Machine learning baseline prototyping tools
Project description
ShortcutML
Description
Sometimes, we don’t want to pay attention to detail very much in some machine learning phase. We are in a hurry and only need to prototype our ideas as fast as possible. Then, this library is for you. It’s not making any magic optimization, but it’s summarising all code that maybe you would write when in this kind of situation. This library will be fully customizable from model selection until scoring metrics in the future.
Installation
Via pip
# Install via pip
$ pip install shortcutml
Via git clone
# Clone the repo
$ git clone https://github.com/SulthanAbiyyu/ShortcutML
# Change working dir to shortcutml
$ cd shortcutml
# Install requirements
$ python3 -m pip install -r requirements.txt
# Run one-time setup
$ python3 install.py
Usage
BaselineModel
from shortcutml.model_selection import BaselineModel
bm = BaselineModel(type="regression") # other type option: "classification"
# Evaluate baseline model
bm.evaluate(X_train, X_test, y_train, y_test)
# Plot result
bm.plot_baseline()
# Result dataframe
bm.test_result
# Model lists
bm.classification_models()
bm.regression_models()
# Still not support custom model and scoring
# Default scoring for classification tasks is f1 score and RMSE for regression
TextCleaningIndo
from shortcutml.preprocessing import TextCleaningIndo
tci = TextCleaningIndo()
# Applying all preprocessing process
df["text"] = df["text"].apply(tci.all_preprocessing)
AutoSearchCV
from shortcutml.model_selection import AutoSearchCV
search = AutoSearchCV(model, type="grid") # other type option: "random"
search.search(X,y)
search.cv_results_
Project Plan
- BaselineModel
- TextCleaningIndo
- TextCleaningEnglish
- Fully customizable component
- FeatureSelection -> Pearson, Lasso, Chi Squared, ..
- AutoSearchCV -> Random and Grid
- ..
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Sulthan Abiyyu,
16 January 2022
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file shortcutml-0.8.tar.gz
.
File metadata
- Download URL: shortcutml-0.8.tar.gz
- Upload date:
- Size: 9.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | bae8d9c360c574e24c2635c19651194fb4262b07a0081def876abccba8dcad31 |
|
MD5 | d79267d01f6d1c01370626dfeb4a0485 |
|
BLAKE2b-256 | 4af09a549b302318ff2a78b167fdd421b1bf7ca1ff3cc27d3b7db74d0fbbd356 |
File details
Details for the file shortcutml-0.8-py3-none-any.whl
.
File metadata
- Download URL: shortcutml-0.8-py3-none-any.whl
- Upload date:
- Size: 9.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ae8f59017f27a99ab79b421dfcdcab42318c54f8c6c82a5342b0d3dd095ecff1 |
|
MD5 | c6e394655b5bac6238216efa8aeff6d9 |
|
BLAKE2b-256 | 82c2f0699de8e870a9a1268849aa431484942b6ffb6f9cbc7922f729c56b0bc2 |