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Unverified Commit 7c4b8c0d authored by liuzhe-lz's avatar liuzhe-lz Committed by GitHub
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Make pylint happy (#1649) 

Update python sdk and nni_annotation to pass pylint rules
parent 22316800
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Showing with 197 additions and 184 deletions
.gitignore
View file @ 7c4b8c0d
......@@ -69,3 +69,6 @@ build
*.egg-info
.vscode
# In case you place source code in ~/nni/
/experiments
src/sdk/pynni/nni/__init__.py
View file @ 7c4b8c0d
......@@ -19,16 +19,14 @@
# ==================================================================================================
# pylint: disable=wildcard-import
from .trial import *
from .smartparam import *
from .nas_utils import training_update
class NoMoreTrialError(Exception):
def __init__(self,ErrorInfo):
def __init__(self, ErrorInfo):
super().__init__(self)
self.errorinfo=ErrorInfo
self.errorinfo = ErrorInfo
def __str__(self):
return self.errorinfo
src/sdk/pynni/nni/__main__.py
View file @ 7c4b8c0d
......@@ -27,9 +27,10 @@ import logging
import json
import importlib
from .common import enable_multi_thread, enable_multi_phase
from .constants import ModuleName, ClassName, ClassArgs, AdvisorModuleName, AdvisorClassName
from nni.common import enable_multi_thread, enable_multi_phase
from nni.msg_dispatcher import MsgDispatcher
from .msg_dispatcher import MsgDispatcher
logger = logging.getLogger('nni.main')
logger.debug('START')
......@@ -44,7 +45,7 @@ def augment_classargs(input_class_args, classname):
input_class_args[key] = value
return input_class_args
def create_builtin_class_instance(classname, jsonstr_args, is_advisor = False):
def create_builtin_class_instance(classname, jsonstr_args, is_advisor=False):
if is_advisor:
if classname not in AdvisorModuleName or \
importlib.util.find_spec(AdvisorModuleName[classname]) is None:
......@@ -130,6 +131,31 @@ def main():
if args.advisor_class_name:
# advisor is enabled and starts to run
_run_advisor(args)
else:
# tuner (and assessor) is enabled and starts to run
tuner = _create_tuner(args)
if args.assessor_class_name:
assessor = _create_assessor(args)
else:
assessor = None
dispatcher = MsgDispatcher(tuner, assessor)
try:
dispatcher.run()
tuner._on_exit()
if assessor is not None:
assessor._on_exit()
except Exception as exception:
logger.exception(exception)
tuner._on_error()
if assessor is not None:
assessor._on_error()
raise
def _run_advisor(args):
if args.advisor_class_name in AdvisorModuleName:
dispatcher = create_builtin_class_instance(
args.advisor_class_name,
......@@ -147,10 +173,9 @@ def main():
except Exception as exception:
logger.exception(exception)
raise
else:
# tuner (and assessor) is enabled and starts to run
tuner = None
assessor = None
def _create_tuner(args):
if args.tuner_class_name in ModuleName:
tuner = create_builtin_class_instance(
args.tuner_class_name,
......@@ -161,11 +186,12 @@ def main():
args.tuner_class_filename,
args.tuner_class_name,
args.tuner_args)
if tuner is None:
raise AssertionError('Failed to create Tuner instance')
return tuner
if args.assessor_class_name:
def _create_assessor(args):
if args.assessor_class_name in ModuleName:
assessor = create_builtin_class_instance(
args.assessor_class_name,
......@@ -178,20 +204,8 @@ def main():
args.assessor_args)
if assessor is None:
raise AssertionError('Failed to create Assessor instance')
return assessor
dispatcher = MsgDispatcher(tuner, assessor)
try:
dispatcher.run()
tuner._on_exit()
if assessor is not None:
assessor._on_exit()
except Exception as exception:
logger.exception(exception)
tuner._on_error()
if assessor is not None:
assessor._on_error()
raise
if __name__ == '__main__':
try:
......
src/sdk/pynni/nni/assessor.py
View file @ 7c4b8c0d
......@@ -31,7 +31,6 @@ class AssessResult(Enum):
Bad = False
class Assessor(Recoverable):
# pylint: disable=no-self-use,unused-argument
def assess_trial(self, trial_job_id, trial_history):
"""Determines whether a trial should be killed. Must override.
......@@ -46,21 +45,20 @@ class Assessor(Recoverable):
trial_job_id: identifier of the trial (str).
success: True if the trial successfully completed; False if failed or terminated.
"""
pass
def load_checkpoint(self):
"""Load the checkpoint of assessr.
path: checkpoint directory for assessor
"""
checkpoin_path = self.get_checkpoint_path()
_logger.info('Load checkpoint ignored by assessor, checkpoint path: %s' % checkpoin_path)
_logger.info('Load checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
def save_checkpoint(self):
"""Save the checkpoint of assessor.
path: checkpoint directory for assessor
"""
checkpoin_path = self.get_checkpoint_path()
_logger.info('Save checkpoint ignored by assessor, checkpoint path: %s' % checkpoin_path)
_logger.info('Save checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
def _on_exit(self):
pass
......
src/sdk/pynni/nni/batch_tuner/batch_tuner.py
View file @ 7c4b8c0d
......@@ -100,7 +100,7 @@ class BatchTuner(Tuner):
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
if len(self.values) == 0:
if not self.values:
logger.info("Search space has not been initialized, skip this data import")
return
......
src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py
View file @ 7c4b8c0d
......@@ -51,7 +51,7 @@ 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
......@@ -80,7 +80,7 @@ def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-
return params_id
class Bracket(object):
class Bracket:
"""
A bracket in BOHB, all the information of a bracket is managed by
an instance of this class.
......@@ -98,7 +98,7 @@ class Bracket(object):
max_budget : float
The largest budget to consider. Needs to be larger than min_budget!
The budgets will be geometrically distributed
:math:`a^2 + b^2 = c^2 \sim \eta^k` for :math:`k\in [0, 1, ... , num\_subsets - 1]`.
:math:`a^2 + b^2 = c^2 \\sim \\eta^k` for :math:`k\\in [0, 1, ... , num\\_subsets - 1]`.
optimize_mode: str
optimize mode, 'maximize' or 'minimize'
"""
......@@ -169,7 +169,7 @@ class Bracket(object):
If we have generated new trials after this trial end, we will return a new trial parameters.
Otherwise, we will return None.
"""
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.s, self.i, i, self.num_finished_configs[i], self.num_configs_to_run[i])
......@@ -377,8 +377,10 @@ class BOHB(MsgDispatcherBase):
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
self.curr_s = self.s_max
self.brackets[self.curr_s] = Bracket(s=self.curr_s, s_max=self.s_max, eta=self.eta,
max_budget=self.max_budget, optimize_mode=self.optimize_mode)
self.brackets[self.curr_s] = Bracket(
s=self.curr_s, s_max=self.s_max, eta=self.eta,
max_budget=self.max_budget, optimize_mode=self.optimize_mode
)
next_n, next_r = self.brackets[self.curr_s].get_n_r()
logger.debug(
'new SuccessiveHalving iteration, next_n=%d, next_r=%d', next_n, next_r)
......@@ -643,14 +645,14 @@ class BOHB(MsgDispatcherBase):
"""
_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
budget_exist_flag = False
barely_params = dict()
......@@ -662,7 +664,7 @@ class BOHB(MsgDispatcherBase):
barely_params[keys] = _params[keys]
if not budget_exist_flag:
_budget = self.max_budget
logger.info("Set \"TRIAL_BUDGET\" value to %s (max budget)" %self.max_budget)
logger.info("Set \"TRIAL_BUDGET\" value to %s (max budget)", self.max_budget)
if self.optimize_mode is OptimizeMode.Maximize:
reward = -_value
else:
......
src/sdk/pynni/nni/bohb_advisor/config_generator.py
View file @ 7c4b8c0d
......@@ -28,7 +28,6 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import logging
import traceback
import ConfigSpace
import ConfigSpace.hyperparameters
......@@ -39,7 +38,7 @@ import statsmodels.api as sm
logger = logging.getLogger('BOHB_Advisor')
class CG_BOHB(object):
class CG_BOHB:
def __init__(self, configspace, min_points_in_model=None,
top_n_percent=15, num_samples=64, random_fraction=1/3,
bandwidth_factor=3, min_bandwidth=1e-3):
......@@ -77,8 +76,8 @@ class CG_BOHB(object):
self.min_points_in_model = len(self.configspace.get_hyperparameters())+1
if self.min_points_in_model < len(self.configspace.get_hyperparameters())+1:
logger.warning('Invalid min_points_in_model value. Setting it to %i'%(len(self.configspace.get_hyperparameters())+1))
self.min_points_in_model =len(self.configspace.get_hyperparameters())+1
logger.warning('Invalid min_points_in_model value. Setting it to %i', len(self.configspace.get_hyperparameters()) + 1)
self.min_points_in_model = len(self.configspace.get_hyperparameters()) + 1
self.num_samples = num_samples
self.random_fraction = random_fraction
......@@ -107,9 +106,9 @@ class CG_BOHB(object):
self.kde_models = dict()
def largest_budget_with_model(self):
if len(self.kde_models) == 0:
return(-float('inf'))
return(max(self.kde_models.keys()))
if not self.kde_models:
return -float('inf')
return max(self.kde_models.keys())
def sample_from_largest_budget(self, info_dict):
"""We opted for a single multidimensional KDE compared to the
......@@ -162,11 +161,11 @@ class CG_BOHB(object):
val = minimize_me(vector)
if not np.isfinite(val):
logger.warning('sampled vector: %s has EI value %s'%(vector, val))
logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
logger.warning("l(x) = %s"%(l(vector)))
logger.warning("g(x) = %s"%(g(vector)))
logger.warning('sampled vector: %s has EI value %s', vector, val)
logger.warning("data in the KDEs:\n%s\n%s", kde_good.data, kde_bad.data)
logger.warning("bandwidth of the KDEs:\n%s\n%s", kde_good.bw, kde_bad.bw)
logger.warning("l(x) = %s", l(vector))
logger.warning("g(x) = %s", g(vector))
# right now, this happens because a KDE does not contain all values for a categorical parameter
# this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
......@@ -181,19 +180,15 @@ class CG_BOHB(object):
best_vector = vector
if best_vector is None:
logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
logger.debug("Sampling based optimization with %i samples failed -> using random configuration", self.num_samples)
sample = self.configspace.sample_configuration().get_dictionary()
info_dict['model_based_pick'] = False
else:
logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
for i, hp_value in enumerate(best_vector):
if isinstance(
self.configspace.get_hyperparameter(
self.configspace.get_hyperparameter_by_idx(i)
),
ConfigSpace.hyperparameters.CategoricalHyperparameter
):
logger.debug('best_vector: %s, %s, %s, %s', best_vector, best, l(best_vector), g(best_vector))
for i, _ in enumerate(best_vector):
hp = self.configspace.get_hyperparameter(self.configspace.get_hyperparameter_by_idx(i))
if isinstance(hp, ConfigSpace.hyperparameters.CategoricalHyperparameter):
best_vector[i] = int(np.rint(best_vector[i]))
sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
......@@ -224,12 +219,12 @@ class CG_BOHB(object):
# If no model is available, sample from prior
# also mix in a fraction of random configs
if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
if not self.kde_models.keys() or np.random.rand() < self.random_fraction:
sample = self.configspace.sample_configuration()
info_dict['model_based_pick'] = False
if sample is None:
sample, info_dict= self.sample_from_largest_budget(info_dict)
sample, info_dict = self.sample_from_largest_budget(info_dict)
sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
configuration_space=self.configspace,
......@@ -245,10 +240,10 @@ class CG_BOHB(object):
for i in range(array.shape[0]):
datum = np.copy(array[i])
nan_indices = np.argwhere(np.isnan(datum)).flatten()
while(np.any(nan_indices)):
while np.any(nan_indices):
nan_idx = nan_indices[0]
valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
if len(valid_indices) > 0:
valid_indices = np.argwhere(np.isfinite(array[:, nan_idx])).flatten()
if valid_indices:
# pick one of them at random and overwrite all NaN values
row_idx = np.random.choice(valid_indices)
datum[nan_indices] = array[row_idx, nan_indices]
......@@ -260,8 +255,8 @@ class CG_BOHB(object):
else:
datum[nan_idx] = np.random.randint(t)
nan_indices = np.argwhere(np.isnan(datum)).flatten()
return_array[i,:] = datum
return(return_array)
return_array[i, :] = datum
return return_array
def new_result(self, loss, budget, parameters, update_model=True):
"""
......@@ -305,7 +300,7 @@ class CG_BOHB(object):
# a) if not enough points are available
if len(self.configs[budget]) <= self.min_points_in_model - 1:
logger.debug("Only %i run(s) for budget %f available, need more than %s \
-> can't build model!"%(len(self.configs[budget]), budget, self.min_points_in_model+1))
-> can't build model!", len(self.configs[budget]), budget, self.min_points_in_model+1)
return
# b) during warnm starting when we feed previous results in and only update once
if not update_model:
......@@ -345,5 +340,5 @@ class CG_BOHB(object):
}
# update probs for the categorical parameters for later sampling
logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n'
%(budget, n_good, n_bad, np.min(train_losses)))
logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n',
budget, n_good, n_bad, np.min(train_losses))
src/sdk/pynni/nni/common.py
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
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()
......@@ -75,10 +72,9 @@ class Compressor:
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 +89,17 @@ 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
......@@ -112,10 +108,9 @@ class Pruner(Compressor):
class Quantizer(Compressor):
"""Base quantizer for pytorch quantizer"""
def __init__(self, config_list):
super().__init__(config_list)
"""
Base quantizer for pytorch quantizer
"""
def __call__(self, model):
self.compress(model)
......@@ -130,15 +125,15 @@ 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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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
View file @ 7c4b8c0d
......@@ -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(
......
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