Merge pull request #211 from microsoft/master

merge master

src/sdk/pynni/nni/common.py

@@ -41,8 +41,8 @@ class _LoggerFileWrapper(TextIOBase):
 
     def write(self, s):
         if s != '\n':
-            time = datetime.now().strftime(_time_format)
-            self.file.write('[{}] PRINT '.format(time) + s + '\n')
+            cur_time = datetime.now().strftime(_time_format)
+            self.file.write('[{}] PRINT '.format(cur_time) + s + '\n')
             self.file.flush()
         return len(s)
 

src/sdk/pynni/nni/compression/tensorflow/builtin_pruners.py

@@ -92,5 +92,5 @@ class AGP_Pruner(Pruner):
     def update_epoch(self, epoch, sess):
         sess.run(self.assign_handler)
         sess.run(tf.assign(self.now_epoch, int(epoch)))
-        for k in self.if_init_list.keys():
+        for k in self.if_init_list:
             self.if_init_list[k] = True

src/sdk/pynni/nni/compression/tensorflow/builtin_quantizers.py

@@ -2,7 +2,7 @@ import logging
 import tensorflow as tf
 from .compressor import Quantizer
 
-__all__ = [ 'NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer' ]
+__all__ = ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer']
 
 _logger = logging.getLogger(__name__)
 
@@ -12,7 +12,7 @@ class NaiveQuantizer(Quantizer):
     """
     def __init__(self, config_list):
         super().__init__(config_list)
-        self.layer_scale = { }
+        self.layer_scale = {}
 
     def quantize_weight(self, weight, config, op_name, **kwargs):
         new_scale = tf.reduce_max(tf.abs(weight)) / 127

src/sdk/pynni/nni/compression/tensorflow/compressor.py

-import tensorflow as tf
 import logging
+import tensorflow as tf
 from . import default_layers
 
 _logger = logging.getLogger(__name__)
@@ -51,17 +51,14 @@ class Compressor:
         Compressors can optionally overload this method to do model-specific initialization.
         It is guaranteed that only one model will be bound to each compressor instance.
         """
-        pass
 
     def update_epoch(self, epoch, sess):
         """If user want to update mask every epoch, user can override this method
         """
-        pass
 
     def step(self, sess):
         """If user want to update mask every step, user can override this method
         """
-        pass
 
 
     def _instrument_layer(self, layer, config):
@@ -84,10 +81,9 @@ class Compressor:
 
 
 class Pruner(Compressor):
-    """Abstract base TensorFlow pruner"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base TensorFlow pruner
+    """
 
     def calc_mask(self, weight, config, op, op_type, op_name):
         """Pruners should overload this method to provide mask for weight tensors.
@@ -105,7 +101,7 @@ class Pruner(Compressor):
         # not sure what will happen if the weight is calculated from other operations
         weight_index = _detect_weight_index(layer)
         if weight_index is None:
-            _logger.warning('Failed to detect weight for layer {}'.format(layer.name))
+            _logger.warning('Failed to detect weight for layer %s', layer.name)
             return
         weight_op = layer.op.inputs[weight_index].op
         weight = weight_op.inputs[0]
@@ -115,10 +111,9 @@ class Pruner(Compressor):
 
 
 class Quantizer(Compressor):
-    """Abstract base TensorFlow quantizer"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base TensorFlow quantizer
+    """
 
     def quantize_weight(self, weight, config, op, op_type, op_name):
         raise NotImplementedError("Quantizer must overload quantize_weight()")
@@ -126,7 +121,7 @@ class Quantizer(Compressor):
     def _instrument_layer(self, layer, config):
         weight_index = _detect_weight_index(layer)
         if weight_index is None:
-            _logger.warning('Failed to detect weight for layer {}'.format(layer.name))
+            _logger.warning('Failed to detect weight for layer %s', layer.name)
             return
         weight_op = layer.op.inputs[weight_index].op
         weight = weight_op.inputs[0]
@@ -138,7 +133,7 @@ def _detect_weight_index(layer):
     index = default_layers.op_weight_index.get(layer.type)
     if index is not None:
         return index
-    weight_indices = [ i for i, op in enumerate(layer.op.inputs) if op.name.endswith('Variable/read') ]
+    weight_indices = [i for i, op in enumerate(layer.op.inputs) if op.name.endswith('Variable/read')]
     if len(weight_indices) == 1:
         return weight_indices[0]
     return None

src/sdk/pynni/nni/compression/torch/builtin_pruners.py

@@ -102,5 +102,5 @@ class AGP_Pruner(Pruner):
     def update_epoch(self, epoch):
         if epoch > 0:
             self.now_epoch = epoch
-            for k in self.if_init_list.keys():
+            for k in self.if_init_list:
                 self.if_init_list[k] = True

src/sdk/pynni/nni/compression/torch/builtin_quantizers.py

@@ -2,7 +2,7 @@ import logging
 import torch
 from .compressor import Quantizer
 
-__all__ = [ 'NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer' ]
+__all__ = ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer']
 
 logger = logging.getLogger(__name__)
 
@@ -62,7 +62,7 @@ class DoReFaQuantizer(Quantizer):
 
     def quantize_weight(self, weight, config, **kwargs):
         out = weight.tanh()
-        out = out /( 2 * out.abs().max()) + 0.5
+        out = out / (2 * out.abs().max()) + 0.5
         out = self.quantize(out, config['q_bits'])
         out = 2 * out -1
         return out

src/sdk/pynni/nni/compression/torch/compressor.py

-import torch
 import logging
+import torch
 from . import default_layers
 
 _logger = logging.getLogger(__name__)
@@ -43,17 +43,14 @@ class Compressor:
         Users can optionally overload this method to do model-specific initialization.
         It is guaranteed that only one model will be bound to each compressor instance.
         """
-        pass
 
     def update_epoch(self, epoch):
         """if user want to update model every epoch, user can override this method
         """
-        pass
 
     def step(self):
         """if user want to update model every step, user can override this method
         """
-        pass
 
     def _instrument_layer(self, layer, config):
         raise NotImplementedError()
@@ -61,10 +58,8 @@ class Compressor:
     def _select_config(self, layer):
         ret = None
         for config in self._config_list:
-            op_types = config.get('op_types')
-            if op_types == 'default':
-                op_types = default_layers.weighted_modules
-            if op_types and layer.type not in op_types:
+            config['op_types'] = self._expand_config_op_types(config)
+            if layer.type not in config['op_types']:
                 continue
             if config.get('op_names') and layer.name not in config['op_names']:
                 continue
@@ -73,12 +68,21 @@ class Compressor:
             return None
         return ret
 
+    def _expand_config_op_types(self, config):
+        if config is None:
+            return []
+        expanded_op_types = []
+        for op_type in config.get('op_types', []):
+            if op_type == 'default':
+                expanded_op_types.extend(default_layers.weighted_modules)
+            else:
+                expanded_op_types.append(op_type)
+        return expanded_op_types
 
 class Pruner(Compressor):
-    """Abstract base PyTorch pruner"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base PyTorch pruner
+    """
 
     def calc_mask(self, weight, config, op, op_type, op_name):
         """Pruners should overload this method to provide mask for weight tensors.
@@ -93,17 +97,17 @@ class Pruner(Compressor):
         # create a wrapper forward function to replace the original one
         assert layer._forward is None, 'Each model can only be compressed once'
         if not _check_weight(layer.module):
-            _logger.warning('Module {} does not have parameter "weight"'.format(layer.name))
+            _logger.warning('Module %s does not have parameter "weight"', layer.name)
             return
         layer._forward = layer.module.forward
 
-        def new_forward(*input):
+        def new_forward(*inputs):
             # apply mask to weight
             old_weight = layer.module.weight.data
             mask = self.calc_mask(old_weight, config, op=layer.module, op_type=layer.type, op_name=layer.name)
             layer.module.weight.data = old_weight.mul(mask)
             # calculate forward
-            ret = layer._forward(*input)
+            ret = layer._forward(*inputs)
             # recover original weight
             layer.module.weight.data = old_weight
             return ret
@@ -112,14 +116,9 @@ class Pruner(Compressor):
 
 
 class Quantizer(Compressor):
-    """Base quantizer for pytorch quantizer"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
-
-    def __call__(self, model):
-        self.compress(model)
-        return model
+    """
+    Base quantizer for pytorch quantizer
+    """
 
     def quantize_weight(self, weight, config, op, op_type, op_name):
         """user should know where dequantize goes and implement it in quantize method
@@ -130,15 +129,15 @@ class Quantizer(Compressor):
     def _instrument_layer(self, layer, config):
         assert layer._forward is None, 'Each model can only be compressed once'
         if not _check_weight(layer.module):
-            _logger.warning('Module {} does not have parameter "weight"'.format(layer.name))
+            _logger.warning('Module %s does not have parameter "weight"', layer.name)
             return
         layer._forward = layer.module.forward
 
-        def new_forward(*input):
+        def new_forward(*inputs):
             weight = layer.module.weight.data
             new_weight = self.quantize_weight(weight, config, op=layer.module, op_type=layer.type, op_name=layer.name)
             layer.module.weight.data = new_weight
-            return layer._forward(*input)
+            return layer._forward(*inputs)
 
         layer.module.forward = new_forward
 

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -50,7 +50,7 @@ class CurvefittingAssessor(Assessor):
             self.higher_better = False
         else:
             self.higher_better = True
-            logger.warning('unrecognized optimize_mode', optimize_mode)
+            logger.warning('unrecognized optimize_mode %s', optimize_mode)
         # Start forecasting when historical data reaches start step
         self.start_step = start_step
         # Record the compared threshold
@@ -81,9 +81,9 @@ class CurvefittingAssessor(Assessor):
             else:
                 self.set_best_performance = True
                 self.completed_best_performance = self.trial_history[-1]
-            logger.info('Updated complted best performance, trial job id:', trial_job_id)
+            logger.info('Updated complted best performance, trial job id: %s', trial_job_id)
         else:
-            logger.info('No need to update, trial job id: ', trial_job_id)
+            logger.info('No need to update, trial job id: %s', trial_job_id)
 
     def assess_trial(self, trial_job_id, trial_history):
         """assess whether a trial should be early stop by curve fitting algorithm
@@ -105,7 +105,7 @@ class CurvefittingAssessor(Assessor):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        self.trial_job_id = trial_job_id
+        trial_job_id = trial_job_id
         self.trial_history = trial_history
         if not self.set_best_performance:
             return AssessResult.Good
@@ -122,7 +122,7 @@ class CurvefittingAssessor(Assessor):
             # Predict the final result
             curvemodel = CurveModel(self.target_pos)
             predict_y = curvemodel.predict(trial_history)
-            logger.info('Prediction done. Trial job id = ', trial_job_id, '. Predict value = ', predict_y)
+            logger.info('Prediction done. Trial job id = %s. Predict value = %s', trial_job_id, predict_y)
             if predict_y is None:
                 logger.info('wait for more information to predict precisely')
                 return AssessResult.Good
@@ -130,7 +130,10 @@ class CurvefittingAssessor(Assessor):
 
             end_time = datetime.datetime.now()
             if (end_time - start_time).seconds > 60:
-                logger.warning('Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = ', self.trial_job_id, 'Trial History = ', self.trial_history)
+                logger.warning(
+                    'Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = %s Trial History = %s',
+                    trial_job_id, self.trial_history
+                )
 
             if self.higher_better:
                 if predict_y > standard_performance:
@@ -142,4 +145,4 @@ class CurvefittingAssessor(Assessor):
                 return AssessResult.Bad
 
         except Exception as exception:
-            logger.exception('unrecognize exception in curvefitting_assessor', exception)
+            logger.exception('unrecognize exception in curvefitting_assessor %s', exception)

src/sdk/pynni/nni/curvefitting_assessor/model_factory.py

@@ -18,7 +18,7 @@
 import logging
 import numpy as np
 from scipy import optimize
-from .curvefunctions import *
+from .curvefunctions import *  # pylint: disable=wildcard-import,unused-wildcard-import
 
 # Number of curve functions we prepared, more details can be found in "curvefunctions.py"
 NUM_OF_FUNCTIONS = 12
@@ -33,7 +33,7 @@ LEAST_FITTED_FUNCTION = 4
 
 logger = logging.getLogger('curvefitting_Assessor')
 
-class CurveModel(object):
+class CurveModel:
     """Build a Curve Model to predict the performance
 
     Algorithm: https://github.com/Microsoft/nni/blob/master/src/sdk/pynni/nni/curvefitting_assessor/README.md
@@ -83,7 +83,7 @@ class CurveModel(object):
                 # Ignore exceptions caused by numerical calculations
                 pass
             except Exception as exception:
-                logger.critical("Exceptions in fit_theta:", exception)
+                logger.critical("Exceptions in fit_theta: %s", exception)
 
     def filter_curve(self):
         """filter the poor performing curve
@@ -113,7 +113,7 @@ class CurveModel(object):
             if y < median + epsilon and y > median - epsilon:
                 self.effective_model.append(model)
         self.effective_model_num = len(self.effective_model)
-        logger.info('List of effective model: ', self.effective_model)
+        logger.info('List of effective model: %s', self.effective_model)
 
     def predict_y(self, model, pos):
         """return the predict y of 'model' when epoch = pos
@@ -303,7 +303,7 @@ class CurveModel(object):
         """
         init_weight = np.ones((self.effective_model_num), dtype=np.float) / self.effective_model_num
         self.weight_samples = np.broadcast_to(init_weight, (NUM_OF_INSTANCE, self.effective_model_num))
-        for i in range(NUM_OF_SIMULATION_TIME):
+        for _ in range(NUM_OF_SIMULATION_TIME):
             # sample new value from Q(i, j)
             new_values = np.random.randn(NUM_OF_INSTANCE, self.effective_model_num) * STEP_SIZE + self.weight_samples
             new_values = self.normalize_weight(new_values)

src/sdk/pynni/nni/curvefitting_assessor/test.py

@@ -15,6 +15,7 @@
 # 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.
 
+
 import numpy as np
 import unittest
 

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

@@ -40,7 +40,7 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
             _type = x[NodeType.TYPE]
             name = name + '-' + _type
             if _type == 'choice':
-                if oldy != None:
+                if oldy is not None:
                     _index = oldy[NodeType.INDEX]
                     y += json2space(x[NodeType.VALUE][_index],
                                     oldy[NodeType.VALUE], name=name+'[%d]' % _index)
@@ -49,15 +49,13 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
             y.append(name)
         else:
             for key in x.keys():
-                y += json2space(x[key], (oldy[key] if oldy !=
-                                         None else None), name+"[%s]" % str(key))
+                y += json2space(x[key], oldy[key] if oldy else None, name+"[%s]" % str(key))
     elif isinstance(x, list):
         for i, x_i in enumerate(x):
             if isinstance(x_i, dict):
                 if NodeType.NAME not in x_i.keys():
                     raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y += json2space(x_i, (oldy[i] if oldy !=
-                                  None else None), name+"[%d]" % i)
+            y += json2space(x_i, oldy[i] if oldy else None, name + "[%d]" % i)
     return y
 
 def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
@@ -74,36 +72,49 @@ def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeTyp
                     _index = random_state.randint(len(_value))
                     y = {
                         NodeType.INDEX: _index,
-                        NodeType.VALUE: json2parameter(x[NodeType.VALUE][_index],
+                        NodeType.VALUE: json2parameter(
+                            x[NodeType.VALUE][_index],
                             is_rand,
                             random_state,
                             None,
                             Rand,
-                                                             name=name+"[%d]" % _index)
+                            name=name+"[%d]" % _index
+                        )
                     }
                 else:
-                    y = eval('parameter_expressions.' +
-                             _type)(*(_value + [random_state]))
+                    y = getattr(parameter_expressions, _type)(*(_value + [random_state]))
             else:
                 y = copy.deepcopy(oldy)
         else:
             y = dict()
             for key in x.keys():
-                y[key] = json2parameter(x[key], is_rand, random_state, oldy[key]
-                                        if oldy != None else None, Rand, name + "[%s]" % str(key))
+                y[key] = json2parameter(
+                    x[key],
+                    is_rand,
+                    random_state,
+                    oldy[key] if oldy else None,
+                    Rand,
+                    name + "[%s]" % str(key)
+                )
     elif isinstance(x, list):
         y = list()
         for i, x_i in enumerate(x):
             if isinstance(x_i, dict):
                 if NodeType.NAME not in x_i.keys():
                     raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y.append(json2parameter(x_i, is_rand, random_state, oldy[i]
-                                    if oldy != None else None, Rand, name + "[%d]" % i))
+            y.append(json2parameter(
+                x_i,
+                is_rand,
+                random_state,
+                oldy[i] if oldy else None,
+                Rand,
+                name + "[%d]" % i
+            ))
     else:
         y = copy.deepcopy(x)
     return y
 
-class Individual(object):
+class Individual:
     """
     Indicidual class to store the indv info.
     """

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

@@ -151,16 +151,14 @@ class GPTuner(Tuner):
         """
         _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))
             _completed_num += 1
             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
             self.supplement_data_num += 1
             _parameter_id = '_'.join(

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

@@ -139,7 +139,7 @@ class TargetSpace():
         except AssertionError:
             raise ValueError(
                 "Size of array ({}) is different than the ".format(len(x)) +
-                "expected number of parameters ({}).".format(self.dim())
+                "expected number of parameters ({}).".format(self.dim)
             )
 
         params = {}

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

@@ -37,8 +37,8 @@ def _match_val_type(vals, bounds):
         _type = bound['_type']
         if _type == "choice":
             # Find the closest integer in the array, vals_bounds
-            vals_new.append(
-                min(bound['_value'], key=lambda x: abs(x - vals[i])))
+            # pylint: disable=cell-var-from-loop
+            vals_new.append(min(bound['_value'], key=lambda x: abs(x - vals[i])))
         elif _type in ['quniform', 'randint']:
             vals_new.append(np.around(vals[i]))
         else:

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

@@ -23,8 +23,8 @@ gridsearch_tuner.py including:
 '''
 
 import copy
-import numpy as np
 import logging
+import numpy as np
 
 import nni
 from nni.tuner import Tuner
@@ -44,7 +44,8 @@ class GridSearchTuner(Tuner):
     Type 'choice' will select one of the options. Note that it can also be nested.
 
     Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the interval
-    It will be sampled in a way that the first sampled value is 'low', and each of the following values is 'interval' larger than the value in front of it.
+    It will be sampled in a way that the first sampled value is 'low',
+    and each of the following values is 'interval' larger than the value in front of it.
 
     Type 'randint' gives all possible intergers in range[low, high). Note that 'high' is not included.
     '''
@@ -132,7 +133,7 @@ class GridSearchTuner(Tuner):
 
     def generate_parameters(self, parameter_id, **kwargs):
         self.count += 1
-        while (self.count <= len(self.expanded_search_space)-1):
+        while self.count <= len(self.expanded_search_space) - 1:
             _params_tuple = convert_dict2tuple(self.expanded_search_space[self.count])
             if _params_tuple in self.supplement_data:
                 self.count += 1
@@ -153,14 +154,14 @@ class GridSearchTuner(Tuner):
         """
         _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))
             _completed_num += 1
             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
             _params_tuple = convert_dict2tuple(_params)
             self.supplement_data[_params_tuple] = True

src/sdk/pynni/nni/hyperband_advisor/hyperband_advisor.py

@@ -32,7 +32,7 @@ from nni.common import multi_phase_enabled
 from nni.msg_dispatcher_base import MsgDispatcherBase
 from nni.protocol import CommandType, send
 from nni.utils import NodeType, OptimizeMode, MetricType, extract_scalar_reward
-import nni.parameter_expressions as parameter_expressions
+from nni import parameter_expressions
 
 _logger = logging.getLogger(__name__)
 
@@ -49,7 +49,7 @@ def create_parameter_id():
     int
         parameter id
     """
-    global _next_parameter_id  # pylint: disable=global-statement
+    global _next_parameter_id
     _next_parameter_id += 1
     return _next_parameter_id - 1
 
@@ -102,8 +102,7 @@ def json2parameter(ss_spec, random_state):
                 _index = random_state.randint(len(_value))
                 chosen_params = json2parameter(ss_spec[NodeType.VALUE][_index], random_state)
             else:
-                chosen_params = eval('parameter_expressions.' +  # pylint: disable=eval-used
-                                     _type)(*(_value + [random_state]))
+                chosen_params = getattr(parameter_expressions, _type)(*(_value + [random_state]))
         else:
             chosen_params = dict()
             for key in ss_spec.keys():
@@ -140,8 +139,8 @@ class Bracket():
         self.bracket_id = s
         self.s_max = s_max
         self.eta = eta
-        self.n = math.ceil((s_max + 1) * (eta ** s) / (s + 1) - _epsilon)  # pylint: disable=invalid-name
-        self.r = R / eta ** s  # pylint: disable=invalid-name
+        self.n = math.ceil((s_max + 1) * (eta ** s) / (s + 1) - _epsilon)
+        self.r = R / eta ** s
         self.i = 0
         self.hyper_configs = []  # [ {id: params}, {}, ... ]
         self.configs_perf = []  # [ {id: [seq, acc]}, {}, ... ]
@@ -197,7 +196,7 @@ class Bracket():
         i: int
             the ith round
         """
-        global _KEY  # pylint: disable=global-statement
+        global _KEY
         self.num_finished_configs[i] += 1
         _logger.debug('bracket id: %d, round: %d %d, finished: %d, all: %d', self.bracket_id, self.i, i,
                       self.num_finished_configs[i], self.num_configs_to_run[i])
@@ -226,7 +225,7 @@ class Bracket():
             return [[key, value] for key, value in hyper_configs.items()]
         return None
 
-    def get_hyperparameter_configurations(self, num, r, searchspace_json, random_state):  # pylint: disable=invalid-name
+    def get_hyperparameter_configurations(self, num, r, searchspace_json, random_state):
         """Randomly generate num hyperparameter configurations from search space
 
         Parameters
@@ -239,7 +238,7 @@ class Bracket():
         list
             a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...]
         """
-        global _KEY  # pylint: disable=global-statement
+        global _KEY
         assert self.i == 0
         hyperparameter_configs = dict()
         for _ in range(num):
@@ -285,7 +284,7 @@ class Hyperband(MsgDispatcherBase):
     def __init__(self, R=60, eta=3, optimize_mode='maximize'):
         """B = (s_max + 1)R"""
         super(Hyperband, self).__init__()
-        self.R = R  # pylint: disable=invalid-name
+        self.R = R
         self.eta = eta
         self.brackets = dict()  # dict of Bracket
         self.generated_hyper_configs = []  # all the configs waiting for run

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

@@ -51,13 +51,13 @@ def json2space(in_x, name=NodeType.ROOT):
             name = name + '-' + _type
             _value = json2space(in_x[NodeType.VALUE], name=name)
             if _type == 'choice':
-                out_y = eval('hp.hp.choice')(name, _value)
+                out_y = hp.hp.choice(name, _value)
             elif _type == 'randint':
                 out_y = hp.hp.randint(name, _value[1] - _value[0])
             else:
                 if _type in ['loguniform', 'qloguniform']:
                     _value[:2] = np.log(_value[:2])
-                out_y = eval('hp.hp.' + _type)(name, *_value)
+                out_y = getattr(hp.hp, _type)(name, *_value)
         else:
             out_y = dict()
             for key in in_x.keys():
@@ -191,6 +191,7 @@ def _add_index(in_x, parameter):
                     return {NodeType.INDEX: pos, NodeType.VALUE: item}
         else:
             return parameter
+    return None  # note: this is not written by original author, feel free to modify if you think it's incorrect
 
 
 class HyperoptTuner(Tuner):
@@ -409,8 +410,8 @@ class HyperoptTuner(Tuner):
 
         misc_by_id = {m['tid']: m for m in miscs}
         for m in miscs:
-            m['idxs'] = dict([(key, []) for key in idxs])
-            m['vals'] = dict([(key, []) for key in idxs])
+            m['idxs'] = {key: [] for key in idxs}
+            m['vals'] = {key: [] for key in idxs}
 
         for key in idxs:
             assert len(idxs[key]) == len(vals[key])
@@ -433,7 +434,7 @@ class HyperoptTuner(Tuner):
         total_params : dict
             parameter suggestion
         """
-        if self.parallel and len(self.total_data)>20 and len(self.running_data) and self.optimal_y is not None:
+        if self.parallel and len(self.total_data) > 20 and self.running_data and self.optimal_y is not None:
             self.CL_rval = copy.deepcopy(self.rval)
             if self.constant_liar_type == 'mean':
                 _constant_liar_y = self.optimal_y[0] / self.optimal_y[1]
@@ -481,8 +482,7 @@ class HyperoptTuner(Tuner):
         """
         _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))
             _completed_num += 1
             if self.algorithm_name == 'random_search':
                 return
@@ -491,9 +491,7 @@ class HyperoptTuner(Tuner):
             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
             self.supplement_data_num += 1
             _parameter_id = '_'.join(

src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py

@@ -42,7 +42,7 @@ class MedianstopAssessor(Assessor):
             self.high_better = False
         else:
             self.high_better = True
-            logger.warning('unrecognized optimize_mode', optimize_mode)
+            logger.warning('unrecognized optimize_mode %s', optimize_mode)
 
     def _update_data(self, trial_job_id, trial_history):
         """update data
@@ -121,10 +121,10 @@ class MedianstopAssessor(Assessor):
             best_history = min(trial_history)
 
         avg_array = []
-        for id in self.completed_avg_history:
-            if len(self.completed_avg_history[id]) >= curr_step:
-                avg_array.append(self.completed_avg_history[id][curr_step - 1])
-        if len(avg_array) > 0:
+        for id_ in self.completed_avg_history:
+            if len(self.completed_avg_history[id_]) >= curr_step:
+                avg_array.append(self.completed_avg_history[id_][curr_step - 1])
+        if avg_array:
             avg_array.sort()
             if self.high_better:
                 median = avg_array[(len(avg_array)-1) // 2]

src/sdk/pynni/nni/medianstop_assessor/test.py

@@ -22,7 +22,6 @@ import random
 from .medianstop_assessor import MedianstopAssessor
 from nni.assessor import AssessResult
 
-
 logger = logging.getLogger('nni.contrib.medianstop_assessor')
 logger.debug('START')