Using Machine Learning to learn how to Compress
Project description
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
andread_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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
shrynk-0.0.18.tar.gz
(2.8 MB
view hashes)
Built Distribution
Close
Hashes for shrynk-0.0.18-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 61a92288a908acf541cdb80f38775c704d5ab71460f6f997acd91414d96f2691 |
|
MD5 | 4441d9781fed8f7eb5f22f74143b6233 |
|
BLAKE2b-256 | 4521b71d17994887263afbb945603614bc013b068195bb524a418b9452270887 |