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gmihaila / repository
This is where I put things I find useful that speed up my work with Machine Learning. Ever looked in your old projects to reuse those cool functions you created before? Well, this repo is designed to be a Python Library of functions I created in my previous project that can be reused. I also share some Notebooks Tutorials and Python Code Snippets.
Machine Learning Things is a lightweight python library that contains functions and code snippets that I use in my everyday research with Machine Learning, Deep Learning, NLP.
I created this repo because I was tired of always looking up same code from older projects and I wanted to gain some experience in building a Python library. By making this available to everyone it gives me easy access to code I use frequently and it can help others in their machine learning work. If you find any bugs or something doesn't make sense please feel free to open an issue.
That is not all! This library also contains Python code snippets and notebooks that speed up my Machine Learning workflow.
Note:
Feb 5, 2022 Thank you all again for your support and kindness! This package is available on pypi now! pip install ml-thingsJuly 16, 2021 Thank you all for your support and kindness! As I promissed I will move this repo to pip install modules.Snippets: Curated list of Python snippets I frequently use.
Comments: Sample on how I like to comment my code. It is still a work in progress.
: Machine learning projects that I converted to tutorials and posted online.
This repo is tested with Python 3.6+.
It's always good practice to install ml_things in a virtual environment. If you guidance on using Python's virtual environments you can check out the user guide here.
You can install ml_things with pip from GitHub:
pip install git+https://github.com/gmihaila/ml_things
Or from pypi:
pip install ml-things
All function implemented in the ml_things module.
Array manipulation related function that can be useful when working with machine learning.
Pad variable length array to a fixed numpy array. It can handle single arrays [1,2,3] or nested arrays [[1,2],[3]].
By default it will padd zeros to the maximum length of row detected:
>>> from ml_things import pad_array
>>> pad_array(variable_length_array=[[1,2],[3],[4,5,6]])
array([[1., 2., 0.],
[3., 0., 0.],
[4., 5., 6.]])
It can also pad to a custom size and with cusotm values:
>>> pad_array(variable_length_array=[[1,2],[3],[4,5,6]], fixed_length=5, pad_value=99)
array([[ 1., 2., 99., 99., 99.],
[ 3., 99., 99., 99., 99.],
[ 4., 5., 6., 99., 99.]])
Split a list into batches/chunks. Last batch size is remaining of list values. Note: This is also called chunking. I call it batches since I use it more in ML.
The last batch will be the reamining values:
>>> from ml_things import batch_array
>>> batch_array(list_values=[1,2,3,4,5,6,7,8,8,9,8,6,5,4,6], batch_size=4)
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 9, 8, 6], [5, 4, 6]]
Plot related function that can be useful when working with machine learning.
Create plot from a single array of values.
All arguments are optimized for quick plots. Change the magnify arguments to vary the size of the plot:
>>> from ml_things import plot_array
>>> plot_array([1,3,5,3,7,5,8,10], path='plot_array.png', magnify=0.1, use_title='A Random Plot', start_step=0.3, step_size=0.1, points_values=True, use_ylabel='Thid', use_xlabel='This')
Create plot from a single array of values.
All arguments are optimized for quick plots. Change the magnify arguments to vary the size of the plot:
>>> from ml_things import plot_dict
>>> plot_dict({'train_acc':[1,3,5,3,7,5,8,10],
'valid_acc':[4,8,9]}, use_linestyles=['-', '--'], magnify=0.1,
start_step=0.3, step_size=0.1,path='plot_dict.png', points_values=[True, False], use_title='Title')
This function prints and plots the confusion matrix. Normalization can be applied by setting normalize=True.
All arguments are optimized for quick plots. Change the magnify arguments to vary the size of the plot:
>>> from ml_things import plot_confusion_matrix
>>> plot_confusion_matrix(y_true=[1,0,1,1,0,1], y_pred=[0,1,1,1,0,1], magnify=0.1, use_title='My Confusion Matrix', path='plot_confusion_matrix.png');
Confusion matrix, without normalization
array([[1, 1],
[1, 3]])
Text related function that can be useful when working with machine learning.
Clean text using various techniques:
>>> from ml_things import clean_text
>>> clean_text("ThIs is $$$%. \t\t\n \\ so dirtyyy$$ text :'(. omg!!!", full_clean=True)
'this is so dirtyyy text omg'
Web related function that can be useful when working with machine learning.
Download file from url. It will return the path of the downloaded file:
>>> from ml_things import download_from
>>> download_from(url='https://raw.githubusercontent.com/gmihaila/ml_things/master/setup.py', path='.')
'./setup.py'
This is a very large variety of Python snippets without a certain theme. I put them in the most frequently used ones while keeping a logical order. I like to have them as simple and as efficient as possible.
| Name | Description |
|---|---|
| Read FIle | One liner to read any file. |
| Write File | One liner to write a string to a file. |
| Debug | Start debugging after this line. |
| Pip Install GitHub | Install library directly from GitHub using pip. |
| Parse Argument | Parse arguments given when running a .py file. |
| Doctest | How to run a simple unittesc using function documentaiton. Useful when need to do unittest inside notebook. |
| Fix Text | Since text data is always messy, I always use it. It is great in fixing any bad Unicode. |
| Current Date | How to get current date in Python. I use this when need to name log files. |
| Current Time | Get current time in Python. |
| Remove Punctuation | The fastest way to remove punctuation in Python3. |
| PyTorch-Dataset | Code sample on how to create a PyTorch Dataset. |
These are a few snippets of how I like to comment my code. I saw a lot of different ways of how people comment their code. One thing is for sure: any comment is better than no comment.
I try to follow as much as I can the PEP 8 — the Style Guide for Python Code.
When I comment a function or class:
# required import for variables type declaration
from typing import List, Optional, Tuple, Dict
def my_function(function_argument: str, another_argument: Optional[List[int]] = None,
another_argument_: bool = True) -> Dict[str, int]
r"""Function/Class main comment.
More details with enough spacing to make it easy to follow.
Arguments:
function_argument (:obj:`str`):
A function argument description.
another_argument (:obj:`List[int]`, `optional`):
This argument is optional and it will have a None value attributed inside the function.
another_argument_ (:obj:`bool`, `optional`, defaults to :obj:`True`):
This argument is optional and it h
Selected from shared topics, language and repository description—not editorial ratings.
Anshika102005 /
This repository is a comprehensive guide to my Machine Learning journey, starting from the fundamentals and gradually progressing to advanced topics. It reflects my current learning phase, where I am exploring concepts step by step and implementing them through Google Colab and Jupyter Notebooks.
Final Note: Being grateful.
| PyTorch-Device | How to setup device in PyTorch to detect if GPU is available. |