Python & ML Snippets Extension

Introduction

The Python & ML Snippets Extension for VS Code is a productivity booster designed to help developers quickly write Python and Machine Learning code snippets with ease. This extension includes a rich collection of commonly used code patterns for both Python and ML projects. It speeds up coding by offering simple, easy-to-use snippets for everyday tasks, such as data manipulation, model training, and visualization.

Whether you're building a machine learning model, performing data preprocessing, or plotting graphs, this extension will save you time by providing quick access to essential code snippets.

Features

• Python Snippets: Includes various Python coding patterns like loops, conditionals, functions, and basic imports.
• Machine Learning Snippets: Features snippets for common tasks in ML workflows such as data splitting, model training, evaluation, and hyperparameter tuning.
• Data Visualization: Easy-to-use snippets for generating various types of plots and charts (e.g., scatter plots, line charts, histograms).
• Quick Start: Add snippets to your code with just a few keystrokes.
• Customization: Edit or extend snippets to suit your coding style and requirements.
• Cross-Project Use: Whether you are working on a Python project or an ML project, the extension provides essential support for both.

Installation

1. Open VS Code.
2. Go to the Extensions view by clicking on the Extensions icon in the Activity Bar on the side of the window.
3. Search for "Python & ML Snippets".
4. Click Install.

Alternatively, you can install directly from the VS Code Marketplace.

Usage

Once installed, you can start using the snippets right away. Here's how:

1. Start typing the snippet's prefix (e.g., forloop, modeltrain, etc.).
2. Press Enter or Tab to insert the corresponding code into your editor.

Example Snippets

1. Python Loop

Prefix: forloop

for i in range(10):
    print(i)

2. Importing Pandas

Prefix: importpandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df)

3. Model Training

Prefix: modeltrain

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

4. SHAP Summary Plot

Prefix: shap

import shap
explainer = shap.TreeExplainer(rf_model)
shap_values = explainer.shap_values(X_train_scaled)
shap.summary_plot(shap_values, X_train_scaled)

5. Scatter Plot

Prefix: scatterplot

import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('Scatter plot of features')
plt.show()

Snippet List

Python Snippets

• Basic print statement (print)
• For loop (forloop)
• While loop (whileloop)
• If-else statement (ifelse)
• F-string formatting (fstring)
• Importing NumPy (importnumpy)
• Importing Pandas (importpandas)

Machine Learning Snippets

• Model Training (modeltrain)
• Model Prediction (modelpredict)
• Confusion Matrix (confmat)
• PCA (pca)
• SMOTE for imbalanced data (smote)
• RandomizedSearchCV for hyperparameter tuning (randomsearch)
• SHAP summary plot (shap)
• Voting Classifier for ensemble models (votingclassifier)
• Save model with joblib (savejoblib)
• Model Evaluation (accuracy, precision, recall, f1-score) (evalmodel)
• Scatter plot (scatterplot)
• Line chart (linechart)
• Bar chart (barplot)
• Histogram (histogram)

Contributing

We welcome contributions from the community! If you'd like to contribute to this extension, feel free to:

1. Fork the repository.
2. Create a new branch.
3. Make your changes or add new snippets.
4. Submit a pull request.

License

This project is licensed under the MIT License - see the (LICENSE) file for details.

Happy Coding! 🚀