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Using Machine Learning to learn how to Compress ⚡️
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Latest commit de670b7 Oct 31, 2019

README.md

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Try it live at https://shrynk.ai

Features

  • ✓ Compress your data smartly based on Machine Learning
  • ✓ Takes User Requirements in the form of weights for size, write_time and read_time
  • ✓ Trains & caches a model based on compression methods available in the system using packaged data
  • CLI for compressing and decompressing

CLI

shrynk compress myfile.json       # will yield e.g. myfile.json.gz or myfile.json.bz2
shrynk decompress myfile.json.gz  # will yield myfile.json

shrynk compress myfile.csv --size 0 --write 1 --read 0

shrynk benchmark myfile.csv                  # shows benchmark results
shrynk benchmark --predict myfile.csv        # will also show the current prediction
shrynk benchmark --save --predict myfile.csv # will add the result to the training data too

Usage

Installation:

pip install shrynk

Then in Python:

from shrynk import save, load
file_path = save(my_df, "mypath.csv")
# e.g. mypath.csv.bz2
loaded_df = load(file_path)

Add your own data

If you want more control you can do the following:

import pandas as pd
from shrynk import PandasCompressor

df = pd.DataFrame({"a": [1, 2, 3]})

pdc = PandasCompressor("default")
pdc.run_benchmarks(df) # adds data to the default

pdc.train_model(size=3, write=1, read=1)

pdc.predict(df)
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