optimal-regressors 0.8.0

A Python library to find optimal configurations for regressors

OptimalRegressors

OptimalRegressors is a Python library designed to find the optimal configurations for decision tree and random forest regressors. It automates hyperparameter tuning, such as determining the optimal number of leaf nodes, using validation data to improve model performance.

Features

• Automatically optimizes hyperparameters for:
  • DecisionTreeRegressor
  • RandomForestRegressor
• Allows custom candidate values for hyperparameter tuning.
• Optionally fits the optimized model for direct use.
• Simple, lightweight, and easy to use.

Installation

To install OptimalRegressors, follow the steps below:

Clone the Repository

git clone https://github.com/RehanTaneja/OptimalRegressors.git
cd OptimalRegressors

Install Dependencies

Use pip to install the required dependencies:

pip install -r requirements.txt

Usage

regressors.py

OptimalDecisionTreeRegressor

• Parameters:
  • trainX,trainy Training data (features & labels)
  • valX,valy Validation data (features & labels)
  • candidate_nodes (list, optional): A list of max_leaf_nodes values to test. Default: [5, 50, 500, 5000]
  • candidate_splits (list, optional): A list of min_sample_split values to test. Default: [2,3,4,5,6,7,8,9,10]
  • candidate_leaves (list,optional): A list of min_sample_leaf values to test. Default: [1,2,3,4,5]
  • candidate_depths (list,optional): A list of max_depth values to test. Default: [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
  • fit (bool, optional): Whether to fit the returned model on the training data. Default: False
• Returns:
  • model: A DecisionTreeRegressor instance with the optimal configuration

Find the best max_leaf_nodes for a decision tree:

from OptimalRegressor import OptimalDecisionTreeRegressor

# Example data (replace with your dataset)
trainX, trainy = [[1], [2], [3]], [2.5, 3.5, 5.0]
valX, valy = [[1.5], [2.5]], [3.0, 4.0]

# Get the optimal DecisionTreeRegressor
model, nodes = OptimalDecisionTreeRegressor(trainX, trainy, valX, valy)

print("Optimal max_leaf_nodes:", nodes)

OptimalRandomForestRegressor

• Parameters:
  • trainX, trainy: Training data (features and labels)
  • vsalX, valy: Validation data (features and labels)
  • candidate_nodes (list, optional): A list of max_leaf_nodes values to test. Default: [5, 50, 500, 5000]
  • candidate_estimators (list,optional): A list of n_estimator values to test. Default: [50,100,200,300,400,500]
  • fit (bool, optional): Whether to fit the returned model on the training data. Default: False
• Returns:
  • model: A RandomForestRegressor instance with the optimal configuration

Find the best max_leaf_nodes value for a random forest regressor

from OptimalRegressor import OptimalRandomForestRegressor

# Example data (replace with your dataset)
trainX, trainy = [[1], [2], [3]], [2.5, 3.5, 5.0]
valX, valy = [[1.5], [2.5]], [3.0, 4.0]

# Get the optimal RandomForestRegressor
model, nodes = OptimalRandomForestRegressor(trainX, trainy, valX, valy)

print("Optimal max_leaf_nodes:", nodes)

OptimalDecisionTreeRegressors.py

OptimalMaxLeafNodes

• Parameters:
• candidate_nodes: A list of max_leaf_nodes values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for max_leaf_node with minimum minimum absolute error

OptimalMinSampleSplit

• Parameters:
• candidate_splits: A list of min_sample_split values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for min_sample_split with minimum minimum absolute error

OptimalMinSampleLeaf

• Parameters:
• candidate_nleaves: A list of min_sample_leaf values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for min_sample_leaf with minimum minimum absolute error

OptimalMaxDepth

• Parameters:
• candidate_nodes: A list of max_depth values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for max_depth with minimum minimum absolute error

OptimalRandomForestRegressors.py

OptimalMaxLeafNodes

• Parameters:
• candidate_nodes: A list of max_leaf_nodes values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for max_leaf_node with minimum minimum absolute error

OptimalNEstimators

• Parameters:
• candidate_estimators: A list of n_estimators values to test
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for n_estimators with minimum minimum absolute error

OptimalBootstrap

• Parameters:
• trainX, trainy: Training data (features and labels)
• valX, valy: Validation data (features and labels)
• mae: Original Mean Absolute Error
• Returns:
  • Optimal value for bootstrap with minimum minimum absolute error

Repository Structure

OptimalRegressors/ │ ├── OptimalDecisionTreeRegressors.py # Methods for getting optimal hyperparameters for DecisionTree |-- OptimalRandomForestRegressors.py # Methods for getting optimal hyperparameters for RandomForest |-- regressor.py # Main library file ├── requirements.txt # Python dependencies ├── LICENSE # License information └── README.md # Project documentation