TinyShift

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.

Features

• Data Drift Detection: Categorical and continuous data drift monitoring with multiple distance metrics
• Outlier Detection: HBOS, PCA-based and SPAD outlier detection algorithms
• Classification Model Evaluation: Calibration curves, confusion matrices, score distributions, and production confidence analysis
• Time Series Analysis: Seasonality decomposition, trend analysis, forecasting diagnostics, and forecast stabilization
• Forecast Stability: Metrics and interpolation methods for stable forecasting

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:

pip install tinyshift

Development Installation

Clone and install from source:

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:

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:

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_predicts = detector.predict(analysis_data)

3. Outlier Detection

TinyShift includes sklearn-compatible outlier detection algorithms:

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.predict(X_test)

# PCA-based outlier detection
pca_detector = PCAReconstructionError()
pca_detector.fit(X_train)
pca_scores = pca_detector.predict(X_test)

4. Binary Classification Model Evaluation

Evaluate and visualize classification model performance for production deployment:

from tinyshift.plot import (
    reliability_curve,
    score_distribution, 
    confusion_matrix,
    efficiency_curve,
    beta_confidence_analysis
)

# Model calibration assessment
reliability_curve(
    clf=classifier,
    X=X_test,
    y=y_test,
    model_name="RandomForestClassifier",
    n_bins=15
)

# Analyze prediction confidence patterns
score_distribution(clf, X_test, nbins=20)

# Performance evaluation with interactive confusion matrix
confusion_matrix(clf, X_test, y_test, percentage_by_class=True)

# Conformal prediction analysis
efficiency_curve(conformal_classifier, X_test)

# Production deployment confidence analysis
beta_confidence_analysis(
    alpha=95, 
    beta_param=5, 
    fig_type=None
)

5. Time Series Analysis and Diagnostics

TinyShift provides comprehensive time series analysis capabilities:

from tinyshift.plot import seasonal_decompose
from tinyshift.series import (
    trend_significance, 
    foreca, 
    sample_entropy,
    permutation_entropy,
    theoretical_limit,
    hurst_exponent,
    hampel_filter,
    bollinger_bands
)

seasonal_decompose(
    time_series, 
    periods=[7, 365],  # Weekly and yearly patterns
    width=1200, 
    height=800
)

# Test for significant trends
r_squared, p_value = trend_significance(time_series)

# Assess forecastability
forecastability = foreca(time_series)
print(f"Forecastability (Omega): {forecastability}")

# Measure complexity and regularity
complexity = sample_entropy(time_series, m=2, tolerance=0.2)
print(f"Sample Entropy: {complexity}")

# Measure ordinal complexity
perm_entropy = permutation_entropy(time_series, m=3, delay=1, normalize=True)
print(f"Permutation Entropy: {perm_entropy}")

# Calculate theoretical predictability limit
theo_limit = theoretical_limit(time_series, m=3, delay=1)
print(f"Theoretical Limit (Πmax): {theo_limit}")

# Detect long-term memory
hurst, p_value = hurst_exponent(time_series)
print(f"Hurst Exponent: {hurst}, P-value: {p_value}")

# Outlier detection in time series
outliers = hampel_filter(time_series, window_size=5)
outliers = bollinger_bands(time_series, window_size=20)

# Plot lag analysis with PAMI (Permutation Auto-Mutual Information)
from tinyshift.plot import pami
pami(time_series, nlags=20, m=3, delay=1, normalize=False)

6. Forecast Accuracy Metrics

TinyShift also includes forecast evaluation utilities in the series metrics module, implemented in tinyshift/series/metric.py. These functions help compare forecasting models using aggregate error, bias, and baseline-relative performance:

import pandas as pd
from tinyshift.series import wape, pbias, score, rae, fva_rae

# Example evaluation dataframe
# df must contain actual values in the 'y' column and model predictions as columns

wape_df = wape(df, models=["model_a", "model_b"], id_col="unique_id", target_col="y")
pbias_df = pbias(df, models=["model_a", "model_b"], id_col="unique_id", target_col="y")
score_df = score(df, models=["model_a", "model_b"], id_col="unique_id", target_col="y")
rae_df = rae(df, models=["model_a", "model_b"], baseline_col="naive", id_col="unique_id", target_col="y")

# Single-series Forecast Value Added (FVA) analysis
fva = fva_rae(y_true, y_pred, nlags=1, baseline_type="naive")
print(f"FVA RAE: {fva}")

These utilities cover:

• wape: weighted absolute percentage error for overall accuracy
• pbias: percent bias to detect over- or under-forecasting
• score: composite score combining WAPE and absolute bias
• rae: relative absolute error versus a baseline model
• fva_rae: lead-time-aware RAE for Forecast Value Added analysis

7. Forecast Stability and Interpolation

TinyShift includes forecast stability metrics and interpolation methods:

from tinyshift.series import (
    macv, mach,           # Mean Absolute Change metrics
    mascv, masch,         # Mean Absolute Scaled Change metrics
    rmsscv, rmssch,       # Root Mean Squared Scaled Change metrics
    vi, hpi, hfi          # Interpolation methods
)

# Calculate forecast stability metrics
vertical_stability = macv(y_hat, y_hat_t_minus_1)
horizontal_stability = mach(y_hat) 

# Scaled stability metrics
scaled_v_stability = mascv(y_train, y_hat, y_hat_t_minus_1, seasonality=12)
scaled_h_stability = masch(y_train, y_hat, seasonality=12)

# Apply forecast stabilization techniques
# Vertical Interpolation
stable_forecast = vi(y_hat, anchor, w_s=0.3)

# Horizontal Partial Interpolation
smooth_forecast = hpi(y_hat, w_s=0.4)

# Horizontal Full Interpolation
fully_stable_forecast = hfi(y_hat, w_s=0.5)

8. Advanced Modeling Tools

from tinyshift.modelling import filter_features_by_vif
from tinyshift.stats import bootstrap_bca_interval

#Residualizer
residualizer = FeatureResidualizer()
residualizer.fit(X_train[preprocess_columns], corrcoef=0.70)

#Train
X_train = X_train.astype({x: float for x in preprocess_columns})
X_train.loc[:, preprocess_columns] = residualizer.transform(X_train[preprocess_columns])

# Detect multicollinearity
mask = filter_features_by_vif(X_train, trehshold=5, verbose=True)
X_train.columns = X_train.columns[mask]
X_test.columns = X_test.columns[mask]

#Test
X_test = X_test.astype({x: float for x in preprocess_columns})
X_test.loc[:, preprocess_columns] = residualizer.transform(X_test[preprocess_columns])

# 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
│   └── README.md              # Module documentation
│   ├── analyzer.py             # Transaction pattern analysis
│   └── encoder.py              # Data encoder
├── drift/                      # Data drift detection 
│   └── README.md              # Module documentation
│   ├── base.py                 # Base drift detection classes  
│   ├── categorical.py          # CatDrift for categorical features
│   └── continuous.py           # ConDrift for numerical features
├── examples/                   # Jupyter notebook examples
│   ├── decomp_mstl_ml.ipynb   # MSTL decomposition and ML examples
│   ├── drift.ipynb            # Drift detection examples
│   ├── outlier.ipynb          # Outlier detection demos
│   ├── series.ipynb           # Time series analysis
│   ├── transaction_analyzer.ipynb # Transaction analysis examples
│   └── ts_diagnostics.ipynb   # Time series diagnostics
├── modelling/                  # ML modeling utilities
│   ├── README.md              # Module documentation
│   ├── multicollinearity.py   # VIF-based multicollinearity detection
│   ├── residualizer.py        # Residualizer Feature
│   └── scaler.py              # Custom scaling transformations
├── outlier/                    # Outlier detection algorithms
│   └── README.md              # Module documentation
│   ├── 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  
│   ├── README.md              # Module documentation
│   ├── calibration.py          # Binary Classification model evaluation plots
│   ├── correlation.py          # Correlation analysis plots
│   └── diagnostic.py           # Time series diagnostics plots
├── series/                     # Time series analysis tools
│   └── README.md              # Module documentation
│   ├── forecastability.py     # Forecast quality and complexity metrics
│   ├── interpolation.py       # Forecast stabilization methods
│   ├── outlier.py             # Time series outlier detection
│   ├── stability.py           # Forecast stability metrics
│   └── 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

Development Setup

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 file for details.

🙏 Acknowledgments

• Inspired by Nannyml