Metadata-Version: 1.2
Name: tune-easy
Version: 0.2.1
Summary: tune-easy: A hyperparameter tuning tool, extremely easy to use.
Home-page: https://github.com/c60evaporator/tune-easy
Author: Kenta Nakamura
Author-email: c60evaporator@gmail.com
Maintainer: Kenta Nakamura
Maintainer-email: c60evaporator@gmail.com
License: BSD 3-Clause
Download-URL: https://github.com/c60evaporator/tune-easy
Description: =========
        tune-easy
        =========
        
        |python| |pypi| |license|
        
        .. |python| image:: https://img.shields.io/pypi/pyversions/tune-easy
           :target: https://www.python.org/
        
        .. |pypi| image:: https://img.shields.io/pypi/v/tune-easy?color=blue
           :target: https://pypi.org/project/tune-easy/
        
        .. |license| image:: https://img.shields.io/pypi/l/tune-easy?color=blue
           :target: https://github.com/c60evaporator/tune-easy/blob/master/LICENSE
        
        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
        ============================
        
        .. code-block:: python
        
            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
        
        .. image:: https://user-images.githubusercontent.com/59557625/140383755-bca64ab3-1593-47ef-8401-affcd0b20a0a.png
           :width: 320px
        
        .. image:: https://user-images.githubusercontent.com/59557625/145702196-50f6781e-2ca2-4cbf-9344-ab58cb08d34b.png
           :width: 480px
        
        If you want to know usage of the other classes, see `API Reference
        <https://c60evaporator.github.io/tune-easy/all_in_one_tuning.html>`__ and `Examples
        <https://github.com/c60evaporator/tune-easy/tree/master/examples/all_in_one_tuning>`__
        
        Example of Detailed Tuning
        ==========================
        
        .. code-block:: python
        
            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
        
        .. image:: https://user-images.githubusercontent.com/59557625/145702586-8b341344-625c-46b3-a9ee-89cb592b1800.png
           :width: 320px
        
        .. image:: https://user-images.githubusercontent.com/59557625/145702594-cc4b2194-2ed0-40b0-8a83-94ebd8162818.png
           :width: 480px
        
        .. image:: https://user-images.githubusercontent.com/59557625/145702643-70e3b1f2-66aa-4619-9703-57402b3669aa.png
           :width: 320px
        
        If you want to know usage of the other classes, see `API Reference
        <https://c60evaporator.github.io/tune-easy/each_estimators.html>`__ and `Examples
        <https://github.com/c60evaporator/tune-easy/tree/master/examples/method_examples>`__
        
        Example of MLflow logging
        =========================
        
        .. code-block:: python
        
            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
        
        .. image:: https://user-images.githubusercontent.com/59557625/147270240-f779cf1f-b216-42a2-8156-37169511ec3e.png
           :width: 640px
        
        If you want to know usage of the other classes, see `API Reference
        <https://c60evaporator.github.io/tune-easy/all_in_one_tuning.html#tune_easy.all_in_one_tuning.AllInOneTuning.all_in_one_tuning>`__ and `Examples
        <https://github.com/c60evaporator/tune-easy/tree/master/examples/mlflow>`__
        
        
        ============
        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 <https://pypi.org/project/tune-easy/>`__
        
        .. code-block:: console
        
            $ 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: c60evaporator@gmail.com
        or Twitter: https://twitter.com/c60evaporator
        This project is hosted at https://github.com/c60evaporator/param-tuning-utility
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Multimedia :: Graphics
Classifier: Framework :: Matplotlib
Requires-Python: >=3.6
