https://github.com/is-leeroy-jenkins/sake
Sake is your go-to, modular machine learning framework for Budget Execution data analysis built in Python with Scikit, XGBoost, PyTorch, and TensorFlow.
https://github.com/is-leeroy-jenkins/sake
bud federal-government finance generative-ai mach
Last synced: about 2 months ago
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Sake is your go-to, modular machine learning framework for Budget Execution data analysis built in Python with Scikit, XGBoost, PyTorch, and TensorFlow.
- Host: GitHub
- URL: https://github.com/is-leeroy-jenkins/sake
- Owner: is-leeroy-jenkins
- Created: 2025-04-06T14:40:10.000Z (2 months ago)
- Default Branch: master
- Last Pushed: 2025-04-06T15:20:05.000Z (2 months ago)
- Last Synced: 2025-04-06T15:41:09.211Z (2 months ago)
- Topics: bud, federal-government, finance, generative-ai, mach
- Language: Jupyter Notebook
- Homepage:
- Size: 11.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
#### Sake
- Sake is your go-to, modular machine learning framework for Budget Execution data analysis built in Python with **Scikit**, **XGBoost**, **PyTorch**, and **TensorFlow**. Designed for rapid experimentation, visualization, and benchmarking of both **classification** and **regression** models, it provides a structured yet extensible workflow that’s equally useful for teaching, prototyping, and real-world application development.
## 🔬 Data Source
- File A (Account Balances) published monthly by agencies on [USASpending](https://www.usaspending.gov/download_center/custom_account_data?about-the-data=file-a)
- Required by the DATA Act.
- Pulled automatically from data in the [Governmentwide Treasury Account Symbol Adjusted Trial Balance System (GTAS)](https://www.fiscal.treasury.gov/gtas/)
- Contains Budgetary resources, obligation, and outlay data for all the relevant [Treasury Account Symbols (TAS)](https://tfx.treasury.gov/taxonomy/term/10257) in a reporting agency.
- It includes both award and non-award spending (grouped together), and crosswalks with the SF 133 report.
## 🚀 Features
### 🔄 Unified Evaluation Pipeline
Easily run multiple models through a single function `train_and_evaluate()`, which handles:
- Model training
- Accuracy computation
- Confusion matrix generation (for classifiers)
- Performance reporting (classification or regression metrics)
### 🧠 Dual Model Support
Out-of-the-box support for both:
- **Classification models** such as Logistic Regression, SVM, Random Forest, XGBoost
- **Regression models** such as Linear Regression, Ridge, SVR, Gradient Boosting
### 📊 Visual Performance Reports
- Heatmaps of confusion matrices
- Auto-generated `classification_report` with precision, recall, F1-score
- Regression summary with metrics like MAE, MSE, R²
- Tabular performance summary across all models
### 📁 Custom Dataset Integration
- Use default Scikit-Learn datasets or plug in your own CSV
- Built-in support for label encoding and numeric feature conversion
- Easy integration with Pandas for pre-processing pipelines
### 🧠 Deep Learning Ready
- Expandable with PyTorch and TensorFlow architectures
- Importable modules for CNNs, RNNs, and Transformers
### 🧪 Educational & Research Utility
- Ideal for teaching ML fundamentals in a comparative format
- Benchmarking for internal ML pipelines and research reproducibility
---
## 🧠 Classification Models
| Model | Module |
|------------------------|-------------------------------|
| Logistic Regression | `sklearn.linear_model` |
| Support Vector Machine | `sklearn.svm` |
| Decision Tree | `sklearn.tree` |
| Random Forest | `sklearn.ensemble` |
| k-Nearest Neighbors | `sklearn.neighbors` |
| Gaussian Naive Bayes | `sklearn.naive_bayes` |
| XGBoost Classifier | `xgboost.XGBClassifier` |
---
## 📉 Regression Models
| Model | Module |
|-----------------------------|--------------------------------------|
| Linear Regression | `sklearn.linear_model.LinearRegression` |
| Ridge Regression | `sklearn.linear_model.Ridge` |
| Support Vector Regressor | `sklearn.svm.SVR` |
| Decision Tree Regressor | `sklearn.tree.DecisionTreeRegressor` |
| Random Forest Regressor | `sklearn.ensemble.RandomForestRegressor` |
| Gradient Boosting Regressor | `sklearn.ensemble.GradientBoostingRegressor` |
| k-NN Regressor | `sklearn.neighbors.KNeighborsRegressor` |
___
## 📦 Dependencies
| Package | Description | Link |
|------------------|------------------------------------------------------------------|-------------------------------------------------------|
| numpy | Numerical computing library | [numpy.org](https://numpy.org/) |
| pandas | Data manipulation and DataFrames | [pandas.pydata.org](https://pandas.pydata.org/) |
| matplotlib | Plotting and visualization | [matplotlib.org](https://matplotlib.org/) |
| seaborn | Statistical data visualization | [seaborn.pydata.org](https://seaborn.pydata.org/) |
| scikit-learn | ML modeling and metrics | [scikit-learn.org](https://scikit-learn.org/stable/) |
| xgboost | Gradient boosting framework (optional) | [xgboost.readthedocs.io](https://xgboost.readthedocs.io/) |
| torch | PyTorch deep learning library | [pytorch.org](https://pytorch.org/) |
| tensorflow | End-to-end ML platform | [tensorflow.org](https://www.tensorflow.org/) |
| openai | OpenAI’s Python API client | [openai-python](https://github.com/openai/openai-python) |
| requests | HTTP requests for API and web access | [requests.readthedocs.io](https://requests.readthedocs.io/) |
| PySimpleGUI | GUI framework for desktop apps | [pysimplegui.readthedocs.io](https://pysimplegui.readthedocs.io/) |
| typing | Type hinting standard library | [typing Docs](https://docs.python.org/3/library/typing.html) |
| pyodbc | ODBC database connector | [pyodbc GitHub](https://github.com/mkleehammer/pyodbc) |
| fitz | PDF document parser via PyMuPDF | [pymupdf](https://pymupdf.readthedocs.io/) |
| pillow | Image processing library | [python-pillow.org](https://python-pillow.org/) |
| openpyxl | Excel file processing | [openpyxl Docs](https://openpyxl.readthedocs.io/) |
| soundfile | Read/write sound file formats | [pysoundfile](https://pysoundfile.readthedocs.io/) |
| sounddevice | Audio I/O interface | [sounddevice Docs](https://python-sounddevice.readthedocs.io/) |
| loguru | Structured, elegant logging | [loguru GitHub](https://github.com/Delgan/loguru) |
| statsmodels | Statistical tests and regression diagnostics | [statsmodels.org](https://www.statsmodels.org/) |
| dotenv | Load environment variables from `.env` | [python-dotenv GitHub](https://github.com/theskumar/python-dotenv) |
| python-dotenv | Same as above (modern usage) | [python-dotenv](https://saurabh-kumar.com/python-dotenv/) |
---
## 🧪 How to Run
```bash
git clone https://github.com/your-username/balance-projector.git
cd balance-projector
pip install -r requirements.txt
jupyter notebook balances.ipynb
```
---
### 📁 Customize Dataset
Replace dataset ingestion cell with:
```python
import pandas as pd
df = pd.read_csv("your_dataset.csv")
X = df.drop("target_column", axis=1)
y = df["target_column"]
```
---
### 📊 Outputs
- R², MAE, MSE for each model
- Bar plots of performance scores
- Visual predicted vs. actual scatter charts
- Residual error analysis
---
## 🔮 Roadmap
- [ ] Add time series models (Prophet, ARIMA)
- [ ] Integrate GridSearchCV for model tuning
- [ ] SHAP-based interpretability
- [ ] Flask/FastAPI API for deploying forecasts
- [ ] LLM summarization of forecast outcomes
---
## 🤝 Contributing
1. 🍴 Fork the project
2. 🔧 Create a branch: `git checkout -b feat/new-feature`
3. ✅ Commit and push changes
4. 📬 Submit a pull request
---
## 📜 License
This project is licensed under the **MIT License**.
---