4.7. Storage¶
4.7.1. Description¶
The Storage
is an object that is responsible for storing extracted features
as computed from Marker step of the pipeline. If the pipeline is
provided with a storage-like
object, the extracted features are stored via
that object else they are kept in memory.
Storage is meant to be used inside the DataGrabber context but you can operate on them outside the context as long as the processed data is in the memory and the Python runtime has not garbage-collected it.
The Markers are responsible for defining what storage kind
(matrix
, vector
, timeseries
, scalar_table
) they support for
which data type by overriding its get_output_type
method. The storage object in turn declares and provides implementation for
specific storage kind. For example, SQLiteFeatureStorage
supports
saving matrix
, vector
and timeseries
via store_matrix
,
store_vector
and store_timeseries
methods respectively.
For storage interfaces not supported by junifer
yet, you can either make
your own Storage
by providing a concrete implementation of
BaseFeatureStorage
or open an issue on junifer Github and we can
help you out.
4.7.2. Storage Types¶
Storage Type |
Description |
Options |
Reference |
---|---|---|---|
|
A 2D square matrix with row and column names |
col_names , row_names , matrix_kind , diagonal row_header_col_name (only for
HDF5FeatureStorage.store_matrix() ) |
|
|
A 1D row vector of values with column names |
|
|
|
A 2D square or non-square matrix of scalar values with column names |
|
|
|
A 2D square or non-square matrix of scalar values with row name, column
name and row header column name
|
|
4.7.3. Storage Interfaces¶
Storage class |
File extension |
File type |
Storage kinds |
---|---|---|---|
|
SQLite |
|
|
|
HDF5 |
|