Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def generate(self, descriptors):
model_ids = self.search_tree.adj_list.keys()
target_graph = None
father_id = None
descriptors = deepcopy(descriptors)
elem_class = Elem
if self.optimizemode is OptimizeMode.Maximize:
... | [
"Generate new architecture.\n Args:\n descriptors: All the searched neural architectures.\n Returns:\n graph: An instance of Graph. A morphed neural network with weights.\n father_id: The father node ID in the search tree.\n "
] |
Please provide a description of the function: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 me... | [] |
Please provide a description of the function:def add_child(self, u, v):
''' add child to search tree itself.
Arguments:
u {int} -- father id
v {int} -- child id
'''
if u == -1:
self.root = v
self.adj_list[v] = []
return
... | [] |
Please provide a description of the function:def get_dict(self, u=None):
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 | [
" A recursive function to return the content of the tree in a dict."
] |
Please provide a description of the function:def train_with_graph(p_graph, qp_pairs, dev_qp_pairs):
'''
Train a network from a specific graph.
'''
global sess
with tf.Graph().as_default():
train_model = GAG(cfg, embed, p_graph)
train_model.build_net(is_training=True)
tf.get_v... | [] |
Please provide a description of the function:def generate_multiple_parameters(self, parameter_id_list):
result = []
for parameter_id in parameter_id_list:
try:
_logger.debug("generating param for {}".format(parameter_id))
res = self.generate_parameter... | [
"Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.\n Call 'generate_parameters()' by 'count' times by default.\n User code must override either this function or 'generate_parameters()'.\n If there's no more trial, user should raise nni.NoMoreTrialError exce... |
Please provide a description of the function:def graph_loads(graph_json):
'''
Load graph
'''
layers = []
for layer in graph_json['layers']:
layer_info = Layer(layer['graph_type'], layer['input'], layer['output'], layer['size'], layer['hash_id'])
layer_info.is_delete = layer['is_delet... | [] |
Please provide a description of the function:def update_hash(self, layers: Iterable):
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'... | [
"\n Calculation of `hash_id` of Layer. Which is determined by the properties of itself, and the `hash_id`s of input layers\n "
] |
Please provide a description of the function:def update_hash(self):
_logger.debug('update hash')
layer_in_cnt = [len(layer.input) for layer in self.layers]
topo_queue = deque([i for i, layer in enumerate(self.layers) if not layer.is_delete and layer.graph_type == LayerType.input.value])... | [
"\n update hash id of each layer, in topological order/recursively\n hash id will be used in weight sharing\n "
] |
Please provide a description of the function:def init_logger(logger_file_path, log_level_name='info'):
log_level = log_level_map.get(log_level_name, logging.INFO)
logger_file = open(logger_file_path, 'w')
fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
logging.Formatter.co... | [
"Initialize root logger.\n This will redirect anything from logging.getLogger() as well as stdout to specified file.\n logger_file_path: path of logger file (path-like object).\n "
] |
Please provide a description of the function: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), activatio... | [] |
Please provide a description of the function: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) / 25... | [] |
Please provide a description of the function:def train(args, params):
'''
Train model
'''
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
# nni
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validat... | [] |
Please provide a description of the function:def on_epoch_end(self, epoch, logs={}):
'''
Run on end of each epoch
'''
LOG.debug(logs)
nni.report_intermediate_result(logs["val_acc"]) | [] |
Please provide a description of the function:def get_all_config(self):
'''get all of config values'''
return json.dumps(self.config, indent=4, sort_keys=True, separators=(',', ':')) | [] |
Please provide a description of the function:def set_config(self, key, value):
'''set {key:value} paris to self.config'''
self.config = self.read_file()
self.config[key] = value
self.write_file() | [] |
Please provide a description of the function:def write_file(self):
'''save config to local file'''
if self.config:
try:
with open(self.config_file, 'w') as file:
json.dump(self.config, file)
except IOError as error:
print('Error... | [] |
Please provide a description of the function:def add_experiment(self, id, port, time, file_name, platform):
'''set {key:value} paris to self.experiment'''
self.experiments[id] = {}
self.experiments[id]['port'] = port
self.experiments[id]['startTime'] = time
self.experiments[id]['... | [] |
Please provide a description of the function:def update_experiment(self, id, key, value):
'''Update experiment'''
if id not in self.experiments:
return False
self.experiments[id][key] = value
self.write_file()
return True | [] |
Please provide a description of the function:def remove_experiment(self, id):
'''remove an experiment by id'''
if id in self.experiments:
self.experiments.pop(id)
self.write_file() | [] |
Please provide a description of the function:def write_file(self):
'''save config to local file'''
try:
with open(self.experiment_file, 'w') as file:
json.dump(self.experiments, file)
except IOError as error:
print('Error:', error)
return | [] |
Please provide a description of the function: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:
... | [] |
Please provide a description of the function: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... | [] |
Please provide a description of the function: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_t... | [] |
Please provide a description of the function: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.a... | [] |
Please provide a description of the function: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 | [] |
Please provide a description of the function: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_pair... | [] |
Please provide a description of the function: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)... | [] |
Please provide a description of the function: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,
... | [] |
Please provide a description of the function: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']:
... | [] |
Please provide a description of the function: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']
... | [] |
Please provide a description of the function:def get_buckets(min_length, max_length, bucket_count):
'''
Get bucket by length.
'''
if bucket_count <= 0:
return [max_length]
unit_length = int((max_length - min_length) // (bucket_count))
buckets = [min_length + unit_length *
... | [] |
Please provide a description of the function:def tokenize(self, text):
'''
tokenize function in Tokenizer.
'''
start = -1
tokens = []
for i, character in enumerate(text):
if character == ' ' or character == '\t':
if start >= 0:
... | [] |
Please provide a description of the function:def generate_new_id(self):
self.events.append(Event())
indiv_id = self.indiv_counter
self.indiv_counter += 1
return indiv_id | [
"\n generate new id and event hook for new Individual\n "
] |
Please provide a description of the function:def init_population(self, population_size, graph_max_layer, graph_min_layer):
population = []
graph = Graph(max_layer_num=graph_max_layer, min_layer_num=graph_min_layer,
inputs=[Layer(LayerType.input.value, output=[4, 5], size='... | [
"\n initialize populations for evolution tuner\n "
] |
Please provide a description of the function:def generate_parameters(self, parameter_id):
logger.debug('acquiring lock for param {}'.format(parameter_id))
self.thread_lock.acquire()
logger.debug('lock for current thread acquired')
if not self.population:
logger.debug... | [
"Returns a set of trial graph config, as a serializable object.\n An example configuration:\n ```json\n {\n \"shared_id\": [\n \"4a11b2ef9cb7211590dfe81039b27670\",\n \"370af04de24985e5ea5b3d72b12644c9\",\n \"11f646e9f650f5f3fedc12b6349ec6... |
Please provide a description of the function:def receive_trial_result(self, parameter_id, parameters, value):
'''
Record an observation of the objective function
parameter_id : int
parameters : dict of parameters
value: final metrics of the trial, including reward
'''
... | [] |
Please provide a description of the function:def _update_data(self, trial_job_id, trial_history):
if trial_job_id not in self.running_history:
self.running_history[trial_job_id] = []
self.running_history[trial_job_id].extend(trial_history[len(self.running_history[trial_job_id]):]) | [
"update data\n\n Parameters\n ----------\n trial_job_id: int\n trial job id\n trial_history: list\n The history performance matrix of each trial\n "
] |
Please provide a description of the function:def trial_end(self, trial_job_id, success):
if trial_job_id in self.running_history:
if success:
cnt = 0
history_sum = 0
self.completed_avg_history[trial_job_id] = []
for each in sel... | [
"trial_end\n \n Parameters\n ----------\n trial_job_id: int\n trial job id\n success: bool\n True if succssfully finish the experiment, False otherwise\n "
] |
Please provide a description of the function:def assess_trial(self, trial_job_id, trial_history):
curr_step = len(trial_history)
if curr_step < self.start_step:
return AssessResult.Good
try:
num_trial_history = [float(ele) for ele in trial_history]
excep... | [
"assess_trial\n \n Parameters\n ----------\n trial_job_id: int\n trial job id\n trial_history: list\n The history performance matrix of each trial\n\n Returns\n -------\n bool\n AssessResult.Good or AssessResult.Bad\n\n ... |
Please provide a description of the function:def copyHdfsDirectoryToLocal(hdfsDirectory, localDirectory, hdfsClient):
'''Copy directory from HDFS to local'''
if not os.path.exists(localDirectory):
os.makedirs(localDirectory)
try:
listing = hdfsClient.list_status(hdfsDirectory)
except Exc... | [] |
Please provide a description of the function:def copyHdfsFileToLocal(hdfsFilePath, localFilePath, hdfsClient, override=True):
'''Copy file from HDFS to local'''
if not hdfsClient.exists(hdfsFilePath):
raise Exception('HDFS file {} does not exist!'.format(hdfsFilePath))
try:
file_status = hd... | [] |
Please provide a description of the function:def copyDirectoryToHdfs(localDirectory, hdfsDirectory, hdfsClient):
'''Copy directory from local to HDFS'''
if not os.path.exists(localDirectory):
raise Exception('Local Directory does not exist!')
hdfsClient.mkdirs(hdfsDirectory)
result = True
fo... | [] |
Please provide a description of the function:def copyFileToHdfs(localFilePath, hdfsFilePath, hdfsClient, override=True):
'''Copy a local file to HDFS directory'''
if not os.path.exists(localFilePath):
raise Exception('Local file Path does not exist!')
if os.path.isdir(localFilePath):
raise E... | [] |
Please provide a description of the function:def load_data():
'''Load dataset, use boston dataset'''
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=99, test_size=0.25)
#normalize data
ss_X = StandardScaler()
ss_y = StandardScal... | [] |
Please provide a description of the function:def get_model(PARAMS):
'''Get model according to parameters'''
model_dict = {
'LinearRegression': LinearRegression(),
'SVR': SVR(),
'KNeighborsRegressor': KNeighborsRegressor(),
'DecisionTreeRegressor': DecisionTreeRegressor()
}
... | [] |
Please provide a description of the function:def run(X_train, X_test, y_train, y_test, PARAMS):
'''Train model and predict result'''
model.fit(X_train, y_train)
predict_y = model.predict(X_test)
score = r2_score(y_test, predict_y)
LOG.debug('r2 score: %s' % score)
nni.report_final_result(score) | [] |
Please provide a description of the function:def add_skip_connection(self, u, v, connection_type):
if connection_type not in [self.CONCAT_CONNECT, self.ADD_CONNECT]:
raise ValueError(
"connection_type should be NetworkDescriptor.CONCAT_CONNECT "
"or NetworkDe... | [
" Add a skip-connection to the descriptor.\n Args:\n u: Number of convolutional layers before the starting point.\n v: Number of convolutional layers before the ending point.\n connection_type: Must be either CONCAT_CONNECT or ADD_CONNECT.\n "
] |
Please provide a description of the function:def to_json(self):
''' NetworkDescriptor to json representation
'''
skip_list = []
for u, v, connection_type in self.skip_connections:
skip_list.append({"from": u, "to": v, "type": connection_type})
return {"node_list": se... | [] |
Please provide a description of the function:def add_layer(self, layer, input_node_id):
if isinstance(input_node_id, Iterable):
layer.input = list(map(lambda x: self.node_list[x], input_node_id))
output_node_id = self._add_node(Node(layer.output_shape))
for node_id i... | [
"Add a layer to the Graph.\n Args:\n layer: An instance of the subclasses of StubLayer in layers.py.\n input_node_id: An integer. The ID of the input node of the layer.\n Returns:\n output_node_id: An integer. The ID of the output node of the layer.\n "
] |
Please provide a description of the function:def _add_node(self, node):
node_id = len(self.node_list)
self.node_to_id[node] = node_id
self.node_list.append(node)
self.adj_list[node_id] = []
self.reverse_adj_list[node_id] = []
return node_id | [
"Add a new node to node_list and give the node an ID.\n Args:\n node: An instance of Node.\n Returns:\n node_id: An integer.\n "
] |
Please provide a description of the function:def _add_edge(self, layer, input_id, output_id):
if layer in self.layer_to_id:
layer_id = self.layer_to_id[layer]
if input_id not in self.layer_id_to_input_node_ids[layer_id]:
self.layer_id_to_input_node_ids[layer_id]... | [
"Add a new layer to the graph. The nodes should be created in advance."
] |
Please provide a description of the function:def _redirect_edge(self, u_id, v_id, new_v_id):
layer_id = None
for index, edge_tuple in enumerate(self.adj_list[u_id]):
if edge_tuple[0] == v_id:
layer_id = edge_tuple[1]
self.adj_list[u_id][index] = (new_... | [
"Redirect the layer to a new node.\n Change the edge originally from `u_id` to `v_id` into an edge from `u_id` to `new_v_id`\n while keeping all other property of the edge the same.\n "
] |
Please provide a description of the function:def _replace_layer(self, layer_id, new_layer):
old_layer = self.layer_list[layer_id]
new_layer.input = old_layer.input
new_layer.output = old_layer.output
new_layer.output.shape = new_layer.output_shape
self.layer_list[layer_i... | [
"Replace the layer with a new layer."
] |
Please provide a description of the function:def topological_order(self):
q = Queue()
in_degree = {}
for i in range(self.n_nodes):
in_degree[i] = 0
for u in range(self.n_nodes):
for v, _ in self.adj_list[u]:
in_degree[v] += 1
for i... | [
"Return the topological order of the node IDs from the input node to the output node."
] |
Please provide a description of the function:def _get_pooling_layers(self, start_node_id, end_node_id):
layer_list = []
node_list = [start_node_id]
assert self._depth_first_search(end_node_id, layer_list, node_list)
ret = []
for layer_id in layer_list:
layer ... | [
"Given two node IDs, return all the pooling layers between them."
] |
Please provide a description of the function:def _depth_first_search(self, target_id, layer_id_list, node_list):
assert len(node_list) <= self.n_nodes
u = node_list[-1]
if u == target_id:
return True
for v, layer_id in self.adj_list[u]:
layer_id_list.app... | [
"Search for all the layers and nodes down the path.\n A recursive function to search all the layers and nodes between the node in the node_list\n and the node with target_id."
] |
Please provide a description of the function:def _search(self, u, start_dim, total_dim, n_add):
if (u, start_dim, total_dim, n_add) in self.vis:
return
self.vis[(u, start_dim, total_dim, n_add)] = True
for v, layer_id in self.adj_list[u]:
layer = self.layer_list[... | [
"Search the graph for all the layers to be widened caused by an operation.\n It is an recursive function with duplication check to avoid deadlock.\n It searches from a starting node u until the corresponding layers has been widened.\n Args:\n u: The starting node ID.\n sta... |
Please provide a description of the function:def to_deeper_model(self, target_id, new_layer):
self.operation_history.append(("to_deeper_model", target_id, new_layer))
input_id = self.layer_id_to_input_node_ids[target_id][0]
output_id = self.layer_id_to_output_node_ids[target_id][0]
... | [
"Insert a relu-conv-bn block after the target block.\n Args:\n target_id: A convolutional layer ID. The new block should be inserted after the block.\n new_layer: An instance of StubLayer subclasses.\n "
] |
Please provide a description of the function:def to_wider_model(self, pre_layer_id, n_add):
self.operation_history.append(("to_wider_model", pre_layer_id, n_add))
pre_layer = self.layer_list[pre_layer_id]
output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
dim = layer_... | [
"Widen the last dimension of the output of the pre_layer.\n Args:\n pre_layer_id: The ID of a convolutional layer or dense layer.\n n_add: The number of dimensions to add.\n "
] |
Please provide a description of the function:def _insert_new_layers(self, new_layers, start_node_id, end_node_id):
new_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
temp_output_id = new_node_id
for layer in new_layers[:-1]:
temp_output_id = self.add_layer(l... | [
"Insert the new_layers after the node with start_node_id."
] |
Please provide a description of the function:def to_add_skip_model(self, start_id, end_id):
self.operation_history.append(("to_add_skip_model", start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
st... | [
"Add a weighted add skip-connection from after start node to end node.\n Args:\n start_id: The convolutional layer ID, after which to start the skip-connection.\n end_id: The convolutional layer ID, after which to end the skip-connection.\n "
] |
Please provide a description of the function:def to_concat_skip_model(self, start_id, end_id):
self.operation_history.append(("to_concat_skip_model", start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
... | [
"Add a weighted add concatenate connection from after start node to end node.\n Args:\n start_id: The convolutional layer ID, after which to start the skip-connection.\n end_id: The convolutional layer ID, after which to end the skip-connection.\n "
] |
Please provide a description of the function:def extract_descriptor(self):
main_chain = self.get_main_chain()
index_in_main_chain = {}
for index, u in enumerate(main_chain):
index_in_main_chain[u] = index
ret = NetworkDescriptor()
for u in main_chain:
... | [
"Extract the the description of the Graph as an instance of NetworkDescriptor."
] |
Please provide a description of the function:def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None | [] |
Please provide a description of the function:def get_main_chain_layers(self):
main_chain = self.get_main_chain()
ret = []
for u in main_chain:
for v, layer_id in self.adj_list[u]:
if v in main_chain and u in main_chain:
ret.append(layer_id... | [
"Return a list of layer IDs in the main chain."
] |
Please provide a description of the function:def get_main_chain(self):
pre_node = {}
distance = {}
for i in range(self.n_nodes):
distance[i] = 0
pre_node[i] = i
for i in range(self.n_nodes - 1):
for u in range(self.n_nodes):
fo... | [
"Returns the main chain node ID list."
] |
Please provide a description of the function:def run(self):
_logger.info('Start dispatcher')
if dispatcher_env_vars.NNI_MODE == 'resume':
self.load_checkpoint()
while True:
command, data = receive()
if data:
data = json_tricks.loads(d... | [
"Run the tuner.\n This function will never return unless raise.\n "
] |
Please provide a description of the function:def command_queue_worker(self, command_queue):
while True:
try:
# set timeout to ensure self.stopping is checked periodically
command, data = command_queue.get(timeout=3)
try:
se... | [
"Process commands in command queues.\n "
] |
Please provide a description of the function:def enqueue_command(self, command, data):
if command == CommandType.TrialEnd or (command == CommandType.ReportMetricData and data['type'] == 'PERIODICAL'):
self.assessor_command_queue.put((command, data))
else:
self.default_co... | [
"Enqueue command into command queues\n "
] |
Please provide a description of the function:def process_command_thread(self, request):
command, data = request
if multi_thread_enabled():
try:
self.process_command(command, data)
except Exception as e:
_logger.exception(str(e))
... | [
"Worker thread to process a command.\n "
] |
Please provide a description of the function:def match_val_type(vals, vals_bounds, vals_types):
'''
Update values in the array, to match their corresponding type
'''
vals_new = []
for i, _ in enumerate(vals_types):
if vals_types[i] == "discrete_int":
# Find the closest integer i... | [] |
Please provide a description of the function:def rand(x_bounds, x_types):
'''
Random generate variable value within their bounds
'''
outputs = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
... | [] |
Please provide a description of the function:def to_wider_graph(graph):
''' wider graph
'''
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
)
wider_layers = sample(weighted_layer_ids, 1)
... | [] |
Please provide a description of the function:def to_skip_connection_graph(graph):
''' skip connection graph
'''
# The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
weighted_layer_ids = graph.skip_connection_layer_ids()
valid_connection = []
fo... | [] |
Please provide a description of the function:def create_new_layer(layer, n_dim):
''' create new layer for the graph
'''
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim),... | [] |
Please provide a description of the function:def to_deeper_graph(graph):
''' deeper graph
'''
weighted_layer_ids = graph.deep_layer_ids()
if len(weighted_layer_ids) >= Constant.MAX_LAYERS:
return None
deeper_layer_ids = sample(weighted_layer_ids, 1)
for layer_id in deeper_layer_ids:
... | [] |
Please provide a description of the function:def legal_graph(graph):
'''judge if a graph is legal or not.
'''
descriptor = graph.extract_descriptor()
skips = descriptor.skip_connections
if len(skips) != len(set(skips)):
return False
return True | [] |
Please provide a description of the function:def transform(graph):
'''core transform function for graph.
'''
graphs = []
for _ in range(Constant.N_NEIGHBOURS * 2):
random_num = randrange(3)
temp_graph = None
if random_num == 0:
temp_graph = to_deeper_graph(deepcopy(g... | [] |
Please provide a description of the function:def uniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert high > low, 'Upper bound must be larger than lower bound'... | [] |
Please provide a description of the function:def quniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(uniform(low, high, random... | [] |
Please provide a description of the function:def loguniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert low > 0, 'Lower bound must be positive'
return np.... | [] |
Please provide a description of the function:def qloguniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(loguniform(low, high, ... | [] |
Please provide a description of the function:def qnormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(normal(mu, sigma, random_state) / q) * q | [] |
Please provide a description of the function:def lognormal(mu, sigma, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
'''
return np.exp(normal(mu, sigma, random_state)) | [] |
Please provide a description of the function:def qlognormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(lognormal(mu, sigma, random_state) / q) *... | [] |
Please provide a description of the function:def predict(parameters_value, regressor_gp):
'''
Predict by Gaussian Process Model
'''
parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
return mu[0... | [] |
Please provide a description of the function:def rest_get(url, timeout):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http get to url {1}'.format(str(e), url))
return... | [] |
Please provide a description of the function:def rest_post(url, data, timeout, rethrow_exception=False):
'''Call rest post method'''
try:
response = requests.post(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout... | [] |
Please provide a description of the function:def rest_put(url, data, timeout):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
... | [] |
Please provide a description of the function:def rest_delete(url, timeout):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http delete to url {1}'.format(str(e), url))
... | [] |
Please provide a description of the function:def trial_end(self, trial_job_id, success):
if success:
if self.set_best_performance:
self.completed_best_performance = max(self.completed_best_performance, self.trial_history[-1])
else:
self.set_best_p... | [
"update the best performance of completed trial job\n \n Parameters\n ----------\n trial_job_id: int\n trial job id\n success: bool\n True if succssfully finish the experiment, False otherwise\n "
] |
Please provide a description of the function:def assess_trial(self, trial_job_id, trial_history):
self.trial_job_id = trial_job_id
self.trial_history = trial_history
if not self.set_best_performance:
return AssessResult.Good
curr_step = len(trial_history)
if ... | [
"assess whether a trial should be early stop by curve fitting algorithm\n\n Parameters\n ----------\n trial_job_id: int\n trial job id\n trial_history: list\n The history performance matrix of each trial\n\n Returns\n -------\n bool\n ... |
Please provide a description of the function:def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True | [] |
Please provide a description of the function:def generate_parameters(self, parameter_id):
if not self.history:
self.init_search()
new_father_id = None
generated_graph = None
if not self.training_queue:
new_father_id, generated_graph = self.generate()
... | [
"\n Returns a set of trial neural architecture, as a serializable object.\n\n Parameters\n ----------\n parameter_id : int\n "
] |
Please provide a description of the function:def receive_trial_result(self, parameter_id, parameters, value):
reward = extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError("Received parameter_id not in total_data.")
(_, father_id, model_i... | [
" Record an observation of the objective function.\n \n Parameters\n ----------\n parameter_id : int\n parameters : dict\n value : dict/float\n if value is dict, it should have \"default\" key.\n "
] |
Please provide a description of the function:def init_search(self):
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_m... | [
"Call the generators to generate the initial architectures for the search."
] |
Please provide a description of the function:def generate(self):
generated_graph, new_father_id = self.bo.generate(self.descriptors)
if new_father_id is None:
new_father_id = 0
generated_graph = self.generators[0](
self.n_classes, self.input_shape
... | [
"Generate the next neural architecture.\n\n Returns\n -------\n other_info: any object\n Anything to be saved in the training queue together with the architecture.\n generated_graph: Graph\n An instance of Graph.\n "
] |
Please provide a description of the function:def update(self, other_info, graph, metric_value, model_id):
father_id = other_info
self.bo.fit([graph.extract_descriptor()], [metric_value])
self.bo.add_child(father_id, model_id) | [
" Update the controller with evaluation result of a neural architecture.\n\n Parameters\n ----------\n other_info: any object\n In our case it is the father ID in the search tree.\n graph: Graph\n An instance of Graph. The trained neural architecture.\n metri... |
Please provide a description of the function:def add_model(self, metric_value, model_id):
if self.verbose:
logger.info("Saving model.")
# Update best_model text file
ret = {"model_id": model_id, "metric_value": metric_value}
self.history.append(ret)
if model... | [
" Add model to the history, x_queue and y_queue\n\n Parameters\n ----------\n metric_value : float\n graph : dict\n model_id : int\n\n Returns\n -------\n model : dict\n "
] |
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