Merge pull request #12 from Microsoft/master

pull code

README.md

@@ -65,7 +65,8 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
           <li><a href="docs/en_US/Builtin_Tuner.md#NetworkMorphism">Network Morphism</a></li>
           <li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
           <li><a href="docs/en_US/Builtin_Tuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
-        </ul> 
+          <li><a href="docs/en_US/Builtin_Tuner.md#BOHB">BOHB</a></li>
+        </ul>
           <a href="docs/en_US/Builtin_Assessors.md#assessor">Assessor</a> 
         <ul>
           <li><a href="docs/en_US/Builtin_Assessors.md#Medianstop">Median Stop</a></li>

docs/en_US/Builtin_Tuner.md

@@ -18,6 +18,7 @@ Currently we support the following algorithms:
 |[__Hyperband__](#Hyperband)|Hyperband tries to use the limited resource to explore as many configurations as possible, and finds out the promising ones to get the final result. The basic idea is generating many configurations and to run them for the small number of trial budget to find out promising one, then further training those promising ones to select several more promising one.[Reference Paper](https://arxiv.org/pdf/1603.06560.pdf)|
 |[__Network Morphism__](#NetworkMorphism)|Network Morphism provides functions to automatically search for architecture of deep learning models. Every child network inherits the knowledge from its parent network and morphs into diverse types of networks, including changes of depth, width, and skip-connection. Next, it estimates the value of a child network using the historic architecture and metric pairs. Then it selects the most promising one to train. [Reference Paper](https://arxiv.org/abs/1806.10282)|
 |[__Metis Tuner__](#MetisTuner)|Metis offers the following benefits when it comes to tuning parameters: While most tools only predict the optimal configuration, Metis gives you two outputs: (a) current prediction of optimal configuration, and (b) suggestion for the next trial. No more guesswork. While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to re-sample a particular hyper-parameter. [Reference Paper](https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/)|
+|[__BOHB__](#BOHB)|BOHB is a follow-up work of Hyperband. It targets the weakness of Hyperband that new configurations are generated randomly without leveraging finished trials. For the name BOHB, HB means Hyperband, BO means Byesian Optimization. BOHB leverages finished trials by building multiple TPE models, a proportion of new configurations are generated through these models. [Reference Paper](https://arxiv.org/abs/1807.01774)|
 
 <br>
 
@@ -317,3 +318,50 @@ tuner:
   classArgs:
     optimize_mode: maximize
 ```
+
+<br>
+
+<a name="BOHB"></a>
+
+![](https://placehold.it/15/1589F0/000000?text=+) `BOHB Advisor`
+
+> Builtin Tuner Name: **BOHB**
+
+**Installation**
+
+BOHB advisor requires [ConfigSpace](https://github.com/automl/ConfigSpace) package, ConfigSpace need to be installed by following command before first use.
+
+```bash
+nnictl package install --name=BOHB
+```
+
+**Suggested scenario**
+
+Similar to Hyperband, it is suggested when you have limited computation resource but have relatively large search space. It performs well in the scenario that intermediate result (e.g., accuracy) can reflect good or bad of final result (e.g., accuracy) to some extent. In this case, it may converges to a better configuration due to bayesian optimization usage.
+
+**Requirement of classArg**
+
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will target to maximize metrics. If 'minimize', tuner will target to minimize metrics.
+* **min_budget** (*int, optional, default = 1*) - The smallest budget assign to a trial job, (budget could be the number of mini-batches or epochs). Needs to be positive.
+* **max_budget** (*int, optional, default = 3*) - The largest budget assign to a trial job, (budget could be the number of mini-batches or epochs). Needs to be larger than min_budget.
+* **eta** (*int, optional, default = 3*) - In each iteration, a complete run of sequential halving is executed. In it, after evaluating each configuration on the same subset size, only a fraction of 1/eta of them 'advances' to the next round. Must be greater or equal to 2.
+* **min_points_in_model**(*int, optional, default = None*): number of observations to start building a KDE. Default 'None' means dim+1, when the number of completed trial in this budget is equal or larger than `max{dim+1, min_points_in_model}`, BOHB will start to build a KDE model of this budget, then use KDE model to guide the configuration selection. Need to be positive.(dim means the number of hyperparameters in search space)
+* **top_n_percent**(*int, optional, default = 15*): percentage (between 1 and 99, default 15) of the observations that are considered good. Good points and bad points are used for building KDE models. For example, if you have 100 observed trials and top_n_percent is 15, then top 15 point will used for building good point models "l(x)", the remaining 85 point will used for building bad point models "g(x)".
+* **num_samples**(*int, optional, default = 64*): number of samples to optimize EI (default 64). In this case, we will sample "num_samples"(default = 64) points, and compare the result of l(x)/g(x), then return one with the maximum l(x)/g(x) value as the next configuration if the optimize_mode is maximize. Otherwise, we return the smallest one.
+* **random_fraction**(*float, optional, default = 0.33*): fraction of purely random configurations that are sampled from the prior without the model.
+* **bandwidth_factor**(*float, optional, default = 3.0*): to encourage diversity, the points proposed to optimize EI, are sampled from a 'widened' KDE where the bandwidth is multiplied by this factor. Suggest to use default value if you are not familiar with KDE.
+* **min_bandwidth**(*float, optional, default = 0.001*): to keep diversity, even when all (good) samples have the same value for one of the parameters, a minimum bandwidth (default: 1e-3) is used instead of zero. Suggest to use default value if you are not familiar with KDE.
+
+*Please note that currently float type only support decimal representation, you have to use 0.333 instead of 1/3 and 0.001 instead of 1e-3.*
+
+**Usage example**
+
+```yml
+advisor:
+  builtinAdvisorName: BOHB
+  classArgs:
+    optimize_mode: maximize
+    min_budget: 1
+    max_budget: 27
+    eta: 3
+```