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ScratchML

A Python library called ScratchML was created to build the most fundamental Machine Learning models from scratch (using only Numpy), emphasizing producing user-friendly, straightforward, easy-to-use, well-organized implementations for novices and enthusiasts.

Disclaimer: This library is not intended to surpass those that already exist and which are better, more optimized, and with more diversity of implemented algorithms (such as scikit-learn, PyTorch, Keras, and Tensorflow), but rather to provide code that is easier to understand, simple, and friendly for beginners and enthusiasts in the field of artificial intelligence who wish to gain a deeper understanding of how algorithms work or who want to contribute to an open-source repository.

Installation

To install this package, first clone the repository to the directory of your choice using the following command:

git clone https://github.com/rafaelgreca/scratchml.git

Using Virtual Environment

Create a virtual environment (ideally using conda) and install the requirements with the following command:

conda create --name scratchml python=3.11.9
conda activate scratchml
pip install -r requirements/requirements.txt

Using Docker

Build the Docker image using the following command:

sudo docker build -f Dockerfile -t scratchml . --no-cache

Run the Docker container using the following command:

sudo docker run -d -p 8000:5000 --name scratchml scratchml

Usage/Examples

See the examples folder to see some use cases.

Running Tests

Locally

Run the following command on the root folder:

python3 -m unittest discover -p 'test_*.py'

Using Docker

Build the Docker image using the following command:

sudo docker build -f test.Dockerfile -t test_scratchml . --no-cache

Run the Docker container using the following command:

sudo docker run -d -p 8001:5000 --name test_scratchml test_scratchml

Roadmap

Implementations:

• [x] Scalers
  • [x] StandardScaler
  • [x] MinMaxScaler
• [ ] Regularizations
  • [x] L1
  • [x] L2
  • [ ] Batch Normalization
• [x] Activation functions
  • [x] Sigmoid
  • [x] ReLU
  • [x] Linear
  • [x] Softmax
  • [x] TanH
  • [x] ELU
  • [x] Leaky ReLU
  • [x] SoftPlus
  • [x] SELU
• [x] Loss functions
  • [x] Binary Cross Entropy
  • [x] Cross Entropy
• [x] Metrics
  • [x] Regression Metrics
    • [x] Mean Squared Error (MSE)
    • [x] Root Mean Squared Error (RMSE)
    • [x] Mean Absolute Error (MAE)
    • [x] Median Absolute Error (MedAE)
    • [x] Mean Absolute Percentage Error (MAPE)
    • [x] Mean Squared Logarithmic Error (MSLE)
    • [x] Max Error (ME)
    • [x] R Squared (R2)
  • [x] Classification Metrics
    • [x] Accuracy
    • [x] Precision
    • [x] Recall
    • [x] F1-Score
    • [x] Confusion Matrix
    • [x] ROC AUC Score
    • [x] False Positive Rate (FPR)
    • [x] True Positive Rate (TPR)
• [x] Distances
  • [x] Euclidean
  • [x] Manhattan
  • [x] Chebyshev
  • [x] Minkowski
• [x] Encoders
  • [x] One-hot Encoding
  • [x] Label Encoding
• [x] Splitters
  • [x] KFold
  • [x] Stratify KFold
  • [x] Train Test Split
  • [x] Split Into Batches
• [ ] Models
  • [x] Linear Regression
  • [x] Logistic Regression
  • [ ] SVM
    • [x] SVC
    • [ ] SRV
  • [x] KNN
    • [x] KNN Classifier
    • [x] KNN Regressor
  • [x] Naive Bayes
  • [x] Random Forest
    • [x] Random Forest Classifier
    • [x] Random Forest Regressor
  • [x] Decision Tree
    • [x] Decision Tree Classifier
    • [x] Decision Tree Regressor
  • [x] Perceptron
  • [x] MLP
    • [x] MLP Classifier
    • [x] MLP Regressor
  • [x] KMeans
  • [x] PCA

Feedback

If you have any feedback, please feel free to create an issue pointing out whatever you want or reach out to me at rgvieira97@gmail.com

Contributing

Contributions are what makes the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please read carefully the Contributing Guide and the Code of Conduct before contributing.

Acknowledge

We would like to thank all these amazing materials and repositories for their amazing work, which indirectly contributed in some sort or that inspired us to create this project.

• [REPOSITORY] SKADI by Douglas Oliveira
• [REPOSITORY] ML From Scratch by Erik Linder-Norén
• [REPOSITORY] Machine Learning from Scratch by AssemblyAI
• [COURSE] Machine Learning Specialization by Andrew Ng
• [COURSE] Machine Learning From Scratch by AssemblyAI

License

Distributed under the MIT License. See LICENSE for more information.

Authors

A huge shoutout to everyone who contributed to the success of the project. Check everyone here!.