update docstring and pylint

docs/en_US/sdk_reference.rst

@@ -36,6 +36,9 @@ Tuner
 ..  autoclass:: nni.metis_tuner.metis_tuner.MetisTuner
     :members:
 
+..  autoclass:: nni.batch_tuner.batch_tuner.BatchTuner
+    :members:
+
 Assessor
 ------------------------
 ..  autoclass:: nni.assessor.Assessor

src/sdk/pynni/nni/batch_tuner/batch_tuner.py

@@ -31,22 +31,27 @@ class BatchTuner
 CHOICE = 'choice'
 VALUE = '_value'
 
-logger = logging.getLogger('batch_tuner_AutoML')
+LOGGER = logging.getLogger('batch_tuner_AutoML')
 
 class BatchTuner(Tuner):
     """
     BatchTuner is tuner will running all the configure that user want to run batchly.
+
+    Examples
+    --------
     The search space only be accepted like:
+    ```
     {
         'combine_params': { '_type': 'choice',
                              '_value': '[{...}, {...}, {...}]',
                           }
     }
+    ```
     """
 
     def __init__(self):
-        self.count = -1
-        self.values = []
+        self._count = -1
+        self._values = []
 
     def is_valid(self, search_space):
         """
@@ -55,18 +60,26 @@ def is_valid(self, search_space):
         Parameters
         ----------
         search_space : dict
+
+        Returns
+        -------
+        None or list
+            If valid, return candidate values; else return None.
         """
         if not len(search_space) == 1:
             raise RuntimeError('BatchTuner only supprt one combined-paramreters key.')
 
         for param in search_space:
             param_type = search_space[param][TYPE]
             if not param_type == CHOICE:
-                raise RuntimeError('BatchTuner only supprt one combined-paramreters type is choice.')
-            else:
-                if isinstance(search_space[param][VALUE], list):
-                    return search_space[param][VALUE]
-                raise RuntimeError('The combined-paramreters value in BatchTuner is not a list.')
+                raise RuntimeError('BatchTuner only supprt \
+                                    one combined-paramreters type is choice.')
+
+            if isinstance(search_space[param][VALUE], list):
+                return search_space[param][VALUE]
+
+            raise RuntimeError('The combined-paramreters \
+                                value in BatchTuner is not a list.')
         return None
 
     def update_search_space(self, search_space):
@@ -76,49 +89,57 @@ def update_search_space(self, search_space):
         ----------
         search_space : dict
         """
-        self.values = self.is_valid(search_space)
+        self._values = self.is_valid(search_space)
 
     def generate_parameters(self, parameter_id, **kwargs):
         """Returns a dict of trial (hyper-)parameters, as a serializable object.
 
         Parameters
         ----------
         parameter_id : int
+
+        Returns
+        -------
+        dict
+            A candidate parameter group.
         """
-        self.count += 1
-        if self.count > len(self.values) - 1:
+        self._count += 1
+        if self._count > len(self._values) - 1:
             raise nni.NoMoreTrialError('no more parameters now.')
-        return self.values[self.count]
+        return self._values[self._count]
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
         pass
 
     def import_data(self, data):
         """Import additional data for tuning
+
         Parameters
         ----------
         data:
             a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
         """
-        if not self.values:
-            logger.info("Search space has not been initialized, skip this data import")
+        if not self._values:
+            LOGGER.info("Search space has not been initialized, skip this data import")
             return
 
-        self.values = self.values[(self.count+1):]
-        self.count = -1
+        self._values = self._values[(self._count+1):]
+        self._count = -1
 
         _completed_num = 0
         for trial_info in data:
-            logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
+            LOGGER .info("Importing data, current processing \
+                            progress %s / %s", _completed_num, len(data))
             # simply validate data format
             assert "parameter" in trial_info
             _params = trial_info["parameter"]
             assert "value" in trial_info
             _value = trial_info['value']
             if not _value:
-                logger.info("Useless trial data, value is %s, skip this trial data.", _value)
+                LOGGER.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
             _completed_num += 1
-            if _params in self.values:
-                self.values.remove(_params)
-        logger.info("Successfully import data to batch tuner, total data: %d, imported data: %d.", len(data), _completed_num)
+            if _params in self._values:
+                self._values.remove(_params)
+        LOGGER .info("Successfully import data to batch tuner, \
+                        total data: %d, imported data: %d.", len(data), _completed_num)

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

@@ -32,7 +32,9 @@
 
 
 def json2space(x, oldy=None, name=NodeType.ROOT):
-    """Change search space from json format to hyperopt format
+    """
+    Change search space from json format to hyperopt format
+
     """
     y = list()
     if isinstance(x, dict):
@@ -59,7 +61,9 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
     return y
 
 def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
-    """Json to pramaters.
+    """
+    Json to pramaters.
+
     """
     if isinstance(x, dict):
         if NodeType.TYPE in x.keys():
@@ -117,13 +121,25 @@ def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeTyp
 class Individual:
     """
     Indicidual class to store the indv info.
+
+    Attributes
+    ----------
+    config : str
+        Search space.
+    info : str
+        The str to save information of individual.
+    result : float
+        The final metric of a individual.
+    store_dir : str
+    save_dir : str
     """
 
     def __init__(self, config=None, info=None, result=None, save_dir=None):
         """
         Parameters
         ----------
         config : str
+            A config to represent a group of parameters.
         info : str
         result : float
         save_dir : str
@@ -140,6 +156,8 @@ def __str__(self):
 
     def mutation(self, config=None, info=None, save_dir=None):
         """
+        Mutation by reset state information.
+
         Parameters
         ----------
         config : str
@@ -177,8 +195,11 @@ def __init__(self, optimize_mode, population_size=32):
         self.population = None
         self.space = None
 
+
     def update_search_space(self, search_space):
-        """Update search space.
+        """
+        Update search space.
+
         Search_space contains the information that user pre-defined.
 
         Parameters
@@ -191,31 +212,39 @@ def update_search_space(self, search_space):
         self.random_state = np.random.RandomState()
         self.population = []
         is_rand = dict()
+
         for item in self.space:
             is_rand[item] = True
+
         for _ in range(self.population_size):
             config = json2parameter(
                 self.searchspace_json, is_rand, self.random_state)
             self.population.append(Individual(config=config))
 
+
     def generate_parameters(self, parameter_id, **kwargs):
-        """Returns a dict of trial (hyper-)parameters, as a serializable object.
+        """
+        This function will returns a dict of trial (hyper-)parameters, as a serializable object.
 
         Parameters
         ----------
         parameter_id : int
 
         Returns
         -------
-        config : dict
+        dict
+            A group of candaidte parameters that evolution tuner generated.
         """
         if not self.population:
             raise RuntimeError('The population is empty')
+
         pos = -1
+
         for i in range(len(self.population)):
             if self.population[i].result is None:
                 pos = i
                 break
+
         if pos != -1:
             indiv = copy.deepcopy(self.population[pos])
             self.population.pop(pos)
@@ -230,6 +259,7 @@ def generate_parameters(self, parameter_id, **kwargs):
                                self.population[0].config)
             is_rand = dict()
             mutation_pos = space[random.randint(0, len(space)-1)]
+
             for i in range(len(self.space)):
                 is_rand[self.space[i]] = (self.space[i] == mutation_pos)
             config = json2parameter(
@@ -238,21 +268,27 @@ def generate_parameters(self, parameter_id, **kwargs):
             # remove "_index" from config and save params-id
 
             total_config = config
+
         self.total_data[parameter_id] = total_config
         config = split_index(total_config)
+
         return config
 
+
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
-        '''Record the result from a trial
+        """
+        Record the result from a trial
 
         Parameters
         ----------
-        parameters: dict
+        parameter_id : int
+        parameters : dict
         value : dict/float
             if value is dict, it should have "default" key.
             value is final metrics of the trial.
-        '''
+        """
         reward = extract_scalar_reward(value)
+
         if parameter_id not in self.total_data:
             raise RuntimeError('Received parameter_id not in total_data.')
         # restore the paramsters contains "_index"

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

@@ -422,7 +422,8 @@ def miscs_update_idxs_vals(self,
                     misc_by_id[tid]['vals'][key] = [val]
 
     def get_suggestion(self, random_search=False):
-        """get suggestion from hyperopt
+        """
+        get suggestion from hyperopt
 
         Parameters
         ----------
@@ -473,7 +474,8 @@ def get_suggestion(self, random_search=False):
         return total_params
 
     def import_data(self, data):
-        """Import additional data for tuning
+        """
+        Import additional data for tuning
 
         Parameters
         ----------

src/sdk/pynni/nni/metis_tuner/Regression_GMM/CreateModel.py

@@ -16,7 +16,8 @@
 # BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
 # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+# IN THE SOFTWARE.
 
 import os
 import sys
@@ -31,21 +32,25 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
     '''
     Create the Gaussian Mixture Model
     '''
-    samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
+    samples = [samples_x[i] + [samples_y_aggregation[i]]
+               for i in range(0, len(samples_x))]
 
     # Sorts so that we can get the top samples
     samples = sorted(samples, key=itemgetter(-1))
     samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
     samples_goodbatch = samples[0:samples_goodbatch_size]
     samples_badbatch = samples[samples_goodbatch_size:]
 
-    samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in samples_goodbatch]
+    samples_x_goodbatch = [sample_goodbatch[0:-1]
+                           for sample_goodbatch in samples_goodbatch]
     #samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
-    samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in samples_badbatch]
+    samples_x_badbatch = [sample_badbatch[0:-1]
+                          for sample_badbatch in samples_badbatch]
 
     # === Trains GMM clustering models === #
     #sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
-    bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, samples_goodbatch_size - 1))
+    bgmm_goodbatch = mm.BayesianGaussianMixture(
+        n_components=max(1, samples_goodbatch_size - 1))
     bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
     bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
     bgmm_goodbatch.fit(samples_x_goodbatch)
@@ -55,4 +60,3 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
     model['clusteringmodel_good'] = bgmm_goodbatch
     model['clusteringmodel_bad'] = bgmm_badbatch
     return model
-