HyperTune is a Python package that uses cross-validation technique to help you find the best hyperparameter for your pre-trained model. It also helps you retrain your model with the best hyperparameters. It helps you automate the task of improving your model, giving you the opportunity to become productive in your machine-learning projects.
Navigate to your command line and run this command: python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps hypertune
To use Hypertune, make sure you:
- Have your pre-trained model
- Your training dataset
- Understand hyperparameter tuning in machine learning.
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Import tune_hyperparameters function
from hypertune.tune import tune_hyperparameters
# Load a dataset for demonstration (Iris dataset)
data = load_iris()
X, y = data.data, data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a pre-trained model (RandomForestClassifier in this case)
model = RandomForestClassifier(random_state=42)
# Define a grid of hyperparameters to search
param_grid = {
'n_estimators': [10, 50, 100],
'max_depth': [None, 10, 20],
}
# Use the tune_hyperparameters function to tune hyperparameters and obtain the best model
best_model = tune_hyperparameters(model, param_grid, X_train, y_train, scoring='f1_macro')
# The best_model is now the RandomForestClassifier with optimised hyperparameters
# You can use it for predictions
y_pred = best_model.predict(X_test)