buildings_bench.data

Functions and class definitions for loading Torch and Pandas datasets.

Main entry points for loading PyTorch and Pandas datasets:

• load_pretraining() (used for pretraining)
• load_torch_dataset() (used for benchmark tasks)
• load_pandas_dataset() (used for benchmark tasks)

Available PyTorch Datasets:

• Buildings900K (used for pretraining)
• TorchBuildingsDataset (used for benchmark tasks)
• PandasTransformerDataset (used for benchmark tasks)

---

load_pretraining

buildings_bench.data.load_pretraining(name, num_buildings_ablation=-1, apply_scaler_transform='', scaler_transform_path=None, weather_inputs=None, custom_idx_filename='', context_len=168, pred_len=24)

Pre-training datasets: buildings-900k-train, buildings-900k-val

Parameters:

Name	Type	Description	Default
name	str	Name of the dataset to load.	required
num_buildings_ablation	int	Number of buildings to use for pre-training. If -1, use all buildings.	-1
apply_scaler_transform	str	If not using quantized load or unscaled loads, applies a {boxcox,standard} scaling transform to the load. Default: ''.	''
scaler_transform_path	Path	Path to data for transform, e.g., pickled data for BoxCox transform.	None
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Default: None.	None
custom_idx_filename	str	customized index filename. Default: ''	''
context_len	int	Length of the context. Defaults to 168.	168
pred_len	int	Length of the prediction horizon. Defaults to 24.	24

Returns:

Type	Description
Dataset	torch.utils.data.Dataset: Dataset for pretraining.

load_torch_dataset

buildings_bench.data.load_torch_dataset(name, dataset_path=None, apply_scaler_transform='', scaler_transform_path=None, weather_inputs=None, include_outliers=False, context_len=168, pred_len=24)

Load datasets by name.

Parameters:

Name	Type	Description	Default
name	str	Name of the dataset to load.	required
dataset_path	Path	Path to the benchmark data. Optional.	None
apply_scaler_transform	str	If not using quantized load or unscaled loads, applies a {boxcox,standard} scaling transform to the load. Default: ''.	''
scaler_transform_path	Path	Path to data for transform, e.g., pickled data for BoxCox transform.	None
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Default: None.	None
include_outliers	bool	Use version of BuildingsBench with outliers.	False
context_len	int	Length of the context. Defaults to 168.	168
pred_len	int	Length of the prediction horizon. Defaults to 24.	24

Returns:

Name	Type	Description
dataset	Union[TorchBuildingDatasetsFromCSV, TorchBuildingDatasetFromParquet]	Dataset for benchmarking.

load_pandas_dataset

buildings_bench.data.load_pandas_dataset(name, dataset_path=None, feature_set='engineered', weather_inputs=None, apply_scaler_transform='', scaler_transform_path=None, include_outliers=False)

Load datasets by name.

Parameters:

Name	Type	Description	Default
name	str	Name of the dataset to load.	required
dataset_path	Path	Path to the benchmark data. Optional.	None
feature_set	str	Feature set to use. Default: 'engineered'.	'engineered'
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Default: None.	None
apply_scaler_transform	str	If not using quantized load or unscaled loads, applies a {boxcox,standard} scaling transform to the load. Default: ''.	''
scaler_transform_path	Path	Path to data for transform, e.g., pickled data for BoxCox transform.	None
include_outliers	bool	Use version of BuildingsBench with outliers.	False

Returns:

Name	Type	Description
dataset	PandasBuildingDatasetsFromCSV	Generator of Pandas datasets for benchmarking.

---

The Buildings-900K PyTorch Dataset

buildings_bench.data.buildings900K.Buildings900K

Bases: Dataset

This is an indexed dataset for the Buildings-900K dataset. It uses an index file to quickly load a sub-sequence from a time series in a multi-building Parquet file. The index file is a tab separated file with the following columns:

1. Building-type-and-year (e.g., comstock_tmy3_release_1)
2. Census region (e.g., by_puma_midwest)
3. PUMA ID
4. Building ID
5. Hour of year pointer (e.g., 0070)

The sequence pointer is used to extract the slice [pointer - context length : pointer + pred length] for a given building ID.

The time series are not stored chronologically and must be sorted by timestamp after loading.

Each dataloader worker has its own file pointer to the index file. This is to avoid weird multiprocessing errors from sharing a file pointer. We 'seek' to the correct line in the index file for random access.

__init__(dataset_path, index_file, context_len=168, pred_len=24, apply_scaler_transform='', scaler_transform_path=None, weather_inputs=None)

Parameters:

Name	Type	Description	Default
dataset_path	Path	Path to the pretraining dataset.	required
index_file	str	Name of the index file	required
context_len	int	Length of the context. Defaults to 168. The index file has to be generated with the same context length.	168
pred_len	int	Length of the prediction horizon. Defaults to 24. The index file has to be generated with the same pred length.	24
apply_scaler_transform	str	Apply a scaler transform to the load. Defaults to ''.	''
scaler_transform_path	Path	Path to the scaler transform. Defaults to None.	None
weather_inputs	List[str]	list of weather features to use. Default: None.	None

__read_index_file(index_file)

Extract metadata from index file.

collate_fn()

Returns a function taking only one argument (the list of items to be batched).

init_fp()

Each worker needs to open its own file pointer to avoid weird multiprocessing errors from sharing a file pointer.

This is not called in the main process. This is called in the DataLoader worker_init_fn. The file is opened in binary mode which lets us disable buffering.

---

TorchBuildingDataset

buildings_bench.data.datasets.TorchBuildingDataset

Bases: Dataset

PyTorch Dataset for a single building's energy timeseries (a Pandas Dataframe) with a timestamp index and a power column.

Used to iterate over mini-batches of 192-hour timeseries (168 hours of context, 24 hours prediction horizon).

__init__(dataframe, building_latlon, building_type, context_len=168, pred_len=24, sliding_window=24, apply_scaler_transform='', scaler_transform_path=None, is_leap_year=False, weather_dataframe=None, weather_transform_path=None)

Parameters:

Name	Type	Description	Default
dataframe	DataFrame	Pandas DataFrame with a timestamp index and a 'power' column.	required
building_latlon	List[float]	Latitude and longitude of the building.	required
building_type	BuildingTypes	Building type for the dataset.	required
context_len	int	Length of context. Defaults to 168.	168
pred_len	int	Length of prediction. Defaults to 24.	24
sliding_window	int	Stride for sliding window to split timeseries into test samples. Defaults to 24.	24
apply_scaler_transform	str	Apply scaler transform {boxcox,standard} to the load. Defaults to ''.	''
scaler_transform_path	Path	Path to the pickled data for BoxCox transform. Defaults to None.	None
is_leap_year	bool	Is the year a leap year? Defaults to False.	False
weather_dataframe	DataFrame	Weather timeseries data. Defaults to None.	None
weather_transform_path	Path	Path to the pickled data for weather transform. Defaults to None.	None

PandasTransformerDataset

buildings_bench.data.datasets.PandasTransformerDataset

Bases: Dataset

Create a Torch Dataset from a Pandas DataFrame.

Used to iterate over mini-batches of e.g, 192-hour (168 hours context + 24 hour pred horizon) timeseries.

__init__(df, context_len=168, pred_len=24, sliding_window=24, weather_inputs=None)

Parameters:

Name	Type	Description	Default
df	DataFrame	Pandas DataFrame with columns: load, latitude, longitude, hour of day, day of week, day of year, building type	required
context_len	int	Length of context.. Defaults to 168.	168
pred_len	int	Length of prediction sequence for the forecasting model. Defaults to 24.	24
sliding_window	int	Stride for sliding window to split timeseries into test samples. Defaults to 24.	24
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Defaults to None. The df is assumed to already have the weather inputs in the list as columns.	None

TorchBuildingDatasetsFromParquet

buildings_bench.data.datasets.TorchBuildingDatasetFromParquet

Generate PyTorch Datasets out of EULP parquet files.

Each file has multiple buildings (with same Lat/Lon and building type) and each building is a column. All time series are for the same year.

Attributes:

Name	Type	Description
building_datasets	dict	Maps unique building ids to a TorchBuildingDataset.

__init__(data_path, parquet_datasets, building_latlons, building_types, weather_inputs=None, context_len=168, pred_len=24, sliding_window=24, apply_scaler_transform='', scaler_transform_path=None, leap_years=None)

Parameters:

Name	Type	Description	Default
data_path	Path	Path to the dataset	required
parquet_datasets	List[str]	List of paths to a parquet file, each has a timestamp index and multiple columns, one per building.	required
building_latlons	List[List[float]]	List of latlons for each parquet file.	required
building_types	List[BuildingTypes]	List of building types for each parquet file.	required
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Default: None.	None
context_len	int	Length of context. Defaults to 168.	168
pred_len	int	Length of prediction. Defaults to 24.	24
sliding_window	int	Stride for sliding window to split timeseries into test samples. Defaults to 24.	24
apply_scaler_transform	str	Apply scaler transform {boxcox,standard} to the load. Defaults to ''.	''
scaler_transform_path	Path	Path to the pickled data for BoxCox transform. Defaults to None.	None
leap_years	List[int]	List of leap years. Defaults to None.	None

__iter__()

Generator to iterate over the building datasets.

Yields:

Type	Description
Tuple[str, TorchBuildingDataset]	A pair of building id, TorchBuildingDataset objects.

TorchBuildingDatasetsFromCSV

buildings_bench.data.datasets.TorchBuildingDatasetsFromCSV

TorchBuildingDatasetsFromCSV

Generate PyTorch Datasets from a list of CSV files.

Attributes:

Name	Type	Description
building_datasets	dict	Maps unique building ids to a list of tuples (year, TorchBuildingDataset).

__init__(data_path, building_year_files, building_latlon, building_type, weather_inputs=None, context_len=168, pred_len=24, sliding_window=24, apply_scaler_transform='', scaler_transform_path=None, leap_years=None)

Parameters:

Name	Type	Description	Default
data_path	Path	Path to the dataset	required
building_year_files	List[str]	List of paths to a csv file, each has a timestamp index and multiple columns, one per building.	required
building_type	BuildingTypes	Building type for the dataset.	required
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Defaults to None.	None
context_len	int	Length of context. Defaults to 168.	168
pred_len	int	Length of prediction sequence for the forecasting model. Defaults to 24.	24
sliding_window	int	Stride for sliding window to split timeseries into test samples. Defaults to 24.	24
apply_scaler_transform	str	Apply scaler transform {boxcox,standard} to the load. Defaults to ''.	''
scaler_transform_path	Path	Path to the pickled data for BoxCox transform. Defaults to None.	None
leap_years	List[int]	List of leap years. Defaults to None.	None

__iter__()

A Generator for TorchBuildingDataset objects.

Yields:

Type	Description
Tuple[str, ConcatDataset]	A tuple of the building id and a ConcatDataset of the TorchBuildingDataset objects for all years.

PandasBuildingDatasetsFromCSV

buildings_bench.data.datasets.PandasBuildingDatasetsFromCSV

Generate Pandas Dataframes from a list of CSV files.

Can be used with sklearn models or tree-based models that require Pandas Dataframes. In this case, use 'features' = 'engineered' to generate a dataframe with engineered features.

Create a dictionary of building datasets from a list of csv files. Used as a generator to iterate over Pandas Dataframes for each building. The Pandas Dataframe contain all of the years of data for the building.

Attributes:

Name	Type	Description
building_datasets	dict	Maps unique building ids to a list of tuples (year, Dataframe).

__init__(data_path, building_year_files, building_latlon, building_type, weather_inputs=None, features='transformer', apply_scaler_transform='', scaler_transform_path=None, leap_years=[])

Parameters:

Name	Type	Description	Default
data_path	Path	Path to the dataset	required
building_year_files	List[str]	List of paths to a csv file, each has a timestamp index and multiple columns, one per building.	required
building_type	BuildingTypes	Building type for the dataset.	required
weather_inputs	List[str]	list of weather feature names to use as additional inputs. Defaults to None.	None
features	str	Type of features to use. Defaults to 'transformer'. {'transformer','engineered'} 'transformer' features: load, latitude, longitude, hour of day, day of week, day of year, building type 'engineered' features are an expansive list of mainly calendar-based features, useful for traditional ML models.	'transformer'
apply_scaler_transform	str	Apply scaler transform {boxcox,standard} to the load. Defaults to ''.	''
scaler_transform_path	Path	Path to the pickled data for BoxCox transform. Defaults to None.	None
leap_years	List[int]	List of leap years. Defaults to None.	[]

__iter__()

Generator for iterating over the dataset.

Yields:

Type	Description
Tuple[str, DataFrame]	A pair of building id and Pandas dataframe. The dataframe has all years concatenated.