Sparse Methods Missing from 0.3.0

[tbellamey]

What happened + What you expected to happen

I attempted to import the BottomUpSparse and MinTraceSparse methods from hierarchicalforecast.methods

However, upon inspection of the class, I noticed that release 0.3.0 doesn't contain either of these sparse methods:
NAME
hierarchicalforecast.methods - # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/methods.ipynb.

CLASSES
HReconciler(builtins.object)
BottomUp
ERM
MiddleOut
MinTrace
OptimalCombination
TopDown

I used the standard pip install command: pip install hierarchicalforecast

Is there a different version that I should install to access these sparse methods?

Versions / Dependencies

---

dateutil 2.8.2
hierarchicalforecast 0.3.0
matplotlib 3.7.1
numpy 1.23.5
pandas 2.0.2
session_info 1.0.0
statsforecast 1.6.0

---

IPython 8.14.0
jupyter_client 8.2.0
jupyter_core 5.3.0
notebook 6.5.4

Python 3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0]
Linux-4.18.0-372.16.1.0.1.el8_6.x86_64-x86_64-with-glibc2.35

Reproduction script

from hierarchicalforecast.methods import BottomUp, TopDown, MinTrace, ERM, OptimalCombination, MiddleOut, BottomUpSparse, MinTraceSparse

Issue Severity

Low: It annoys or frustrates me.

[jmoralez]

Hey @tbellamey, we just released 0.4.0. You should be able to use those methods if you upgrade your installation.

[tbellamey]

Hi @jmoralez , I upgraded my installation to 0.4.0. However, upon running my script (no code changed), I am now getting the below error. This seems to be inducing an error in using StatsForecast AutoETS models now. I also tried StatsForecast HoltWinters and received the same error.

This error did not raise with the same dataset & script when running 0.3.0 - any ideas what might have changed the behavior from 0.3.0 to 0.4.0? I can also open this as a new issue for tracking.

---

NotImplementedError Traceback (most recent call last)
Cell In[8], line 109
98 #valid_agg_reset = valid_agg.reset_index()
100 model = StatsForecast(models=[
102 AutoETS(season_length=12,model='AAA',alias='AutoETS_AAA')
(...)
107 ],
108 freq='MS', n_jobs=1, verbose=True)
--> 109 model.fit(train_agg)
111 p = model.forecast(h=h_months, fitted=True)
112 p_fitted = model.forecast_fitted_values()

File ~/lib/python3.10/site-packages/statsforecast/core.py:880, in StatsForecast.fit(self, df, sort_df, prediction_intervals)
878 self.prepare_fit(df, sort_df)
879 if self.n_jobs == 1:
--> 880 self.fitted = self.ga.fit(models=self.models)
881 else:
882 self.fitted = self._fit_parallel()

File ~/lib/python3.10/site-packages/statsforecast/core.py:77, in GroupedArray.fit(self, models)
75 for i_model, model in enumerate(models):
76 new_model = model.new()
---> 77 fm[i, i_model] = new_model.fit(y=y, X=X)
78 return fm

File ~/lib/python3.10/site-packages/statsforecast/models.py:650, in AutoETS.fit(self, y, X)
628 def fit(
629 self,
630 y: np.ndarray,
631 X: Optional[np.ndarray] = None,
632 ):
633 """Fit the Exponential Smoothing model.
634
635 Fit an Exponential Smoothing model to a time series (numpy array) y
(...)
648 Exponential Smoothing fitted model.
649 """
--> 650 self.model_ = ets_f(
651 y, m=self.season_length, model=self.model, damped=self.damped
652 )
653 self.model_["actual_residuals"] = y - self.model_["fitted"]
654 self._store_cs(y=y, X=X)

File ~/lib/python3.10/site-packages/statsforecast/ets.py:1241, in ets_f(y, m, model, damped, alpha, beta, gamma, phi, additive_only, blambda, biasadj, lower, upper, opt_crit, nmse, bounds, ic, restrict, allow_multiplicative_trend, use_initial_values, maxit)
1238 # ses for non-optimized tiny datasets
1239 if n <= npars + 4:
1240 # we need HoltWintersZZ function
-> 1241 raise NotImplementedError("tiny datasets")
1242 # fit model (assuming only one nonseasonal model)
1243 if errortype == "Z":

NotImplementedError: tiny datasets

[jmoralez]

Closing since the original issue is solved.