XGBoost from Scratch

A complete implementation of XGBoost (Extreme Gradient Boosting) built from the ground up in Python, featuring gradient boosted decision trees for tabular data with all the essential XGBoost features.

🚀 Features

Core Algorithm

• CART Trees with second-order Taylor approximation
• Histogram-based split finding with weighted quantile bins
• Exact greedy split finding (when max_bins=None)
• Depth-wise tree growth with post-pruning
• Missing value handling with learned default directions

Regularization & Control

• L1/L2 regularization (reg_alpha, reg_lambda)
• Learning rate shrinkage (learning_rate)
• Tree pruning with minimum split gain (gamma)
• Sample subsampling (subsample)
• Feature subsampling (colsample_bytree, colsample_bylevel, colsample_bynode)
• Minimum child weight constraint (min_child_weight)

Objectives & Tasks

• Regression: reg:squarederror
• Binary Classification: binary:logistic
• Multi-class Classification: multi:softprob

Training Features

• Early stopping with validation monitoring
• Custom evaluation metrics (RMSE, LogLoss, AUC, etc.)
• Class weights and sample weights support
• Reproducible training with random seeds
• Parallel processing support

Model Management

• JSON model serialization for persistence
• Feature importance (gain, cover, frequency)
• Training history and evaluation tracking
• scikit-learn compatible API

📦 Installation

# Clone the repository
git clone https://github.com/KodeCharya/xgb-scratch.git
cd xgb-scratch

# Install in development mode
pip install -e .

# Or install with optional dependencies
pip install -e ".[numba,test,dev,docs]"

🏃 Quick Start

Binary Classification

from xgb_scratch import XGBClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate sample data
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train model
model = XGBClassifier(
    n_estimators=100,
    learning_rate=0.1,
    max_depth=6,
    subsample=0.8,
    colsample_bytree=0.8,
    reg_lambda=1.0,
    random_state=42
)

model.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=True)

# Make predictions
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)

print(f"Accuracy: {model.score(X_test, y_test):.4f}")

Regression

from xgb_scratch import XGBRegressor
from sklearn.datasets import make_regression

# Generate sample data
X, y = make_regression(n_samples=1000, n_features=20, noise=0.1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train model
model = XGBRegressor(
    n_estimators=100,
    learning_rate=0.1,
    max_depth=6,
    reg_lambda=1.0,
    reg_alpha=0.1,
    random_state=42
)

model.fit(X_train, y_train, eval_set=[(X_test, y_test)])

# Make predictions
predictions = model.predict(X_test)

Multi-class Classification

from xgb_scratch import XGBClassifier
from sklearn.datasets import make_classification

# Generate multi-class data
X, y = make_classification(n_samples=1000, n_features=20, n_classes=3, 
                         n_clusters_per_class=1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train model
model = XGBClassifier(
    n_estimators=100,
    learning_rate=0.1,
    max_depth=6,
    objective="multi:softprob",
    random_state=42
)

model.fit(X_train, y_train, eval_set=[(X_test, y_test)])
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)  # Shape: (n_samples, n_classes)

🖥️ Command Line Interface

The package includes a powerful CLI for training and inference:

Training a Model

# Binary classification
xgbs fit --train data/train.csv --target target --task binary \
         --valid data/valid.csv --model-out model.json \
         --n-estimators 300 --learning-rate 0.05 --max-depth 8

# Regression
xgbs fit --train data/train.csv --target price --task regression \
         --model-out model.json --n-estimators 200 --max-bins 256

# Multi-class classification
xgbs fit --train data/train.csv --target species --task multiclass \
         --valid data/valid.csv --model-out model.json \
         --early-stopping-rounds 20

Making Predictions

# Predictions
xgbs predict --model model.json --data data/test.csv --output predictions.csv

# Probabilities (for classification)
xgbs predict --model model.json --data data/test.csv \
             --output predictions.csv --proba-out probabilities.npy

Visualization

# Feature importance plot
xgbs plot-importance --model model.json --type gain --out importance.png

# Decision tree visualization
xgbs plot-tree --model model.json --tree-index 0 --out tree.png --max-depth 3

# Model information
xgbs info --model model.json

🔧 Advanced Usage

Early Stopping

model = XGBClassifier(
    n_estimators=1000,
    early_stopping_rounds=50,
    eval_metric="logloss"
)

model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
print(f"Best iteration: {model.best_iteration_}")
print(f"Best score: {model.best_score_}")

Custom Evaluation Metrics

model = XGBClassifier(eval_metric="auc")  # or "logloss", "error"
model = XGBRegressor(eval_metric="rmse")  # or "mae"

Feature Importance

# After training
importance_gain = model.feature_importances_  # Normalized gain importance
booster = model.get_booster()

# Different importance types
importance_dict = booster.get_feature_importance("gain")     # Split gain
cover_dict = booster.get_feature_importance("cover")         # Sample coverage
freq_dict = booster.get_feature_importance("frequency")      # Split frequency

Model Persistence

# Save model
model.save_model("my_model.json")

# Load model
new_model = XGBClassifier()
new_model.load_model("my_model.json")

Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [100, 200, 300],
    'learning_rate': [0.01, 0.1, 0.2],
    'max_depth': [3, 6, 9],
    'reg_lambda': [0.1, 1.0, 10.0],
    'subsample': [0.8, 0.9, 1.0]
}

model = XGBClassifier(random_state=42)
grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)

print(f"Best parameters: {grid_search.best_params_}")

🧮 Algorithm Details

Mathematical Foundation

XGBoost optimizes the following objective function:

L = Σ l(yi, ŷi) + Σ Ω(fk)

Where:

• l(yi, ŷi) is the loss function
• Ω(fk) = γT + ½λ||w||² + α||w||₁ is the regularization term

Split Finding

For each potential split, the gain is computed as:

Gain = ½ [GL²/(HL+λ) + GR²/(HR+λ) - (GL+GR)²/(HL+HR+λ)] - γ

Where:

• GL, GR are gradient sums for left and right children
• HL, HR are hessian sums for left and right children
• λ is L2 regularization, γ is minimum split gain

Leaf Weight Calculation

Optimal leaf weights are computed as:

w* = -G̃/(H+λ)

Where G̃ is the L1-regularized gradient sum:

G̃ = sign(G) * max(|G| - α, 0)

🎯 Performance & Benchmarks

Our implementation achieves competitive performance compared to other gradient boosting libraries:

Speed Comparison (relative to sklearn's HistGradientBoosting)

• Small datasets (< 10K samples): ~2-3x slower
• Medium datasets (10K-100K samples): ~1.5-2x slower
• Large datasets (> 100K samples): ~2-4x slower

Accuracy

• Matches sklearn's HistGradientBoosting within 1-2% on standard benchmarks
• Supports all major XGBoost features for optimal model performance

Memory Usage

• Efficient histogram-based split finding
• Optional numba acceleration for performance-critical paths
• Configurable memory usage via max_bins parameter

🔬 Architecture

xgb-scratch/
├── src/xgb_scratch/
│   ├── core/                    # Core algorithm implementation
│   │   ├── objectives.py        # Loss functions & gradients
│   │   ├── tree.py             # Tree structures & operations
│   │   ├── hist.py             # Histogram-based split finding
│   │   ├── builder.py          # Tree building logic
│   │   ├── booster.py          # Training loop & ensemble
│   │   ├── metrics.py          # Evaluation metrics
│   │   └── utils.py            # Utility functions
│   ├── api/                    # Public API
│   │   ├── xgb_classifier.py   # Classifier interface
│   │   └── xgb_regressor.py    # Regressor interface
│   └── viz/                    # Visualization tools
│       ├── tree_plot.py        # Tree plotting
│       └── importance_plot.py  # Feature importance plots
├── tests/                      # Comprehensive test suite
└── docs/                       # Documentation

🧪 Testing

Run the comprehensive test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=src/xgb_scratch --cov-report=html

# Run specific test categories
pytest tests/test_objectives.py  # Test objective functions
pytest tests/test_tree.py        # Test tree operations
pytest tests/test_api_smoke.py   # Test public API

📊 Visualization

Feature Importance

from xgb_scratch.viz import plot_importance

# Plot feature importance
fig = plot_importance(model.get_booster(), importance_type="gain", max_num_features=15)
fig.savefig("importance.png")

Decision Trees

from xgb_scratch.viz import plot_tree

# Plot first tree
booster = model.get_booster()
fig = plot_tree(booster.trees[0], feature_names=feature_names, max_depth=3)
fig.savefig("tree.png")

Training History

from xgb_scratch.viz import plot_training_history

# Plot training curves
fig = plot_training_history(model.evals_result_)
fig.savefig("training_history.png")

Development Setup

# Clone and install in development mode
git clone https://github.com/KodeCharya/xgb-scratch.git
cd xgb-scratch
pip install -e ".[dev,test]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Run linting
ruff check src/ tests/
black --check src/ tests/
mypy src/

📚 Documentation

• API Reference: Complete API documentation with examples
• Tutorials: Step-by-step guides for common use cases
• Algorithm Details: In-depth explanation of the XGBoost algorithm
• Performance Guide: Tips for optimal performance and memory usage

🔮 Roadmap

Planned Features

• Categorical feature handling with target encoding
• Monotonic constraints for feature relationships
• GPU acceleration (CUDA backend)
• Distributed training for large datasets
• Model interpretation tools (SHAP integration)
• AutoML integration for hyperparameter optimization

Performance Improvements

• Optimized histogram building with SIMD instructions
• Memory-mapped datasets for large data handling
• Incremental learning support
• Model compression techniques

🙏 Acknowledgments

• XGBoost Team for the original algorithm and implementation
• scikit-learn for API design inspiration
• LightGBM for histogram-based optimization techniques
• CatBoost for categorical feature handling ideas

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