update docstring and pylint (#1662)

* update docstring of batchtuner

* update docstring of batch tuner

* update docstring of evolution tuner

* update docstring and pylint of metis_tuner

* fix pylint related to logger in metis_tuner

* fix pylint

* update

* fix pylint in metis_tuner

* update in networkmorphsim_tuner

* update

* update

* update docstring in hyperopt_tuner

* update batch_tuner

* delete unused space

* update in metis

* update sdk_reference.rst

* update netowrkmorhism

* update networkmorphsim

* update batch_tuner

* update batch_tuner

* update

* update metis

* roll back to print

* update Returns

* update

* delete white space

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 @@ TYPE = '_type'
 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):
         """
@@ -55,6 +60,11 @@ class BatchTuner(Tuner):
         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.')
@@ -62,11 +72,14 @@ class BatchTuner(Tuner):
         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):
@@ -76,7 +89,7 @@ class BatchTuner(Tuner):
         ----------
         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.
@@ -84,41 +97,49 @@ class BatchTuner(Tuner):
         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 @@ import nni.parameter_expressions as parameter_expressions
 
 
 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():
@@ -117,6 +121,17 @@ 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):
@@ -124,6 +139,7 @@ class Individual:
         Parameters
         ----------
         config : str
+            A config to represent a group of parameters.
         info : str
         result : float
         save_dir : str
@@ -140,6 +156,8 @@ class Individual:
 
     def mutation(self, config=None, info=None, save_dir=None):
         """
+        Mutation by reset state information.
+
         Parameters
         ----------
         config : str
@@ -177,8 +195,11 @@ class EvolutionTuner(Tuner):
         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
@@ -191,15 +212,19 @@ class EvolutionTuner(Tuner):
         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
         ----------
@@ -207,15 +232,19 @@ class EvolutionTuner(Tuner):
 
         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 @@ class EvolutionTuner(Tuner):
                                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 @@ class EvolutionTuner(Tuner):
             # 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 @@ class HyperoptTuner(Tuner):
                     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 @@ class HyperoptTuner(Tuner):
         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
@@ -31,7 +32,8 @@ 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))
@@ -39,13 +41,16 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
     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
-    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.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 random
@@ -33,14 +34,17 @@ CONSTRAINT_UPPERBOUND = None
 CONSTRAINT_PARAMS_IDX = []
 
 
-def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
+def _ratio_scores(parameters_value, clusteringmodel_gmm_good,
+                  clusteringmodel_gmm_bad):
     '''
     The ratio is smaller the better
     '''
-    ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
+    ratio = clusteringmodel_gmm_good.score(
+        [parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
     sigma = 0
     return ratio, sigma
 
+
 def selection_r(x_bounds,
                 x_types,
                 clusteringmodel_gmm_good,
@@ -60,6 +64,7 @@ def selection_r(x_bounds,
 
     return outputs
 
+
 def selection(x_bounds,
               x_types,
               clusteringmodel_gmm_good,
@@ -69,13 +74,14 @@ def selection(x_bounds,
     '''
     Select the lowest mu value
     '''
-    results = lib_acquisition_function.next_hyperparameter_lowest_mu(\
-                    _ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],\
-                    x_bounds, x_types, minimize_starting_points, \
-                    minimize_constraints_fun=minimize_constraints_fun)
+    results = lib_acquisition_function.next_hyperparameter_lowest_mu(
+        _ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],
+        x_bounds, x_types, minimize_starting_points,
+        minimize_constraints_fun=minimize_constraints_fun)
 
     return results
 
+
 def _rand_with_constraints(x_bounds, x_types):
     '''
     Random generate the variable with constraints
@@ -96,6 +102,7 @@ def _rand_with_constraints(x_bounds, x_types):
                 outputs[i] = random.randint(x_bounds[i][0], x_bounds[i][1])
     return outputs
 
+
 def _minimize_constraints_fun_summation(x):
     '''
     Minimize constraints fun summation

src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py

@@ -17,7 +17,9 @@
 # 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.
-
+"""
+OutlierDectection.py
+"""
 
 import os
 import sys
@@ -30,19 +32,21 @@ sys.path.insert(1, os.path.join(sys.path[0], '..'))
 
 
 def _outlierDetection_threaded(inputs):
-    '''
+    """
     Detect the outlier
-    '''
+    """
     [samples_idx, samples_x, samples_y_aggregation] = inputs
-    sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
-                        % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
+    sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"
+                     % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
     outlier = None
 
-    # Create a diagnostic regression model which removes the sample that we want to evaluate
-    diagnostic_regressor_gp = gp_create_model.create_model(\
-                                    samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
-                                    samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
-    mu, sigma = gp_prediction.predict(samples_x[samples_idx], diagnostic_regressor_gp['model'])
+    # Create a diagnostic regression model which removes the sample that we
+    # want to evaluate
+    diagnostic_regressor_gp = gp_create_model.create_model(
+        samples_x[0:samples_idx] + samples_x[samples_idx + 1:],
+        samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
+    mu, sigma = gp_prediction.predict(
+        samples_x[samples_idx], diagnostic_regressor_gp['model'])
 
     # 2.33 is the z-score for 98% confidence level
     if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
@@ -52,16 +56,18 @@ def _outlierDetection_threaded(inputs):
                    "difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)}
     return outlier
 
+
 def outlierDetection_threaded(samples_x, samples_y_aggregation):
-    '''
+    """
     Use Multi-thread to detect the outlier
-    '''
+    """
     outliers = []
 
-    threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
-                            for samples_idx in range(0, len(samples_x))]
+    threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]
+                      for samples_idx in range(0, len(samples_x))]
     threads_pool = ThreadPool(min(4, len(threads_inputs)))
-    threads_results = threads_pool.map(_outlierDetection_threaded, threads_inputs)
+    threads_results = threads_pool.map(
+        _outlierDetection_threaded, threads_inputs)
     threads_pool.close()
     threads_pool.join()
 
@@ -69,15 +75,13 @@ def outlierDetection_threaded(samples_x, samples_y_aggregation):
         if threads_result is not None:
             outliers.append(threads_result)
         else:
-            print("error here.")
+            print("Error: threads_result is None.")
 
     outliers = outliers if outliers else None
     return outliers
 
+
 def outlierDetection(samples_x, samples_y_aggregation):
-    '''
-    TODO
-    '''
     outliers = []
     for samples_idx, _ in enumerate(samples_x):
         #sys.stderr.write("[%s] DEBUG: Evaluating %d of %d samples\n"
@@ -92,7 +96,8 @@ def outlierDetection(samples_x, samples_y_aggregation):
             outliers.append({"samples_idx": samples_idx,
                              "expected_mu": mu,
                              "expected_sigma": sigma,
-                             "difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)})
+                             "difference": \
+                                abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)})
 
     outliers = outliers if outliers else None
     return outliers

src/sdk/pynni/nni/metis_tuner/lib_acquisition_function.py

@@ -16,7 +16,11 @@
 # 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.
+"""
+lib_acquisition_function.py
+"""
 
 import sys
 import numpy
@@ -33,9 +37,9 @@ def next_hyperparameter_expected_improvement(fun_prediction,
                                              samples_y_aggregation,
                                              minimize_starting_points,
                                              minimize_constraints_fun=None):
-    '''
+    """
     "Expected Improvement" acquisition function
-    '''
+    """
     best_x = None
     best_acquisition_value = None
     x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
@@ -70,6 +74,7 @@ def next_hyperparameter_expected_improvement(fun_prediction,
 
     return outputs
 
+
 def _expected_improvement(x, fun_prediction, fun_prediction_args,
                           x_bounds, x_types, samples_y_aggregation,
                           minimize_constraints_fun):
@@ -77,7 +82,8 @@ def _expected_improvement(x, fun_prediction, fun_prediction_args,
     x = lib_data.match_val_type(x, x_bounds, x_types)
 
     expected_improvement = sys.maxsize
-    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+    if (minimize_constraints_fun is None) or (
+            minimize_constraints_fun(x) is True):
         mu, sigma = fun_prediction(x, *fun_prediction_args)
 
         loss_optimum = min(samples_y_aggregation)
@@ -87,7 +93,7 @@ def _expected_improvement(x, fun_prediction, fun_prediction_args,
         with numpy.errstate(divide="ignore"):
             Z = scaling_factor * (mu - loss_optimum) / sigma
             expected_improvement = scaling_factor * (mu - loss_optimum) * \
-                                        norm.cdf(Z) + sigma * norm.pdf(Z)
+                norm.cdf(Z) + sigma * norm.pdf(Z)
             expected_improvement = 0.0 if sigma == 0.0 else expected_improvement
 
         # We want expected_improvement to be as large as possible
@@ -101,9 +107,9 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
                                           x_bounds, x_types,
                                           minimize_starting_points,
                                           minimize_constraints_fun=None):
-    '''
+    """
     "Lowest Confidence" acquisition function
-    '''
+    """
     best_x = None
     best_acquisition_value = None
     x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
@@ -120,10 +126,12 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
                              x_types,
                              minimize_constraints_fun))
 
-        if (best_acquisition_value) is None or (res.fun < best_acquisition_value):
+        if (best_acquisition_value) is None or (
+                res.fun < best_acquisition_value):
             res.x = numpy.ndarray.tolist(res.x)
             res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
-            if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
+            if (minimize_constraints_fun is None) or (
+                    minimize_constraints_fun(res.x) is True):
                 best_acquisition_value = res.fun
                 best_x = res.x
 
@@ -134,13 +142,15 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
                    'expected_sigma': sigma, 'acquisition_func': "lc"}
     return outputs
 
+
 def _lowest_confidence(x, fun_prediction, fun_prediction_args,
                        x_bounds, x_types, minimize_constraints_fun):
     # This is only for step-wise optimization
     x = lib_data.match_val_type(x, x_bounds, x_types)
 
     ci = sys.maxsize
-    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+    if (minimize_constraints_fun is None) or (
+            minimize_constraints_fun(x) is True):
         mu, sigma = fun_prediction(x, *fun_prediction_args)
         ci = (sigma * 1.96 * 2) / mu
         # We want ci to be as large as possible
@@ -156,9 +166,9 @@ def next_hyperparameter_lowest_mu(fun_prediction,
                                   x_bounds, x_types,
                                   minimize_starting_points,
                                   minimize_constraints_fun=None):
-    '''
+    """
     "Lowest Mu" acquisition function
-    '''
+    """
     best_x = None
     best_acquisition_value = None
     x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
@@ -169,13 +179,15 @@ def next_hyperparameter_lowest_mu(fun_prediction,
                        x0=starting_point.reshape(1, -1),
                        bounds=x_bounds_minmax,
                        method="L-BFGS-B",
-                       args=(fun_prediction, fun_prediction_args, \
+                       args=(fun_prediction, fun_prediction_args,
                              x_bounds, x_types, minimize_constraints_fun))
 
-        if (best_acquisition_value is None) or (res.fun < best_acquisition_value):
+        if (best_acquisition_value is None) or (
+                res.fun < best_acquisition_value):
             res.x = numpy.ndarray.tolist(res.x)
             res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
-            if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
+            if (minimize_constraints_fun is None) or (
+                    minimize_constraints_fun(res.x) is True):
                 best_acquisition_value = res.fun
                 best_x = res.x
 
@@ -189,14 +201,14 @@ def next_hyperparameter_lowest_mu(fun_prediction,
 
 def _lowest_mu(x, fun_prediction, fun_prediction_args,
                x_bounds, x_types, minimize_constraints_fun):
-    '''
+    """
     Calculate the lowest mu
-    '''
+    """
     # This is only for step-wise optimization
     x = lib_data.match_val_type(x, x_bounds, x_types)
 
     mu = sys.maxsize
-    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+    if (minimize_constraints_fun is None) or (
+            minimize_constraints_fun(x) is True):
         mu, _ = fun_prediction(x, *fun_prediction_args)
     return mu
-    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py

@@ -16,7 +16,11 @@
 # 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.
+"""
+lib_constraint_summation.py
+"""
 
 import math
 import random
@@ -39,6 +43,7 @@ def check_feasibility(x_bounds, lowerbound, upperbound):
     return (x_bounds_lowerbound <= lowerbound <= x_bounds_upperbound) or \
            (x_bounds_lowerbound <= upperbound <= x_bounds_upperbound)
 
+
 def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
     '''
     Key idea is that we try to move towards upperbound, by randomly choose one
@@ -55,7 +60,8 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
             if x_types[i] == "discrete_int":
                 x_idx_sorted.append([i, len(x_bounds[i])])
             elif (x_types[i] == "range_int") or (x_types[i] == "range_continuous"):
-                x_idx_sorted.append([i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
+                x_idx_sorted.append(
+                    [i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
         x_idx_sorted = sorted(x_idx_sorted, key=itemgetter(1))
 
         for _ in range(max_retries):
@@ -77,12 +83,13 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
                                     temp.append(j)
                             # Randomly pick a number from the integer array
                             if temp:
-                                outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
+                                outputs[x_idx] = temp[random.randint(
+                                    0, len(temp) - 1)]
 
                         elif (x_types[x_idx] == "range_int") or \
-                                    (x_types[x_idx] == "range_continuous"):
-                            outputs[x_idx] = random.randint(x_bounds[x_idx][0],
-                                                            min(x_bounds[x_idx][-1], budget_max))
+                                (x_types[x_idx] == "range_continuous"):
+                            outputs[x_idx] = random.randint(
+                                x_bounds[x_idx][0], min(x_bounds[x_idx][-1], budget_max))
 
                 else:
                     # The last x that we need to assign a random number
@@ -91,26 +98,28 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
 
                     # This check:
                     # is our smallest possible value going to overflow the available budget space,
-                    # and is our largest possible value going to underflow the lower bound
+                    # and is our largest possible value going to underflow the
+                    # lower bound
                     if (x_bounds[x_idx][0] <= budget_max) and \
                             (x_bounds[x_idx][-1] >= randint_lowerbound):
                         if x_types[x_idx] == "discrete_int":
                             temp = []
                             for j in x_bounds[x_idx]:
-                                # if (j <= budget_max) and (j >= randint_lowerbound):
+                                # if (j <= budget_max) and (j >=
+                                # randint_lowerbound):
                                 if randint_lowerbound <= j <= budget_max:
                                     temp.append(j)
                             if temp:
-                                outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
+                                outputs[x_idx] = temp[random.randint(
+                                    0, len(temp) - 1)]
                         elif (x_types[x_idx] == "range_int") or \
                                 (x_types[x_idx] == "range_continuous"):
-                            outputs[x_idx] = random.randint(randint_lowerbound,
-                                                            min(x_bounds[x_idx][1], budget_max))
+                            outputs[x_idx] = random.randint(
+                                randint_lowerbound, min(
+                                    x_bounds[x_idx][1], budget_max))
                 if outputs[x_idx] is None:
                     break
-                else:
-                    budget_allocated += outputs[x_idx]
+                budget_allocated += outputs[x_idx]
             if None not in outputs:
                 break
     return outputs
-    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/lib_data.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 math
 import random
@@ -56,7 +57,7 @@ def rand(x_bounds, x_types):
             temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
             outputs.append(temp)
         elif x_types[i] == "range_int":
-            temp = random.randint(x_bounds[i][0], x_bounds[i][1] -1)
+            temp = random.randint(x_bounds[i][0], x_bounds[i][1] - 1)
             outputs.append(temp)
         elif x_types[i] == "range_continuous":
             temp = random.uniform(x_bounds[i][0], x_bounds[i][1])

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

@@ -16,7 +16,11 @@
 # 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.
+"""
+metis_tuner.py
+"""
 
 import copy
 import logging
@@ -51,10 +55,45 @@ class MetisTuner(Tuner):
 
     More algorithm information you could reference here:
     https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/
+
+    Attributes
+    ----------
+        optimize_mode : str
+            optimize_mode is a string that including two mode "maximize" and "minimize"
+
+        no_resampling : bool
+            True or False.
+            Should Metis consider re-sampling as part of the search strategy?
+            If you are confident that the training dataset is noise-free,
+            then you do not need re-sampling.
+
+        no_candidates : bool
+            True or False.
+            Should Metis suggest parameters for the next benchmark?
+            If you do not plan to do more benchmarks,
+            Metis can skip this step.
+
+        selection_num_starting_points : int
+            How many times Metis should try to find the global optimal in the search space?
+            The higher the number, the longer it takes to output the solution.
+
+        cold_start_num : int
+            Metis need some trial result to get cold start.
+            when the number of trial result is less than
+            cold_start_num, Metis will randomly sample hyper-parameter for trial.
+
+        exploration_probability: float
+            The probability of Metis to select parameter from exploration instead of exploitation.
     """
 
-    def __init__(self, optimize_mode="maximize", no_resampling=True, no_candidates=False,
-                 selection_num_starting_points=600, cold_start_num=10, exploration_probability=0.9):
+    def __init__(
+            self,
+            optimize_mode="maximize",
+            no_resampling=True,
+            no_candidates=False,
+            selection_num_starting_points=600,
+            cold_start_num=10,
+            exploration_probability=0.9):
         """
         Parameters
         ----------
@@ -62,23 +101,34 @@ class MetisTuner(Tuner):
             optimize_mode is a string that including two mode "maximize" and "minimize"
 
         no_resampling : bool
-            True or False. Should Metis consider re-sampling as part of the search strategy?
-        If you are confident that the training dataset is noise-free, then you do not need re-sampling.
-
-        no_candidates: bool
-            True or False. Should Metis suggest parameters for the next benchmark?
-        If you do not plan to do more benchmarks, Metis can skip this step.
-
-        selection_num_starting_points: int
-            how many times Metis should try to find the global optimal in the search space?
-        The higher the number, the longer it takes to output the solution.
+            True or False.
+            Should Metis consider re-sampling as part of the search strategy?
+            If you are confident that the training dataset is noise-free,
+            then you do not need re-sampling.
+
+        no_candidates : bool
+            True or False.
+            Should Metis suggest parameters for the next benchmark?
+            If you do not plan to do more benchmarks,
+            Metis can skip this step.
+
+        selection_num_starting_points : int
+            How many times Metis should try to find the global optimal in the search space?
+            The higher the number, the longer it takes to output the solution.
+
+        cold_start_num : int
+            Metis need some trial result to get cold start.
+            when the number of trial result is less than
+            cold_start_num, Metis will randomly sample hyper-parameter for trial.
+
+        exploration_probability : float
+            The probability of Metis to select parameter from exploration instead of exploitation.
 
-        cold_start_num: int
-            Metis need some trial result to get cold start. when the number of trial result is less than
-        cold_start_num, Metis will randomly sample hyper-parameter for trial.
+        x_bounds : list
+            The constration of parameters.
 
-        exploration_probability: float
-            The probability of Metis to select parameter from exploration instead of exploitation.
+        x_types : list
+            The type of parameters.
         """
 
         self.samples_x = []
@@ -101,7 +151,8 @@ class MetisTuner(Tuner):
 
 
     def update_search_space(self, search_space):
-        """Update the self.x_bounds and self.x_types by the search_space.json
+        """
+        Update the self.x_bounds and self.x_types by the search_space.json
 
         Parameters
         ----------
@@ -120,12 +171,20 @@ class MetisTuner(Tuner):
                 key_range = search_space[key]['_value']
                 idx = self.key_order.index(key)
                 if key_type == 'quniform':
-                    if key_range[2] == 1 and key_range[0].is_integer() and key_range[1].is_integer():
-                        self.x_bounds[idx] = [key_range[0], key_range[1]+1]
+                    if key_range[2] == 1 and key_range[0].is_integer(
+                    ) and key_range[1].is_integer():
+                        self.x_bounds[idx] = [key_range[0], key_range[1] + 1]
                         self.x_types[idx] = 'range_int'
                     else:
                         low, high, q = key_range
-                        bounds = np.clip(np.arange(np.round(low/q), np.round(high/q)+1) * q, low, high)
+                        bounds = np.clip(
+                            np.arange(
+                                np.round(
+                                    low / q),
+                                np.round(
+                                    high / q) + 1) * q,
+                            low,
+                            high)
                         self.x_bounds[idx] = bounds
                         self.x_types[idx] = 'discrete_int'
                 elif key_type == 'randint':
@@ -139,22 +198,28 @@ class MetisTuner(Tuner):
 
                     for key_value in key_range:
                         if not isinstance(key_value, (int, float)):
-                            raise RuntimeError("Metis Tuner only support numerical choice.")
+                            raise RuntimeError(
+                                "Metis Tuner only support numerical choice.")
 
                     self.x_types[idx] = 'discrete_int'
                 else:
-                    logger.info("Metis Tuner doesn't support this kind of variable: %s", key_type)
-                    raise RuntimeError("Metis Tuner doesn't support this kind of variable: " + str(key_type))
+                    logger.info(
+                        "Metis Tuner doesn't support this kind of variable: %s",
+                        str(key_type))
+                    raise RuntimeError(
+                        "Metis Tuner doesn't support this kind of variable: %s" %
+                        str(key_type))
         else:
             logger.info("The format of search space is not a dict.")
             raise RuntimeError("The format of search space is not a dict.")
 
-        self.minimize_starting_points = _rand_init(self.x_bounds, self.x_types, \
-                                                        self.selection_num_starting_points)
+        self.minimize_starting_points = _rand_init(
+            self.x_bounds, self.x_types, self.selection_num_starting_points)
 
 
     def _pack_output(self, init_parameter):
-        """Pack the output
+        """
+        Pack the output
 
         Parameters
         ----------
@@ -167,14 +232,18 @@ class MetisTuner(Tuner):
         output = {}
         for i, param in enumerate(init_parameter):
             output[self.key_order[i]] = param
+
         return output
 
 
     def generate_parameters(self, parameter_id, **kwargs):
-        """Generate next parameter for trial
+        """
+        Generate next parameter for trial
+
         If the number of trial result is lower than cold start number,
         metis will first random generate some parameters.
-        Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixture Model.
+        Otherwise, metis will choose the parameters by
+        the Gussian Process Model and the Gussian Mixture Model.
 
         Parameters
         ----------
@@ -188,26 +257,34 @@ class MetisTuner(Tuner):
             init_parameter = _rand_init(self.x_bounds, self.x_types, 1)[0]
             results = self._pack_output(init_parameter)
         else:
-            self.minimize_starting_points = _rand_init(self.x_bounds, self.x_types, \
-                                                       self.selection_num_starting_points)
-            results = self._selection(self.samples_x, self.samples_y_aggregation, self.samples_y,
-                                      self.x_bounds, self.x_types,
-                                      threshold_samplessize_resampling=(None if self.no_resampling is True else 50),
-                                      no_candidates=self.no_candidates,
-                                      minimize_starting_points=self.minimize_starting_points,
-                                      minimize_constraints_fun=self.minimize_constraints_fun)
-
-        logger.info("Generate paramageters:\n%s", results)
+            self.minimize_starting_points = _rand_init(
+                self.x_bounds, self.x_types, self.selection_num_starting_points)
+            results = self._selection(
+                self.samples_x,
+                self.samples_y_aggregation,
+                self.samples_y,
+                self.x_bounds,
+                self.x_types,
+                threshold_samplessize_resampling=(
+                    None if self.no_resampling is True else 50),
+                no_candidates=self.no_candidates,
+                minimize_starting_points=self.minimize_starting_points,
+                minimize_constraints_fun=self.minimize_constraints_fun)
+
+        logger.info("Generate paramageters: \n%s", str(results))
         return results
 
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """Tuner receive result from trial.
+        """
+        Tuner receive result from trial.
 
         Parameters
         ----------
         parameter_id : int
+            The id of parameters, generated by nni manager.
         parameters : dict
+            A group of parameters that trial has tried.
         value : dict/float
             if value is dict, it should have "default" key.
         """
@@ -216,8 +293,8 @@ class MetisTuner(Tuner):
             value = -value
 
         logger.info("Received trial result.")
-        logger.info("value is :%s", value)
-        logger.info("parameter is : %s", parameters)
+        logger.info("value is : %s", str(value))
+        logger.info("parameter is : %s", str(parameters))
 
         # parse parameter to sample_x
         sample_x = [0 for i in range(len(self.key_order))]
@@ -244,11 +321,19 @@ class MetisTuner(Tuner):
             self.samples_y_aggregation.append([value])
 
 
-    def _selection(self, samples_x, samples_y_aggregation, samples_y,
-                   x_bounds, x_types, max_resampling_per_x=3,
-                   threshold_samplessize_exploitation=12,
-                   threshold_samplessize_resampling=50, no_candidates=False,
-                   minimize_starting_points=None, minimize_constraints_fun=None):
+    def _selection(
+            self,
+            samples_x,
+            samples_y_aggregation,
+            samples_y,
+            x_bounds,
+            x_types,
+            max_resampling_per_x=3,
+            threshold_samplessize_exploitation=12,
+            threshold_samplessize_resampling=50,
+            no_candidates=False,
+            minimize_starting_points=None,
+            minimize_constraints_fun=None):
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -259,7 +344,8 @@ class MetisTuner(Tuner):
         samples_size_unique = len(samples_y)
 
         # ===== STEP 1: Compute the current optimum =====
-        gp_model = gp_create_model.create_model(samples_x, samples_y_aggregation)
+        gp_model = gp_create_model.create_model(
+            samples_x, samples_y_aggregation)
         lm_current = gp_selection.selection(
             "lm",
             samples_y_aggregation,
@@ -278,7 +364,7 @@ class MetisTuner(Tuner):
         })
 
         if no_candidates is False:
-            # ===== STEP 2: Get recommended configurations for exploration =====
+            # ===== STEP 2: Get recommended configurations for exploration ====
             results_exploration = gp_selection.selection(
                 "lc",
                 samples_y_aggregation,
@@ -303,25 +389,31 @@ class MetisTuner(Tuner):
             else:
                 logger.info("DEBUG: No suitable exploration candidates were")
 
-            # ===== STEP 3: Get recommended configurations for exploitation =====
+            # ===== STEP 3: Get recommended configurations for exploitation ===
             if samples_size_all >= threshold_samplessize_exploitation:
                 logger.info("Getting candidates for exploitation...\n")
                 try:
-                    gmm = gmm_create_model.create_model(samples_x, samples_y_aggregation)
+                    gmm = gmm_create_model.create_model(
+                        samples_x, samples_y_aggregation)
 
                     if ("discrete_int" in x_types) or ("range_int" in x_types):
-                        results_exploitation = gmm_selection.selection(x_bounds, x_types,
-                                                                       gmm['clusteringmodel_good'],
-                                                                       gmm['clusteringmodel_bad'],
-                                                                       minimize_starting_points,
-                                                                       minimize_constraints_fun=minimize_constraints_fun)
+                        results_exploitation = gmm_selection.selection(
+                            x_bounds,
+                            x_types,
+                            gmm['clusteringmodel_good'],
+                            gmm['clusteringmodel_bad'],
+                            minimize_starting_points,
+                            minimize_constraints_fun=minimize_constraints_fun)
                     else:
-                        # If all parameters are of "range_continuous", let's use GMM to generate random starting points
-                        results_exploitation = gmm_selection.selection_r(x_bounds, x_types,
-                                                                         gmm['clusteringmodel_good'],
-                                                                         gmm['clusteringmodel_bad'],
-                                                                         num_starting_points=self.selection_num_starting_points,
-                                                                         minimize_constraints_fun=minimize_constraints_fun)
+                        # If all parameters are of "range_continuous",
+                        # let's use GMM to generate random starting points
+                        results_exploitation = gmm_selection.selection_r(
+                            x_bounds,
+                            x_types,
+                            gmm['clusteringmodel_good'],
+                            gmm['clusteringmodel_bad'],
+                            num_starting_points=self.selection_num_starting_points,
+                            minimize_constraints_fun=minimize_constraints_fun)
 
                     if results_exploitation is not None:
                         if _num_past_samples(results_exploitation['hyperparameter'], samples_x, samples_y) == 0:
@@ -335,24 +427,30 @@ class MetisTuner(Tuner):
                             }
                             candidates.append(temp_candidate)
 
-                            logger.info("DEBUG: 1 exploitation_gmm candidate selected\n")
+                            logger.info(
+                                "DEBUG: 1 exploitation_gmm candidate selected\n")
                             logger.info(temp_candidate)
                     else:
-                        logger.info("DEBUG: No suitable exploitation_gmm candidates were found\n")
+                        logger.info(
+                            "DEBUG: No suitable exploitation_gmm candidates were found\n")
 
                 except ValueError as exception:
                     # The exception: ValueError: Fitting the mixture model failed
                     # because some components have ill-defined empirical covariance
                     # (for instance caused by singleton or collapsed samples).
-                    # Try to decrease the number of components, or increase reg_covar.
-                    logger.info("DEBUG: No suitable exploitation_gmm candidates were found due to exception.")
+                    # Try to decrease the number of components, or increase
+                    # reg_covar.
+                    logger.info(
+                        "DEBUG: No suitable exploitation_gmm \
+                        candidates were found due to exception.")
                     logger.info(exception)
 
             # ===== STEP 4: Get a list of outliers =====
             if (threshold_samplessize_resampling is not None) and \
-                        (samples_size_unique >= threshold_samplessize_resampling):
+                    (samples_size_unique >= threshold_samplessize_resampling):
                 logger.info("Getting candidates for re-sampling...\n")
-                results_outliers = gp_outlier_detection.outlierDetection_threaded(samples_x, samples_y_aggregation)
+                results_outliers = gp_outlier_detection.outlierDetection_threaded(
+                    samples_x, samples_y_aggregation)
 
                 if results_outliers is not None:
                     for results_outlier in results_outliers:  # pylint: disable=not-an-iterable
@@ -365,11 +463,13 @@ class MetisTuner(Tuner):
                     logger.info("DEBUG: %d re-sampling candidates selected\n")
                     logger.info(temp_candidate)
                 else:
-                    logger.info("DEBUG: No suitable resampling candidates were found\n")
+                    logger.info(
+                        "DEBUG: No suitable resampling candidates were found\n")
 
             if candidates:
-                # ===== STEP 5: Compute the information gain of each candidate towards the optimum =====
-                logger.info("Evaluating information gain of %d candidates...\n")
+                # ===== STEP 5: Compute the information gain of each candidate
+                logger.info(
+                    "Evaluating information gain of %d candidates...\n")
                 next_improvement = 0
 
                 threads_inputs = [[
@@ -377,36 +477,45 @@ class MetisTuner(Tuner):
                     minimize_constraints_fun, minimize_starting_points
                 ] for candidate in candidates]
                 threads_pool = ThreadPool(4)
-                # Evaluate what would happen if we actually sample each candidate
-                threads_results = threads_pool.map(_calculate_lowest_mu_threaded, threads_inputs)
+                # Evaluate what would happen if we actually sample each
+                # candidate
+                threads_results = threads_pool.map(
+                    _calculate_lowest_mu_threaded, threads_inputs)
                 threads_pool.close()
                 threads_pool.join()
 
                 for threads_result in threads_results:
                     if threads_result['expected_lowest_mu'] < lm_current['expected_mu']:
                         # Information gain
-                        temp_improvement = threads_result['expected_lowest_mu'] - lm_current['expected_mu']
+                        temp_improvement = threads_result['expected_lowest_mu'] - \
+                            lm_current['expected_mu']
 
                         if next_improvement > temp_improvement:
                             next_improvement = temp_improvement
                             next_candidate = threads_result['candidate']
             else:
-                # ===== STEP 6: If we have no candidates, randomly pick one =====
+                # ===== STEP 6: If we have no candidates, randomly pick one ===
                 logger.info(
                     "DEBUG: No candidates from exploration, exploitation,\
                                  and resampling. We will random a candidate for next_candidate\n"
                 )
 
-                next_candidate = _rand_with_constraints(x_bounds, x_types) \
-                                    if minimize_starting_points is None else minimize_starting_points[0]
-                next_candidate = lib_data.match_val_type(next_candidate, x_bounds, x_types)
-                expected_mu, expected_sigma = gp_prediction.predict(next_candidate, gp_model['model'])
-                next_candidate = {'hyperparameter': next_candidate, 'reason': "random",
-                                  'expected_mu': expected_mu, 'expected_sigma': expected_sigma}
-
-        # ===== STEP 7 =====
-        # If current optimal hyperparameter occurs in the history or exploration probability is less than the threshold,
-        # take next config as exploration step
+                next_candidate = _rand_with_constraints(
+                    x_bounds,
+                    x_types) if minimize_starting_points is None else minimize_starting_points[0]
+                next_candidate = lib_data.match_val_type(
+                    next_candidate, x_bounds, x_types)
+                expected_mu, expected_sigma = gp_prediction.predict(
+                    next_candidate, gp_model['model'])
+                next_candidate = {
+                    'hyperparameter': next_candidate,
+                    'reason': "random",
+                    'expected_mu': expected_mu,
+                    'expected_sigma': expected_sigma}
+
+        # STEP 7: If current optimal hyperparameter occurs in the history
+        # or exploration probability is less than the threshold, take next
+        # config as exploration step
         outputs = self._pack_output(lm_current['hyperparameter'])
         ap = random.uniform(0, 1)
         if outputs in self.total_data or ap <= self.exploration_probability:
@@ -419,11 +528,13 @@ class MetisTuner(Tuner):
         return outputs
 
     def import_data(self, data):
-        """Import additional data for tuning
+        """
+        Import additional data for tuning
+
         Parameters
         ----------
-        data:
-            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        data : a list of dict
+               each of which has at least two keys: 'parameter' and 'value'.
         """
         _completed_num = 0
         for trial_info in data:
@@ -437,18 +548,26 @@ class MetisTuner(Tuner):
                 logger.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
             self.supplement_data_num += 1
-            _parameter_id = '_'.join(["ImportData", str(self.supplement_data_num)])
+            _parameter_id = '_'.join(
+                ["ImportData", str(self.supplement_data_num)])
             self.total_data.append(_params)
-            self.receive_trial_result(parameter_id=_parameter_id, parameters=_params, value=_value)
+            self.receive_trial_result(
+                parameter_id=_parameter_id,
+                parameters=_params,
+                value=_value)
         logger.info("Successfully import data to metis tuner.")
 
+
 def _rand_with_constraints(x_bounds, x_types):
     outputs = None
     x_bounds_withconstraints = [x_bounds[i] for i in CONSTRAINT_PARAMS_IDX]
     x_types_withconstraints = [x_types[i] for i in CONSTRAINT_PARAMS_IDX]
 
-    x_val_withconstraints = lib_constraint_summation.rand(x_bounds_withconstraints,\
-                                x_types_withconstraints, CONSTRAINT_LOWERBOUND, CONSTRAINT_UPPERBOUND)
+    x_val_withconstraints = lib_constraint_summation.rand(
+        x_bounds_withconstraints,
+        x_types_withconstraints,
+        CONSTRAINT_LOWERBOUND,
+        CONSTRAINT_UPPERBOUND)
     if not x_val_withconstraints:
         outputs = [None] * len(x_bounds)
 
@@ -462,12 +581,18 @@ def _rand_with_constraints(x_bounds, x_types):
 
 
 def _calculate_lowest_mu_threaded(inputs):
-    [candidate, samples_x, samples_y, x_bounds, x_types, minimize_constraints_fun, minimize_starting_points] = inputs
+    [candidate, samples_x, samples_y, x_bounds, x_types,
+     minimize_constraints_fun, minimize_starting_points] = inputs
 
     outputs = {"candidate": candidate, "expected_lowest_mu": None}
 
-    for expected_mu in [candidate['expected_mu'] + 1.96 * candidate['expected_sigma'],
-                        candidate['expected_mu'] - 1.96 * candidate['expected_sigma']]:
+    for expected_mu in [
+            candidate['expected_mu'] +
+            1.96 *
+            candidate['expected_sigma'],
+            candidate['expected_mu'] -
+            1.96 *
+            candidate['expected_sigma']]:
         temp_samples_x = copy.deepcopy(samples_x)
         temp_samples_y = copy.deepcopy(samples_y)
 
@@ -480,8 +605,10 @@ def _calculate_lowest_mu_threaded(inputs):
             temp_samples_y.append([expected_mu])
 
         # Aggregates multiple observation of the sample sampling points
-        temp_y_aggregation = [statistics.median(temp_sample_y) for temp_sample_y in temp_samples_y]
-        temp_gp = gp_create_model.create_model(temp_samples_x, temp_y_aggregation)
+        temp_y_aggregation = [statistics.median(
+            temp_sample_y) for temp_sample_y in temp_samples_y]
+        temp_gp = gp_create_model.create_model(
+            temp_samples_x, temp_y_aggregation)
         temp_results = gp_selection.selection(
             "lm",
             temp_y_aggregation,
@@ -491,7 +618,8 @@ def _calculate_lowest_mu_threaded(inputs):
             minimize_starting_points,
             minimize_constraints_fun=minimize_constraints_fun)
 
-        if outputs["expected_lowest_mu"] is None or outputs["expected_lowest_mu"] > temp_results['expected_mu']:
+        if outputs["expected_lowest_mu"] is None \
+            or outputs["expected_lowest_mu"] > temp_results['expected_mu']:
             outputs["expected_lowest_mu"] = temp_results['expected_mu']
 
     return outputs
@@ -510,18 +638,19 @@ def _rand_init(x_bounds, x_types, selection_num_starting_points):
     '''
     Random sample some init seed within bounds.
     '''
-    return [lib_data.rand(x_bounds, x_types) for i \
-                    in range(0, selection_num_starting_points)]
+    return [lib_data.rand(x_bounds, x_types) for i
+            in range(0, selection_num_starting_points)]
 
 
 def get_median(temp_list):
-    """Return median
+    """
+    Return median
     """
     num = len(temp_list)
     temp_list.sort()
     print(temp_list)
     if num % 2 == 0:
-        median = (temp_list[int(num/2)] + temp_list[int(num/2) - 1]) / 2
+        median = (temp_list[int(num / 2)] + temp_list[int(num / 2) - 1]) / 2
     else:
-        median = temp_list[int(num/2)]
+        median = temp_list[int(num / 2)]
     return median

src/sdk/pynni/nni/networkmorphism_tuner/bayesian.py

@@ -38,7 +38,7 @@ from nni.networkmorphism_tuner.layers import is_layer
 def layer_distance(a, b):
     """The distance between two layers."""
     # pylint: disable=unidiomatic-typecheck
-    if type(a) != type(b):
+    if not isinstance(a, type(b)):
         return 1.0
     if is_layer(a, "Conv"):
         att_diff = [
@@ -96,7 +96,8 @@ def skip_connection_distance(a, b):
         return 1.0
     len_a = abs(a[1] - a[0])
     len_b = abs(b[1] - b[0])
-    return (abs(a[0] - b[0]) + abs(len_a - len_b)) / (max(a[0], b[0]) + max(len_a, len_b))
+    return (abs(a[0] - b[0]) + abs(len_a - len_b)) / \
+        (max(a[0], b[0]) + max(len_a, len_b))
 
 
 def skip_connections_distance(list_a, list_b):
@@ -161,7 +162,8 @@ class IncrementalGaussianProcess:
     def incremental_fit(self, train_x, train_y):
         """ Incrementally fit the regressor. """
         if not self._first_fitted:
-            raise ValueError("The first_fit function needs to be called first.")
+            raise ValueError(
+                "The first_fit function needs to be called first.")
 
         train_x, train_y = np.array(train_x), np.array(train_y)
 
@@ -174,7 +176,7 @@ class IncrementalGaussianProcess:
         temp_distance_matrix = np.concatenate((up_k, down_k), axis=0)
         k_matrix = bourgain_embedding_matrix(temp_distance_matrix)
         diagonal = np.diag_indices_from(k_matrix)
-        diagonal = (diagonal[0][-len(train_x) :], diagonal[1][-len(train_x) :])
+        diagonal = (diagonal[0][-len(train_x):], diagonal[1][-len(train_x):])
         k_matrix[diagonal] += self.alpha
 
         try:
@@ -186,7 +188,8 @@ class IncrementalGaussianProcess:
         self._y = np.concatenate((self._y, train_y), axis=0)
         self._distance_matrix = temp_distance_matrix
 
-        self._alpha_vector = cho_solve((self._l_matrix, True), self._y)  # Line 3
+        self._alpha_vector = cho_solve(
+            (self._l_matrix, True), self._y)  # Line 3
 
         return self
 
@@ -209,7 +212,8 @@ class IncrementalGaussianProcess:
 
         self._l_matrix = cholesky(k_matrix, lower=True)  # Line 2
 
-        self._alpha_vector = cho_solve((self._l_matrix, True), self._y)  # Line 3
+        self._alpha_vector = cho_solve(
+            (self._l_matrix, True), self._y)  # Line 3
 
         self._first_fitted = True
         return self
@@ -227,7 +231,9 @@ class IncrementalGaussianProcess:
 
         # compute inverse K_inv of K based on its Cholesky
         # decomposition L and its inverse L_inv
-        l_inv = solve_triangular(self._l_matrix.T, np.eye(self._l_matrix.shape[0]))
+        l_inv = solve_triangular(
+            self._l_matrix.T, np.eye(
+                self._l_matrix.shape[0]))
         k_inv = l_inv.dot(l_inv.T)
         # Compute variance of predictive distribution
         y_var = np.ones(len(train_x), dtype=np.float)
@@ -378,7 +384,11 @@ class BayesianOptimizer:
                         continue
 
                     temp_acq_value = self.acq(temp_graph)
-                    pq.put(elem_class(temp_acq_value, elem.father_id, temp_graph))
+                    pq.put(
+                        elem_class(
+                            temp_acq_value,
+                            elem.father_id,
+                            temp_graph))
                     descriptors.append(temp_graph.extract_descriptor())
                     if self._accept_new_acq_value(opt_acq, temp_acq_value):
                         opt_acq = temp_acq_value

src/sdk/pynni/nni/networkmorphism_tuner/graph.py

@@ -249,7 +249,8 @@ class Graph:
                 self.reverse_adj_list[v_id].remove(edge_tuple)
                 break
         self.reverse_adj_list[new_v_id].append((u_id, layer_id))
-        for index, value in enumerate(self.layer_id_to_output_node_ids[layer_id]):
+        for index, value in enumerate(
+                self.layer_id_to_output_node_ids[layer_id]):
             if value == v_id:
                 self.layer_id_to_output_node_ids[layer_id][index] = new_v_id
                 break
@@ -350,7 +351,8 @@ class Graph:
                 self._replace_layer(layer_id, new_layer)
 
             elif is_layer(layer, "BatchNormalization"):
-                new_layer = wider_bn(layer, start_dim, total_dim, n_add, self.weighted)
+                new_layer = wider_bn(
+                    layer, start_dim, total_dim, n_add, self.weighted)
                 self._replace_layer(layer_id, new_layer)
                 self._search(v, start_dim, total_dim, n_add)
 
@@ -405,7 +407,8 @@ class Graph:
             target_id: A convolutional layer ID. The new block should be inserted after the block.
             new_layer: An instance of StubLayer subclasses.
         """
-        self.operation_history.append(("to_deeper_model", target_id, new_layer))
+        self.operation_history.append(
+            ("to_deeper_model", target_id, new_layer))
         input_id = self.layer_id_to_input_node_ids[target_id][0]
         output_id = self.layer_id_to_output_node_ids[target_id][0]
         if self.weighted:
@@ -478,14 +481,20 @@ class Graph:
         pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
         end_node_id = self.layer_id_to_output_node_ids[end_id][0]
 
-        skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
+        skip_output_id = self._insert_pooling_layer_chain(
+            start_node_id, end_node_id)
 
         # Add the conv layer
-        new_conv_layer = get_conv_class(self.n_dim)(filters_start, filters_end, 1)
+        new_conv_layer = get_conv_class(
+            self.n_dim)(
+                filters_start,
+                filters_end,
+                1)
         skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
 
         # Add the add layer.
-        add_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
+        add_input_node_id = self._add_node(
+            deepcopy(self.node_list[end_node_id]))
         add_layer = StubAdd()
 
         self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
@@ -504,7 +513,8 @@ class Graph:
             weights = np.zeros((filters_end, filters_start) + filter_shape)
             bias = np.zeros(filters_end)
             new_conv_layer.set_weights(
-                (add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+                (add_noise(weights, np.array([0, 1])), add_noise(
+                    bias, np.array([0, 1])))
             )
 
     def to_concat_skip_model(self, start_id, end_id):
@@ -513,7 +523,8 @@ class Graph:
             start_id: The convolutional layer ID, after which to start the skip-connection.
             end_id: The convolutional layer ID, after which to end the skip-connection.
         """
-        self.operation_history.append(("to_concat_skip_model", start_id, end_id))
+        self.operation_history.append(
+            ("to_concat_skip_model", start_id, end_id))
         filters_end = self.layer_list[end_id].output.shape[-1]
         filters_start = self.layer_list[start_id].output.shape[-1]
         start_node_id = self.layer_id_to_output_node_ids[start_id][0]
@@ -521,9 +532,11 @@ class Graph:
         pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
         end_node_id = self.layer_id_to_output_node_ids[end_id][0]
 
-        skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
+        skip_output_id = self._insert_pooling_layer_chain(
+            start_node_id, end_node_id)
 
-        concat_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
+        concat_input_node_id = self._add_node(
+            deepcopy(self.node_list[end_node_id]))
         self._redirect_edge(pre_end_node_id, end_node_id, concat_input_node_id)
 
         concat_layer = StubConcatenate()
@@ -532,7 +545,10 @@ class Graph:
             self.node_list[skip_output_id],
         ]
         concat_output_node_id = self._add_node(Node(concat_layer.output_shape))
-        self._add_edge(concat_layer, concat_input_node_id, concat_output_node_id)
+        self._add_edge(
+            concat_layer,
+            concat_input_node_id,
+            concat_output_node_id)
         self._add_edge(concat_layer, skip_output_id, concat_output_node_id)
         concat_layer.output = self.node_list[concat_output_node_id]
         self.node_list[concat_output_node_id].shape = concat_layer.output_shape
@@ -559,7 +575,8 @@ class Graph:
             )
             bias = np.zeros(filters_end)
             new_conv_layer.set_weights(
-                (add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+                (add_noise(weights, np.array([0, 1])), add_noise(
+                    bias, np.array([0, 1])))
             )
 
     def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
@@ -568,7 +585,8 @@ class Graph:
             new_layer = deepcopy(layer)
             if is_layer(new_layer, "Conv"):
                 filters = self.node_list[start_node_id].shape[-1]
-                new_layer = get_conv_class(self.n_dim)(filters, filters, 1, layer.stride)
+                new_layer = get_conv_class(self.n_dim)(
+                    filters, filters, 1, layer.stride)
                 if self.weighted:
                     init_conv_weight(new_layer)
             else:
@@ -601,8 +619,10 @@ class Graph:
                     temp_v = v
                     temp_layer_id = layer_id
                     skip_type = None
-                    while not (temp_v in index_in_main_chain and temp_u in index_in_main_chain):
-                        if is_layer(self.layer_list[temp_layer_id], "Concatenate"):
+                    while not (
+                            temp_v in index_in_main_chain and temp_u in index_in_main_chain):
+                        if is_layer(
+                                self.layer_list[temp_layer_id], "Concatenate"):
                             skip_type = NetworkDescriptor.CONCAT_CONNECT
                         if is_layer(self.layer_list[temp_layer_id], "Add"):
                             skip_type = NetworkDescriptor.ADD_CONNECT
@@ -711,7 +731,8 @@ class Graph:
 
     def wide_layer_ids(self):
         return (
-            self._conv_layer_ids_in_order()[:-1] + self._dense_layer_ids_in_order()[:-1]
+            self._conv_layer_ids_in_order(
+            )[:-1] + self._dense_layer_ids_in_order()[:-1]
         )
 
     def skip_connection_layer_ids(self):
@@ -810,7 +831,8 @@ class KerasModel:
         topo_node_list = self.graph.topological_order
         output_id = topo_node_list[-1]
         input_id = topo_node_list[0]
-        input_tensor = keras.layers.Input(shape=graph.node_list[input_id].shape)
+        input_tensor = keras.layers.Input(
+            shape=graph.node_list[input_id].shape)
 
         node_list = deepcopy(self.graph.node_list)
         node_list[input_id] = input_tensor
@@ -838,7 +860,8 @@ class KerasModel:
         output_tensor = keras.layers.Activation("softmax", name="activation_add")(
             output_tensor
         )
-        self.model = keras.models.Model(inputs=input_tensor, outputs=output_tensor)
+        self.model = keras.models.Model(
+            inputs=input_tensor, outputs=output_tensor)
 
         if graph.weighted:
             for index, layer in enumerate(self.layers):
@@ -892,7 +915,8 @@ class JSONModel:
 
         for layer_id, item in enumerate(graph.layer_list):
             layer = graph.layer_list[layer_id]
-            layer_information = layer_description_extractor(layer, graph.node_to_id)
+            layer_information = layer_description_extractor(
+                layer, graph.node_to_id)
             layer_list.append((layer_id, layer_information))
 
         data["node_list"] = node_list
@@ -938,7 +962,8 @@ def json_to_graph(json_model: str):
 
     graph.input_shape = input_shape
     vis = json_model["vis"]
-    graph.vis = {tuple(item): True for item in vis} if vis is not None else None
+    graph.vis = {
+        tuple(item): True for item in vis} if vis is not None else None
     graph.weighted = json_model["weighted"]
     layer_id_to_input_node_ids = json_model["layer_id_to_input_node_ids"]
     graph.layer_id_to_input_node_ids = {

src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py

@@ -40,7 +40,8 @@ def to_wider_graph(graph):
     '''
     weighted_layer_ids = graph.wide_layer_ids()
     weighted_layer_ids = list(
-        filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
+        filter(
+            lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
     )
     wider_layers = sample(weighted_layer_ids, 1)
 
@@ -58,12 +59,14 @@ def to_wider_graph(graph):
 def to_skip_connection_graph(graph):
     ''' skip connection graph
     '''
-    # The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
+    # The last conv layer cannot be widen since wider operator cannot be done
+    # over the two sides of flatten.
     weighted_layer_ids = graph.skip_connection_layer_ids()
     valid_connection = []
-    for skip_type in sorted([NetworkDescriptor.ADD_CONNECT, NetworkDescriptor.CONCAT_CONNECT]):
+    for skip_type in sorted(
+            [NetworkDescriptor.ADD_CONNECT, NetworkDescriptor.CONCAT_CONNECT]):
         for index_a in range(len(weighted_layer_ids)):
-            for index_b in range(len(weighted_layer_ids))[index_a + 1 :]:
+            for index_b in range(len(weighted_layer_ids))[index_a + 1:]:
                 valid_connection.append((index_a, index_b, skip_type))
 
     if not valid_connection:
@@ -84,9 +87,14 @@ def create_new_layer(layer, n_dim):
 
     input_shape = layer.output.shape
     dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
-    conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim), StubReLU]
+    conv_deeper_classes = [
+        get_conv_class(n_dim),
+        get_batch_norm_class(n_dim),
+        StubReLU]
     if is_layer(layer, "ReLU"):
-        conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim)]
+        conv_deeper_classes = [
+            get_conv_class(n_dim),
+            get_batch_norm_class(n_dim)]
         dense_deeper_classes = [StubDense, get_dropout_class(n_dim)]
     elif is_layer(layer, "Dropout"):
         dense_deeper_classes = [StubDense, StubReLU]

src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py

@@ -52,7 +52,8 @@ def deeper_conv_block(conv_layer, kernel_size, weighted=True):
 
     if weighted:
         new_conv_layer.set_weights(
-            (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+            (add_noise(weight, np.array([0, 1])),
+             add_noise(bias, np.array([0, 1])))
         )
         new_weights = [
             add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
@@ -74,7 +75,8 @@ def dense_to_deeper_block(dense_layer, weighted=True):
     new_dense_layer = StubDense(units, units)
     if weighted:
         new_dense_layer.set_weights(
-            (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+            (add_noise(weight, np.array([0, 1])),
+             add_noise(bias, np.array([0, 1])))
         )
     return [StubReLU(), new_dense_layer]
 
@@ -97,8 +99,11 @@ def wider_pre_dense(layer, n_add, weighted=True):
         teacher_index = rand[i]
         new_weight = teacher_w[teacher_index, :]
         new_weight = new_weight[np.newaxis, :]
-        student_w = np.concatenate((student_w, add_noise(new_weight, student_w)), axis=0)
-        student_b = np.append(student_b, add_noise(teacher_b[teacher_index], student_b))
+        student_w = np.concatenate(
+            (student_w, add_noise(new_weight, student_w)), axis=0)
+        student_b = np.append(
+            student_b, add_noise(
+                teacher_b[teacher_index], student_b))
 
     new_pre_layer = StubDense(layer.input_units, n_units2 + n_add)
     new_pre_layer.set_weights((student_w, student_b))
@@ -209,7 +214,7 @@ def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
             student_w[:, : start_dim * n_units_each_channel],
             add_noise(new_weight, student_w),
             student_w[
-                :, start_dim * n_units_each_channel : total_dim * n_units_each_channel
+                :, start_dim * n_units_each_channel: total_dim * n_units_each_channel
             ],
         ),
         axis=1,
@@ -225,7 +230,8 @@ def add_noise(weights, other_weights):
     '''
     w_range = np.ptp(other_weights.flatten())
     noise_range = NOISE_RATIO * w_range
-    noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
+    noise = np.random.uniform(-noise_range / 2.0,
+                              noise_range / 2.0, weights.shape)
     return np.add(noise, weights)
 
 
@@ -236,7 +242,8 @@ def init_dense_weight(layer):
     weight = np.eye(units)
     bias = np.zeros(units)
     layer.set_weights(
-        (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+        (add_noise(weight, np.array([0, 1])),
+         add_noise(bias, np.array([0, 1])))
     )
 
 
@@ -256,7 +263,8 @@ def init_conv_weight(layer):
     bias = np.zeros(n_filters)
 
     layer.set_weights(
-        (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
+        (add_noise(weight, np.array([0, 1])),
+         add_noise(bias, np.array([0, 1])))
     )
 
 

src/sdk/pynni/nni/networkmorphism_tuner/layers.py

@@ -28,8 +28,10 @@ from nni.networkmorphism_tuner.utils import Constant
 
 
 class AvgPool(nn.Module):
-    '''AvgPool Module.
-    '''
+    """
+    AvgPool Module.
+    """
+
     def __init__(self):
         super().__init__()
 
@@ -39,8 +41,10 @@ class AvgPool(nn.Module):
 
 
 class GlobalAvgPool1d(AvgPool):
-    '''GlobalAvgPool1d Module.
-    '''
+    """
+    GlobalAvgPool1d Module.
+    """
+
     def forward(self, input_tensor):
         return functional.avg_pool1d(input_tensor, input_tensor.size()[2:]).view(
             input_tensor.size()[:2]
@@ -48,8 +52,10 @@ class GlobalAvgPool1d(AvgPool):
 
 
 class GlobalAvgPool2d(AvgPool):
-    '''GlobalAvgPool2d Module.
-    '''
+    """
+    GlobalAvgPool2d Module.
+    """
+
     def forward(self, input_tensor):
         return functional.avg_pool2d(input_tensor, input_tensor.size()[2:]).view(
             input_tensor.size()[:2]
@@ -57,8 +63,10 @@ class GlobalAvgPool2d(AvgPool):
 
 
 class GlobalAvgPool3d(AvgPool):
-    '''GlobalAvgPool3d Module.
-    '''
+    """
+    GlobalAvgPool3d Module.
+    """
+
     def forward(self, input_tensor):
         return functional.avg_pool3d(input_tensor, input_tensor.size()[2:]).view(
             input_tensor.size()[:2]
@@ -66,70 +74,86 @@ class GlobalAvgPool3d(AvgPool):
 
 
 class StubLayer:
-    '''StubLayer Module. Base Module.
-    '''
+    """
+    StubLayer Module. Base Module.
+    """
+
     def __init__(self, input_node=None, output_node=None):
         self.input = input_node
         self.output = output_node
         self.weights = None
 
     def build(self, shape):
-        '''build shape.
-        '''
+        """
+        build shape.
+        """
 
     def set_weights(self, weights):
-        '''set weights.
-        '''
+        """
+        set weights.
+        """
         self.weights = weights
 
     def import_weights(self, torch_layer):
-        '''import weights.
-        '''
+        """
+        import weights.
+        """
 
     def import_weights_keras(self, keras_layer):
-        '''import weights from keras layer.
-        '''
+        """
+        import weights from keras layer.
+        """
 
     def export_weights(self, torch_layer):
-        '''export weights.
-        '''
+        """
+        export weights.
+        """
 
     def export_weights_keras(self, keras_layer):
-        '''export weights to keras layer.
-        '''
+        """
+        export weights to keras layer.
+        """
 
     def get_weights(self):
-        '''get weights.
-        '''
+        """
+        get weights.
+        """
         return self.weights
 
     def size(self):
-        '''size().
-        '''
+        """
+        size().
+        """
         return 0
 
     @property
     def output_shape(self):
-        '''output shape.
-        '''
+        """
+        output shape.
+        """
         return self.input.shape
 
     def to_real_layer(self):
-        '''to real layer.
-        '''
+        """
+        to real layer.
+        """
 
     def __str__(self):
-        '''str() function to print.
-        '''
+        """
+        str() function to print.
+        """
         return type(self).__name__[4:]
 
 
 class StubWeightBiasLayer(StubLayer):
-    '''StubWeightBiasLayer Module to set the bias.
-    '''
+    """
+    StubWeightBiasLayer Module to set the bias.
+    """
+
     def import_weights(self, torch_layer):
         self.set_weights(
-            (torch_layer.weight.data.cpu().numpy(), torch_layer.bias.data.cpu().numpy())
+            (torch_layer.weight.data.cpu().numpy(),
+             torch_layer.bias.data.cpu().numpy())
         )
 
     def import_weights_keras(self, keras_layer):
@@ -144,8 +168,10 @@ class StubWeightBiasLayer(StubLayer):
 
 
 class StubBatchNormalization(StubWeightBiasLayer):
-    '''StubBatchNormalization Module. Batch Norm.
-    '''
+    """
+    StubBatchNormalization Module. Batch Norm.
+    """
+
     def __init__(self, num_features, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
         self.num_features = num_features
@@ -175,29 +201,37 @@ class StubBatchNormalization(StubWeightBiasLayer):
 
 
 class StubBatchNormalization1d(StubBatchNormalization):
-    '''StubBatchNormalization1d Module.
-    '''
+    """
+    StubBatchNormalization1d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.BatchNorm1d(self.num_features)
 
 
 class StubBatchNormalization2d(StubBatchNormalization):
-    '''StubBatchNormalization2d Module.
-    '''
+    """
+    StubBatchNormalization2d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.BatchNorm2d(self.num_features)
 
 
 class StubBatchNormalization3d(StubBatchNormalization):
-    '''StubBatchNormalization3d Module.
-    '''
+    """
+    StubBatchNormalization3d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.BatchNorm3d(self.num_features)
 
 
 class StubDense(StubWeightBiasLayer):
-    '''StubDense Module. Linear.
-    '''
+    """
+    StubDense Module. Linear.
+    """
+
     def __init__(self, input_units, units, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
         self.input_units = input_units
@@ -208,7 +242,9 @@ class StubDense(StubWeightBiasLayer):
         return (self.units,)
 
     def import_weights_keras(self, keras_layer):
-        self.set_weights((keras_layer.get_weights()[0].T, keras_layer.get_weights()[1]))
+        self.set_weights(
+            (keras_layer.get_weights()[0].T,
+             keras_layer.get_weights()[1]))
 
     def export_weights_keras(self, keras_layer):
         keras_layer.set_weights((self.weights[0].T, self.weights[1]))
@@ -221,9 +257,12 @@ class StubDense(StubWeightBiasLayer):
 
 
 class StubConv(StubWeightBiasLayer):
-    '''StubConv Module. Conv.
-    '''
-    def __init__(self, input_channel, filters, kernel_size, stride=1, input_node=None, output_node=None):
+    """
+    StubConv Module. Conv.
+    """
+
+    def __init__(self, input_channel, filters, kernel_size,
+                 stride=1, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
         self.input_channel = input_channel
         self.filters = filters
@@ -242,13 +281,16 @@ class StubConv(StubWeightBiasLayer):
         return tuple(ret)
 
     def import_weights_keras(self, keras_layer):
-        self.set_weights((keras_layer.get_weights()[0].T, keras_layer.get_weights()[1]))
+        self.set_weights(
+            (keras_layer.get_weights()[0].T,
+             keras_layer.get_weights()[1]))
 
     def export_weights_keras(self, keras_layer):
         keras_layer.set_weights((self.weights[0].T, self.weights[1]))
 
     def size(self):
-        return (self.input_channel * self.kernel_size * self.kernel_size + 1) * self.filters
+        return (self.input_channel * self.kernel_size *
+                self.kernel_size + 1) * self.filters
 
     @abstractmethod
     def to_real_layer(self):
@@ -272,8 +314,10 @@ class StubConv(StubWeightBiasLayer):
 
 
 class StubConv1d(StubConv):
-    '''StubConv1d Module.
-    '''
+    """
+    StubConv1d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Conv1d(
             self.input_channel,
@@ -285,8 +329,10 @@ class StubConv1d(StubConv):
 
 
 class StubConv2d(StubConv):
-    '''StubConv2d Module.
-    '''
+    """
+    StubConv2d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Conv2d(
             self.input_channel,
@@ -298,8 +344,10 @@ class StubConv2d(StubConv):
 
 
 class StubConv3d(StubConv):
-    '''StubConv3d Module.
-    '''
+    """
+    StubConv3d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Conv3d(
             self.input_channel,
@@ -311,8 +359,10 @@ class StubConv3d(StubConv):
 
 
 class StubAggregateLayer(StubLayer):
-    '''StubAggregateLayer Module.
-    '''
+    """
+    StubAggregateLayer Module.
+    """
+
     def __init__(self, input_nodes=None, output_node=None):
         if input_nodes is None:
             input_nodes = []
@@ -320,8 +370,8 @@ class StubAggregateLayer(StubLayer):
 
 
 class StubConcatenate(StubAggregateLayer):
-    '''StubConcatenate Module.
-    '''
+    """StubConcatenate Module.
+    """
     @property
     def output_shape(self):
         ret = 0
@@ -335,8 +385,9 @@ class StubConcatenate(StubAggregateLayer):
 
 
 class StubAdd(StubAggregateLayer):
-    '''StubAdd Module.
-    '''
+    """
+    StubAdd Module.
+    """
     @property
     def output_shape(self):
         return self.input[0].shape
@@ -346,8 +397,9 @@ class StubAdd(StubAggregateLayer):
 
 
 class StubFlatten(StubLayer):
-    '''StubFlatten Module.
-    '''
+    """
+    StubFlatten Module.
+    """
     @property
     def output_shape(self):
         ret = 1
@@ -360,22 +412,28 @@ class StubFlatten(StubLayer):
 
 
 class StubReLU(StubLayer):
-    '''StubReLU Module.
-    '''
+    """
+    StubReLU Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.ReLU()
 
 
 class StubSoftmax(StubLayer):
-    '''StubSoftmax Module.
-    '''
+    """
+    StubSoftmax Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.LogSoftmax(dim=1)
 
 
 class StubDropout(StubLayer):
-    '''StubDropout Module.
-    '''
+    """
+    StubDropout Module.
+    """
+
     def __init__(self, rate, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
         self.rate = rate
@@ -386,36 +444,45 @@ class StubDropout(StubLayer):
 
 
 class StubDropout1d(StubDropout):
-    '''StubDropout1d Module.
-    '''
+    """
+    StubDropout1d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Dropout(self.rate)
 
 
 class StubDropout2d(StubDropout):
-    '''StubDropout2d Module.
-    '''
+    """
+    StubDropout2d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Dropout2d(self.rate)
 
 
 class StubDropout3d(StubDropout):
-    '''StubDropout3d Module.
-    '''
+    """
+    StubDropout3d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.Dropout3d(self.rate)
 
 
 class StubInput(StubLayer):
-    '''StubInput Module.
-    '''
+    """
+    StubInput Module.
+    """
+
     def __init__(self, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
 
 
 class StubPooling(StubLayer):
-    '''StubPooling Module.
-    '''
+    """
+    StubPooling Module.
+    """
 
     def __init__(self,
                  kernel_size=None,
@@ -444,30 +511,37 @@ class StubPooling(StubLayer):
 
 
 class StubPooling1d(StubPooling):
-    '''StubPooling1d Module.
-    '''
+    """
+    StubPooling1d Module.
+    """
 
     def to_real_layer(self):
         return torch.nn.MaxPool1d(self.kernel_size, stride=self.stride)
 
 
 class StubPooling2d(StubPooling):
-    '''StubPooling2d Module.
-    '''
+    """
+    StubPooling2d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.MaxPool2d(self.kernel_size, stride=self.stride)
 
 
 class StubPooling3d(StubPooling):
-    '''StubPooling3d Module.
-    '''
+    """
+    StubPooling3d Module.
+    """
+
     def to_real_layer(self):
         return torch.nn.MaxPool3d(self.kernel_size, stride=self.stride)
 
 
 class StubGlobalPooling(StubLayer):
-    '''StubGlobalPooling Module.
-    '''
+    """
+    StubGlobalPooling Module.
+    """
+
     def __init__(self, input_node=None, output_node=None):
         super().__init__(input_node, output_node)
 
@@ -481,49 +555,63 @@ class StubGlobalPooling(StubLayer):
 
 
 class StubGlobalPooling1d(StubGlobalPooling):
-    '''StubGlobalPooling1d Module.
-    '''
+    """
+    StubGlobalPooling1d Module.
+    """
+
     def to_real_layer(self):
         return GlobalAvgPool1d()
 
 
 class StubGlobalPooling2d(StubGlobalPooling):
-    '''StubGlobalPooling2d Module.
-    '''
+    """
+    StubGlobalPooling2d Module.
+    """
+
     def to_real_layer(self):
         return GlobalAvgPool2d()
 
 
 class StubGlobalPooling3d(StubGlobalPooling):
-    '''StubGlobalPooling3d Module.
-    '''
+    """
+    StubGlobalPooling3d Module.
+    """
+
     def to_real_layer(self):
         return GlobalAvgPool3d()
 
 
 class TorchConcatenate(nn.Module):
-    '''TorchConcatenate Module.
-    '''
+    """
+    TorchConcatenate Module.
+    """
+
     def forward(self, input_list):
         return torch.cat(input_list, dim=1)
 
 
 class TorchAdd(nn.Module):
-    '''TorchAdd Module.
-    '''
+    """
+    TorchAdd Module.
+    """
+
     def forward(self, input_list):
         return input_list[0] + input_list[1]
 
 
 class TorchFlatten(nn.Module):
-    '''TorchFlatten Module.
-    '''
+    """
+    TorchFlatten Module.
+    """
+
     def forward(self, input_tensor):
         return input_tensor.view(input_tensor.size(0), -1)
 
+
 def keras_dropout(layer, rate):
-    '''keras dropout layer.
-    '''
+    """
+    Keras dropout layer.
+    """
 
     from keras import layers
 
@@ -539,8 +627,9 @@ def keras_dropout(layer, rate):
 
 
 def to_real_keras_layer(layer):
-    ''' real keras layer.
-    '''
+    """
+    Real keras layer.
+    """
     from keras import layers
 
     if is_layer(layer, "Dense"):
@@ -574,10 +663,14 @@ def to_real_keras_layer(layer):
 
 
 def is_layer(layer, layer_type):
-    '''judge the layer type.
-    Returns:
+    """
+    Judge the layer type.
+
+    Returns
+    -------
+    bool
         boolean -- True or False
-    '''
+    """
 
     if layer_type == "Input":
         return isinstance(layer, StubInput)
@@ -607,8 +700,9 @@ def is_layer(layer, layer_type):
 
 
 def layer_description_extractor(layer, node_to_id):
-    '''get layer description.
-    '''
+    """
+    Get layer description.
+    """
 
     layer_input = layer.input
     layer_output = layer.output
@@ -641,7 +735,8 @@ def layer_description_extractor(layer, node_to_id):
             layer.units,
         ]
     elif isinstance(layer, (StubBatchNormalization,)):
-        return (type(layer).__name__, layer_input, layer_output, layer.num_features)
+        return (type(layer).__name__, layer_input,
+                layer_output, layer.num_features)
     elif isinstance(layer, (StubDropout,)):
         return (type(layer).__name__, layer_input, layer_output, layer.rate)
     elif isinstance(layer, StubPooling):
@@ -658,8 +753,8 @@ def layer_description_extractor(layer, node_to_id):
 
 
 def layer_description_builder(layer_information, id_to_node):
-    '''build layer from description.
-    '''
+    """build layer from description.
+    """
     layer_type = layer_information[0]
 
     layer_input_ids = layer_information[1]
@@ -696,8 +791,9 @@ def layer_description_builder(layer_information, id_to_node):
 
 
 def layer_width(layer):
-    '''get layer width.
-    '''
+    """
+    Get layer width.
+    """
 
     if is_layer(layer, "Dense"):
         return layer.units

src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py

@@ -17,11 +17,13 @@
 # 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.
 # ==================================================================================================
+"""
+networkmorphsim_tuner.py
+"""
 
 import logging
 import os
 
-
 from nni.tuner import Tuner
 from nni.utils import OptimizeMode, extract_scalar_reward
 from nni.networkmorphism_tuner.bayesian import BayesianOptimizer
@@ -34,7 +36,35 @@ logger = logging.getLogger("NetworkMorphism_AutoML")
 
 
 class NetworkMorphismTuner(Tuner):
-    """NetworkMorphismTuner is a tuner which using network morphism techniques."""
+    """
+    NetworkMorphismTuner is a tuner which using network morphism techniques.
+
+    Attributes
+    ----------
+    n_classes : int
+        The class number or output node number (default: ``10``)
+    input_shape : tuple
+        A tuple including: (input_width, input_width, input_channel)
+    t_min : float
+        The minimum temperature for simulated annealing. (default: ``Constant.T_MIN``)
+    beta : float
+        The beta in acquisition function. (default: ``Constant.BETA``)
+    algorithm_name : str
+        algorithm name used in the network morphism (default: ``"Bayesian"``)
+    optimize_mode : str
+        optimize mode "minimize" or "maximize" (default: ``"minimize"``)
+    verbose : bool
+        verbose to print the log (default: ``True``)
+    bo : BayesianOptimizer
+        The optimizer used in networkmorphsim tuner.
+    max_model_size : int
+        max model size to the graph (default: ``Constant.MAX_MODEL_SIZE``)
+    default_model_len : int
+        default model length (default: ``Constant.MODEL_LEN``)
+    default_model_width : int
+        default model width (default: ``Constant.MODEL_WIDTH``)
+    search_space : dict
+    """
 
     def __init__(
             self,
@@ -52,36 +82,8 @@ class NetworkMorphismTuner(Tuner):
             default_model_len=Constant.MODEL_LEN,
             default_model_width=Constant.MODEL_WIDTH,
     ):
-        """ initilizer of the NetworkMorphismTuner.
-
-        Parameters
-        ----------
-        task : str
-            task mode, such as "cv","common" etc. (default: {"cv"})
-        input_width : int
-            input sample shape (default: {32})
-        input_channel : int
-            input sample shape (default: {3})
-        n_output_node : int
-            output node number (default: {10})
-        algorithm_name : str
-            algorithm name used in the network morphism (default: {"Bayesian"})
-        optimize_mode : str
-            optimize mode "minimize" or "maximize" (default: {"minimize"})
-        path : str
-            default mode path to save the model file (default: {"model_path"})
-        verbose : bool
-            verbose to print the log (default: {True})
-        beta : float
-            The beta in acquisition function. (default: {Constant.BETA})
-        t_min : float
-            The minimum temperature for simulated annealing. (default: {Constant.T_MIN})
-        max_model_size : int
-            max model size to the graph (default: {Constant.MAX_MODEL_SIZE})
-        default_model_len : int
-            default model length (default: {Constant.MODEL_LEN})
-        default_model_width : int
-            default model width (default: {Constant.MODEL_WIDTH})
+        """
+        initilizer of the NetworkMorphismTuner.
         """
 
         if not os.path.exists(path):
@@ -92,7 +94,8 @@ class NetworkMorphismTuner(Tuner):
         elif task == "common":
             self.generators = [MlpGenerator]
         else:
-            raise NotImplementedError('{} task not supported in List ["cv","common"]')
+            raise NotImplementedError(
+                '{} task not supported in List ["cv","common"]')
 
         self.n_classes = n_output_node
         self.input_shape = (input_width, input_width, input_channel)
@@ -106,7 +109,8 @@ class NetworkMorphismTuner(Tuner):
         self.verbose = verbose
         self.model_count = 0
 
-        self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode, self.beta)
+        self.bo = BayesianOptimizer(
+            self, self.t_min, self.optimize_mode, self.beta)
         self.training_queue = []
         self.descriptors = []
         self.history = []
@@ -117,6 +121,7 @@ class NetworkMorphismTuner(Tuner):
 
         self.search_space = dict()
 
+
     def update_search_space(self, search_space):
         """
         Update search space definition in tuner by search_space in neural architecture.
@@ -140,7 +145,8 @@ class NetworkMorphismTuner(Tuner):
             new_father_id, generated_graph = self.generate()
             new_model_id = self.model_count
             self.model_count += 1
-            self.training_queue.append((generated_graph, new_father_id, new_model_id))
+            self.training_queue.append(
+                (generated_graph, new_father_id, new_model_id))
             self.descriptors.append(generated_graph.extract_descriptor())
 
         graph, father_id, model_id = self.training_queue.pop(0)
@@ -153,12 +159,15 @@ class NetworkMorphismTuner(Tuner):
         return json_out
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """ Record an observation of the objective function.
+        """
+        Record an observation of the objective function.
 
         Parameters
         ----------
         parameter_id : int
+            the id of a group of paramters that generated by nni manager.
         parameters : dict
+            A group of parameters.
         value : dict/float
             if value is dict, it should have "default" key.
         """
@@ -175,8 +184,11 @@ class NetworkMorphismTuner(Tuner):
         self.add_model(reward, model_id)
         self.update(father_id, graph, reward, model_id)
 
+
     def init_search(self):
-        """Call the generators to generate the initial architectures for the search."""
+        """
+        Call the generators to generate the initial architectures for the search.
+        """
         if self.verbose:
             logger.info("Initializing search.")
         for generator in self.generators:
@@ -191,14 +203,16 @@ class NetworkMorphismTuner(Tuner):
         if self.verbose:
             logger.info("Initialization finished.")
 
+
     def generate(self):
-        """Generate the next neural architecture.
+        """
+        Generate the next neural architecture.
 
         Returns
         -------
-        other_info: any object
+        other_info : any object
             Anything to be saved in the training queue together with the architecture.
-        generated_graph: Graph
+        generated_graph : Graph
             An instance of Graph.
         """
         generated_graph, new_father_id = self.bo.generate(self.descriptors)
@@ -211,7 +225,8 @@ class NetworkMorphismTuner(Tuner):
         return new_father_id, generated_graph
 
     def update(self, other_info, graph, metric_value, model_id):
-        """ Update the controller with evaluation result of a neural architecture.
+        """
+        Update the controller with evaluation result of a neural architecture.
 
         Parameters
         ----------
@@ -228,7 +243,8 @@ class NetworkMorphismTuner(Tuner):
         self.bo.add_child(father_id, model_id)
 
     def add_model(self, metric_value, model_id):
-        """ Add model to the history, x_queue and y_queue
+        """
+        Add model to the history, x_queue and y_queue
 
         Parameters
         ----------
@@ -252,16 +268,21 @@ class NetworkMorphismTuner(Tuner):
             file.close()
         return ret
 
+
     def get_best_model_id(self):
-        """ Get the best model_id from history using the metric value
+        """
+        Get the best model_id from history using the metric value
         """
 
         if self.optimize_mode is OptimizeMode.Maximize:
-            return max(self.history, key=lambda x: x["metric_value"])["model_id"]
+            return max(self.history, key=lambda x: x["metric_value"])[
+                "model_id"]
         return min(self.history, key=lambda x: x["metric_value"])["model_id"]
 
+
     def load_model_by_id(self, model_id):
-        """Get the model by model_id
+        """
+        Get the model by model_id
 
         Parameters
         ----------
@@ -281,7 +302,8 @@ class NetworkMorphismTuner(Tuner):
         return load_model
 
     def load_best_model(self):
-        """ Get the best model by model id
+        """
+        Get the best model by model id
 
         Returns
         -------
@@ -291,7 +313,8 @@ class NetworkMorphismTuner(Tuner):
         return self.load_model_by_id(self.get_best_model_id())
 
     def get_metric_value_by_id(self, model_id):
-        """ Get the model metric valud by its model_id
+        """
+        Get the model metric valud by its model_id
 
         Parameters
         ----------

src/sdk/pynni/nni/networkmorphism_tuner/nn.py

@@ -92,17 +92,25 @@ class CnnGenerator(NetworkGenerator):
         for i in range(model_len):
             output_node_id = graph.add_layer(StubReLU(), output_node_id)
             output_node_id = graph.add_layer(
-                self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id
+                self.batch_norm(
+                    graph.node_list[output_node_id].shape[-1]), output_node_id
             )
             output_node_id = graph.add_layer(
-                self.conv(temp_input_channel, model_width, kernel_size=3, stride=stride),
+                self.conv(
+                    temp_input_channel,
+                    model_width,
+                    kernel_size=3,
+                    stride=stride),
                 output_node_id,
             )
             temp_input_channel = model_width
-            if pooling_len == 0 or ((i + 1) % pooling_len == 0 and i != model_len - 1):
-                output_node_id = graph.add_layer(self.pooling(), output_node_id)
+            if pooling_len == 0 or (
+                    (i + 1) % pooling_len == 0 and i != model_len - 1):
+                output_node_id = graph.add_layer(
+                    self.pooling(), output_node_id)
 
-        output_node_id = graph.add_layer(self.global_avg_pooling(), output_node_id)
+        output_node_id = graph.add_layer(
+            self.global_avg_pooling(), output_node_id)
         output_node_id = graph.add_layer(
             self.dropout(Constant.CONV_DROPOUT_RATE), output_node_id
         )
@@ -111,7 +119,11 @@ class CnnGenerator(NetworkGenerator):
             output_node_id,
         )
         output_node_id = graph.add_layer(StubReLU(), output_node_id)
-        graph.add_layer(StubDense(model_width, self.n_output_node), output_node_id)
+        graph.add_layer(
+            StubDense(
+                model_width,
+                self.n_output_node),
+            output_node_id)
         return graph
 
 
@@ -145,7 +157,8 @@ class MlpGenerator(NetworkGenerator):
         if model_width is None:
             model_width = Constant.MODEL_WIDTH
         if isinstance(model_width, list) and not len(model_width) == model_len:
-            raise ValueError("The length of 'model_width' does not match 'model_len'")
+            raise ValueError(
+                "The length of 'model_width' does not match 'model_len'")
         elif isinstance(model_width, int):
             model_width = [model_width] * model_len
 
@@ -162,5 +175,9 @@ class MlpGenerator(NetworkGenerator):
             output_node_id = graph.add_layer(StubReLU(), output_node_id)
             n_nodes_prev_layer = width
 
-        graph.add_layer(StubDense(n_nodes_prev_layer, self.n_output_node), output_node_id)
+        graph.add_layer(
+            StubDense(
+                n_nodes_prev_layer,
+                self.n_output_node),
+            output_node_id)
         return graph

src/sdk/pynni/nni/networkmorphism_tuner/test_networkmorphism_tuner.py

@@ -59,9 +59,12 @@ class NetworkMorphismTestCase(TestCase):
             graph_recover.layer_id_to_input_node_ids,
         )
         self.assertEqual(graph_init.adj_list, graph_recover.adj_list)
-        self.assertEqual(graph_init.reverse_adj_list, graph_recover.reverse_adj_list)
         self.assertEqual(
-            len(graph_init.operation_history), len(graph_recover.operation_history)
+            graph_init.reverse_adj_list,
+            graph_recover.reverse_adj_list)
+        self.assertEqual(
+            len(graph_init.operation_history), len(
+                graph_recover.operation_history)
         )
         self.assertEqual(graph_init.n_dim, graph_recover.n_dim)
         self.assertEqual(graph_init.conv, graph_recover.conv)
@@ -71,7 +74,8 @@ class NetworkMorphismTestCase(TestCase):
         node_list_init = [node.shape for node in graph_init.node_list]
         node_list_recover = [node.shape for node in graph_recover.node_list]
         self.assertEqual(node_list_init, node_list_recover)
-        self.assertEqual(len(graph_init.node_to_id), len(graph_recover.node_to_id))
+        self.assertEqual(len(graph_init.node_to_id),
+                         len(graph_recover.node_to_id))
         layer_list_init = [
             layer_description_extractor(item, graph_init.node_to_id)
             for item in graph_init.layer_list
@@ -82,7 +86,8 @@ class NetworkMorphismTestCase(TestCase):
         ]
         self.assertEqual(layer_list_init, layer_list_recover)
 
-        node_to_id_init = [graph_init.node_to_id[node] for node in graph_init.node_list]
+        node_to_id_init = [graph_init.node_to_id[node]
+                           for node in graph_init.node_list]
         node_to_id_recover = [
             graph_recover.node_to_id[node] for node in graph_recover.node_list
         ]
@@ -192,8 +197,8 @@ class NetworkMorphismTestCase(TestCase):
         """
 
         tuner = NetworkMorphismTuner()
-        tuner.add_model(0.8,  0)
-        tuner.add_model(0.9,  1)
+        tuner.add_model(0.8, 0)
+        tuner.add_model(0.9, 1)
         self.assertEqual(tuner.get_best_model_id(), 1)