ra-pickles 0.8.5

A simple package that allows creating a pickles dataset and fast random access

RA Pickles

In many applications, elements from a large dataset, that cannot be stored in memory, need to be sampled (with or without replacement). This package simplifies this process by allowing random access to elements in large datasets. Most python objects as dataset elements can be used as pickles are used at the backend.

A dataset will be represented by a directory, stored in combination of meta and data files. As file identifiers are randomly generated, two datasets can be added to each other create a larger dataset without additional work. This also allows multiple applications to add elements to the dataset in parallel.

Example

First import all the packages

from ra_pickles import RandomAccessPicklesReader, RandomAccessPicklesWriter
import shutil
import os
import numpy as np

So to create a dataset in test_folder, use the following code. 100 here specifies number of dataset samples in each file. If there are more than 100 files, multiple files will be created.

fold = 'test_folder'
writer = RandomAccessPicklesWriter(100, fold)

Now entries to the dataset can be added as follows:

for i in range(30):
    # d is the dataset element which can be any python object
    d = np.ones(4) * i
    
    # and you add it to the dataset
    writer.add(d)
writer.close()

Here, 30 numpy arrays have been added.

To access contents in the dataset, use the following code:

reader = RandomAccessPicklesReader(fold)
print("Total", reader.get_total())

print("Trying to retreive")
for i in range(30):
    print("Retrieving",i)
    a = reader.get_element(i)
    print(i, a[0])

Multiple elements can also be retrieved in parallel for fast access. To do so, first, retrieval threads must be started (and closed at the end):

reader.start_parallel_retrieval_threads(n_theads=5)
data = reader.get_multi_in_parallel([1,2,3])
reader.close_parallel_retrieval_threads()
reader.close()

Good luck!