超参数优化 贝叶斯优化框架
Tune your Machine Learning models with open-source optimization libraries
使用开源优化库调整机器学习模型
介绍(Introduction)
Hyper-parameters are the parameters used to control the behavior of the algorithm while building the model. These parameters cannot be learned from the regular training process. They need to be assigned before training the model.
超参数是用于在构建模型时控制算法行为的参数。 这些参数无法从常规培训过程中学习。 在训练模型之前，需要先分配它们。
Example: n_neighbors (KNN), kernel (SVC) , max_depth & criterion (Decision Tree Classifier) etc.
例如： n_neighbors (KNN)，内核(SVC)， max_depth和标准(决策树分类器)等。
Hyperparameter optimization or tuning in machine learning is the process of selecting the best combination of hyper-parameters that deliver the best performance.
机器学习中的超参数优化或调整是选择可提供最佳性能的超参数的最佳组合的过程。
Various automatic optimization techniques exist, and each has its own strengths and drawbacks when applied to different types of problems.
存在各种自动优化技术，当应用于不同类型的问题时，每种技术都有其自身的优缺点。
Example: Grid Search, Random Search, Bayesian Search, etc.
示例：网格搜索，随机搜索，贝叶斯搜索等。
Scikit-learn is one of the frameworks we could use for Hyperparameter optimization, but there are other frameworks that could even perform better.
Scikit-learn是我们可以用于超参数优化的框架之一，但是还有其他一些框架甚至可以表现更好。
- Ray-Tune 雷·图恩
- Optuna奥图纳
- Hyperopt超级选择
- mlmachine机器
- Polyaxon多轴突
- BayesianOptimization贝叶斯优化
- Talos塔罗斯
- SHERPA夏尔巴人
- Scikit-OptimizeScikit优化
- GPyOptGPyOpt
1.雷声 (1. Ray-Tune)
Tune is a Python library for experiment execution and hyperparameter tuning at any scale.[GitHub]
Tune是一个Python库，可用于任意规模的实验执行和超参数调整。[ GitHub ]
主要特征 (Key Features)
Launch a multi-node distributed hyperparameter sweep in less than ten lines of code.
少于十行代码即可启动多节点分布式超参数扫描。
Supports any machine learning framework, including PyTorch, XGBoost, MXNet, and Keras.
支持任何机器学习框架，包括PyTorch，XGBoost，MXNet和Keras 。
Choose among the state of the art algorithms such as Population Based Training (PBT), BayesOptSearch, HyperBand/ASHA.
在最新的算法中进行选择，例如基于人口的训练(PBT) ， BayesOptSearch ， HyperBand / ASHA 。
Tune’s Search Algorithms are wrappers around open-source optimization libraries such as HyperOpt, SigOpt, Dragonfly, and Facebook Ax.
Tune的搜索算法围绕着开放源代码优化库(例如HyperOpt，SigOpt，Dragonfly和Facebook Ax)进行包装。
- Automatically visualize results with TensorBoard. 使用TensorBoard自动显示结果。
#Tune for Scikit Learn
#Scikit学习调音
Installation: pip install ray[tune] tune-sklearn
安装：pip install ray [tune] tune-sklearn
# from sklearn.model_selection import GridSearchCV
from ray.tune.sklearn import TuneGridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
X = iris.data
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state = 14)
# Example parameters to tune from SGDClassifier
parameter_grid = {"alpha": [1e-4, 1e-1, 1], "epsilon": [0.01, 0.1]}
tune_search = TuneGridSearchCV(
SGDClassifier(),
parameter_grid,
early_stopping=True,
max_iters=10)
tune_search.fit(x_train, y_train)
#best set of perameter
print(tune_search.best_params_)
#best score with best set of perameters
print(tune_search.best_score)2.奥图纳(2. Optuna)
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.
Optuna是一个自动超参数优化软件框架，专门为机器学习而设计。
主要特征 (Key Features)
Easy parallelization
轻松并行化
Quick visualization
快速可视化
Efficient optimization algorithms
高效的优化算法
Lightweight, versatile, and platform-agnostic architecture
轻巧，通用且与平台无关的架构
Pythonic search spaces
Pythonic搜索空间
Installation: pip install optuna
安装：pip install optuna
import optuna
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import KFold , cross_val_score
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state = 14)
def objective(trial):
optimizer = trial.suggest_categorical('algorithm', ['auto','ball_tree','kd_tree','brute'])
rf_max_depth = trial.suggest_int("k_n_neighbors", 2, 10, log=True)
knn = KNeighborsClassifier(n_neighbors=rf_max_depth,algorithm=optimizer)
score = cross_val_score(knn, X_train,y_train, n_jobs=-1, cv=3)
accuracy = score.mean()
return accuracy
if __name__ == "__main__":
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=10)
print(study.best_trial)
#best parameter combination
study.best_params
#score achieved with best parameter combination
study.best_value3. Hyperopt(3. Hyperopt)
Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
Hyperopt是一个Python库，用于在尴尬的搜索空间上进行串行和并行优化，搜索空间可能包括实值，离散和条件维。
Hyperopt currently it supports three algorithms :
Hyperopt当前支持三种算法：
Random Search
随机搜寻
Tree of Parzen Estimators (TPE)
Parzen估算器树(TPE)
Adaptive TPE
自适应TPE
主要特征 (Key Features)
Search space (you can create very complex parameter spaces)
搜索空间(您可以创建非常复杂的参数空间)
Persisting and restarting (you can save important information and later load and then resume the optimization process)
持久并重新启动(您可以保存重要信息并在以后加载，然后继续优化过程)
Speed and Parallelization (you can distribute your computation over a cluster of machines)
速度和并行化(您可以将计算分布在一组计算机上)
Installation: pip install hyperopt
安装：pip install hyperopt
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials , space_eval
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
iris = datasets.load_iris()
X = iris.data
y = iris.target
def hyperopt_train_test(params):
clf = RandomForestClassifier(**params)
return cross_val_score(clf, X, y).mean()
space = {
'max_depth': hp.choice('max_depth', range(1,20)),
'max_features': hp.choice('max_features', range(1,5)),
'n_estimators': hp.choice('n_estimators', range(1,20)),
'criterion': hp.choice('criterion', ["gini", "entropy"])
}
best = 0
def f(params):
global best
acc = hyperopt_train_test(params)
if acc > best:
best = acc
print( 'new best:', best, params)
return {'loss': -acc, 'status': STATUS_OK}
trials = Trials()
best = fmin(f, space, algo=tpe.suggest, max_evals=300, trials=trials)
print(best)4. mlmachine(4. mlmachine)
mlmachine is a Python package that facilitates clean and organized notebook-based machine learning experimentation and accomplishes many key aspects of the experimentation life cycle.
mlmachine是一个Python软件包，可促进干净且有组织的基于笔记本的机器学习实验，并完成了实验生命周期的许多关键方面。
mlmachine performs Hyperparameter Tuning with Bayesian Optimization on multiple estimators in one shot and includes functionality for visualizing model performance and parameter selections.
mlmachine一口气对多个估计量执行带贝叶斯优化的超参数调整，并具有可视化模型性能和参数选择的功能。
A well explained article on mlmachine.
关于mlmachine的详尽解释的文章。
Installation: pip install mlmachine
安装方式：pip install mlmachine
5. Polyaxon (5. Polyaxon)
Polyaxon is a platform for building, training, and monitoring large scale deep learning applications. It makes a system to solve reproducibility, automation, and scalability for machine learning applications.
Polyaxon是用于构建，培训和监视大规模深度学习应用程序的平台。 它使系统能够解决机器学习应用程序的可重复性，自动化和可扩展性。
The way Polyaxon performs hyperparameter tuning is by providing a selection of customizable search algorithms. Polyaxon supports both simple approaches such as random search and grid search, and provides a simple interface for advanced approaches, such as Hyperband and Bayesian Optimization, it also integrates with tools such as Hyperopt, and provides an interface for running custom iterative processes. All these search algorithms run in an asynchronous way, and support concurrency and routing to leverage your cluster(s)’s resources to the maximum.
Polyaxon执行超参数调整的方式是提供一系列可定制的搜索算法。 Polyaxon支持简单方法(例如random search和grid search ，并为高级方法(例如Hyperband和Bayesian Optimization提供简单的界面，还与Hyperopt等工具集成，并提供用于运行自定义迭代过程的界面。 所有这些搜索算法均以异步方式运行，并支持并发和路由，以最大程度地利用群集的资源。
主要特征 (Key Features)
Easy-to-use: Polyaxon’s Optimization Engine is a built-in service and can be used easily by adding a
matrixsection to your operations, you can run hyperparameter tuning using the CLI, client, and the dashboard.易于使用：Polyaxon的优化引擎是一项内置服务，可以通过在操作中添加
matrix部分来轻松使用，您可以使用CLI，客户端和仪表板运行超参数调整。- Scalability: Tuning hyperparameters or neural architectures requires leveraging a large amount of computation resources, using Polyaxon you can run hundreds of trials in parallel and intuitively track their progress. 可扩展性：调整超参数或神经体系结构需要利用大量的计算资源，使用Polyaxon，您可以并行运行数百个试验并直观地跟踪其进度。
- Flexibility: Besides the rich built-in algorithms, Polyaxon allows users to customize various hyperparameter tuning algorithms, neural architecture search algorithms, early stopping algorithms, etc. 灵活性：除了丰富的内置算法，Polyaxon还允许用户自定义各种超参数调整算法，神经体系结构搜索算法，提前停止算法等。
- Efficiency: We are intensively working on more efficient model tuning from both system-level and algorithm level. For example, leveraging early feedback to speedup tuning procedure. 效率：我们正在从系统级和算法级集中精力进行更有效的模型调整。 例如，利用早期反馈来加快调整过程。
Installation: pip install -U polyaxon
安装：pip install -U polyaxon
6.贝叶斯优化 (6. Bayesian Optimization)
Bayesian Optimization is another framework that is a pure Python implementation of Bayesian global optimization with Gaussian processes. This is a constrained global optimization package built upon Bayesian inference and Gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for the optimization of high-cost functions, situations where the balance between exploration and exploitation is important.
贝叶斯优化是另一个框架，它是具有高斯过程的贝叶斯全局优化的纯Python实现。 这是基于贝叶斯推理和高斯过程的受约束的全局优化程序包，该程序包尝试在尽可能少的迭代中找到未知函数的最大值。 该技术特别适合于高成本功能的优化，在勘探和开发之间的平衡很重要的情况下。
Installation: pip install bayesian-optimization
安装：pip install贝叶斯优化
7.塔洛斯(7. Talos)
Talos radically changes the ordinary Keras workflow by fully automating hyperparameter tuning and model evaluation. Talos exposes Keras functionality entirely and there is no new syntax or templates to learn.
Talos通过完全自动化超参数调整和模型评估，从根本上改变了普通的Keras工作流程。 Talos完全公开了Keras功能，没有新的语法或模板需要学习。
主要特征 (Key Features)
Single-line optimize-to-predict pipeline
talos.Scan(x, y, model, params).predict(x_test, y_test)单行优化预测管道
talos.Scan(x, y, model, params).predict(x_test, y_test)- Automated hyperparameter optimization 自动化超参数优化
- Model generalization evaluator模型概括评估器
- Experiment analytics实验分析
- Pseudo, Quasi, and Quantum Random search options伪，拟和量子随机搜索选项
- Grid search网格搜索
- Probabilistic optimizers概率优化器
- Single file custom optimization strategies单文件自定义优化策略
Installation: pip install talos
安装：pip install talos
8.夏尔巴人 (8. SHERPA)
SHERPA is a Python library for hyperparameter tuning of machine learning models.
SHERPA是一个Python库，用于机器学习模型的超参数调整。
它提供： (It provides:)
- hyperparameter optimization for machine learning researchers 机器学习研究人员的超参数优化
- a choice of hyperparameter optimization algorithms超参数优化算法的选择
- parallel computation that can be fitted to the user’s needs可以满足用户需求的并行计算
- a live dashboard for the exploratory analysis of results.一个实时的仪表板，用于对结果进行探索性分析。
Installation: pip install parameter-sherpa
安装：pip install参数-sherpa
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import time
import sherpa
import sherpa.algorithms.bayesian_optimization as bayesian_optimization
parameters = [sherpa.Discrete('n_estimators', [2, 50]),
sherpa.Choice('criterion', ['gini', 'entropy']),
sherpa.Continuous('max_features', [0.1, 0.9])]
algorithm = bayesian_optimization.GPyOpt(max_concurrent=1,
model_type='GP_MCMC',
acquisition_type='EI_MCMC',
max_num_trials=10)
X, y = load_breast_cancer(return_X_y=True)
study = sherpa.Study(parameters=parameters,
algorithm=algorithm,
lower_is_better=False)
for trial in study:
print("Trial ", trial.id, " with parameters ", trial.parameters)
clf = RandomForestClassifier(criterion=trial.parameters['criterion'],
max_features=trial.parameters['max_features'],
n_estimators=trial.parameters['n_estimators'],
random_state=0)
scores = cross_val_score(clf, X, y, cv=5)
print("Score: ", scores.mean())
study.add_observation(trial, iteration=1, objective=scores.mean())
study.finalize(trial)
print(study.get_best_result())9. Scikit优化(9. Scikit-Optimize)
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts. Scikit-Optimize provides support for tuning the hyperparameters of ML algorithms offered by the scikit-learn library, so-called hyperparameter optimization.
Scikit-Optimize或skopt是一个简单高效的库，可最大限度地减少(非常)昂贵且嘈杂的黑盒功能。 它实现了几种基于顺序模型优化的方法。 skopt旨在在许多情况下易于访问和使用。 Scikit-Optimize支持调整scikit-learn库提供的ML算法的超参数，即所谓的超参数优化。
The library is built on top of NumPy, SciPy and Scikit-Learn.
该库基于NumPy，SciPy和Scikit-Learn构建。
Installation: pip install scikit-optimize
安装：pip install scikit-optimize
from skopt import BayesSearchCV
import warnings
warnings.filterwarnings("ignore")
# parameter ranges are specified by one of below
from skopt.space import Real, Categorical, Integer
knn = KNeighborsClassifier()
#defining hyper-parameter grid
grid_param = { 'n_neighbors' : list(range(2,11)) ,
'algorithm' : ['auto','ball_tree','kd_tree','brute'] }
#initializing Bayesian Search
Bayes = BayesSearchCV(knn , grid_param , n_iter=30 , random_state=14)
Bayes.fit(X_train,y_train)
#best parameter combination
Bayes.best_params_
#score achieved with best parameter combination
Bayes.best_score_
#all combinations of hyperparameters
Bayes.cv_results_['params']
#average scores of cross-validation
Bayes.cv_results_['mean_test_score']10. GPyOpt(10. GPyOpt)
GPyOpt is a tool for optimization (minimization) of black-box functions using Gaussian processes. It has been implemented in Python by the group of Machine Learning (at SITraN) of the University of Sheffield.GPyOpt is based on GPy, a library for Gaussian process modeling in Python. It can handle large data sets via sparse Gaussian process models.
GPyOpt是使用高斯过程优化(最小化)黑盒功能的工具。 谢菲尔德大学的机器学习小组(位于SITraN)已在Python中实现了它。GPyOpt基于GPy ， GPy是一个用于Python中高斯过程建模的库。 它可以通过稀疏的高斯过程模型处理大型数据集。
主要特征 (Key Features)
Bayesian optimization with arbitrary restrictions
具有任意限制的贝叶斯优化
Parallel Bayesian optimization
并行贝叶斯优化
Mixing different types of variables
混合不同类型的变量
Tuning scikit-learn models
调整scikit学习模型
Integrating the model hyper parameters
集成模型超参数
External objective evaluation
外部目标评估
Installation: pip install gpyopt
安装：pip install gpyopt
import GPy
import GPyOpt
from GPyOpt.methods import BayesianOptimization
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.datasets import load_iris
from scipy.stats import uniform
from xgboost import XGBRegressor
import numpy as np
iris = load_iris()
X = iris.data
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state = 14)
bds = [{'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)},
{'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)},
{'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}]
# Optimization objective
def cv_score(parameters):
parameters = parameters[0]
score = cross_val_score(
XGBRegressor(learning_rate=parameters[0],
gamma=int(parameters[1]),
max_depth=int(parameters[2])),
X, y, scoring='neg_mean_squared_error').mean()
score = np.array(score)
return score
optimizer = GPyOpt.methods.BayesianOptimization(f = cv_score, # function to optimize
domain = bds, # box-constraints of the problem
acquisition_type ='LCB', # LCB acquisition
acquisition_weight = 0.1) # Exploration exploitation
x_best = np.exp(optimizer.X[np.argmin(optimizer.Y)])
print("Best parameters: learning_rate="+str(x_best[0])+",gamma="+str(x_best[1])+",max_depth="+str(x_best[2]))感谢您的阅读！(Thank you for reading!)
Any feedback and comments are, greatly appreciated!
任何反馈和评论都非常感谢！
Some of my other posts you may find interesting,
您可能会发现我的其他一些有趣的帖子，
翻译自: https://towardsdatascience.com/10-hyperparameter-optimization-frameworks-8bc87bc8b7e3
超参数优化 贝叶斯优化框架