GuardianDataset#

class pepbench.datasets.GuardianDataset(base_path: path_t, groupby_cols: Sequence[str] | None = None, subset_index: Sequence[str] | None = None, *, return_clean: bool = True, exclude_no_recorded_data: bool = True, exclude_noisy_data: bool = True, use_cache: bool = True, only_labeled: bool = False, label_type: str = 'rater_01')[source]#

Dataset class for the Guardian Dataset.

This class is the tpcp dataset class for the Guardian dataset. It provides access to the Task Force Monitor (TFM) data (for ECG and ICG), the reference annotations for the ECG and ICG annotations, as well as metadata like age, gender, and BMI.

Attributes:

icgIcgRawDataFrame: Return the ICG signal.
ecgEcgRawDataFrame: Return the ECG signal.
sampling_rate_ecgint: Return the sampling rate of the ECG signal.
sampling_rate_icgint: Return the sampling rate of the ICG signal.
heartbeatsHeartbeatSegmentationDataFrame: Segment heartbeats from the ECG data and return the heartbeat borders.
reference_pepDataFrame: Return the reference PEP values.
reference_heartbeatsDataFrame: Return the reference heartbeats.
reference_labels_ecgDataFrame: Return the reference labels for the ECG signal.
reference_labels_icgDataFrame: Return the reference labels for the ICG signal.
ageDataFrame: Return the age of the selected participants.
genderDataFrame: Return the gender of the selected participants.
bmiDataFrame: Compute the BMI of the selected participants and return it.
metadataDataFrame: Return metadata for the selected participants.
timelogDataFrame: Timelog data, indicating the start and end of each experimental phase, as a pandas DataFrame.

Methods

`as_attrs`()	Return a version of the Dataset class that can be subclassed using `attrs` defined classes.
`as_dataclass`()	Return a version of the Dataset class that can be subclassed using dataclasses.
`assert_is_single`(groupby_cols, property_name)	Raise error if index does contain more than one group/row with the given groupby settings.
`assert_is_single_group`(property_name)	Raise error if index does contain more than one group/row.
`clone`()	Create a new instance of the class with all parameters copied over.
`create_index`()	Create the full index for the dataset.
`create_string_group_labels`(label_cols)	Generate a list of string labels for each group/row in the dataset.
`get_params`([deep])	Get parameters for this algorithm.
`get_subset`(*[, group_labels, index, bool_map])	Get a subset of the dataset.
`groupby`(groupby_cols)	Return a copy of the dataset grouped by the specified columns.
`index_as_tuples`()	Get all datapoint labels of the dataset (i.e. a list of the rows of the index as named tuples).
`is_single`(groupby_cols)	Return True if index contains only one row/group with the given groupby settings.
`is_single_group`()	Return True if index contains only one group.
`iter_level`(level)	Return generator object containing a subset for every category from the selected level.
`set_params`(**params)	Set the parameters of this Algorithm.

create_group_labels

__init__(base_path: path_t, groupby_cols: Sequence[str] | None = None, subset_index: Sequence[str] | None = None, *, return_clean: bool = True, exclude_no_recorded_data: bool = True, exclude_noisy_data: bool = True, use_cache: bool = True, only_labeled: bool = False, label_type: str = 'rater_01') → None[source]#

Initialize a new GuardianDataset instance.

Parameters:

base_pathPath or str: Path to the root directory of the Guardian dataset.
return_cleanbool: Whether to return the preprocessed/cleaned ECG and ICG data when accessing the respective properties. Default: True.
exclude_no_recorded_databool, optional: Whether to exclude participants with no recorded data. Default: True.
exclude_noisy_databool, optional: Whether to exclude participants with noisy data. Default: True.
use_cachebool, optional: Whether to use caching for loading TFM data. Default: True.
only_labeledbool, optional: Whether to only return segments that are labeled (i.e., cut the data to the labeling borders). This is necessary when using the dataset for evaluating the performance of PEP extraction algorithms or for training ML-based PEP extraction algorithms. Default: False.
label_type: str, optional: Which annotations to use. Can be either “rater_01”, “rater_02”, or “average”. Default: “rater_01”.

create_index() → DataFrame[source]#

Create the full index for the dataset.

This needs to be implemented by the subclass.

Warning

Make absolutely sure that the dataframe you return is deterministic and does not change between runs! This can lead to some nasty bugs! We try to catch them internally, but it is not always possible. As tips, avoid reliance on random numbers and make sure that the order is not depend on things like file system order, when creating an index by scanning a directory. Particularly nasty are cases when using non-sorted container like set, that sometimes maintain their order, but sometimes don’t. At the very least, we recommend to sort the final dataframe you return in create_index.

property sampling_rates: dict[str, int]#

Return the sampling rates of the ECG and ICG signals.

Returns:

dict: Dictionary with the sampling rates of the ECG and ICG signals in Hz.

property sampling_rate_ecg: int#

Return the sampling rate of the ECG signal.

Returns:

int: Sampling rate of the ECG signal in Hz.

property sampling_rate_icg: int#

Return the sampling rate of the ICG signal.

Returns:

int: Sampling rate of the ICG signal in Hz.

property date: Series | Timestamp#

Return the recording date of the participant(s).

Returns:

pd.Series or pd.Timestamp: Recording date of the participant(s). If only a single participant is selected, a single timestamp is returned. If multiple participants are selected, a Series with the recording dates for each participant is returned.

property tfm_data: DataFrame | dict[str, DataFrame]#

Return the Task Force Monitor (TFM) data.

The data is loaded from the raw TFM data files and returned as DataFrame. The data can only be accessed for a single participant and a single phase or for a single participant and all phases.

Returns:

DataFrame or dict: TFM data as DataFrame or dictionary of DataFrame if multiple phases are selected.

Raises:

ValueError: If the data is accessed for multiple participants or multiple phases (that are not all phases).

property icg: DataFrame#

Return the ICG signal.

If return_clean is set to True in the __init__, the ICG signal is preprocessed and cleaned using the IcgPreprocessingBandpass algorithm before returning it.

Returns:

DataFrame: ICG signal as DataFrame.

property ecg: DataFrame#

Return the ECG signal.

If return_clean is set to True in the __init__, the ECG signal is preprocessed and cleaned using the EcgPreprocessingNeurokit algorithm before returning it.

Returns:

DataFrame: ECG signal as DataFrame.

property labeling_borders: DataFrame#

Return the labeling borders for a selected participant and phase(s).

Returns:

DataFrame: Labeling borders as DataFrame.

property reference_heartbeats: DataFrame#

Return the reference heartbeats.

Returns:

DataFrame: Reference heartbeats as a pandas DataFrame

property reference_labels_ecg: DataFrame#

Return the reference labels for the ECG signal.

Returns:

DataFrame: Reference labels for the ECG signal as a pandas DataFrame

property reference_labels_icg: DataFrame#

Return the reference labels for the ICG signal.

Returns:

DataFrame: Reference labels for the ICG signal as a pandas DataFrame

property heartbeats: DataFrame#

Segment heartbeats from the ECG data and return the heartbeat borders.

Returns:

DataFrame: Heartbeats as a pandas DataFrame.

property metadata: DataFrame#

Return metadata for the selected participants.

Returns:

DataFrame: Metadata as a pandas DataFrame.

property age: DataFrame#

Return the age of the selected participants.

Returns:

DataFrame: Age as a pandas DataFrame.

classmethod as_attrs()[source]#

Return a version of the Dataset class that can be subclassed using attrs defined classes.

Note, this requires attrs to be installed!

classmethod as_dataclass()[source]#: Return a version of the Dataset class that can be subclassed using dataclasses.

assert_is_single(groupby_cols: list[str] | str | None, property_name) → None[source]#

Raise error if index does contain more than one group/row with the given groupby settings.

This should be used when implementing access to data values, which can only be accessed when only a single trail/participant/etc. exist in the dataset.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.
property_name: Name of the property this check is used in. Used to format the error message.

assert_is_single_group(property_name) → None[source]#

Raise error if index does contain more than one group/row.

Note that this is different from assert_is_single as it is aware of the current grouping. Instead of checking that a certain combination of columns is left in the dataset, it checks that only a single group exists with the already selected grouping as defined by self.groupby_cols.

Parameters:

property_name: Name of the property this check is used in. Used to format the error message.

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

create_string_group_labels(label_cols: str | list[str]) → list[str][source]#

Generate a list of string labels for each group/row in the dataset.

Note

This has a different use case than the dataset-wide groupby. Using groupby reduces the effective size of the dataset to the number of groups. This method produces a group label for each group/row that is already in the dataset, without changing the dataset.

The output of this method can be used in combination with GroupKFold as the group label.

Parameters:

label_cols: The columns that should be included in the label. If the dataset is already grouped, this must be a subset of self.groupby_cols.

property gender: DataFrame#

Return the gender of the selected participants.

Returns:

DataFrame: Gender as a pandas DataFrame, recoded as {“M”: “Male”, “F”: “Female”}

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params: Parameter names mapped to their values.

get_subset(*, group_labels: list[tuple[str, ...]] | None = None, index: DataFrame | None = None, bool_map: Sequence[bool] | None = None, **kwargs: list[str] | str) → Self[source]#

Get a subset of the dataset.

Note

All arguments are mutable exclusive!

Parameters:

group_labels: A valid row locator or slice that can be passed to self.grouped_index.loc[locator, :]. This basically needs to be a subset of self.group_labels. Note that this is the only indexer that works on the grouped index. All other indexers work on the pure index.
index: pd.DataFrame that is a valid subset of the current dataset index.
bool_map: bool-map that is used to index the current index-dataframe. The list must be of same length as the number of rows in the index.
**kwargs: The key must be the name of an index column. The value is a list containing strings that correspond to the categories that should be kept. For examples see above.

Returns:

subset: New dataset object filtered by specified parameters.

property group: GroupLabelT#: Get the current group label. Deprecated, use group_label instead.

property group_label: GroupLabelT#

Get the current group label.

The group is defined by the current groupby settings.

Note, this attribute can only be used, if there is just a single group. This will return a named tuple. The tuple will contain only one entry if there is only a single groupby column or column in the index. The elements of the named tuple will have the same names as the groupby columns and will be in the same order.

property group_labels: list[GroupLabelT]#

Get all group labels of the dataset based on the set groupby level.

This will return a list of named tuples. The tuples will contain only one entry if there is only one groupby level or index column.

The elements of the named tuples will have the same names as the groupby columns and will be in the same order.

Note, that if one of the groupby levels/index columns is not a valid Python attribute name (e.g. in contains spaces or starts with a number), the named tuple will not contain the correct column name! For more information see the documentation of the rename parameter of collections.namedtuple.

For some examples and additional explanation see this example.

groupby(groupby_cols: list[str] | str | None) → Self[source]#

Return a copy of the dataset grouped by the specified columns.

This does not change the order of the rows of the dataset index.

Each unique group represents a single data point in the resulting dataset.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.

property grouped_index: DataFrame#: Return the index with the groupby columns set as multiindex.

property groups: list[GroupLabelT]#: Get the current group labels. Deprecated, use group_labels instead.

property index: DataFrame#: Get index.

index_as_tuples() → list[GroupLabelT][source]#: Get all datapoint labels of the dataset (i.e. a list of the rows of the index as named tuples).

property index_is_unchanged: bool#

Returns True if the index is the same as the one created by create_index.

This can be used to check, if the index represents a subset or the actual full index. Note, that this is independent of the groupby_cols setting.

Note

Under the hood this uses the attrs functionality of pandas to store a hash of the original index on the dataframe. If the index is modified or a new index is created, this property does either not exist anymore or the content is modified.

is_single(groupby_cols: list[str] | str | None) → bool[source]#

Return True if index contains only one row/group with the given groupby settings.

If groupby_cols=None this checks if there is only a single row left. If you want to check if there is only a single group within the current grouping, use is_single_group instead.

Parameters:

groupby_cols: None (no grouping) or a valid subset of the columns available in the dataset index.

is_single_group() → bool[source]#: Return True if index contains only one group.

iter_level(level: str) → Iterator[Self][source]#

Return generator object containing a subset for every category from the selected level.

Parameters:

level: Optional str that sets the level which shall be used for iterating. This must be one of the columns names of the index.

Returns:

subset: New dataset object containing only one category in the specified level.

property reference_pep: DataFrame#

Return the reference PEP values.

Returns:

DataFrame: Reference PEP values as a pandas DataFrame

set_params(**params: Any) → Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

property shape: tuple[int]#

Get the shape of the dataset.

This only reports a single dimension. This is equal to the number of rows in the index, if self.groupby_cols=None. Otherwise, it is equal to the number of unique groups.

property bmi: DataFrame#

Compute the BMI of the selected participants and return it.

Returns:

DataFrame: BMI as a pandas DataFrame.

GuardianDataset#

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