#1546 add doc for gp-tuner: support only numerical values (#1551)

* add doc & err catch for gp-tuner: support only numerical values

docs/en_US/Tuner/BuiltinTuner.md

@@ -308,8 +308,7 @@ tuner:
 
 > Built-in Tuner Name: **MetisTuner**
 
-Note that the only acceptable types of search space are `choice`, `quniform`, `uniform` and `randint`.
-
+Note that the only acceptable types of search space are `quniform`, `uniform` and `randint` and numerical `choice`. Only numerical values are supported since the values will be used to evaluate the 'distance' between different points.
 **Suggested scenario**
 
 Similar to TPE and SMAC, Metis is a black-box tuner. If your system takes a long time to finish each trial, Metis is more favorable than other approaches such as random search. Furthermore, Metis provides guidance on the subsequent trial. Here is an [example](https://github.com/Microsoft/nni/tree/master/examples/trials/auto-gbdt/search_space_metis.json) about the use of Metis. User only need to send the final result like `accuracy` to tuner, by calling the NNI SDK. [Detailed Description](./MetisTuner.md)
@@ -381,7 +380,7 @@ advisor:
 
 > Built-in Tuner Name: **GPTuner**
 
-Note that the only acceptable types of search space are `choice`, `randint`, `uniform`, `quniform`,  `loguniform`, `qloguniform`.
+Note that the only acceptable types of search space are `randint`, `uniform`, `quniform`,  `loguniform`, `qloguniform`, and numerical `choice`. Only numerical values are supported since the values will be used to evaluate the 'distance' between different points.
 
 **Suggested scenario**
 

docs/en_US/Tuner/GPTuner.md

@@ -7,4 +7,6 @@ Bayesian optimization works by constructing a posterior distribution of function
 
 GP Tuner is designed to minimize/maximize the number of steps required to find a combination of parameters that are close to the optimal combination. To do so, this method uses a proxy optimization problem (finding the maximum of the acquisition function) that, albeit still a hard problem, is cheaper (in the computational sense) and common tools can be employed. Therefore Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor.
 
+Note that the only acceptable types of search space are `randint`, `uniform`, `quniform`,  `loguniform`, `qloguniform`, and numerical `choice`.
+
 This optimization approach is described in Section 3 of [Algorithms for Hyper-Parameter Optimization](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf). 

docs/en_US/Tuner/MetisTuner.md

@@ -15,6 +15,6 @@ It finds the global optimal point in the Gaussian Process space. This point repr
 
 It identifies the next hyper-parameter candidate. This is achieved by inferring the potential information gain of exploration, exploitation, and re-sampling.
 
-Note that the only acceptable types of search space are `choice`, `quniform`, `uniform` and `randint`.
+Note that the only acceptable types of search space are `quniform`, `uniform` and `randint` and numerical `choice`.
 
 More details can be found in our paper: https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -94,7 +94,7 @@ All types of sampling strategies and their parameter are listed here:
 
 Known Limitations:
 
-* Note that Metis Tuner only supports numerical `choice` now
+* GP Tuner and Metis Tuner support only **numerical values** in search space(`choice` type values can be no-numeraical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the 'distance' between different points, it's hard to get the true distance between no-numerical values.
 
 * Note that for nested search space:
 

src/sdk/pynni/nni/gp_tuner/target_space.py

@@ -55,6 +55,14 @@ def __init__(self, pbounds, random_state=None):
             [item[1] for item in sorted(pbounds.items(), key=lambda x: x[0])]
         )
 
+        # check values type
+        for _bound in self._bounds:
+            if _bound['_type'] == 'choice':
+                try:
+                    [float(val) for val in _bound['_value']]
+                except ValueError:
+                    raise ValueError("GP Tuner supports only numerical values")
+
         # preallocated memory for X and Y points
         self._params = np.empty(shape=(0, self.dim))
         self._target = np.empty(shape=(0))