| Name | tinyshift JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | A small toolbox for mlops |
| upload_time | 2025-11-04 15:39:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
mlops
toolbox
machine-learning
|
| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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| coveralls test coverage |
No coveralls.
|
# TinyShift
<p align="center">
<img src="https://github.com/user-attachments/assets/34668d33-459d-4dc3-b598-342130bf7db3" alt="tinyshift_full_logo" width="400" height="400">
</p>
**TinyShift** is a lightweight, sklearn-compatible Python library designed for **data drift detection**, **outlier identification**, and **MLOps monitoring** in production machine learning systems. The library provides modular, easy-to-use tools for detecting when data distributions or model performance change over time, with comprehensive visualization capabilities.
For enterprise-grade solutions, consider [Nannyml](https://github.com/NannyML/nannyml).
## Features
- **Data Drift Detection**: Categorical and continuous data drift monitoring with multiple distance metrics
- **Outlier Detection**: **HBOS**, **PCA-based** and **SPAD** outlier detection algorithms
- **Time Series Analysis**: Seasonality decomposition, trend analysis, and forecasting diagnostics
## Technologies Used
- **Python 3.10+**
- **Scikit-learn 1.3.0+**
- **Pandas 2.3.0+**
- **NumPy**
- **SciPy**
- **Statsmodels 0.14.5+**
- **Plotly 5.22.0+** (optional, for plotting)
## 📦 Installation
Install TinyShift using pip:
```bash
pip install tinyshift
```
### Development Installation
Clone and install from source:
```bash
git clone https://github.com/HeyLucasLeao/tinyshift.git
cd tinyshift
pip install -e .
```
## 📖 Quick Start
### 1. Categorical Data Drift Detection
TinyShift provides sklearn-compatible drift detectors that follow the familiar `fit()` and `score()` pattern:
```python
import pandas as pd
from tinyshift.drift import CatDrift
# Load your data
df = pd.read_csv("data.csv")
reference_data = df[df["date"] < '2024-07-01']
analysis_data = df[df["date"] >= '2024-07-01']
# Initialize and fit the drift detector
detector = CatDrift(
freq="D", # Daily frequency
func="chebyshev", # Distance metric
drift_limit="auto", # Automatic threshold detection
method="expanding" # Comparison method
)
# Fit on reference data
detector.fit(reference_data)
# Score new data for drift
drift_scores = detector.predict(analysis_data)
print(drift_scores)
```
Available distance metrics for **categorical** data:
- `"chebyshev"`: Maximum absolute difference between distributions
- `"jensenshannon"`: Jensen-Shannon divergence
- `"psi"`: Population Stability Index
### 2. Continuous Data Drift Detection
For numerical features, use the continuous drift detector:
```python
from tinyshift.drift import ConDrift
# Initialize continuous drift detector
detector = ConDrift(
freq="W", # Weekly frequency
func="ws", # Wasserstein distance
drift_limit="auto",
method="expanding"
)
# Fit and score
detector.fit(reference_data)
drift_scores = detector.score(analysis_data)
```
### 3. Outlier Detection
TinyShift includes sklearn-compatible outlier detection algorithms:
```python
from tinyshift.outlier import SPAD, HBOS, PCAReconstructionError
# SPAD (Simple Probabilistic Anomaly Detector)
spad = SPAD(plus=True)
spad.fit(X_train)
outlier_scores = spad.decision_function(X_test)
outlier_labels = spad.predict(X_test)
# HBOS (Histogram-Based Outlier Score)
hbos = HBOS(dynamic_bins=True)
hbos.fit(X_train, nbins="fd")
scores = hbos.decision_function(X_test)
# PCA-based outlier detection
pca_detector = PCAReconstructionError()
pca_detector.fit(X_train)
pca_scores = pca_detector.decision_function(X_test)
```
### 4. Time Series Analysis and Diagnostics
TinyShift provides time series analysis capabilities:
```python
from tinyshift.plot import seasonal_decompose
from tinyshift.series import trend_significance, permutation_auto_mutual_information
# Seasonal decomposition with multiple periods
seasonal_decompose(
time_series,
periods=[7, 365], # Weekly and yearly patterns
width=1200,
height=800
)
# Test for significant trends
trend_result = trend_significance(time_series, alpha=0.05)
print(f"Significant trend: {trend_result}")
# Stationary Analysis
fig = stationarity_analysis(time_series)
```
### 5. Advanced Modeling Tools
```python
from tinyshift.modelling import filter_features_by_vif
from tinyshift.stats import bootstrap_bca_interval
# Detect multicollinearity
mask = filter_features_by_vif(X, trehshold=5, verbose=True)
X.columns[mask]
# Bootstrap confidence intervals
confidence_interval = bootstrap_bca_interval(
data,
statistic=np.mean,
alpha=0.05,
n_bootstrap=1000
)
```
## 📁 Project Structure
```
tinyshift/
├── association_mining/ # Market basket analysis tools
│ ├── analyzer.py # Transaction pattern analysis
│ └── encoder.py # Data encoder
├── drift/ # Data drift detection
│ ├── base.py # Base drift detection classes
│ ├── categorical.py # CatDrift for categorical features
│ └── continuous.py # ConDrift for numerical features
├── examples/ # Jupyter notebook examples
│ ├── drift.ipynb # Drift detection examples
│ ├── outlier.ipynb # Outlier detection demos
│ ├── series.ipynb # Time series analysis
│ └── transaction_analyzer.ipynb
├── modelling/ # ML modeling utilities
│ ├── multicollinearity.py # VIF-based multicollinearity detection
│ ├── residualizer.py # Residualizer Feature
│ └── scaler.py # Custom scaling transformations
├── outlier/ # Outlier detection algorithms
│ ├── base.py # Base outlier detection classes
│ ├── hbos.py # Histogram-Based Outlier Score
│ ├── pca.py # PCA-based outlier detection
│ └── spad.py # Simple Probabilistic Anomaly Detector
├── plot/ # Visualization capabilities
│ ├── correlation.py # Correlation analysis plots
│ └── diagnostic.py # Time series diagnostics plots
├── series/ # Time series analysis tools
│ ├── forecastability.py # Forecast quality metrics
│ ├── outlier.py # Time series outlier detection
│ └── stats.py # Statistical analysis functions
└── stats/ # Statistical utilities
├── bootstrap_bca.py # Bootstrap confidence intervals
├── statistical_interval.py # Statistical interval estimation
└── utils.py # General statistical utilities
```
```
tinyshift
├── LICENSE
├── README.md
├── poetry.lock
├── pyproject.toml
├── tinyshift
│ ├── association_mining
│ │ ├── README.md
│ │ ├── __init__.py
│ │ ├── analyzer.py
│ │ └── encoder.py
│ ├── examples
│ │ ├── outlier.ipynb
│ │ ├── tracker.ipynb
│ │ └── transaction_analyzer.ipynb
│ ├── modelling
│ │ ├── __init__.py
│ │ ├── multicollinearity.py
│ │ ├── residualizer.py
│ │ └── scaler.py
│ ├── outlier
│ │ ├── README.md
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── hbos.py
│ │ ├── pca.py
│ │ └── spad.py
│ ├── plot
│ │ ├── __init__.py
│ │ ├── correlation.py
│ │ └── plot.py
│ ├── series
│ │ ├── README.md
│ │ ├── __init__.py
│ │ ├── forecastability.py
│ │ ├── outlier.py
│ │ └── stats.py
│ ├── stats
│ │ ├── __init__.py
│ │ ├── bootstrap_bca.py
│ │ ├── series.py
│ │ ├── statistical_interval.py
│ │ └── utils.py
│ ├── tests
│ │ ├── test.pca.py
│ │ ├── test_hbos.py
│ │ └── test_spad.py
│ └── drift
│ ├── __init__.py
│ ├── base.py
│ ├── categorical.py
│ ├── continuous.py
```
### Development Setup
```bash
git clone https://github.com/HeyLucasLeao/tinyshift.git
cd tinyshift
pip install -e ".[all]"
```
## 📋 Requirements
- **Python**: 3.10+
- **Core Dependencies**:
- pandas (>2.3.0)
- scikit-learn (>1.3.0)
- statsmodels (>=0.14.5)
- **Optional Dependencies**:
- plotly (>5.22.0) - for visualization
- kaleido (<=0.2.1) - for static plot export
- nbformat (>=5.10.4) - for notebook support
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Inspired by [Nannyml](https://github.com/NannyML/nannyml)
Raw data
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"description": "# TinyShift\n<p align=\"center\">\n <img src=\"https://github.com/user-attachments/assets/34668d33-459d-4dc3-b598-342130bf7db3\" alt=\"tinyshift_full_logo\" width=\"400\" height=\"400\">\n</p>\n**TinyShift** is a lightweight, sklearn-compatible Python library designed for **data drift detection**, **outlier identification**, and **MLOps monitoring** in production machine learning systems. The library provides modular, easy-to-use tools for detecting when data distributions or model performance change over time, with comprehensive visualization capabilities.\n\nFor enterprise-grade solutions, consider [Nannyml](https://github.com/NannyML/nannyml).\n\n## Features\n\n- **Data Drift Detection**: Categorical and continuous data drift monitoring with multiple distance metrics\n- **Outlier Detection**: **HBOS**, **PCA-based** and **SPAD** outlier detection algorithms \n- **Time Series Analysis**: Seasonality decomposition, trend analysis, and forecasting diagnostics\n\n## Technologies Used\n\n- **Python 3.10+** \n- **Scikit-learn 1.3.0+**\n- **Pandas 2.3.0+** \n- **NumPy**\n- **SciPy**\n- **Statsmodels 0.14.5+**\n- **Plotly 5.22.0+** (optional, for plotting)\n\n## \ud83d\udce6 Installation\n\nInstall TinyShift using pip:\n\n```bash\npip install tinyshift\n```\n\n### Development Installation\n\nClone and install from source:\n\n```bash\ngit clone https://github.com/HeyLucasLeao/tinyshift.git\ncd tinyshift\npip install -e .\n```\n\n## \ud83d\udcd6 Quick Start\n\n### 1. Categorical Data Drift Detection\n\nTinyShift provides sklearn-compatible drift detectors that follow the familiar `fit()` and `score()` pattern:\n\n```python\nimport pandas as pd\nfrom tinyshift.drift import CatDrift\n\n# Load your data\ndf = pd.read_csv(\"data.csv\")\nreference_data = df[df[\"date\"] < '2024-07-01']\nanalysis_data = df[df[\"date\"] >= '2024-07-01'] \n\n# Initialize and fit the drift detector\ndetector = CatDrift(\n freq=\"D\", # Daily frequency\n func=\"chebyshev\", # Distance metric\n drift_limit=\"auto\", # Automatic threshold detection\n method=\"expanding\" # Comparison method\n)\n\n# Fit on reference data\ndetector.fit(reference_data)\n\n# Score new data for drift\ndrift_scores = detector.predict(analysis_data)\nprint(drift_scores)\n```\n\nAvailable distance metrics for **categorical** data:\n- `\"chebyshev\"`: Maximum absolute difference between distributions\n- `\"jensenshannon\"`: Jensen-Shannon divergence \n- `\"psi\"`: Population Stability Index\n\n### 2. Continuous Data Drift Detection\n\nFor numerical features, use the continuous drift detector:\n\n```python\nfrom tinyshift.drift import ConDrift\n\n# Initialize continuous drift detector\ndetector = ConDrift(\n freq=\"W\", # Weekly frequency \n func=\"ws\", # Wasserstein distance\n drift_limit=\"auto\",\n method=\"expanding\"\n)\n\n# Fit and score\ndetector.fit(reference_data)\ndrift_scores = detector.score(analysis_data)\n```\n\n### 3. Outlier Detection\n\nTinyShift includes sklearn-compatible outlier detection algorithms:\n\n```python\nfrom tinyshift.outlier import SPAD, HBOS, PCAReconstructionError\n\n# SPAD (Simple Probabilistic Anomaly Detector)\nspad = SPAD(plus=True)\nspad.fit(X_train)\n\noutlier_scores = spad.decision_function(X_test)\noutlier_labels = spad.predict(X_test)\n\n# HBOS (Histogram-Based Outlier Score)\nhbos = HBOS(dynamic_bins=True)\nhbos.fit(X_train, nbins=\"fd\")\nscores = hbos.decision_function(X_test)\n\n# PCA-based outlier detection\npca_detector = PCAReconstructionError()\npca_detector.fit(X_train)\npca_scores = pca_detector.decision_function(X_test)\n```\n### 4. Time Series Analysis and Diagnostics\n\nTinyShift provides time series analysis capabilities:\n\n```python\nfrom tinyshift.plot import seasonal_decompose\nfrom tinyshift.series import trend_significance, permutation_auto_mutual_information\n\n# Seasonal decomposition with multiple periods\nseasonal_decompose(\n time_series, \n periods=[7, 365], # Weekly and yearly patterns\n width=1200, \n height=800\n)\n\n# Test for significant trends\ntrend_result = trend_significance(time_series, alpha=0.05)\nprint(f\"Significant trend: {trend_result}\")\n\n# Stationary Analysis\nfig = stationarity_analysis(time_series)\n```\n\n### 5. Advanced Modeling Tools\n\n```python\nfrom tinyshift.modelling import filter_features_by_vif\nfrom tinyshift.stats import bootstrap_bca_interval\n\n# Detect multicollinearity\nmask = filter_features_by_vif(X, trehshold=5, verbose=True)\nX.columns[mask]\n\n# Bootstrap confidence intervals\nconfidence_interval = bootstrap_bca_interval(\n data, \n statistic=np.mean, \n alpha=0.05, \n n_bootstrap=1000\n)\n```\n\n## \ud83d\udcc1 Project Structure\n\n```\ntinyshift/\n\u251c\u2500\u2500 association_mining/ # Market basket analysis tools\n\u2502 \u251c\u2500\u2500 analyzer.py # Transaction pattern analysis\n\u2502 \u2514\u2500\u2500 encoder.py # Data encoder\n\u251c\u2500\u2500 drift/ # Data drift detection \n\u2502 \u251c\u2500\u2500 base.py # Base drift detection classes \n\u2502 \u251c\u2500\u2500 categorical.py # CatDrift for categorical features\n\u2502 \u2514\u2500\u2500 continuous.py # ConDrift for numerical features\n\u251c\u2500\u2500 examples/ # Jupyter notebook examples\n\u2502 \u251c\u2500\u2500 drift.ipynb # Drift detection examples\n\u2502 \u251c\u2500\u2500 outlier.ipynb # Outlier detection demos\n\u2502 \u251c\u2500\u2500 series.ipynb # Time series analysis\n\u2502 \u2514\u2500\u2500 transaction_analyzer.ipynb\n\u251c\u2500\u2500 modelling/ # ML modeling utilities\n\u2502 \u251c\u2500\u2500 multicollinearity.py # VIF-based multicollinearity detection\n\u2502 \u251c\u2500\u2500 residualizer.py # Residualizer Feature\n\u2502 \u2514\u2500\u2500 scaler.py # Custom scaling transformations\n\u251c\u2500\u2500 outlier/ # Outlier detection algorithms\n\u2502 \u251c\u2500\u2500 base.py # Base outlier detection classes\n\u2502 \u251c\u2500\u2500 hbos.py # Histogram-Based Outlier Score\n\u2502 \u251c\u2500\u2500 pca.py # PCA-based outlier detection \n\u2502 \u2514\u2500\u2500 spad.py # Simple Probabilistic Anomaly Detector\n\u251c\u2500\u2500 plot/ # Visualization capabilities \n\u2502 \u251c\u2500\u2500 correlation.py # Correlation analysis plots\n\u2502 \u2514\u2500\u2500 diagnostic.py # Time series diagnostics plots\n\u251c\u2500\u2500 series/ # Time series analysis tools\n\u2502 \u251c\u2500\u2500 forecastability.py # Forecast quality metrics\n\u2502 \u251c\u2500\u2500 outlier.py # Time series outlier detection\n\u2502 \u2514\u2500\u2500 stats.py # Statistical analysis functions\n\u2514\u2500\u2500 stats/ # Statistical utilities\n \u251c\u2500\u2500 bootstrap_bca.py # Bootstrap confidence intervals\n \u251c\u2500\u2500 statistical_interval.py # Statistical interval estimation\n \u2514\u2500\u2500 utils.py # General statistical utilities\n```\n\n```\ntinyshift\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 poetry.lock\n\u251c\u2500\u2500 pyproject.toml\n\u251c\u2500\u2500 tinyshift\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 association_mining\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 analyzer.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 encoder.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 examples\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 outlier.ipynb\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 tracker.ipynb\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 transaction_analyzer.ipynb\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 modelling\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 multicollinearity.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 residualizer.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 scaler.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 outlier\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 base.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 hbos.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 pca.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 spad.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 plot\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 correlation.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 plot.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 series\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 forecastability.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 outlier.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 stats.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 stats\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 bootstrap_bca.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 series.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 statistical_interval.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 utils.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 tests\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 test.pca.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 test_hbos.py\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 test_spad.py\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 drift\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 base.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 categorical.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 continuous.py\n```\n\n\n### Development Setup\n\n```bash\ngit clone https://github.com/HeyLucasLeao/tinyshift.git\ncd tinyshift\npip install -e \".[all]\"\n```\n\n## \ud83d\udccb Requirements\n\n- **Python**: 3.10+\n- **Core Dependencies**: \n - pandas (>2.3.0)\n - scikit-learn (>1.3.0) \n - statsmodels (>=0.14.5)\n- **Optional Dependencies**:\n - plotly (>5.22.0) - for visualization\n - kaleido (<=0.2.1) - for static plot export\n - nbformat (>=5.10.4) - for notebook support\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- Inspired by [Nannyml](https://github.com/NannyML/nannyml)\n\n",
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