UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.

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UltraOpt is a simple and efficient library to minimize expensive and noisy black-box functions, it can be used in many fields, such as HyperParameter Optimization(HPO) and Automatic Machine Learning(AutoML).

After absorbing the advantages of existing optimization libraries such as HyperOpt^[5], SMAC3^[3], scikit-optimize^[4] and HpBandSter^[2], we develop UltraOpt , which implement a new bayesian optimization algorithm : Embedding-Tree-Parzen-Estimator(ETPE), which is better than HyperOpt' TPE algorithm in our experiments. Besides, The optimizer of UltraOpt is redesigned to adapt HyperBand & SuccessiveHalving Evaluation Strategies^[6][7] and MapReduce & Async Communication Conditions. Finally, you can visualize Config Space and optimization process & results by UltraOpt's tool function. Enjoy it !

Other Language: 中文README

• Documentation

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  • 中文文档

• Tutorials

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  • 中文教程

Table of Contents

• Installation
• Quick Start
  • Using UltraOpt in HPO
  • Using UltraOpt in AutoML
• Our Advantages
  • Advantage One: ETPE optimizer is more competitive
  • Advantage Two: UltraOpt is more adaptable to distributed computing
  • Advantage Three: UltraOpt is more function comlete and user friendly
• Citation
• Referance

Installation

UltraOpt requires Python 3.6 or higher.

You can install the latest release by pip:

bash pip install ultraopt

You can download the repository and manual installation:

bash git clone https://github.com/auto-flow/ultraopt.git && cd ultraopt python setup.py install

Quick Start

Using UltraOpt in HPO

Let's learn what UltraOpt doing with several examples (you can try it on your Jupyter Notebook).

You can learn Basic-Tutorial in here, and HDL's Definition in here.

Before starting a black box optimization task, you need to provide two things:

• parameter domain, or the Config Space
• objective function, accept config (config is sampled from Config Space), return loss

Let's define a Random Forest's HPO Config Space by UltraOpt's HDL (Hyperparameter Description Language):

python HDL = { "n_estimators": {"_type": "int_quniform","_value": [10, 200, 10], "_default": 100}, "criterion": {"_type": "choice","_value": ["gini", "entropy"],"_default": "gini"}, "max_features": {"_type": "choice","_value": ["sqrt","log2"],"_default": "sqrt"}, "min_samples_split": {"_type": "int_uniform", "_value": [2, 20],"_default": 2}, "min_samples_leaf": {"_type": "int_uniform", "_value": [1, 20],"_default": 1}, "bootstrap": {"_type": "choice","_value": [True, False],"_default": True}, "random_state": 42 }

And then define an objective function:

python from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_digits from sklearn.model_selection import cross_val_score, StratifiedKFold from ultraopt.hdl import layering_config X, y = load_digits(return_X_y=True) cv = StratifiedKFold(5, True, 0) def evaluate(config: dict) -> float: model = RandomForestClassifier(**layering_config(config)) return 1 - float(cross_val_score(model, X, y, cv=cv).mean())

Now, we can start an optimization process:

python from ultraopt import fmin result = fmin(eval_func=evaluate, config_space=HDL, optimizer="ETPE", n_iterations=30) result

``` 100%|██████████| 30/30 [00:36<00:00, 1.23s/trial, best loss: 0.023]

+-----------------------------------+ | HyperParameters | Optimal Value | +-------------------+---------------+ | bootstrap | True:bool | | criterion | gini | | max_features | log2 | | min_samples_leaf | 1 | | min_samples_split | 2 | | n_estimators | 200 | +-------------------+---------------+ | Optimal Loss | 0.0228 | +-------------------+---------------+ | Num Configs | 30 | +-------------------+---------------+ ```

Finally, make a simple visualizaiton:

python result.plot_convergence()

You can visualize high dimensional interaction by facebook's hiplot:

python !pip install hiplot result.plot_hi(target_name="accuracy", loss2target_func=lambda x:1-x)

Using UltraOpt in AutoML

Let's try a more complex example: solve AutoML's CASH Problem <sup>[1]</sup> (Combination problem of Algorithm Selection and Hyperparameter optimization) by BOHB algorithm<sup>[2]</sup> (Combine HyperBand<sup>[6]</sup> Evaluation Strategies with UltraOpt's ETPE optimizer) .

You can learn Conditional Parameter and complex HDL's Definition in here, AutoML implementation tutorial in here and Multi-Fidelity Optimization in here.

First of all, let's define a CASH HDL :

python HDL = { 'classifier(choice)':{ "RandomForestClassifier": { "n_estimators": {"_type": "int_quniform","_value": [10, 200, 10], "_default": 100}, "criterion": {"_type": "choice","_value": ["gini", "entropy"],"_default": "gini"}, "max_features": {"_type": "choice","_value": ["sqrt","log2"],"_default": "sqrt"}, "min_samples_split": {"_type": "int_uniform", "_value": [2, 20],"_default": 2}, "min_samples_leaf": {"_type": "int_uniform", "_value": [1, 20],"_default": 1}, "bootstrap": {"_type": "choice","_value": [True, False],"_default": True}, "random_state": 42 }, "KNeighborsClassifier": { "n_neighbors": {"_type": "int_loguniform", "_value": [1,100],"_default": 3}, "weights" : {"_type": "choice", "_value": ["uniform", "distance"],"_default": "uniform"}, "p": {"_type": "choice", "_value": [1, 2],"_default": 2}, }, } }

And then, define a objective function with an additional parameter budget to adapt to HyperBand<sup>[6]</sup> evaluation strategy:

python from sklearn.neighbors import KNeighborsClassifier import numpy as np def evaluate(config: dict, budget: float) -> float: layered_dict = layering_config(config) AS_HP = layered_dict['classifier'].copy() AS, HP = AS_HP.popitem() ML_model = eval(AS)(**HP) scores = [] for i, (train_ix, valid_ix) in enumerate(cv.split(X, y)): rng = np.random.RandomState(i) size = int(train_ix.size * budget) train_ix = rng.choice(train_ix, size, replace=False) X_train,y_train = X[train_ix, :],y[train_ix] X_valid,y_valid = X[valid_ix, :],y[valid_ix] ML_model.fit(X_train, y_train) scores.append(ML_model.score(X_valid, y_valid)) score = np.mean(scores) return 1 - score

You should instance a multi_fidelity_iter_generator object for the purpose of using HyperBand<sup>[6]</sup> Evaluation Strategy :

python from ultraopt.multi_fidelity import HyperBandIterGenerator hb = HyperBandIterGenerator(min_budget=1/4, max_budget=1, eta=2) hb.get_table()

	iter 0			iter 1		iter 2
	stage 0	stage 1	stage 2	stage 0	stage 1	stage 0
num_config	4	2	1	2	1	3
budget	1/4	1/2	1	1/2	1	1

let's combine HyperBand Evaluation Strategies with UltraOpt's ETPE optimizer , and then start an optimization process:

python result = fmin(eval_func=evaluate, config_space=HDL, optimizer="ETPE", # using bayesian optimizer: ETPE multi_fidelity_iter_generator=hb, # using HyperBand n_jobs=3, # 3 threads n_iterations=20) result

``` 100%|██████████| 88/88 [00:11<00:00, 7.48trial/s, max budget: 1.0, best loss: 0.012]

+--------------------------------------------------------------------------------------------------------------------------+ | HyperParameters | Optimal Value | +-----------------------------------------------------+----------------------+----------------------+----------------------+ | classifier:choice | KNeighborsClassifier | KNeighborsClassifier | KNeighborsClassifier | | classifier:KNeighborsClassifier:n_neighbors | 4 | 1 | 3 | | classifier:KNeighborsClassifier:p | 2:int | 2:int | 2:int | | classifier:KNeighborsClassifier:weights | distance | uniform | uniform | | classifier:RandomForestClassifier:bootstrap | - | - | - | | classifier:RandomForestClassifier:criterion | - | - | - | | classifier:RandomForestClassifier:max_features | - | - | - | | classifier:RandomForestClassifier:min_samples_leaf | - | - | - | | classifier:RandomForestClassifier:min_samples_split | - | - | - | | classifier:RandomForestClassifier:n_estimators | - | - | - | | classifier:RandomForestClassifier:random_state | - | - | - | +-----------------------------------------------------+----------------------+----------------------+----------------------+ | Budgets | 1/4 | 1/2 | 1 (max) | +-----------------------------------------------------+----------------------+----------------------+----------------------+ | Optimal Loss | 0.0328 | 0.0178 | 0.0122 | +-----------------------------------------------------+----------------------+----------------------+----------------------+ | Num Configs | 28 | 28 | 32 | +-----------------------------------------------------+----------------------+----------------------+----------------------+ ```

You can visualize optimization process in multi-fidelity scenarios:

python import pylab as plt plt.rcParams['figure.figsize'] = (16, 12) plt.subplot(2, 2, 1) result.plot_convergence_over_time(); plt.subplot(2, 2, 2) result.plot_concurrent_over_time(num_points=200); plt.subplot(2, 2, 3) result.plot_finished_over_time(); plt.subplot(2, 2, 4) result.plot_correlation_across_budgets();

Our Advantages

Advantage One: ETPE optimizer is more competitive

We implement 4 kinds of optimizers(listed in the table below), and ETPE optimizer is our original creation, which is proved to be better than other TPE based optimizers such as HyperOpt's TPE and HpBandSter's BOHB in our experiments.

Our experimental code is public available in here, experimental documentation can be found in here .

|Optimizer|Description| |-----|---| |ETPE| Embedding-Tree-Parzen-Estimator, is our original creation, converting high-cardinality categorical variables to low-dimension continuous variables based on TPE algorithm, and some other aspects have also been improved, is proved to be better than HyperOpt's TPE in our experiments. | |Forest |Bayesian Optimization based on Random Forest. Surrogate model import scikit-optimize 's skopt.learning.forest model, and integrate Local Search methods in SMAC3| . |GBRT| Bayesian Optimization based on Gradient Boosting Resgression Tree. Surrogate model import scikit-optimize 's skopt.learning.gbrt model. | |Random| Random Search for baseline or dummy model. |

Key result figure in experiment (you can see details in experimental documentation ) :

Advantage Two: UltraOpt is more adaptable to distributed computing

You can see this section in the documentation:

• Asynchronous Communication Parallel Strategy

• MapReduce Parallel Strategy

Advantage Three: UltraOpt is more function comlete and user friendly

UltraOpt is more function comlete and user friendly than other optimize library:

| | UltraOpt | HyperOpt |Scikit-Optimize|SMAC3 |HpBandSter | |------------------------------------------|-------------|-------------|---------------|-------------|-------------| |Simple Usage like fmin function |✓ |✓ |✓ |✓ |×| |Simple Config Space Definition |✓ |✓ |✓ |×|×| |Support Conditional Config Space |✓ |✓ |× |✓ |✓ | |Support Serializable Config Space |✓ |×|× |×|×| |Support Visualizing Config Space |✓ |✓ |× |×|×| |Can Analyse Optimization Process & Result |✓ |×|✓ |×|✓ | |Distributed in Cluster |✓ |✓ |× |×|✓ | |Support HyperBand<sup>[6]</sup> & SuccessiveHalving<sup>[7]</sup> |✓ |×|× |✓ |✓ |

Citation

bibtex @misc{Tang_UltraOpt, author = {Qichun Tang}, title = {UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt}, month = January, year = 2021, doi = {10.5281/zenodo.4430148}, version = {v0.1.0}, publisher = {Zenodo}, url = {https://doi.org/10.5281/zenodo.4430148} }

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Reference

[1] Thornton, Chris et al. “Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (2013): n. pag.

[2] Falkner, Stefan et al. “BOHB: Robust and Efficient Hyperparameter Optimization at Scale.” ICML (2018).

[3] Hutter F., Hoos H.H., Leyton-Brown K. (2011) Sequential Model-Based Optimization for General Algorithm Configuration. In: Coello C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg.

[4] https://github.com/scikit-optimize/scikit-optimize

[5] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS'11). Curran Associates Inc., Red Hook, NY, USA, 2546–2554.

[6] Li, L. et al. “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.” J. Mach. Learn. Res. 18 (2017): 185:1-185:52.

[7] Jamieson, K. and Ameet Talwalkar. “Non-stochastic Best Arm Identification and Hyperparameter Optimization.” AISTATS (2016).

Issues

where is the list of methods?

opened on 2021-02-14 03:09:23 by microprediction

Hi, I'm looking for a list of methods I can use. I infer "ETPE" and "Random" are two. Any others? Peter

Releases

v0.1.0 2021-01-10 05:05:54

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.