tune-easy 0.2.1

tune-easy: A hyperparameter tuning tool, extremely easy to use.

tune-easy

A hyperparameter tuning tool, extremely easy to use.

This package supports scikit-learn API estimators, such as SVM and LightGBM.

Usage

Example of All-in-one Tuning

from tune_easy import AllInOneTuning
import seaborn as sns
# Load Dataset
iris = sns.load_dataset("iris")
iris = iris[iris['species'] != 'setosa']  # Select 2 classes
TARGET_VARIALBLE = 'species'  # Target variable
USE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length']  # Explanatory variables
y = iris[OBJECTIVE_VARIALBLE].values
X = iris[USE_EXPLANATORY].values
###### Run All-in-one Tuning######
all_tuner = AllInOneTuning()
all_tuner.all_in_one_tuning(X, y, x_colnames=USE_EXPLANATORY, cv=2)
all_tuner.df_scores

If you want to know usage of the other classes, see API Reference and Examples

Example of Detailed Tuning

from tune_easy import LGBMClassifierTuning
from sklearn.datasets import load_boston
import seaborn as sns
# Load dataset
iris = sns.load_dataset("iris")
iris = iris[iris['species'] != 'setosa']  # Select 2 classes
OBJECTIVE_VARIALBLE = 'species'  # Target variable
USE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length']  # Explanatory variables
y = iris[OBJECTIVE_VARIALBLE].values
X = iris[USE_EXPLANATORY].values
###### Run Detailed Tuning######
tuning = LGBMClassifierTuning(X, y, USE_EXPLANATORY)  # Initialize tuning instance
tuning.plot_first_validation_curve(cv=2)  # Plot first validation curve
tuning.optuna_tuning(cv=2)  # Optimization using Optuna library
tuning.plot_search_history()  # Plot score increase history
tuning.plot_search_map()  # Visualize relationship between parameters and validation score
tuning.plot_best_learning_curve()  # Plot learning curve
tuning.plot_best_validation_curve()  # Plot validation curve

If you want to know usage of the other classes, see API Reference and Examples

Example of MLflow logging

from tune_easy import AllInOneTuning
import seaborn as sns
# Load dataset
iris = sns.load_dataset("iris")
iris = iris[iris['species'] != 'setosa']  # Select 2 classes
TARGET_VARIALBLE = 'species'  # Target variable
USE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length']  # Explanatory variables
y = iris[TARGET_VARIALBLE].values
X = iris[USE_EXPLANATORY].values
###### Run All-in-one Tuning with MLflow logging ######
all_tuner = AllInOneTuning()
all_tuner.all_in_one_tuning(X, y, x_colnames=USE_EXPLANATORY, cv=2,
                             mlflow_logging=True)  # Set MLflow logging argument

If you want to know usage of the other classes, see API Reference and Examples

Requirements

param-tuning-utility 0.2.1 requires

• Python >=3.6

• Scikit-learn >=0.24.2

• Numpy >=1.20.3

• Pandas >=1.2.4

• Matplotlib >=3.3.4

• Seaborn >=0.11.0

• Optuna >=2.7.0

• BayesianOptimization >=1.2.0

• MLFlow >=1.17.0

• LightGBM >=3.3.2

• XGBoost >=1.4.2

• seaborn-analyzer >=0.2.11

Installing tune-easy

Use pip to install the binary wheels on PyPI

$ pip install tune-easy

Support

Bugs may be reported at https://github.com/c60evaporator/tune-easy/issues

Contact

If you have any questions or comments about param-tuning-utility, please feel free to contact me via eMail: [e-mail] or Twitter: https://twitter.com/c60evaporator This project is hosted at https://github.com/c60evaporator/param-tuning-utility