# TinyShift

**TinyShift** is a small experimental Python library designed to detect **data drifts** and **performance drops** in machine learning models over time. The main goal of the project is to provide quick and tiny monitoring tools to help identify when data or model performance unexpectedly change.
For more robust solutions, I highly recommend [Nannyml.](https://github.com/NannyML/nannyml)

## Technologies Used

- **Python 3.x**
- **Scikit-learn**
- **Pandas**
- **NumPy**
- **Plotly**
- **Scipy**

## Installation

To install **TinyShift** in your development environment, use **pip**:


```bash
pip install tinyshift
```
If you prefer to clone the repository and install manually:
```bash
git clone https://github.com/HeyLucasLeao/tinyshift.git
cd tinyshift    
pip install .
```

## Usage
Below are basic examples of how to use TinyShift's features.
### 1. Data Drift Detection
To detect data drift, simply score in a new dataset to compare with the reference data. The DataDriftDetector will calculate metrics to identify significant differences.

```python
from tinyshift.detector import CategoricalDriftDetector

df = pd.DataFrame("examples.csv")
df_reference = df[(df["datetime"] < '2024-07-01')].copy()
df_analysis = df[(df["datetime"] >= '2024-07-01')].copy()

detector = CategoricalDriftDetector(df_reference, 'discrete_1', "datetime", "W", drift_limit='mad')

analysis_score = detector.score(df_analysis, "discrete_1", "datetime")

print(analysis_score)
```

### 2. Performance Tracker
To track model performance over time, use the PerformanceMonitor, which will compare model accuracy on both old and new data.
```python
from tinyshift.tracker import PerformanceTracker

df_reference = pd.read_csv('refence.csv')
df_analysis = pd.read_csv('analysis.csv')
model = load_model('model.pkl') 
df_analysis['prediction'] = model.predict(df_analysis["feature_0"])

tracker = PerformanceTracker(df_reference, 'target', 'prediction', 'datetime', "W")

analysis_score = tracker.score(df_analysis, 'target', 'prediction', 'datetime')

print(analysis_score)
```

### 3. Visualization
TinyShift also provides graphs to visualize the magnitude of drift and performance changes over time.
```python
tracker.plot.scatter(analysis_score, fig_type="png")

tracker.plot.bar(analysis_score, fig_type="png")
```

## Project Structure
The basic structure of the project is as follows:
```
tinyshift
├── LICENSE
├── README.md
├── example.ipynb
├── pyproject.toml
└── tinyshift
    ├── base
    │   ├── __init__.py
    │   └── model.py
    ├── detector
    │   ├── __init__.py
    │   ├── categorical.py
    │   └── continuous.py
    ├── plot
    │   ├── __init__.py
    │   └── plot.py
    └── tracker
        ├── __init__.py
        └── performance.py          
```

### License
This project is licensed under the MIT License - see the LICENSE file for more details.
