This is my first release of the Machine Learning Things package.
From this version onwards I will keep track of the updates.
Installation
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
Current features:
Functions
All function implemented in the ml_things module.
-
Array Functions: Array manipulation related function that can be useful when working with machine learning.
- pad_array: Pad variable length array to a fixed numpy array. It can handle single arrays [1,2,3] or nested arrays [[1,2],[3]].
- batch_array: 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.
-
Plot Functions: Plot related function that can be useful when working with machine learning.
- plot_array: Create plot from a single array of valu
- plot_dict: Create plot from a single array of values.
- plot_confusion_matrix: This function prints and plots the confusion matrix.
-
Text Functions: Text related function that can be useful when working with machine learning.
- clean_text: Clean text using various techniques.
-
Web Related: Web related function that can be useful when working with machine learning.
- download_from: Download file from url. It will return the path of the downloaded file.
Snippets
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. |
| PyTorch-Device | How to setup device in PyTorch to detect if GPU is available. |
Notebooks Tutorials
This is where I keep notebooks of some previous projects which I turnned them into small tutorials. A lot of times I use them as basis for starting a new project.
All of the notebooks are in Google Colab. Never heard of Google Colab? 🙀 You have to check out the Overview of Colaboratory, Introduction to Colab and Python and what I think is a great medium article about it to configure Google Colab Like a Pro.
If you check the /ml_things/notebooks/ a lot of them are not listed here because they are not in a 'polished' form yet. These are the notebooks that are good enough to share with everyone:
| Name | Description | Links |
|---|---|---|
| 🍇 Better Batches with PyTorchText BucketIterator | How to use PyTorchText BucketIterator to sort text data for better batching. |      |
| 🐶 Pretrain Transformers Models in PyTorch using Hugging Face Transformers | Pretrain 67 transformers models on your custom dataset. | |
| 🎻 Fine-tune Transformers in PyTorch using Hugging Face Transformers | Complete tutorial on how to fine-tune 73 transformer models for text classification — no code changes necessary! | |
| ⚙️ Bert Inner Workings in PyTorch using Hugging Face Transformers | Complete tutorial on how an input flows through Bert. | |
| 🎱 GPT2 For Text Classification using Hugging Face 🤗 Transformers | Complete tutorial on how to use GPT2 for text classification. | |