pkg-pyknnclassifier 0.0.3

A k-Nearest Neighbors (KNN) classifier for Python.

pkg_pyknnclassifier

📄 About

Our package, named "pkg_pyknnclassifier," is a comprehensive toolkit for k-Nearest Neighbors (k-NN) modeling and evaluation. It offers a set of functions designed to facilitate various aspects of working with k-NN algorithms, from loading the data, calculating distances to making predictions and assessing model performance. We aim to simplify the process by providing essential functionalities for data manipulation, model evaluation, and scaling.

📦 Functions

This package consists of six functions and explained as below:

• data_loading(str_of_path, target_column): This function loads data from a file path and split into features and target.
• scaling(df, impute_strategy, scale_method): This function allows user to choose the method of data imputation and scaling, and apply to the data.
• calculate_distance(obs_1, obs_2, method = "Euclidean"): This function calculates the Euclidean distance between two observations for the KNN model to find the similarity score.
• find_neighbors(labeled_arraies, unlabeled_array, k): This function finds the indices of the 'k' nearest neighbors in a collection of labeled arrays to a given unlabeled array.
• predict(train_X, train_y, unlabel_df, pred_method, k): This function predicts the labels of the unlabled observations based on the similarity score calculated from Euclidean distance.
• evaluate(y_true, y_pred, metric='accuracy'): This function calculates evaluation metrics such as accuracy, precision, recall, and F1 score for a k-NN model based on true labels and predicted labels.

🛠️ Installation

To sucessfully run the following commands of installation, we would need conda and poetry, guide included in the link (conda, poetry)

1. Clone this repository

$ git clone [e-mail]:UBC-MDS/Group16_knnmodel.git

2. Direct to the root of this repository
3. Create a virtual environment in Conda with Python by the following commands at terminal and activate it:

$ conda create --name pyknnclassifier python=3.11 -y

$ conda activate pyknnclassifier

4. Install this package via poetry, run the following command.

$ poetry install

✅ Testing

To test this package, please run the following command from the root directory of the repository:

$ pytest tests/

• branch coverage could be viewed with the following command:

$ pytest --cov-branch --cov=pkg_pyknnclassifier

Usage

To successfully use our knn model to predict the target, please first ensure you have followed the instruction of installation, and then run the following line in a python notebook.

from pkg_pyknnclassifier.data_loading import data_loading
from pkg_pyknnclassifier.scaling import scaling
from pkg_pyknnclassifier.predict import predict
from pkg_pyknnclassifier.evaluate import evaluate

features, target = data_loading('data/toy_dataset.csv', 'Target')
X_scaled = scaling(features, 'median', 'StandardScaler')
pred = predict(X_scaled, target, X_scaled, 'hard', 3)
accuracy_result = evaluate(target, pred, metric='accuracy')
print("Accuracy:", accuracy_result)

📚 Package Integration within the Python Ecosystem

pkg_pyknnclassifier, while acknowledging the robustness and the capabilities of scikit-learn's KNeighborsClassifier, aims to offer a specialized and streamlined toolkit tailored explicitly for k-Nearest Neighbors classification tasks. As a lightweight and focused alternative, pkg_pyknnclassifier serves users who seek a concise package that offers calculating distances, making predictions, and evaluating k-NN models functions. While scikit-learn covers a broader spectrum of machine learning algorithms, pkg_pyknnclassifier provides a more specialized package, potentially appealing to those who prefer a tailored implementation of their k-NN workflows.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

📜 License

pkg_pyknnclassifier was created by "Bill Wan, Sho Inagaki, Shizhe Zhang, Weiran Zhao". It is licensed under the terms of the MIT license.

📚 Credits

pkg_pyknnclassifier was created with cookiecutter and the py-pkgs-cookiecutter template.