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Favorita promotions data #27

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2 changes: 2 additions & 0 deletions datasetsforecast/_modidx.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,8 @@
'datasetsforecast/favorita.py'),
'datasetsforecast.favorita.FavoritaData.load_preprocessed': ( 'favorita.html#favoritadata.load_preprocessed',
'datasetsforecast/favorita.py'),
'datasetsforecast.favorita.FavoritaData.load_promotions': ( 'favorita.html#favoritadata.load_promotions',
'datasetsforecast/favorita.py'),
'datasetsforecast.favorita.FavoritaRawData': ( 'favorita.html#favoritarawdata',
'datasetsforecast/favorita.py'),
'datasetsforecast.favorita.FavoritaRawData._load_raw_group_data': ( 'favorita.html#favoritarawdata._load_raw_group_data',
Expand Down
74 changes: 65 additions & 9 deletions datasetsforecast/favorita.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
# %% auto 0
__all__ = ['FavoritaRawData', 'FavoritaData']

# %% ../nbs/favorita.ipynb 4
# %% ../nbs/favorita.ipynb 5
import os
import gc
import timeit
Expand All @@ -23,7 +23,7 @@

from .utils import download_file, extract_file, Info #, CodeTimer

# %% ../nbs/favorita.ipynb 7
# %% ../nbs/favorita.ipynb 8
# TODO: @kdgutier `CodeTimer`/`numpy_balance` are shared with hierarchicalforecast.utils
# In case of merging datasetsforecast/hierarchicalforeast we wil need to keep only one.
class CodeTimer:
Expand Down Expand Up @@ -108,7 +108,7 @@ def numpy_bfill(arr):
out = arr[np.arange(idx.shape[0])[:,None], idx]
return out

# %% ../nbs/favorita.ipynb 12
# %% ../nbs/favorita.ipynb 13
def one_hot_encoding(df, index_col):
"""
Encodes dataFrame `df`'s categorical variables skipping `index_col`.
Expand Down Expand Up @@ -175,7 +175,7 @@ def get_levels_from_S_df(S_df):
assert sum([len(lv) for lv in levels]) == S_df.shape[0]
return levels

# %% ../nbs/favorita.ipynb 16
# %% ../nbs/favorita.ipynb 17
# TODO: @kdgutier `make_holidays_distance_df` partially shared with neuralforecast.utils
# In particular some Transformers use a holiday-based global positional encoding.
# Same goes for HINT experiment that uses such general purpose holiday distances.
Expand Down Expand Up @@ -224,7 +224,7 @@ def make_holidays_distance_df(holidays_df, dates):
holidays_distance_df = pd.DataFrame(distance_dict)
return holidays_distance_df

# %% ../nbs/favorita.ipynb 18
# %% ../nbs/favorita.ipynb 19
@dataclass
class Favorita200:
freq: str = 'D'
Expand Down Expand Up @@ -265,7 +265,7 @@ class FavoritaComplete:

FavoritaInfo = Info((Favorita200, Favorita500, FavoritaComplete))

# %% ../nbs/favorita.ipynb 20
# %% ../nbs/favorita.ipynb 21
class FavoritaRawData:
""" Favorita Raw Data

Expand Down Expand Up @@ -454,7 +454,7 @@ def _load_raw_group_data(directory, group, verbose=False):

return filter_items, filter_stores, filter_dates, raw_group_data

# %% ../nbs/favorita.ipynb 26
# %% ../nbs/favorita.ipynb 27
class FavoritaData:
""" Favorita Data

Expand All @@ -463,7 +463,6 @@ class FavoritaData:
January 2013 to August 2017, with a geographic hierarchy of states, cities, and stores.
This wrangling matches that of the DPMN paper.

- [Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2022)."Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures". International Journal Forecasting, special issue.](https://doi.org/10.1016/j.ijforecast.2023.04.007)
"""
@staticmethod
def _get_static_data(filter_items, filter_stores, items, store_info, temporal, verbose=False):
Expand Down Expand Up @@ -593,7 +592,21 @@ def _get_temporal_bottom(temporal, item_store_df, filter_dates, verbose=False):
balanced_df[col] = col_values.flatten()
balanced_df[col] = balanced_df[col].fillna(0)
#check_nans(balanced_df)


#-------------------- with CodeTimer('Promotions'): --------------------#
onpromotion = balanced_df['onpromotion'].values
onpromotion_lag = balanced_df[['unique_id', 'onpromotion']].groupby(['unique_id'])
onpromotion_lag = onpromotion_lag.shift(periods=1, fill_value=0).values.flatten()

onpromotion_start = 1 * ((onpromotion - onpromotion_lag)>0)
onpromotion_start = onpromotion_start.reshape(n_items * n_stores, n_dates)
onpromotion = onpromotion.reshape(n_items * n_stores, n_dates)

promotion_id = np.cumsum(onpromotion_start, axis=1) * onpromotion

balanced_df['onpromotion_start'] = onpromotion_start.flatten()
balanced_df['promotion_id'] = promotion_id.flatten().astype(np.int64)

# Rename variables for StatsForecast/NeuralForecast compatibility
balanced_df.rename(columns={"date": "ds", "unit_sales": "y"}, inplace=True)

Expand Down Expand Up @@ -901,3 +914,46 @@ def load(directory: str, group: str, cache: bool=True, verbose: bool=False):
ds = ds, y = Y_hier.flatten()))

return Y_df, S_df, tags

@staticmethod
def load_promotions(directory: str, group: str, cache: bool=True, verbose: bool=False):
"""
Load Favorita promotions benchmark dataset.

Normally time series forecasting assumes regularly sampled observations from same
generative processes. This dataset aims presents a more intricate problem, forecasting
sales at the promotion event. The unique feature of promotion forecasting is that
promotions are irregular events of varying length; making standard forecasting approach
not directly applicable and calling for the neural architecture innovations.

The dataset contains daily sales trajectories at the item-store level, and its promotions.
The dataset is augmented with calendar variables (month, day of week, day of month), and
is accompanied with static features of the store location as well as the item characteristics.

**Parameters:**<br>
`directory`: str, directory where data will be downloaded and saved.<br>
`group`: str, dataset group name in 'Favorita200', 'Favorita500', 'FavoritaComplete'.<br>
`cache`: bool=False, If `True` saves and loads.<br>
`verbose`: bool=False, wether or not print partial outputs.<br>

**Returns:**<br>
`Y_df`: pd.DataFrame, augmented daily item-store sales and promotions.<br>
`static_df`: pd.DataFrame, store and item level static features.<br>
"""
static_agg, static_bottom, temporal_agg, temporal_bottom, _ = \
FavoritaData.load_preprocessed(directory=directory, group=group,
cache=cache, verbose=verbose)

# Temporal Data
Y_df = temporal_bottom.copy()
Y_df['unique_promotion_id'] = Y_df['unique_id'].astype(str) + \
'_' + Y_df['promotion_id'].astype(str)

temporal_agg = temporal_agg[['item_nbr', 'ds', 'month', 'day_of_week', 'day_of_month']]
Y_df = Y_df.merge(temporal_agg, on=['item_nbr', 'ds'], how='left')

# Static Data
static_agg = static_agg.filter(regex=("family.*|item_nbr|perishable"))
static_df = static_bottom.merge(static_agg, on=['item_nbr'], how='left')

return Y_df, static_df
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