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340 | def fetch_dataset(
name : str,
version: str = 'latest',
raw: bool = False,
kwargs_read_csv: dict = {},
verbose: bool = True
) -> pd.DataFrame:
"""
Fetch a dataset from the skforecast-datasets repository. Available datasets
are: 'h2o', 'items_sales', 'air_pollution', 'fuel_consumption', 'web_visits',
'bike_sharing', 'store_item_demand'.
Parameters
----------
name: str
Name of the dataset to fetch.
version: str, int, default `'latest'`
Version of the dataset to fetch. If 'latest', the lastest version will be
fetched (the one in the main branch). For a list of available versions,
see the repository branches.
raw: bool, default `False`
If True, the raw dataset is fetched. If False, the preprocessed dataset
is fetched. The preprocessing consists of setting the column with the
date/time as index and converting the index to datetime. A frequency is
also set to the index.
kwargs_read_csv: dict, default `{}`
Kwargs to pass to pandas `read_csv` function.
verbose: bool, default `True`
If True, print information about the dataset.
Returns
-------
df: pandas DataFrame
Dataset.
"""
version = 'main' if version == 'latest' else f'{version}'
datasets = {
'h2o': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/h2o.csv'
),
'sep': ',',
'index_col': 'fecha',
'date_format': '%Y-%m-%d',
'freq': 'MS',
'description': (
'Monthly expenditure ($AUD) on corticosteroid drugs that the '
'Australian health system had between 1991 and 2008. '
),
'source': (
'Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice'
'(3rd Edition). http://pkg.robjhyndman.com/fpp3package/,'
'https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.'
)
},
'h2o_exog': {
'url': (
f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
f"skforecast-datasets/{version}/data/h2o_exog.csv"
),
'sep': ',',
'index_col': 'fecha',
'date_format': '%Y-%m-%d',
'freq': 'MS',
'description': (
'Monthly expenditure ($AUD) on corticosteroid drugs that the '
'Australian health system had between 1991 and 2008. Two additional '
'variables (exog_1, exog_2) are simulated.'
),
'source': (
"Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice "
"(3rd Edition). http://pkg.robjhyndman.com/fpp3package/, "
"https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3."
)
},
'fuel_consumption': {
'url' : (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/consumos-combustibles-mensual.csv'
),
'sep': ',',
'index_col': 'Fecha',
'date_format': '%Y-%m-%d',
'freq': 'MS',
'description': (
'Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01.'
),
'source': (
'Obtained from Corporación de Reservas Estratégicas de Productos '
'Petrolíferos and Corporación de Derecho Público tutelada por el '
'Ministerio para la Transición Ecológica y el Reto Demográfico. '
'https://www.cores.es/es/estadisticas'
)
},
'items_sales': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/simulated_items_sales.csv'
),
'sep': ',',
'index_col': 'date',
'date_format': '%Y-%m-%d',
'freq': 'D',
'description': 'Simulated time series for the sales of 3 different items.',
'source': 'Simulated data.'
},
'air_quality_valencia': {
'url': (
f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
f"skforecast-datasets/{version}/data/air_quality_valencia.csv"
),
'sep': ',',
'index_col': 'datetime',
'date_format': '%Y-%m-%d %H:%M:%S',
'freq': 'H',
'description': (
'Hourly measures of several air quimical pollutant (pm2.5, co, no, '
'no2, pm10, nox, o3, so2) at Valencia city.'
),
'source': (
" Red de Vigilancia y Control de la Contaminación Atmosférica, "
"46250054-València - Centre, "
"https://mediambient.gva.es/es/web/calidad-ambiental/datos-historicos."
)
},
'website_visits': {
'url' : (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/visitas_por_dia_web_cienciadedatos.csv'
),
'sep': ',',
'index_col': 'date',
'date_format': '%Y-%m-%d',
'freq': '1D',
'description': (
'Daily visits to the cienciadedatos.net website registered with the '
'google analytics service.'
),
'source': (
"Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo. "
"https://doi.org/10.5281/zenodo.10006330"
)
},
'bike_sharing': {
'url' : (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/bike_sharing_dataset_clean.csv'
),
'sep': ',',
'index_col': 'date_time',
'date_format': '%Y-%m-%d %H:%M:%S',
'freq': 'H',
'description': (
'Hourly usage of the bike share system in the city of Washington D.C. '
'during the years 2011 and 2012. In addition to the number of users per '
'hour, information about weather conditions and holidays is available.'
),
'source': (
"Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning "
"Repository. https://doi.org/10.24432/C5W894."
)
},
'bike_sharing_extended_features': {
'url' : (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/bike_sharing_extended_features.csv'
),
'sep': ',',
'index_col': 'date_time',
'date_format': '%Y-%m-%d %H:%M:%S',
'freq': 'H',
'description': (
'Hourly usage of the bike share system in the city of Washington D.C. '
'during the years 2011 and 2012. In addition to the number of users per '
'hour, the dataset was enriched by introducing supplementary features. '
'Addition includes calendar-based variables (day of the week, hour of '
'the day, month, etc.), indicators for sunlight, incorporation of '
'rolling temperature averages, and the creation of polynomial features '
'generated from variable pairs. All cyclic variables are encoded using '
'sine and cosine functions to ensure accurate representation.'
),
'source': (
"Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning "
"Repository. https://doi.org/10.24432/C5W894."
)
},
'australia_tourism': {
'url' : (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/australia_tourism.csv'
),
'sep': ',',
'index_col': 'date_time',
'date_format': '%Y-%m-%d',
'freq': 'Q',
'description': (
"Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 "
"across Australia. The tourism regions are formed through the "
"aggregation of Statistical Local Areas (SLAs) which are defined by "
"the various State and Territory tourism authorities according to "
"their research and marketing needs."
),
'source': (
"Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to "
"support exploration and modeling of temporal data, Journal of "
"Computational and Graphical Statistics, 29:3, 466-478, "
"doi:10.1080/10618600.2019.1695624."
)
},
'uk_daily_flights': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/uk_daily_flights.csv'
),
'sep': ',',
'index_col': 'Date',
'date_format': '%d/%m/%Y',
'freq': 'D',
'description': 'Daily number of flights in UK from 02/01/2019 to 23/01/2022.',
'source': (
'Experimental statistics published as part of the Economic activity and '
'social change in the UK, real-time indicators release, Published 27 '
'January 2022. Daily flight numbers are available in the dashboard '
'provided by the European Organisation for the Safety of Air Navigation '
'(EUROCONTROL). '
'https://www.ons.gov.uk/economy/economicoutputandproductivity/output/'
'bulletins/economicactivityandsocialchangeintheukrealtimeindicators/latest'
)
},
'wikipedia_visits': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/wikipedia_visits.csv'
),
'sep': ',',
'index_col': 'date',
'date_format': '%Y-%m-%d',
'freq': 'D',
'description': (
'Log daily page views for the Wikipedia page for Peyton Manning. '
'Scraped data using the Wikipediatrend package in R.'
),
'source': (
'https://github.com/facebook/prophet/blob/main/examples/'
'example_wp_log_peyton_manning.csv'
)
},
'vic_electricity': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/vic_electricity.csv'
),
'sep': ',',
'index_col': 'Time',
'date_format': '%Y-%m-%dT%H:%M:%SZ',
'freq': '30min',
'description': 'Half-hourly electricity demand for Victoria, Australia',
'source': (
"O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse "
"Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/, "
"https://github.com/tidyverts/tsibbledata/. "
"https://tsibbledata.tidyverts.org/reference/vic_elec.html"
)
},
'store_sales': {
'url': (
f'https://raw.githubusercontent.com/JoaquinAmatRodrigo/'
f'skforecast-datasets/{version}/data/store_sales.csv'
),
'sep': ',',
'index_col': 'date',
'date_format': '%Y-%m-%d',
'freq': '1D',
'description': (
'This dataset contains 913,000 sales transactions from 2013-01-01 to '
'2017-12-31 for 50 products (SKU) in 10 stores.'
),
'source': (
'The original data was obtained from: inversion. (2018). Store Item '
'Demand Forecasting Challenge. Kaggle. '
'https://kaggle.com/competitions/demand-forecasting-kernels-only'
)
}
}
if name not in datasets.keys():
raise ValueError(
f"Dataset {name} not found. Available datasets are: {list(datasets.keys())}"
)
url = datasets[name]['url']
try:
sep = datasets[name]['sep']
df = pd.read_csv(url, sep=sep, **kwargs_read_csv)
except:
raise ValueError(
f"Error reading dataset {name} from {url}. Try to version = 'latest'"
)
if not raw:
try:
index_col = datasets[name]['index_col']
freq = datasets[name]['freq']
if freq == 'H' and pd.__version__ >= '2.2.0':
freq = "h"
date_format = datasets[name]['date_format']
df = df.set_index(index_col)
df.index = pd.to_datetime(df.index, format=date_format)
df = df.asfreq(freq)
df = df.sort_index()
except:
pass
if verbose:
print(name)
print('-'*len(name))
description = textwrap.fill(datasets[name]['description'], width=80)
source = textwrap.fill(datasets[name]['source'], width=80)
print(description)
print(source)
print(f"Shape of the dataset: {df.shape}")
return df
|