code>None</code>
+`sigmah` | <code>[Optional](#Optional)[[ndarray](#numpy.ndarray)]</code> | Estimated standard deviation of the conditional marginal distribution. Default is None. | <code>None</code>
+`level` | <code>[Optional](#Optional)[[list](#list)[[int](#int)]]</code> | float list 0-100, confidence levels for prediction intervals. Default is None. | <code>None</code>
+`intervals_method` | <code>[Optional](#Optional)[[str](#str)]</code> | Sampler for prediction intervals, one of `normality`, `bootstrap`, `permbu`, `conformal`. Default is None. | <code>None</code>
+`num_samples` | <code>[Optional](#Optional)[[int](#int)]</code> | Number of samples for probabilistic coherent distribution. Default is None. | <code>None</code>
+`seed` | <code>[Optional](#Optional)[[int](#int)]</code> | Seed for reproducibility. Default is None. | <code>None</code>
+`tags` | <code>[Optional](#Optional)[[dict](#dict)[[str](#str), [ndarray](#numpy.ndarray)]]</code> | Tags for hierarchical structure. Default is None. | <code>None</code>
 
 **Returns:**
 
 Name | Type | Description
 ---- | ---- | -----------
 `y_tilde` | <code>[dict](#dict)</code> | Reconciliated y_hat using the Bottom Up approach.
+
 """

tests/test_mlforecast.py

@@ -10,4 +10,5 @@ def test_distributed_dask_lgb(setup_parser):
 ```python
 model_
 ```
+
 """

tests/test_neuralforecast.py

@@ -22,10 +22,11 @@ def test_timeseriesloader(setup_parser):
 
 Name | Type | Description | Default
 ---- | ---- | ----------- | -------
-`dataset` | | Dataset to load data from. | *required*
+`dataset` |  | Dataset to load data from. | *required*
 `batch_size` | <code>[int](#int)</code> | How many samples per batch to load. Defaults to 1. | *required*
 `shuffle` | <code>[bool](#bool)</code> | Set to True to have the data reshuffled at every epoch. Defaults to False. | *required*
 `sampler` | <code>[Sampler](#Sampler) or [Iterable](#Iterable)</code> | Defines the strategy to draw samples from the dataset. | *required*
 `drop_last` | <code>[bool](#bool)</code> | Set to True to drop the last incomplete batch. Defaults to False. | *required*
-`**kwargs` | | Additional keyword arguments for DataLoader. | <code>{}</code>
+`**kwargs` |  | Additional keyword arguments for DataLoader. | <code>{}</code>
+
 """

tests/test_statsforecast.py

@@ -81,7 +81,7 @@ def test_autoarima_prophet(setup_parser):
 
 Name | Type | Description
 ---- | ---- | -----------
-`AutoARIMAProphet` | | Adapter object with `AutoARIMA` fitted model.
+`AutoARIMAProphet` |  | Adapter object with `AutoARIMA` fitted model.
 
 #### `AutoARIMAProphet.predict`
 
@@ -101,5 +101,6 @@ def test_autoarima_prophet(setup_parser):
 
 Type | Description
 ---- | -----------
-| pandas.DataFrame: DataFrame with the forecast components.
+ | pandas.DataFrame: DataFrame with the forecast components.
+
 """

tests/test_utilsforecast.py

@@ -37,4 +37,5 @@ def test_utilsforecast_rmae(setup_parser):
 Type | Description
 ---- | -----------
 <code>[IntoDataFrameT](#narwhals.stable.v2.typing.IntoDataFrameT)</code> | pandas or polars DataFrame: dataframe with one row per id and one column per model.
+
 """