pyhealth.datasets.BaseDataset#

This is the basic base dataset class. Any specific datasets will inherit from this class.

class pyhealth.datasets.BaseDataset(root, tables, dataset_name=None, config_path=None, cache_dir=None, num_workers=1, dev=False)[source]#

Bases: ABC

Abstract base class for all PyHealth datasets.

root#

The root directory where dataset files are stored.

Type:: Path

tables#

List of table names to load.

Type:: List[str]

dataset_name#

Name of the dataset.

Type:: str

config#

Configuration loaded from a YAML file.

Type:: dict

global_event_df#

The global event data frame.

Type:: pl.LazyFrame

dev#

Whether to enable dev mode (limit to 1000 patients).

Type:: bool

create_tmpdir()[source]#

Creates and returns a new temporary directory within the cache.

Returns:: The path to the new temporary directory.
Return type:: Path

clean_tmpdir()[source]#

Cleans up the temporary directory within the cache.

Return type:: None

property global_event_df: LazyFrame#

Returns the path to the cached event dataframe.

Returns:: The path to the cached event dataframe.
Return type:: Path

load_data()[source]#

Loads data from the specified tables.

Returns:: A concatenated lazy frame of all tables.
Return type:: dd.DataFrame

load_table(table_name)[source]#

Loads a table and processes joins if specified.

Parameters:

table_name (str) – The name of the table to load.

Returns:

The processed Dask dataframe for the table.

Return type:

dd.DataFrame

Raises:

ValueError – If the table is not found in the config.
FileNotFoundError – If the CSV file for the table or join is not found.

property unique_patient_ids: List[str]#

Returns a list of unique patient IDs.

Returns:: List of unique patient IDs.
Return type:: List[str]

get_patient(patient_id)[source]#

Retrieves a Patient object for the given patient ID.

Parameters:: patient_id (str) – The ID of the patient to retrieve.
Returns:: The Patient object for the given ID.
Return type:: Patient
Raises:: AssertionError – If the patient ID is not found in the dataset.

iter_patients(df=None)[source]#

Yields Patient objects for each unique patient in the dataset.

Yields:: Iterator[Patient] – An iterator over Patient objects.
Return type:: Iterator[Patient]

stats()[source]#

Prints statistics about the dataset.

Return type:: None

property default_task: Optional[BaseTask]#

Returns the default task for the dataset.

Returns:: The default task, if any.
Return type:: Optional[BaseTask]

set_task(task=None, num_workers=None, input_processors=None, output_processors=None)[source]#

Processes the base dataset to generate the task-specific sample dataset. The cache structure is as follows:

{task_name}_{task_uuid}/        # Cached data for specific task based on task name, schema, and args
    task_df.ld/                 # Intermediate task dataframe based on schema
    samples_{proc_uuid}.ld/     # Final processed samples after applying processors
        schema.pkl              # Saved SampleBuilder schema
        *.bin                   # Processed sample files

Parameters:

task (Optional[BaseTask]) – The task to set. Uses default task if None.
num_workers (int) – Number of workers for multi-threading. Default is self.num_workers.
input_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted input processors. If provided, these will be used instead of creating new ones from task’s input_schema. Defaults to None.
output_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted output processors. If provided, these will be used instead of creating new ones from task’s output_schema. Defaults to None.

Returns:

The generated sample dataset.

Return type:

SampleDataset

Raises:

AssertionError – If no default task is found and task is None.