Metadata-Version: 1.1
Name: hyperparameter_hunter
Version: 0.0.1
Summary: Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Home-page: https://github.com/HunterMcGushion/hyperparameter_hunter
Author: Hunter McGushion
Author-email: hunter@mcgushion.com
License: MIT
Description: HyperparameterHunter
        ====================
        
        HyperparameterHunter provides wrappers for executing machine learning algorithms that
        automatically save the testing conditions/hyperparameters, results, predictions, and
        other data for a wide range of algorithms from many different libraries in a unified
        format. HyperparameterHunter aims to simplify the experimentation and hyperparameter
        tuning process by allowing you to spend less time doing the annoying tasks, and more time
        doing the important ones.
        
        Features
        --------
        * Truly informed hyperparameter optimization that automatically uses past Experiments
        * Eliminate boilerplate code for cross-validation loops, predicting, and scoring
        * Stop worrying about keeping track of hyperparameters, scores, or re-running the same Experiments
        
        Getting Started
        ---------------
        Set up an Environment to organize Experiments and Optimization
        ```python
        from hyperparameter_hunter import Environment, CrossValidationExperiment
        import pandas as pd
        from sklearn.datasets import load_breast_cancer
        from sklearn.model_selection import StratifiedKFold
        from xgboost import XGBClassifier
        
        data = load_breast_cancer
        df = pd.DataFrame(data=data.data, columns=data.feature_names)
        df['target'] = data.target
        
        env = Environment(
        	train_dataset=df,
        	root_results_path='path/to/results/directory',
        	metrics_map=['roc_auc_score'],
        	cross_validation_type=StratifiedKFold,
        	cross_validation_params=dict(n_splits=5, shuffle=2, random_state=32)
        )
        ```
        Individual Experimentation
        ```python
        experiment = CrossValidationExperiment(
        	model_initializer=XGBClassifier,
        	model_init_params=dict(objective='reg:linear', max_depth=3, subsample=0.5)
        )
        ```
        Hyperparameter Optimization
        ```python
        from hyperparameter_hunter import BayesianOptimization, Real, Integer, Categorical
        
        optimizer = BayesianOptimization(
        	iterations=100, read_experiments=True, dimensions=[
        		Integer(name='max_depth', low=2, high=20),
        		Real(name='learning_rate', low=0.0001, high=0.5),
        		Categorical(name='booster', categories=['gbtree', 'gblinear', 'dart'])
        	]
        )
        optimizer.set_experiment_guidelines(
        	model_initializer=XGBClassifier,
        	model_init_params=dict(n_estimators=200, subsample=0.5, learning_rate=0.1)
        )
        optimizer.go()
        ```
        Plenty of examples for different libraries, and algorithms, as well as more advanced
        HyperparameterHunter features can be found in the
        [examples](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples)
        directory.
        
        Tested Libraries
        ----------------
        * [Keras](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/keras_example.py)
        * [scikit-learn](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/sklearn_example.py)
        * [LightGBM](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/lightgbm_example.py)
        * [CatBoost](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/catboost_example.py)
        * [XGBoost](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/simple_example.py)
        * [rgf_python](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/rgf_example.py)
        * ... More on the way
        
        Installation
        ------------
        
        
Keywords: hyperparameter tuning optimization machine learning artificial intelligence neural network keras scikit-learn xgboost catboost lightgbm rgf
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
