text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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Description:
| def load_embedding(path):
'''
return embedding for a specific file by given file path.
'''
EMBEDDING_DIM = 300
embedding_dict = {}
with open(path, 'r', encoding='utf-8') as file:
pairs = [line.strip('\r\n').split() for line in file.readlines()]
for pair in pairs:
if l... |
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Description:
| def generate_predict_json(position1_result, position2_result, ids, passage_tokens):
'''
Generate json by prediction.
'''
predict_len = len(position1_result)
logger.debug('total prediction num is %s', str(predict_len))
answers = {}
for i in range(predict_len):
sample_id = ids[i]
... |
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| def f1_score(prediction, ground_truth):
'''
Calculate the f1 score.
'''
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
... |
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| def _evaluate(dataset, predictions):
'''
Evaluate function.
'''
f1_result = exact_match = total = 0
count = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa_pair in paragraph['qas']:
total += 1
if qa_pair['id'] not in pr... |
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def json2space(in_x, name=ROOT):
""" Change json to search space in hyperopt. Parameters in_x : dict/list/str/int/float The part of json. name : str name could b... |
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if TYPE in in_x.keys():
_type = in_x[TYPE]
name = name + '-' + _type
_value = json2space(in_x[VALUE], name=name)
if _type == 'choice':
out_y = eval('hp.hp.'+_type)(name, _value)
... |
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def json2parameter(in_x, parameter, name=ROOT):
""" Change json to parameters. """ |
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if TYPE in in_x.keys():
_type = in_x[TYPE]
name = name + '-' + _type
if _type == 'choice':
_index = parameter[name]
out_y = {
INDEX: _index,
... |
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def _split_index(params):
""" Delete index infromation from params """ |
if isinstance(params, list):
return [params[0], _split_index(params[1])]
elif isinstance(params, dict):
if INDEX in params.keys():
return _split_index(params[VALUE])
result = dict()
for key in params:
result[key] = _split_index(params[key])
return... |
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def update_search_space(self, search_space):
""" Update search space definition in tuner by search_space in parameters. Will called when first setup experiemnt o... |
self.json = search_space
search_space_instance = json2space(self.json)
rstate = np.random.RandomState()
trials = hp.Trials()
domain = hp.Domain(None, search_space_instance,
pass_expr_memo_ctrl=None)
algorithm = self._choose_tuner(self.algorithm... |
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def receive_trial_result(self, parameter_id, parameters, value):
""" Record an observation of the objective function Parameters parameter_id : int parameters : d... |
reward = extract_scalar_reward(value)
# restore the paramsters contains '_index'
if parameter_id not in self.total_data:
raise RuntimeError('Received parameter_id not in total_data.')
params = self.total_data[parameter_id]
if self.optimize_mode is OptimizeMode.Maxim... |
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def miscs_update_idxs_vals(self, miscs, idxs, vals, assert_all_vals_used=True, idxs_map=None):
""" Unpack the idxs-vals format into the list of dictionaries that... |
if idxs_map is None:
idxs_map = {}
assert set(idxs.keys()) == set(vals.keys())
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])
for key in ... |
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def get_suggestion(self, random_search=False):
"""get suggestion from hyperopt Parameters random_search : bool flag to indicate random search or not (default: {F... |
rval = self.rval
trials = rval.trials
algorithm = rval.algo
new_ids = rval.trials.new_trial_ids(1)
rval.trials.refresh()
random_state = rval.rstate.randint(2**31-1)
if random_search:
new_trials = hp.rand.suggest(new_ids, rval.domain, trials, random_s... |
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| def next_hyperparameter_lowest_mu(fun_prediction,
fun_prediction_args,
x_bounds, x_types,
minimize_starting_points,
minimize_constraints_fun=None):
'''
"Lowest Mu" acquisition ... |
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| 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 No... |
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def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
"""Build char embedding network for the QA model.""" |
max_char_length = self.cfg.max_char_length
inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
self.cfg.dropout, is_training)
inputs = tf.reshape(
inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim])
char_lengths = tf.reshape(cha... |
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def _handle_final_metric_data(self, data):
"""Call tuner to process final results """ |
id_ = data['parameter_id']
value = data['value']
if id_ in _customized_parameter_ids:
self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value)
else:
self.tuner.receive_trial_result(id_, _trial_params[id_], value) |
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def _handle_intermediate_metric_data(self, data):
"""Call assessor to process intermediate results """ |
if data['type'] != 'PERIODICAL':
return
if self.assessor is None:
return
trial_job_id = data['trial_job_id']
if trial_job_id in _ended_trials:
return
history = _trial_history[trial_job_id]
history[data['sequence']] = data['value']
... |
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def _earlystop_notify_tuner(self, data):
"""Send last intermediate result as final result to tuner in case the trial is early stopped. """ |
_logger.debug('Early stop notify tuner data: [%s]', data)
data['type'] = 'FINAL'
if multi_thread_enabled():
self._handle_final_metric_data(data)
else:
self.enqueue_command(CommandType.ReportMetricData, data) |
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def train_eval():
""" train and eval the model """ |
global trainloader
global testloader
global net
(x_train, y_train) = trainloader
(x_test, y_test) = testloader
# train procedure
net.fit(
x=x_train,
y=y_train,
batch_size=args.batch_size,
validation_data=(x_test, y_test),
epochs=args.epochs,
... |
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def get_n_r(self):
"""return the values of n and r for the next round""" |
return math.floor(self.n / self.eta**self.i + _epsilon), math.floor(self.r * self.eta**self.i + _epsilon) |
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def increase_i(self):
"""i means the ith round. Increase i by 1""" |
self.i += 1
if self.i > self.bracket_id:
self.no_more_trial = True |
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def get_hyperparameter_configurations(self, num, r, searchspace_json, random_state):
# pylint: disable=invalid-name """Randomly generate num hyperparameter confi... |
global _KEY # pylint: disable=global-statement
assert self.i == 0
hyperparameter_configs = dict()
for _ in range(num):
params_id = create_bracket_parameter_id(self.bracket_id, self.i)
params = json2paramater(searchspace_json, random_state)
params[_KEY... |
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def _record_hyper_configs(self, hyper_configs):
"""after generating one round of hyperconfigs, this function records the generated hyperconfigs, creates a dict t... |
self.hyper_configs.append(hyper_configs)
self.configs_perf.append(dict())
self.num_finished_configs.append(0)
self.num_configs_to_run.append(len(hyper_configs))
self.increase_i() |
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| def gen_send_stdout_url(ip, port):
'''Generate send stdout url'''
return '{0}:{1}{2}{3}/{4}/{5}'.format(BASE_URL.format(ip), port, API_ROOT_URL, STDOUT_API, NNI_EXP_ID, NNI_TRIAL_JOB_ID) |
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| def gen_send_version_url(ip, port):
'''Generate send error url'''
return '{0}:{1}{2}{3}/{4}/{5}'.format(BASE_URL.format(ip), port, API_ROOT_URL, VERSION_API, NNI_EXP_ID, NNI_TRIAL_JOB_ID) |
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| def validate_digit(value, start, end):
'''validate if a digit is valid'''
if not str(value).isdigit() or int(value) < start or int(value) > end:
raise ValueError('%s must be a digit from %s to %s' % (value, start, end)) |
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| def validate_dispatcher(args):
'''validate if the dispatcher of the experiment supports importing data'''
nni_config = Config(get_config_filename(args)).get_config('experimentConfig')
if nni_config.get('tuner') and nni_config['tuner'].get('builtinTunerName'):
dispatcher_name = nni_config['tuner']['b... |
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| def load_search_space(path):
'''load search space content'''
content = json.dumps(get_json_content(path))
if not content:
raise ValueError('searchSpace file should not be empty')
return content |
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| def update_experiment_profile(args, key, value):
'''call restful server to update experiment profile'''
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
running, _ = check_rest_server_quick(rest_port)
if running:
response = rest_get(experimen... |
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| def import_data(args):
'''import additional data to the experiment'''
validate_file(args.filename)
validate_dispatcher(args)
content = load_search_space(args.filename)
args.port = get_experiment_port(args)
if args.port is not None:
if import_data_to_restful_server(args, content):
... |
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| def import_data_to_restful_server(args, content):
'''call restful server to import data to the experiment'''
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
running, _ = check_rest_server_quick(rest_port)
if running:
response = rest_post(imp... |
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| def setType(key, type):
'''check key type'''
return And(type, error=SCHEMA_TYPE_ERROR % (key, type.__name__)) |
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| def setNumberRange(key, keyType, start, end):
'''check number range'''
return And(
And(keyType, error=SCHEMA_TYPE_ERROR % (key, keyType.__name__)),
And(lambda n: start <= n <= end, error=SCHEMA_RANGE_ERROR % (key, '(%s,%s)' % (start, end))),
) |
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| def keras_dropout(layer, rate):
'''keras dropout layer.
'''
from keras import layers
input_dim = len(layer.input.shape)
if input_dim == 2:
return layers.SpatialDropout1D(rate)
elif input_dim == 3:
return layers.SpatialDropout2D(rate)
elif input_dim == 4:
return laye... |
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| def to_real_keras_layer(layer):
''' real keras layer.
'''
from keras import layers
if is_layer(layer, "Dense"):
return layers.Dense(layer.units, input_shape=(layer.input_units,))
if is_layer(layer, "Conv"):
return layers.Conv2D(
layer.filters,
layer.kernel_si... |
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| def layer_description_extractor(layer, node_to_id):
'''get layer description.
'''
layer_input = layer.input
layer_output = layer.output
if layer_input is not None:
if isinstance(layer_input, Iterable):
layer_input = list(map(lambda x: node_to_id[x], layer_input))
else:
... |
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| def layer_description_builder(layer_information, id_to_node):
'''build layer from description.
'''
# pylint: disable=W0123
layer_type = layer_information[0]
layer_input_ids = layer_information[1]
if isinstance(layer_input_ids, Iterable):
layer_input = list(map(lambda x: id_to_node[x], l... |
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| def layer_width(layer):
'''get layer width.
'''
if is_layer(layer, "Dense"):
return layer.units
if is_layer(layer, "Conv"):
return layer.filters
raise TypeError("The layer should be either Dense or Conv layer.") |
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| def define_params(self):
'''
Define parameters.
'''
input_dim = self.input_dim
hidden_dim = self.hidden_dim
prefix = self.name
self.w_matrix = tf.Variable(tf.random_normal([input_dim, 3 * hidden_dim], stddev=0.1),
name='/'.join(... |
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| def build(self, x, h, mask=None):
'''
Build the GRU cell.
'''
xw = tf.split(tf.matmul(x, self.w_matrix) + self.bias, 3, 1)
hu = tf.split(tf.matmul(h, self.U), 3, 1)
r = tf.sigmoid(xw[0] + hu[0])
z = tf.sigmoid(xw[1] + hu[1])
h1 = tf.tanh(xw[2] + r * hu[2])... |
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| def build_sequence(self, xs, masks, init, is_left_to_right):
'''
Build GRU sequence.
'''
states = []
last = init
if is_left_to_right:
for i, xs_i in enumerate(xs):
h = self.build(xs_i, last, masks[i])
states.append(h)
... |
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def conv2d(x_input, w_matrix):
"""conv2d returns a 2d convolution layer with full stride.""" |
return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME') |
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def max_pool(x_input, pool_size):
"""max_pool downsamples a feature map by 2X.""" |
return tf.nn.max_pool(x_input, ksize=[1, pool_size, pool_size, 1],
strides=[1, pool_size, pool_size, 1], padding='SAME') |
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| def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
# Create the model
# Build the graph for the deep net... |
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| def check_output_command(file_path, head=None, tail=None):
'''call check_output command to read content from a file'''
if os.path.exists(file_path):
if sys.platform == 'win32':
cmds = ['powershell.exe', 'type', file_path]
if head:
cmds += ['|', 'select', '-first',... |
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| def install_package_command(package_name):
'''install python package from pip'''
#TODO refactor python logic
if sys.platform == "win32":
cmds = 'python -m pip install --user {0}'.format(package_name)
else:
cmds = 'python3 -m pip install --user {0}'.format(package_name)
call(cmds, she... |
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| def install_requirements_command(requirements_path):
'''install requirements.txt'''
cmds = 'cd ' + requirements_path + ' && {0} -m pip install --user -r requirements.txt'
#TODO refactor python logic
if sys.platform == "win32":
cmds = cmds.format('python')
else:
cmds = cmds.format('py... |
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| def get_params():
''' Get parameters from command line '''
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default='/tmp/tensorflow/mnist/input_data', help="data directory")
parser.add_argument("--dropout_rate", type=float, default=0.5, help="dropout rate")
parser.add_... |
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| def build_network(self):
'''
Building network for mnist
'''
# Reshape to use within a convolutional neural net.
# Last dimension is for "features" - there is only one here, since images are
# grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
with tf.n... |
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| def get_experiment_time(port):
'''get the startTime and endTime of an experiment'''
response = rest_get(experiment_url(port), REST_TIME_OUT)
if response and check_response(response):
content = convert_time_stamp_to_date(json.loads(response.text))
return content.get('startTime'), content.get(... |
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| def get_experiment_status(port):
'''get the status of an experiment'''
result, response = check_rest_server_quick(port)
if result:
return json.loads(response.text).get('status')
return None |
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| def update_experiment():
'''Update the experiment status in config file'''
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
if not experiment_dict:
return None
for key in experiment_dict.keys():
if isinstance(experiment_dict[key], dict):
... |
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| def check_experiment_id(args):
'''check if the id is valid
'''
update_experiment()
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
if not experiment_dict:
print_normal('There is no experiment running...')
return None
if not args.id:... |
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| def get_config_filename(args):
'''get the file name of config file'''
experiment_id = check_experiment_id(args)
if experiment_id is None:
print_error('Please set the experiment id!')
exit(1)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
... |
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| def convert_time_stamp_to_date(content):
'''Convert time stamp to date time format'''
start_time_stamp = content.get('startTime')
end_time_stamp = content.get('endTime')
if start_time_stamp:
start_time = datetime.datetime.utcfromtimestamp(start_time_stamp // 1000).strftime("%Y/%m/%d %H:%M:%S")
... |
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| def check_rest(args):
'''check if restful server is running'''
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
running, _ = check_rest_server_quick(rest_port)
if not running:
print_normal('Restful server is running...')
else:
pri... |
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| def stop_experiment(args):
'''Stop the experiment which is running'''
experiment_id_list = parse_ids(args)
if experiment_id_list:
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
for experiment_id in experiment_id_list:
print_nor... |
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| def list_experiment(args):
'''Get experiment information'''
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
rest_pid = nni_config.get_config('restServerPid')
if not detect_process(rest_pid):
print_error('Experiment is not running...')
... |
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| def experiment_status(args):
'''Show the status of experiment'''
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
result, response = check_rest_server_quick(rest_port)
if not result:
print_normal('Restful server is not running...')
else:
... |
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| def log_internal(args, filetype):
'''internal function to call get_log_content'''
file_name = get_config_filename(args)
if filetype == 'stdout':
file_full_path = os.path.join(NNICTL_HOME_DIR, file_name, 'stdout')
else:
file_full_path = os.path.join(NNICTL_HOME_DIR, file_name, 'stderr')
... |
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| def log_trial(args):
''''get trial log path'''
trial_id_path_dict = {}
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
rest_pid = nni_config.get_config('restServerPid')
if not detect_process(rest_pid):
print_error('Experiment is not runn... |
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| def webui_url(args):
'''show the url of web ui'''
nni_config = Config(get_config_filename(args))
print_normal('{0} {1}'.format('Web UI url:', ' '.join(nni_config.get_config('webuiUrl')))) |
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| def experiment_list(args):
'''get the information of all experiments'''
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
if not experiment_dict:
print('There is no experiment running...')
exit(1)
update_experiment()
experiment_id_list = ... |
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| def get_time_interval(time1, time2):
'''get the interval of two times'''
try:
#convert time to timestamp
time1 = time.mktime(time.strptime(time1, '%Y/%m/%d %H:%M:%S'))
time2 = time.mktime(time.strptime(time2, '%Y/%m/%d %H:%M:%S'))
seconds = (datetime.datetime.fromtimestamp(time2)... |
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| def show_experiment_info():
'''show experiment information in monitor'''
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
if not experiment_dict:
print('There is no experiment running...')
exit(1)
update_experiment()
experiment_id_list =... |
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| def monitor_experiment(args):
'''monitor the experiment'''
if args.time <= 0:
print_error('please input a positive integer as time interval, the unit is second.')
exit(1)
while True:
try:
os.system('clear')
update_experiment()
show_experiment_info(... |
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def export_trials_data(args):
"""export experiment metadata to csv """ |
nni_config = Config(get_config_filename(args))
rest_port = nni_config.get_config('restServerPort')
rest_pid = nni_config.get_config('restServerPid')
if not detect_process(rest_pid):
print_error('Experiment is not running...')
return
running, response = check_rest_server_quick(rest_p... |
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| def copy_remote_directory_to_local(sftp, remote_path, local_path):
'''copy remote directory to local machine'''
try:
os.makedirs(local_path, exist_ok=True)
files = sftp.listdir(remote_path)
for file in files:
remote_full_path = os.path.join(remote_path, file)
loca... |
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| def create_ssh_sftp_client(host_ip, port, username, password):
'''create ssh client'''
try:
check_environment()
import paramiko
conn = paramiko.Transport(host_ip, port)
conn.connect(username=username, password=password)
sftp = paramiko.SFTPClient.from_transport(conn)
... |
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def json2space(x, oldy=None, name=NodeType.Root.value):
"""Change search space from json format to hyperopt format """ |
y = list()
if isinstance(x, dict):
if NodeType.Type.value in x.keys():
_type = x[NodeType.Type.value]
name = name + '-' + _type
if _type == 'choice':
if oldy != None:
_index = oldy[NodeType.Index.value]
y += jso... |
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def json2paramater(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.Root.value):
"""Json to pramaters. """ |
if isinstance(x, dict):
if NodeType.Type.value in x.keys():
_type = x[NodeType.Type.value]
_value = x[NodeType.Value.value]
name = name + '-' + _type
Rand |= is_rand[name]
if Rand is True:
if _type == 'choice':
... |
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def _split_index(params):
"""Delete index information from params Parameters params : dict Returns ------- result : dict """ |
result = {}
for key in params:
if isinstance(params[key], dict):
value = params[key]['_value']
else:
value = params[key]
result[key] = value
return result |
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def update_search_space(self, search_space):
"""Update search space. Search_space contains the information that user pre-defined. Parameters search_space : dict ... |
self.searchspace_json = search_space
self.space = json2space(self.searchspace_json)
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):
... |
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| def receive_trial_result(self, parameter_id, parameters, value):
'''Record the result from a trial
Parameters
----------
parameters: dict
value : dict/float
if value is dict, it should have "default" key.
value is final metrics of the trial.
'''
... |
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| def load_data(train_path='./data/regression.train', test_path='./data/regression.test'):
'''
Load or create dataset
'''
print('Load data...')
df_train = pd.read_csv(train_path, header=None, sep='\t')
df_test = pd.read_csv(test_path, header=None, sep='\t')
num = len(df_train)
split_num = ... |
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def layer_distance(a, b):
"""The distance between two layers.""" |
# pylint: disable=unidiomatic-typecheck
if type(a) != type(b):
return 1.0
if is_layer(a, "Conv"):
att_diff = [
(a.filters, b.filters),
(a.kernel_size, b.kernel_size),
(a.stride, b.stride),
]
return attribute_difference(att_diff)
if is_... |
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| def attribute_difference(att_diff):
''' The attribute distance.
'''
ret = 0
for a_value, b_value in att_diff:
if max(a_value, b_value) == 0:
ret += 0
else:
ret += abs(a_value - b_value) * 1.0 / max(a_value, b_value)
return ret * 1.0 / len(att_diff) |
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def layers_distance(list_a, list_b):
"""The distance between the layers of two neural networks.""" |
len_a = len(list_a)
len_b = len(list_b)
f = np.zeros((len_a + 1, len_b + 1))
f[-1][-1] = 0
for i in range(-1, len_a):
f[i][-1] = i + 1
for j in range(-1, len_b):
f[-1][j] = j + 1
for i in range(len_a):
for j in range(len_b):
f[i][j] = min(
... |
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def skip_connection_distance(a, b):
"""The distance between two skip-connections.""" |
if a[2] != b[2]:
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)) |
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def skip_connections_distance(list_a, list_b):
"""The distance between the skip-connections of two neural networks.""" |
distance_matrix = np.zeros((len(list_a), len(list_b)))
for i, a in enumerate(list_a):
for j, b in enumerate(list_b):
distance_matrix[i][j] = skip_connection_distance(a, b)
return distance_matrix[linear_sum_assignment(distance_matrix)].sum() + abs(
len(list_a) - len(list_b)
) |
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def vector_distance(a, b):
"""The Euclidean distance between two vectors.""" |
a = np.array(a)
b = np.array(b)
return np.linalg.norm(a - b) |
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def contain(descriptors, target_descriptor):
"""Check if the target descriptor is in the descriptors.""" |
for descriptor in descriptors:
if edit_distance(descriptor, target_descriptor) < 1e-5:
return True
return False |
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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.")
train_x, train_y = np.array(train_x), np.array(train_y)
# Incrementally compute K
up_right_k = edit_distance_matrix(self._x, train_x)
down_left_k = np.transpose(up_right... |
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def first_fit(self, train_x, train_y):
""" Fit the regressor for the first time. """ |
train_x, train_y = np.array(train_x), np.array(train_y)
self._x = np.copy(train_x)
self._y = np.copy(train_y)
self._distance_matrix = edit_distance_matrix(self._x)
k_matrix = bourgain_embedding_matrix(self._distance_matrix)
k_matrix[np.diag_indices_from(k_matrix)] += s... |
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| def acq(self, graph):
''' estimate the value of generated graph
'''
mean, std = self.gpr.predict(np.array([graph.extract_descriptor()]))
if self.optimizemode is OptimizeMode.Maximize:
return mean + self.beta * std
return mean - self.beta * std |
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def get_dict(self, u=None):
""" A recursive function to return the content of the tree in a dict.""" |
if u is None:
return self.get_dict(self.root)
children = []
for v in self.adj_list[u]:
children.append(self.get_dict(v))
ret = {"name": u, "children": children}
return ret |
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def update_hash(self, layers: Iterable):
""" Calculation of `hash_id` of Layer. Which is determined by the properties of itself, and the `hash_id`s of input laye... |
if self.graph_type == LayerType.input.value:
return
hasher = hashlib.md5()
hasher.update(LayerType(self.graph_type).name.encode('ascii'))
hasher.update(str(self.size).encode('ascii'))
for i in self.input:
if layers[i].hash_id is None:
rais... |
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| def create_mnist_model(hyper_params, input_shape=(H, W, 1), num_classes=NUM_CLASSES):
'''
Create simple convolutional model
'''
layers = [
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2,... |
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| def load_mnist_data(args):
'''
Load MNIST dataset
'''
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (np.expand_dims(x_train, -1).astype(np.float) / 255.)[:args.num_train]
x_test = (np.expand_dims(x_test, -1).astype(np.float) / 255.)[:args.num_test]
y_train = keras.utils... |
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| def get_all_config(self):
'''get all of config values'''
return json.dumps(self.config, indent=4, sort_keys=True, separators=(',', ':')) |
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| def remove_experiment(self, id):
'''remove an experiment by id'''
if id in self.experiments:
self.experiments.pop(id)
self.write_file() |
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| def read_file(self):
'''load config from local file'''
if os.path.exists(self.experiment_file):
try:
with open(self.experiment_file, 'r') as file:
return json.load(file)
except ValueError:
return {}
return {} |
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| def load_from_file(path, fmt=None, is_training=True):
'''
load data from file
'''
if fmt is None:
fmt = 'squad'
assert fmt in ['squad', 'csv'], 'input format must be squad or csv'
qp_pairs = []
if fmt == 'squad':
with open(path) as data_file:
data = json.load(data... |
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| def tokenize(qp_pair, tokenizer=None, is_training=False):
'''
tokenize function.
'''
question_tokens = tokenizer.tokenize(qp_pair['question'])
passage_tokens = tokenizer.tokenize(qp_pair['passage'])
if is_training:
question_tokens = question_tokens[:300]
passage_tokens = passage_... |
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| def collect_vocab(qp_pairs):
'''
Build the vocab from corpus.
'''
vocab = set()
for qp_pair in qp_pairs:
for word in qp_pair['question_tokens']:
vocab.add(word['word'])
for word in qp_pair['passage_tokens']:
vocab.add(word['word'])
return vocab |
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| def shuffle_step(entries, step):
'''
Shuffle the step
'''
answer = []
for i in range(0, len(entries), step):
sub = entries[i:i+step]
shuffle(sub)
answer += sub
return answer |
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| def get_batches(qp_pairs, batch_size, need_sort=True):
'''
Get batches data and shuffle.
'''
if need_sort:
qp_pairs = sorted(qp_pairs, key=lambda qp: (
len(qp['passage_tokens']), qp['id']), reverse=True)
batches = [{'qp_pairs': qp_pairs[i:(i + batch_size)]}
for i i... |
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| def get_char_input(data, char_dict, max_char_length):
'''
Get char input.
'''
batch_size = len(data)
sequence_length = max(len(d) for d in data)
char_id = np.zeros((max_char_length, sequence_length,
batch_size), dtype=np.int32)
char_lengths = np.zeros((sequence_length... |
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| def get_word_input(data, word_dict, embed, embed_dim):
'''
Get word input.
'''
batch_size = len(data)
max_sequence_length = max(len(d) for d in data)
sequence_length = max_sequence_length
word_input = np.zeros((max_sequence_length, batch_size,
embed_dim), dtype=np.... |
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| def get_word_index(tokens, char_index):
'''
Given word return word index.
'''
for (i, token) in enumerate(tokens):
if token['char_end'] == 0:
continue
if token['char_begin'] <= char_index and char_index <= token['char_end']:
return i
return 0 |
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| def get_answer_begin_end(data):
'''
Get answer's index of begin and end.
'''
begin = []
end = []
for qa_pair in data:
tokens = qa_pair['passage_tokens']
char_begin = qa_pair['answer_begin']
char_end = qa_pair['answer_end']
word_begin = get_word_index(tokens, char_... |
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