idt-r-optimizer 0.1.5

Advanced hyperparameter optimization using Iterative Decision Tree - Random (IDT-R) algorithm

IDT-R Optimizer: Iterative Decision Tree - Random Optimization

A professional, production-ready Python package for advanced hyperparameter optimization based on the IDT-R (Iterative Decision Tree - Random) algorithm.

Overview

IDT-R is a novel hyperparameter optimization algorithm that combines:

• Decision Tree Surrogate Models: Learn patterns in the objective function landscape
• Intelligent Node Extraction: Identify promising hyperparameter regions from tree leaves
• Constrained Random Sampling: Efficiently explore within high-potential regions
• Iterative Refinement: Progressively narrow down the search space

This approach bridges the gap between simple random/grid search and expensive Bayesian optimization, offering:

• ✅ Fast convergence on realistic problems
• ✅ Interpretable optimization dynamics (see tree structure and leaf regions)
• ✅ Robust sampling within identified promising regions
• ✅ Minimal computational overhead

Installation

From PyPI (Recommended)

pip install idt-r-optimizer

From Source

git clone https://github.com/Narith-Saum/idt_r_optimizer.git
cd idt_r_optimizer
pip install -e .

Requirements

• Python 3.9+
• NumPy >= 1.20.0
• scikit-learn >= 1.0.0

Quick Start

from idt_r_optimizer import IDTROptimizer
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

# Define search space
search_space = {
    "C": (0.1, 10.0),           # Continuous
    "kernel": ["linear", "rbf"],  # Categorical
    "gamma": (0.001, 1.0),      # Continuous
}

# Define objective function
def objective(params):
    X, y = make_classification(n_samples=100, n_features=20, random_state=42)
    model = SVC(**params)
    score = cross_val_score(model, X, y, cv=3, scoring='accuracy').mean()
    return score

# Create optimizer
optimizer = IDTROptimizer(
    search_space=search_space,
    max_iterations=15,
    n_random_init=5,
    n_top_leaves=3,
    n_samples_per_leaf=2,
    verbose=True,
    maximize=True,  # Maximize accuracy
)

# Run optimization
best_params, best_score = optimizer.optimize(objective)

print(f"\n✅ Best Score: {best_score:.4f}")
print(f"✅ Best Params: {best_params}")

Algorithm Details

1. Initialization Phase

• Generate n_random_init random samples from the search space
• Evaluate each using the objective function

2. Iterative Optimization Loop

Step 1: Train Surrogate Model

Train a Decision Tree regressor on all previously evaluated points.

Input: {(params, score), ...}
Output: DecisionTreeRegressor fitted on objective landscape

Step 2: Extract Leaf Nodes

Extract all leaf nodes from the trained tree, identifying their hyperparameter bounds.

For each leaf:

• Determine interval bounds: param_i ∈ [lower_i, upper_i]
• Record predicted average performance in that region

Step 3: Select Best Leaves

Rank leaves by predicted performance and select top n_top_leaves.

Step 4: Random Sampling Within Leaves

For each selected leaf, randomly sample n_samples_per_leaf points:

for leaf in top_leaves:
    for _ in range(n_samples_per_leaf):
        for param in leaf.bounds:
            value = random_uniform(leaf.bounds[param])
        candidates.append(new_point)

Step 5: Evaluate & Update

• Filter duplicates and already-evaluated points
• Evaluate new candidates
• Update history

Step 6: Repeat

Continue until max_iterations or no new candidates remain.

Why IDT-R Works

Aspect	Random Search	Grid Search	Bayesian Opt	IDT-R
Convergence	Slow	Poor for continuous	Excellent	Very Good
Interpretability	None	None	Black box	Tree structure visible
Computational Cost	Low	Medium	High	Low
Scaling	Poor	Very poor	OK	Good
Local Optima	Not stuck	Not stuck	Exploits	Balanced

API Reference

IDTROptimizer

optimizer = IDTROptimizer(
    search_space,              # Dict with parameter ranges
    max_iterations=20,         # Max optimization iterations
    n_random_init=5,           # Initial random samples
    n_top_leaves=3,            # Leaves to sample from per iteration
    n_samples_per_leaf=2,      # Samples per leaf
    tree_max_depth=5,          # Max depth of surrogate tree
    verbose=True,              # Print progress
    maximize=True,             # Maximize or minimize
    seed=None,                 # Random seed
)

Methods

optimize(objective_function) → (best_params, best_score)

Run optimization. Objective function takes param dict, returns float score.

get_best() → (params, score)

Returns best parameters and score found.

get_history() → OptimizationHistory

Returns detailed optimization history with all evaluations.

get_summary() → Dict

Returns summary statistics.

SearchSpace

Define your search space flexibly:

from idt_r_optimizer import SearchSpace

search_space = SearchSpace({
    "learning_rate": (0.0001, 0.1),     # Continuous
    "batch_size": (8, 256),              # Discrete (integer bounds)
    "optimizer": ["adam", "sgd", "rmsprop"],  # Categorical
    "dropout": (0.0, 0.5),               # Continuous
})

OptimizationHistory

Track and analyze results:

history = optimizer.get_history()

# Access records
best_record = history.get_best()
all_scores = history.get_all_scores()
best_params = history.get_best_params()

# Summary statistics
summary = history.get_summary()
print(summary)  # {'n_evaluations': 50, 'best_score': 0.95, 'mean_score': 0.87, ...}

Examples

Example 1: SVM Hyperparameter Tuning

from idt_r_optimizer import IDTROptimizer
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score

# Load data
X, y = load_iris(return_X_y=True)

# Search space
search_space = {
    "C": (0.1, 100.0),
    "kernel": ["linear", "poly", "rbf"],
    "gamma": (0.001, 1.0),
}

# Objective function
def objective(params):
    model = SVC(**params)
    cv_scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')
    return cv_scores.mean()

# Optimize
optimizer = IDTROptimizer(
    search_space,
    max_iterations=20,
    n_top_leaves=2,
    verbose=True,
)
best_params, best_score = optimizer.optimize(objective)

Example 2: Random Forest Tuning

from idt_r_optimizer import IDTROptimizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score

# Generate data
X, y = make_classification(n_samples=1000, n_features=50, n_informative=30)

# Search space
search_space = {
    "n_estimators": (10, 200),
    "max_depth": (3, 30),
    "min_samples_split": (2, 10),
    "min_samples_leaf": (1, 5),
    "criterion": ["gini", "entropy"],
}

# Objective function
def objective(params):
    rf = RandomForestClassifier(n_jobs=-1, **params)
    scores = cross_val_score(rf, X, y, cv=3, scoring='accuracy')
    return scores.mean()

# Optimize
optimizer = IDTROptimizer(
    search_space,
    max_iterations=25,
    n_random_init=10,
    n_top_leaves=3,
    n_samples_per_leaf=3,
    verbose=True,
)
best_params, best_score = optimizer.optimize(objective)

Configuration Guide

Tuning Hyperparameters

n_random_init: Number of initial random samples

• Small (3-5): Fast startup, requires good surrogate early
• Large (10-20): Better initial understanding, slower start

n_top_leaves: Number of leaves to sample from per iteration

• Small (1-2): Exploitative, focuses on best regions
• Large (5-10): Explorative, broader coverage

n_samples_per_leaf: Samples per selected leaf

• Small (1-2): Fewer evaluations, lower computation
• Large (5-10): More evaluations, better coverage per region

tree_max_depth: Decision tree complexity

• Shallow (2-3): Rough surrogate, general patterns
• Deep (6-10): Complex surrogate, overfitting risk

For Different Problem Types

Fast/Cheap Objective (~seconds)

IDTROptimizer(
    search_space,
    max_iterations=50,
    n_random_init=5,
    n_top_leaves=3,
    n_samples_per_leaf=3,
)

Expensive Objective (~hours)

IDTROptimizer(
    search_space,
    max_iterations=10,
    n_random_init=20,
    n_top_leaves=2,
    n_samples_per_leaf=1,
)

Features

✅ Type Support

• Continuous (float) parameters
• Discrete (integer) parameters
• Categorical (choice) parameters
• Mixed spaces

✅ Robustness

• Duplicate detection and removal
• History tracking
• Graceful error handling
• Reproducible (seed support)

✅ Interpretability

• Tree structure visible
• Leaf intervals accessible
• Full evaluation history
• Feature importance available

✅ Performance

• Vectorized operations
• Efficient tree extraction
• Memory-conscious design
• Scales to large parameter spaces

Advantages

vs. Random Search

• 4-10x faster convergence on typical problems
• Identifies promising regions automatically
• Leverages past evaluations

vs. Grid Search

• Handles continuous spaces naturally
• No combinatorial explosion from many parameters
• Adaptive grid refinement

vs. Bayesian Optimization

• Simpler, more interpretable
• Faster: no GP posterior sampling
• More robust: tree structure visible
• Lower computational overhead

Performance Considerations

Memory Usage: O(n_evaluations × n_params)

Time Complexity per Iteration:

• Tree training: O(n_evaluations × log n_params)
• Leaf extraction: O(tree_depth)
• Sampling: O(n_top_leaves × n_samples_per_leaf)

Typical Evaluation Counts:

• Initial random: ~5-10 evaluations
• Per iteration: ~5-20 new evaluations
• Total (20 iterations): 100-410 evaluations

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

Citation

If you use IDT-R in your research, please cite the software and the related research paper:

Software Citation

@software{idt_r_2024,
  title={IDT-R Optimizer: Iterative Decision Tree - Random Hyperparameter Optimization},
  author={{IDT-R Development Team}},
  year={2024},
  url={https://github.com/Narith-Saum/idt_r_optimizer},
  version={0.1.2},
  howpublished={Python Package Index (PyPI)}
}

Related Research

This implementation is based on the IDT (Iterative Decision Tree) algorithm. For the foundational work, please refer to:

@article{saum2022hyperparameter,
  title={Hyperparameter Optimization Using Iterative Decision Tree (IDT)},
  author={Saum, Narith and Sugiura, Satoshi and Piantanakulchai, Mongkut},
  journal={IEEE Access},
  volume={10},
  pages={106812--106827},
  year={2022},
  doi={10.1109/ACCESS.2022.3212387},
  publisher={IEEE}
}

For comprehensive citation formats (APA, Chicago, etc.) and additional references, see CITATIONS.md.

License

MIT License - see LICENSE file for details.

Support

• 📚 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

Changelog

v0.1.0 (2024)

• Initial release
• Core IDT-R algorithm implementation
• Support for continuous, discrete, categorical parameters
• Full history tracking
• Comprehensive examples

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Happy Optimizing! 🚀