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base_source

Module containing BaseSource class.

BaseSource is the abstract data source class from which all concrete data sources must inherit.

Classes​

BaseSource​

class BaseSource(    data_splitter: Optional[DatasetSplitter] = None,    seed: Optional[int] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    ignore_cols: Optional[Union[str, Sequence[str]]] = None,    **kwargs: Any,):

Abstract Base Source from which all other data sources must inherit.

Arguments

  • data_splitter: Approach used for splitting the data into training, test, validation. Defaults to None.
  • seed: Random number seed. Used for setting random seed for all libraries. Defaults to None.
  • modifiers: Dictionary used for modifying paths/ extensions in the dataframe. Defaults to None.
  • ignore_cols: Column/list of columns to be ignored from the data. Defaults to None.

Attributes

  • data: A Dataframe-type object which contains the data.
  • data_splitter: Approach used for splitting the data into training, test, validation.
  • seed: Random number seed. Used for setting random seed for all libraries.

Ancestors​

Subclasses​

  • IterableSource
  • CSVSource
  • bitfount.data.datasources.dummy_module_1729249325677711000.DummySource_1729249325677711000
  • bitfount.data.datasources.dummy_module_1729249325693767000.DummySource_1729249325693767000
  • bitfount.data.datasources.dummy_module_1729249325922265000.DummySource_1729249325922265000

Variables​

  • data : pandas.core.frame.DataFrame - A property containing the underlying dataframe if the data has been loaded.

    Raises: DataNotLoadedError: If the data has not been loaded yet.

  • hash : str - The hash associated with this BaseSource.

    This is the hash of the static information regarding the underlying DataFrame, primarily column names and content types but NOT anything content-related itself. It should be consistent across invocations, even if additional data is added, as long as the DataFrame is still compatible in its format.

    Returns: The hexdigest of the DataFrame hash.

  • is_initialised : bool - Checks if BaseSource was initialised.
  • is_task_running : bool - Returns True if a task is running.

  • iterable : bool - This returns False if the DataSource does not subclass IterableSource.

    However, this property must be re-implemented in IterableSource, therefore it is not necessarily True if the DataSource inherits from IterableSource.

Methods​

get_column​

def get_column(    self, col_name: str, **kwargs: Any,) ‑> Union[numpy.ndarray, pandas.core.series.Series]:

Get a single column from dataset.

Used to iterate over image columns for the purposes of schema generation.

get_column_names​

def get_column_names(self, **kwargs: Any) ‑> collections.abc.Iterable:

Get the column names as an iterable.

get_data​

def get_data(self, **kwargs: Any) ‑> Optional[pandas.core.frame.DataFrame]:

Implement this method to load and return dataset.

get_dtypes​

def get_dtypes(self, **kwargs: Any) ‑> _Dtypes:

Implement this method to get the columns and column types from dataset.

get_project_db_sqlite_columns​

def get_project_db_sqlite_columns(self) ‑> list:

Implement this method to get the required columns.

This is used by the "run on new data only" feature. This is used to add data to the task table in the project database.

get_project_db_sqlite_create_table_query​

def get_project_db_sqlite_create_table_query(self) ‑> str:

Implement this method to return the required columns and types.

This is used by the "run on new data only" feature. This should be in the format that can be used after a "CREATE TABLE" statement and is used to create the task table in the project database.

get_values​

def get_values(self, col_names: list[str], **kwargs: Any) ‑> dict:

Get distinct values from list of columns.

load_data​

def load_data(self, **kwargs: Any) ‑> None:

Load the data for the datasource.

Raises

  • TypeError: If data format is not supported.

FileSystemIterableSource​

class FileSystemIterableSource(    path: Union[os.PathLike, str],    output_path: Optional[Union[os.PathLike, str]] = None,    iterable: bool = True,    fast_load: bool = True,    file_extension: Optional[_SingleOrMulti[str]] = None,    strict: bool = False,    cache_images: bool = False,    file_creation_min_date: Optional[Union[Date, DateTD]] = None,    file_modification_min_date: Optional[Union[Date, DateTD]] = None,    file_creation_max_date: Optional[Union[Date, DateTD]] = None,    file_modification_max_date: Optional[Union[Date, DateTD]] = None,    min_file_size: Optional[float] = None,    max_file_size: Optional[float] = None,    partition_size: int = 16,    data_splitter: Optional[DatasetSplitter] = None,    seed: Optional[int] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    ignore_cols: Optional[Union[str, Sequence[str]]] = None,):

Abstract base source that supports iterating over file-based data.

This is used for Iterable data sources that whose data is stored as files on disk.

Arguments

  • cache_images: Whether to cache images in the file system. Defaults to False. This is ignored if fast_load is True.
  • data_splitter: Approach used for splitting the data into training, test, validation. Defaults to None.
  • fast_load: Whether the data will be loaded in fast mode. This is used to determine whether the data will be iterated over during set up for schema generation and splitting (where necessary). Only relevant if iterable is True, otherwise it is ignored. Defaults to True.
  • file_creation_max_date: The newest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_creation_min_date: The oldest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_extension: File extension(s) of the data files. If None, all files will be searched. Can either be a single file extension or a list of file extensions. Case-insensitive. Defaults to None.
  • file_modification_max_date: The newest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • file_modification_min_date: The oldest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • ignore_cols: Column/list of columns to be ignored from the data. Defaults to None.
  • iterable: Whether the data source is iterable. This is used to determine whether the data source can be used in a streaming context during a task. Defaults to True.
  • max_file_size: The maximum file size in megabytes to consider. If None, all files will be considered. Defaults to None.
  • min_file_size: The minimum file size in megabytes to consider. If None, all files will be considered. Defaults to None.
  • modifiers: Dictionary used for modifying paths/ extensions in the dataframe. Defaults to None.
  • output_path: The path where to save intermediary output files. Defaults to 'preprocessed/'.
  • partition_size: The size of each partition when iterating over the data.
  • path: Path to the directory which contains the data files. Subdirectories will be searched recursively.
  • seed: Random number seed. Used for setting random seed for all libraries. Defaults to None.
  • strict: Whether File loading should be strictly done on files with the explicit file extension provided. If set to True will only load those files in the dataset. Otherwise, it will scan the given path for files of the same type as the provided file extension. Only relevant if file_extension is provided. Defaults to False.

Attributes

  • data: A Dataframe-type object which contains the data.
  • data_splitter: Approach used for splitting the data into training, test, validation.
  • seed: Random number seed. Used for setting random seed for all libraries.

Raises

  • ValueError: If iterable is False or fast_load is False or cache_images is True.

Ancestors​

Subclasses​

Variables​

  • data : pandas.core.frame.DataFrame - A property containing the underlying dataframe if the data has been loaded.

    If the datasource is iterable, this will raise an exception.

    Raises: IterableDataSourceError: If the datasource is set to iterable. DataNotLoadedError: If the data has not been loaded yet.

  • file_names : list - Returns a list of file names in the specified directory.

    This property accounts for files skipped at runtime by filtering them out of the list of cached file names. Files may get skipped at runtime due to errors or because they don't contain any image data and images_only is True. This allows us to skip these files again more quickly if they are still present in the directory.

  • hash : str - The hash associated with this BaseSource.

    This is the hash of the static information regarding the underlying DataFrame, primarily column names and content types but NOT anything content-related itself. It should be consistent across invocations, even if additional data is added, as long as the DataFrame is still compatible in its format.

    Returns: The hexdigest of the DataFrame hash.

  • is_initialised : bool - Checks if BaseSource was initialised.
  • is_task_running : bool - Returns True if a task is running.

  • iterable : bool - Defines whether the data source is iterable.

    This is defined by the user when instantiating the class.

  • path : pathlib.Path - Resolved absolute path to data.

    Provides a consistent version of the path provided by the user which should work throughout regardless of operating system and of directory structure.

  • selected_file_names : list - Returns a list of selected file names.

    Selected file names are affected by the selected_file_names_override and new_file_names_only attributes.

Static methods​

get_num_workers​

def get_num_workers(file_names: list[str]) ‑> int:

Inherited from:

MultiProcessingMixIn.get_num_workers :

Gets the number of workers to use for multiprocessing.

Ensures that the number of workers is at least 1 and at most equal to MAX_NUM_MULTIPROCESSING_WORKERS. If the number of files is less than MAX_NUM_MULTIPROCESSING_WORKERS, then we use the number of files as the number of workers. Unless the number of machine cores is also less than MAX_NUM_MULTIPROCESSING_WORKERS, in which case we use the lower of the two.

Arguments

  • file_names: The list of file names to load.

Returns The number of workers to use for multiprocessing.

Methods​

clear_file_names_cache​

def clear_file_names_cache(self) ‑> None:

Clears the list of selected file names.

This allows the datasource to pick up any new files that have been added to the directory since the last time it was cached.

file_names_iter​

def file_names_iter(    self, as_strs: bool = False,) ‑> Union[collections.abc.Iterator[pathlib.Path], collections.abc.Iterator[str]]:

Iterate over files in a directory, yielding those that match the criteria.

Arguments

  • as_strs: By default the files yielded will be yielded as Path objects. If this is True, yield them as strings instead.

get_column​

def get_column(    self, col_name: str, **kwargs: Any,) ‑> Union[numpy.ndarray, pandas.core.series.Series]:

Loads and returns single column from the dataset.

Arguments

  • col_name: The name of the column which should be loaded.
  • **kwargs: Additional keyword arguments to pass to the load_data method if the data is stale.

Returns The column request as a series.

get_column_names​

def get_column_names(self, **kwargs: Any) ‑> collections.abc.Iterable:

Get column names for fast-load datasource.

get_data​

def get_data(self, **kwargs: Any) ‑> Optional[pandas.core.frame.DataFrame]:

Inherited from:

IterableSource.get_data :

This method must return None if the data source is iterable.

get_dtypes​

def get_dtypes(self, **kwargs: Any) ‑> _Dtypes:

Loads and returns the column names and types of the dataframe.

Arguments

  • **kwargs: Additional keyword arguments to pass to the load_data method if the data is stale.

Returns A mapping from column names to column types.

get_project_db_sqlite_columns​

def get_project_db_sqlite_columns(self) ‑> list:

Returns the required columns to identify a data point.

get_project_db_sqlite_create_table_query​

def get_project_db_sqlite_create_table_query(self) ‑> str:

Returns the required columns and types to identify a data point.

The file name is used as the primary key and the last modified date is used to determine if the file has been updated since the last time it was processed. If there is a conflict on the file name, the row is replaced with the new data to ensure that the last modified date is always up to date.

get_values​

def get_values(self, col_names: list[str], **kwargs: Any) ‑> dict:

Get distinct values from columns in the dataset.

Arguments

  • col_names: The list of the columns whose distinct values should be returned.
  • **kwargs: Additional keyword arguments to pass to the load_data method if the data is stale.

Returns The distinct values of the requested column as a mapping from col name to a series of distinct values.

load_data​

def load_data(self, **kwargs: Any) ‑> None:

Inherited from:

IterableSource.load_data :

Load the data for the datasource.

Raises

  • TypeError: If data format is not supported.

partition​

def partition(    self, iterable: Sequence, partition_size: int = 1,) ‑> collections.abc.Iterable:

Takes an iterable and yields partitions of size partition_size.

The final partition may be less than size partition_size due to the variable length of the iterable.

use_file_multiprocessing​

def use_file_multiprocessing(self, file_names: list[str]) ‑> bool:

Inherited from:

MultiProcessingMixIn.use_file_multiprocessing :

Check if file multiprocessing should be used.

Returns True if file multiprocessing has been enabled by the environment variable and the number of workers would be greater than 1, otherwise False. There is no need to use file multiprocessing if we are just going to use one worker - it would be slower than just loading the data in the main process.

Returns True if file multiprocessing should be used, otherwise False.

yield_data​

def yield_data(    self,    file_names: Optional[list[str]] = None,    use_cache: bool = True,    partition_size: Optional[int] = None,    **kwargs: Any,) ‑> collections.abc.Iterator:

Yields data in batches from files that match the given file names.

Arguments

  • file_names: An optional list of file names to use for yielding data. Otherwise, all files that have already been found will be used. file_names is always provided when this method is called from the Dataset as part of a task.
  • use_cache: Whether the cache should be used to retrieve data for these files. Note that cached data may have some elements, particularly image-related fields such as image data or file paths, replaced with placeholder values when stored in the cache. If datacache is set on the instance, data will be _set in the cache, regardless of this argument.
  • partition_size: The number of file names to load in each iteration.
  • ****kwargs**: Additional keyword arguments.

Raises

  • ValueError: If no file names provided and no files have been found.

FileSystemIterableSourceInferrable​

class FileSystemIterableSourceInferrable(    path: Union[os.PathLike, str],    data_cache: Optional[DataPersister] = None,    infer_class_labels_from_filepaths: bool = False,    output_path: Optional[Union[os.PathLike, str]] = None,    iterable: bool = True,    fast_load: bool = True,    file_extension: Optional[_SingleOrMulti[str]] = None,    strict: bool = False,    cache_images: bool = False,    file_creation_min_date: Optional[Union[Date, DateTD]] = None,    file_modification_min_date: Optional[Union[Date, DateTD]] = None,    file_creation_max_date: Optional[Union[Date, DateTD]] = None,    file_modification_max_date: Optional[Union[Date, DateTD]] = None,    min_file_size: Optional[float] = None,    max_file_size: Optional[float] = None,    partition_size: int = 16,    data_splitter: Optional[DatasetSplitter] = None,    seed: Optional[int] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    ignore_cols: Optional[Union[str, Sequence[str]]] = None,):

Abstract base source that supports iterating over file-based data.

This is used for Iterable data sources that whose data is stored as files on disk.

Arguments

  • cache_images: Whether to cache images in the file system. Defaults to False. This is ignored if fast_load is True.
  • data_cache: A DataPersister instance to use for data caching.
  • data_splitter: Approach used for splitting the data into training, test, validation. Defaults to None.
  • fast_load: Whether the data will be loaded in fast mode. This is used to determine whether the data will be iterated over during set up for schema generation and splitting (where necessary). Only relevant if iterable is True, otherwise it is ignored. Defaults to True.
  • file_creation_max_date: The newest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_creation_min_date: The oldest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_extension: File extension(s) of the data files. If None, all files will be searched. Can either be a single file extension or a list of file extensions. Case-insensitive. Defaults to None.
  • file_modification_max_date: The newest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • file_modification_min_date: The oldest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • ignore_cols: Column/list of columns to be ignored from the data. Defaults to None.
  • infer_class_labels_from_filepaths: Whether class labels should be added to the data based on the filepath of the files. Defaults to the first directory within self.path, but can go a level deeper if the datasplitter is provided with infer_data_split_labels set to true
  • iterable: Whether the data source is iterable. This is used to determine whether the data source can be used in a streaming context during a task. Defaults to True.
  • max_file_size: The maximum file size in megabytes to consider. If None, all files will be considered. Defaults to None.
  • min_file_size: The minimum file size in megabytes to consider. If None, all files will be considered. Defaults to None.
  • modifiers: Dictionary used for modifying paths/ extensions in the dataframe. Defaults to None.
  • output_path: The path where to save intermediary output files. Defaults to 'preprocessed/'.
  • partition_size: The size of each partition when iterating over the data.
  • path: Path to the directory which contains the data files. Subdirectories will be searched recursively.
  • seed: Random number seed. Used for setting random seed for all libraries. Defaults to None.
  • strict: Whether File loading should be strictly done on files with the explicit file extension provided. If set to True will only load those files in the dataset. Otherwise, it will scan the given path for files of the same type as the provided file extension. Only relevant if file_extension is provided. Defaults to False.

Attributes

  • data: A Dataframe-type object which contains the data.
  • data_splitter: Approach used for splitting the data into training, test, validation.
  • seed: Random number seed. Used for setting random seed for all libraries.

Raises

  • ValueError: If iterable is False or fast_load is False or cache_images is True.

Ancestors​

Subclasses​

  • DICOMSource
  • bitfount.data.datasources.ophthalmology.ophthalmology_base_source._OphthalmologySource

Variables​

  • data : pandas.core.frame.DataFrame - A property containing the underlying dataframe if the data has been loaded.

    If the datasource is iterable, this will raise an exception.

    Raises: IterableDataSourceError: If the datasource is set to iterable. DataNotLoadedError: If the data has not been loaded yet.

  • file_names : list - Returns a list of file names in the specified directory.

    This property accounts for files skipped at runtime by filtering them out of the list of cached file names. Files may get skipped at runtime due to errors or because they don't contain any image data and images_only is True. This allows us to skip these files again more quickly if they are still present in the directory.

  • hash : str - The hash associated with this BaseSource.

    This is the hash of the static information regarding the underlying DataFrame, primarily column names and content types but NOT anything content-related itself. It should be consistent across invocations, even if additional data is added, as long as the DataFrame is still compatible in its format.

    Returns: The hexdigest of the DataFrame hash.

  • is_initialised : bool - Checks if BaseSource was initialised.
  • is_task_running : bool - Returns True if a task is running.

  • iterable : bool - Defines whether the data source is iterable.

    This is defined by the user when instantiating the class.

  • path : pathlib.Path - Resolved absolute path to data.

    Provides a consistent version of the path provided by the user which should work throughout regardless of operating system and of directory structure.

  • selected_file_names : list - Returns a list of selected file names.

    Selected file names are affected by the selected_file_names_override and new_file_names_only attributes.

Static methods​

get_num_workers​

def get_num_workers(file_names: list[str]) ‑> int:

Inherited from:

FileSystemIterableSource.get_num_workers :

Gets the number of workers to use for multiprocessing.

Ensures that the number of workers is at least 1 and at most equal to MAX_NUM_MULTIPROCESSING_WORKERS. If the number of files is less than MAX_NUM_MULTIPROCESSING_WORKERS, then we use the number of files as the number of workers. Unless the number of machine cores is also less than MAX_NUM_MULTIPROCESSING_WORKERS, in which case we use the lower of the two.

Arguments

  • file_names: The list of file names to load.

Returns The number of workers to use for multiprocessing.

Methods​

clear_file_names_cache​

def clear_file_names_cache(self) ‑> None:

Inherited from:

FileSystemIterableSource.clear_file_names_cache :

Clears the list of selected file names.

This allows the datasource to pick up any new files that have been added to the directory since the last time it was cached.

file_names_iter​

def file_names_iter(    self, as_strs: bool = False,) ‑> Union[collections.abc.Iterator[pathlib.Path], collections.abc.Iterator[str]]:

Inherited from:

FileSystemIterableSource.file_names_iter :

Iterate over files in a directory, yielding those that match the criteria.

Arguments

  • as_strs: By default the files yielded will be yielded as Path objects. If this is True, yield them as strings instead.

get_column​

def get_column(    self, col_name: str, **kwargs: Any,) ‑> Union[numpy.ndarray, pandas.core.series.Series]:

Inherited from:

FileSystemIterableSource.get_column :

Loads and returns single column from the dataset.

Arguments

  • col_name: The name of the column which should be loaded.
  • **kwargs: Additional keyword arguments to pass to the load_data method if the data is stale.

Returns The column request as a series.

get_column_names​

def get_column_names(self, **kwargs: Any) ‑> collections.abc.Iterable:

Inherited from:

FileSystemIterableSource.get_column_names :

Get column names for fast-load datasource.

get_data​

def get_data(self, **kwargs: Any) ‑> Optional[pandas.core.frame.DataFrame]:

Inherited from:

FileSystemIterableSource.get_data :

This method must return None if the data source is iterable.

get_dtypes​

def get_dtypes(self, **kwargs: Any) ‑> _Dtypes:

Get dtypes for iterable datasource.

get_project_db_sqlite_columns​

def get_project_db_sqlite_columns(self) ‑> list:

Inherited from:

FileSystemIterableSource.get_project_db_sqlite_columns :

Returns the required columns to identify a data point.

get_project_db_sqlite_create_table_query​

def get_project_db_sqlite_create_table_query(self) ‑> str:

Inherited from:

FileSystemIterableSource.get_project_db_sqlite_create_table_query :

Returns the required columns and types to identify a data point.

The file name is used as the primary key and the last modified date is used to determine if the file has been updated since the last time it was processed. If there is a conflict on the file name, the row is replaced with the new data to ensure that the last modified date is always up to date.

get_values​

def get_values(self, col_names: list[str], **kwargs: Any) ‑> dict:

Inherited from:

FileSystemIterableSource.get_values :

Get distinct values from columns in the dataset.

Arguments

  • col_names: The list of the columns whose distinct values should be returned.
  • **kwargs: Additional keyword arguments to pass to the load_data method if the data is stale.

Returns The distinct values of the requested column as a mapping from col name to a series of distinct values.

load_data​

def load_data(self, **kwargs: Any) ‑> None:

Inherited from:

FileSystemIterableSource.load_data :

Load the data for the datasource.

Raises

  • TypeError: If data format is not supported.

partition​

def partition(    self, iterable: Sequence, partition_size: int = 1,) ‑> collections.abc.Iterable:

Inherited from:

FileSystemIterableSource.partition :

Takes an iterable and yields partitions of size partition_size.

The final partition may be less than size partition_size due to the variable length of the iterable.

use_file_multiprocessing​

def use_file_multiprocessing(self, file_names: list[str]) ‑> bool:

Inherited from:

FileSystemIterableSource.use_file_multiprocessing :

Check if file multiprocessing should be used.

Returns True if file multiprocessing has been enabled by the environment variable and the number of workers would be greater than 1, otherwise False. There is no need to use file multiprocessing if we are just going to use one worker - it would be slower than just loading the data in the main process.

Returns True if file multiprocessing should be used, otherwise False.

yield_data​

def yield_data(    self,    file_names: Optional[list[str]] = None,    use_cache: bool = True,    partition_size: Optional[int] = None,    **kwargs: Any,) ‑> collections.abc.Iterator:

Inherited from:

FileSystemIterableSource.yield_data :

Yields data in batches from files that match the given file names.

Arguments

  • file_names: An optional list of file names to use for yielding data. Otherwise, all files that have already been found will be used. file_names is always provided when this method is called from the Dataset as part of a task.
  • use_cache: Whether the cache should be used to retrieve data for these files. Note that cached data may have some elements, particularly image-related fields such as image data or file paths, replaced with placeholder values when stored in the cache. If datacache is set on the instance, data will be _set in the cache, regardless of this argument.
  • partition_size: The number of file names to load in each iteration.
  • ****kwargs**: Additional keyword arguments.

Raises

  • ValueError: If no file names provided and no files have been found.

IterableSource​

class IterableSource(    partition_size: int = 16,    data_splitter: Optional[DatasetSplitter] = None,    seed: Optional[int] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    ignore_cols: Optional[Union[str, Sequence[str]]] = None,):

Abstract base source that supports iterating over the data.

This is used for streaming data in batches as opposed to loading the entire dataset into memory.

Arguments

  • data_splitter: Approach used for splitting the data into training, test, validation. Defaults to None.
  • ignore_cols: Column/list of columns to be ignored from the data. Defaults to None.
  • modifiers: Dictionary used for modifying paths/ extensions in the dataframe. Defaults to None.
  • partition_size: The size of each partition when iterating over the data.
  • seed: Random number seed. Used for setting random seed for all libraries. Defaults to None.

Attributes

  • data: A Dataframe-type object which contains the data.
  • data_splitter: Approach used for splitting the data into training, test, validation.
  • seed: Random number seed. Used for setting random seed for all libraries.

Ancestors​

Subclasses​

Variables​

  • data : pandas.core.frame.DataFrame - A property containing the underlying dataframe if the data has been loaded.

    If the datasource is iterable, this will raise an exception.

    Raises: IterableDataSourceError: If the datasource is set to iterable. DataNotLoadedError: If the data has not been loaded yet.

  • hash : str - The hash associated with this BaseSource.

    This is the hash of the static information regarding the underlying DataFrame, primarily column names and content types but NOT anything content-related itself. It should be consistent across invocations, even if additional data is added, as long as the DataFrame is still compatible in its format.

    Returns: The hexdigest of the DataFrame hash.

  • is_initialised : bool - Checks if BaseSource was initialised.
  • is_task_running : bool - Returns True if a task is running.

  • iterable : bool - Implement this method to define whether the data source is iterable.

    The datasource must inherit from IterableSource if this is True. However, the inverse is not necessarily True.

Methods​

get_column​

def get_column(    self, col_name: str, **kwargs: Any,) ‑> Union[numpy.ndarray, pandas.core.series.Series]:

Get column for iterable datasource.

get_column_names​

def get_column_names(self, **kwargs: Any) ‑> collections.abc.Iterable:

Get column names for iterable datasource.

get_data​

def get_data(self, **kwargs: Any) ‑> Optional[pandas.core.frame.DataFrame]:

This method must return None if the data source is iterable.

get_dtypes​

def get_dtypes(self, **kwargs: Any) ‑> _Dtypes:

Get dtypes for iterable datasource.

get_project_db_sqlite_columns​

def get_project_db_sqlite_columns(self) ‑> list:

Inherited from:

BaseSource.get_project_db_sqlite_columns :

Implement this method to get the required columns.

This is used by the "run on new data only" feature. This is used to add data to the task table in the project database.

get_project_db_sqlite_create_table_query​

def get_project_db_sqlite_create_table_query(self) ‑> str:

Inherited from:

BaseSource.get_project_db_sqlite_create_table_query :

Implement this method to return the required columns and types.

This is used by the "run on new data only" feature. This should be in the format that can be used after a "CREATE TABLE" statement and is used to create the task table in the project database.

get_values​

def get_values(self, col_names: list[str], **kwargs: Any) ‑> dict:

Implement this method to get distinct values from list of columns.

load_data​

def load_data(self, **kwargs: Any) ‑> None:

Inherited from:

BaseSource.load_data :

Load the data for the datasource.

Raises

  • TypeError: If data format is not supported.

yield_data​

def yield_data(self, **kwargs: Any) ‑> collections.abc.Iterator:

Implement this method to yield dataframes.

MultiProcessingMixIn​

class MultiProcessingMixIn():

MixIn class for multiprocessing of _get_data.

Subclasses​

Variables​

  • static image_columns : set
  • static skipped_files : set

Static methods​

get_num_workers​

def get_num_workers(file_names: list[str]) ‑> int:

Gets the number of workers to use for multiprocessing.

Ensures that the number of workers is at least 1 and at most equal to MAX_NUM_MULTIPROCESSING_WORKERS. If the number of files is less than MAX_NUM_MULTIPROCESSING_WORKERS, then we use the number of files as the number of workers. Unless the number of machine cores is also less than MAX_NUM_MULTIPROCESSING_WORKERS, in which case we use the lower of the two.

Arguments

  • file_names: The list of file names to load.

Returns The number of workers to use for multiprocessing.

Methods​

use_file_multiprocessing​

def use_file_multiprocessing(self, file_names: list[str]) ‑> bool:

Check if file multiprocessing should be used.

Returns True if file multiprocessing has been enabled by the environment variable and the number of workers would be greater than 1, otherwise False. There is no need to use file multiprocessing if we are just going to use one worker - it would be slower than just loading the data in the main process.

Returns True if file multiprocessing should be used, otherwise False.