modify loguniform and lognormal (#395)

* modify loguniform and lognormal

* fix bug

* fix bug

* update doc

* update doc

* fix

* update tpe for loguniform

* update tpe for loguniform

* update for loguniform

* update for loguniform

* update loguniform and qloguniform

* update doc

* update

* revert

* revert

* revert

* revert

docs/SearchSpaceSpec.md

@@ -40,12 +40,12 @@ The candidate type and value for variable is here:
 <br/>
 
 * {"_type":"loguniform","_value":[low, high]}
-   * Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
-   * When optimizing, this variable is constrained to the interval [exp(low), exp(high)].
+   * Which means the variable value is a value drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
+   * When optimizing, this variable is constrained to be positive.
 <br/>
 
 * {"_type":"qloguniform","_value":[low, high, q]}
-   * Which means the variable value is a value like round(exp(uniform(low, high)) / q) * q
+   * Which means the variable value is a value like round(loguniform(low, high)) / q) * q
    * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
 <br/>
 
@@ -67,4 +67,8 @@ The candidate type and value for variable is here:
    * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
 <br/>
 
-Note that SMAC only supports a subset of the types above, including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`. In the current version, SMAC does not support cascaded search space (i.e., conditional variable in SMAC).
+Note that SMAC only supports a subset of the types above, including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`. In the current version, SMAC does not support cascaded search space (i.e., conditional variable in SMAC).
\ No newline at end of file
+
+Note that GridSearch Tuner only supports a subset of the types above, including `choic`, `quniform` and `qloguniform`, where q here specifies the number of values that will be sampled. Details about the last two type as follows
+* Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the number of values that will be sampled evenly. Note that q should be at least 2. It will be sampled in a way that the first sampled value is 'low', and each of the following values is (high-low)/q larger that the value in front of it.
+* Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log(low), log(high)] and sample and then change the sampled value back.
\ No newline at end of file

src/sdk/pynni/nni/evolution_tuner/evolution_tuner.py

@@ -35,7 +35,7 @@ import random
 import numpy as np
 
 from nni.tuner import Tuner
-from . import parameter_expressions
+from .. import parameter_expressions
 
 
 @unique

src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py

@@ -44,7 +44,7 @@ class GridSearchTuner(Tuner):
     Note that q should be at least 2.
     It will be sampled in a way that the first sampled value is 'low', and each of the following values is (high-low)/q larger that the value in front of it.
 
-    Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log10(low), log10(high)]
+    Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log(low), log(high)]
     and sample and then change the sampled value back.
     '''
 
@@ -102,8 +102,8 @@ class GridSearchTuner(Tuner):
         if param_type == 'quniform':
             return self._parse_quniform(param_value)
         if param_type == 'qloguniform':
-            param_value[:2] = np.log10(param_value[:2])
-            return list(np.power(10, self._parse_quniform(param_value)))
+            param_value[:2] = np.log(param_value[:2])
+            return list(np.exp(self._parse_quniform(param_value)))
 
         raise RuntimeError("Not supported type: %s" % param_type)
 

src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py

@@ -61,6 +61,8 @@ def json2space(in_x, name=ROOT):
             if _type == 'choice':
                 out_y = eval('hp.hp.'+_type)(name, _value)
             else:
+                if _type in ['loguniform', 'qloguniform']:
+                    _value[:2] = np.log(_value[:2])
                 out_y = eval('hp.hp.' + _type)(name, *_value)
         else:
             out_y = dict()
@@ -75,7 +77,7 @@ def json2space(in_x, name=ROOT):
     return out_y
 
 
-def json2paramater(in_x, paramater, name=ROOT):
+def json2parameter(in_x, parameter, name=ROOT):
     '''
     Change json to parameters.
     '''
@@ -85,22 +87,22 @@ def json2paramater(in_x, paramater, name=ROOT):
             _type = in_x[TYPE]
             name = name + '-' + _type
             if _type == 'choice':
-                _index = paramater[name]
+                _index = parameter[name]
                 out_y = {
                     INDEX: _index,
-                    VALUE: json2paramater(in_x[VALUE][_index], paramater, name=name+'[%d]' % _index)
+                    VALUE: json2parameter(in_x[VALUE][_index], parameter, name=name+'[%d]' % _index)
                 }
             else:
-                out_y = paramater[name]
+                out_y = parameter[name]
         else:
             out_y = dict()
             for key in in_x.keys():
-                out_y[key] = json2paramater(
-                    in_x[key], paramater, name + '[%s]' % str(key))
+                out_y[key] = json2parameter(
+                    in_x[key], parameter, name + '[%s]' % str(key))
     elif isinstance(in_x, list):
         out_y = list()
         for i, x_i in enumerate(in_x):
-            out_y.append(json2paramater(x_i, paramater, name + '[%d]' % i))
+            out_y.append(json2parameter(x_i, parameter, name + '[%d]' % i))
     else:
         logger.info('in_x is not a dict or a list in json2space fuinction %s', str(in_x))
     return out_y
@@ -201,7 +203,7 @@ class HyperoptTuner(Tuner):
                 parameter[key] = None
 
         # remove '_index' from json2parameter and save params-id
-        total_params = json2paramater(self.json, parameter)
+        total_params = json2parameter(self.json, parameter)
         self.total_data[parameter_id] = total_params
         params = _split_index(total_params)
         return params

src/sdk/pynni/nni/evolution_tuner/parameter_expressions.py → src/sdk/pynni/nni/parameter_expressions.py

@@ -46,6 +46,7 @@ def uniform(low, high, random_state):
     high: an float that represent an upper bound
     random_state: an object of numpy.random.RandomState
     '''
+    assert high > low, 'Upper bound must be larger than lower bound'
     return random_state.uniform(low, high)
 
 
@@ -65,7 +66,8 @@ def loguniform(low, high, random_state):
     high: an float that represent an upper bound
     random_state: an object of numpy.random.RandomState
     '''
-    return np.exp(uniform(low, high, random_state))
+    assert low > 0, 'Lower bound must be positive'
+    return np.exp(uniform(np.log(low), np.log(high), random_state))
 
 
 def qloguniform(low, high, q, random_state):

src/sdk/pynni/nni/smartparam.py

@@ -55,10 +55,12 @@ if env_args.platform is None:
         return random.uniform(low, high)
 
     def quniform(low, high, q, name=None):
+        assert high > low, 'Upper bound must be larger than lower bound'
         return round(random.uniform(low, high) / q) * q
 
     def loguniform(low, high, name=None):
-        return math.exp(random.uniform(low, high))
+        assert low > 0, 'Lower bound must be positive'
+        return np.exp(random.uniform(np.log(low), np.log(high)))
 
     def qloguniform(low, high, q, name=None):
         return round(loguniform(low, high) / q) * q
@@ -70,7 +72,7 @@ if env_args.platform is None:
         return round(random.gauss(mu, sigma) / q) * q
 
     def lognormal(mu, sigma, name=None):
-        return math.exp(random.gauss(mu, sigma))
+        return np.exp(random.gauss(mu, sigma))
 
     def qlognormal(mu, sigma, q, name=None):
         return round(lognormal(mu, sigma) / q) * q