[ENH] add length option to _bottom_hier_datagen hierarchical data g…

…enerator, speed up `ReconcilerForecaster` doctest (#4979)

This PR:
* adds a length option to `_bottom_hier_datagen` hierarchical data
generator, allowing to generate much smaller data sets
* speeds up the `ReconcilerForecaster` doctest by using this option, to
generate a much smaller data set in the doctest

Due to the test speed-up, related to
#2890

sktime/forecasting/reconcile.py

@@ -72,6 +72,7 @@ class ReconcilerForecaster(BaseForecaster):
     ...     no_bottom_nodes=3,
     ...     no_levels=1,
     ...     random_seed=123,
+    ...     length=7,
     ... )
     >>> y = agg.fit_transform(y)
     >>> forecaster = NaiveForecaster(strategy="drift")

sktime/utils/_testing/hierarchical.py

@@ -105,19 +105,19 @@ def _bottom_hier_datagen(
     coef_1_max=20,
     coef_2_max=0.1,
     random_seed=None,
+    length=144,
 ):
-    """Hierarchical data generator using the flights dataset.
+    """Hierarchical data generator using the airline dataset.
 
     This function generates bottom level, i.e. not aggregated, time-series
-    from the flights dataset.
+    from the airline dataset (sktime.datasets.load_airline).
 
-    Each series is generated from the flights dataset using a linear model,
+    Each series is generated from the airline dataset using a linear model,
     y = c0 + c1x + c2x^(c3), where the coefficients, intercept, and exponent
     are randomly sampled for each series. The coefficients and intercept are
     sampled between np.arange(0, *_max, 0.01) to keep the values positive. The
     exponent is sampled from [0.5, 1, 1.5, 2].
 
-
     Parameters
     ----------
     no_levels : int, optional
@@ -128,7 +128,9 @@ def _bottom_hier_datagen(
         Maximum possible value of the coefficient or intercept value.
     random_seed : int, optional
         Random seed for reproducability.
-
+    length : int between 1 and 144, optional, default = 144
+        length of base time series. If lowe than 144,
+        the airline dataet is truncated to the specified length, cutting from the end.
 
     Returns
     -------
@@ -139,7 +141,7 @@ def _bottom_hier_datagen(
 
     rng = np.random.default_rng(random_seed)
 
-    base_ts = load_airline()
+    base_ts = load_airline()[:length]
     df = pd.DataFrame(base_ts, index=base_ts.index)
     df.index.rename(None, inplace=True)