Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def url_for(self, *args: str, **kwargs: str) -> URL:
return self._resource.url_for(*args, **kwargs) | [
"Construct url for route with additional params."
] |
Please provide a description of the function:def add_static(self, prefix: str, path: PathLike, *,
name: Optional[str]=None,
expect_handler: Optional[_ExpectHandler]=None,
chunk_size: int=256 * 1024,
show_index: bool=False, follow_symlinks: bool... | [
"Add static files view.\n\n prefix - url prefix\n path - folder with files\n\n "
] |
Please provide a description of the function:def add_head(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_HEAD, path, handler, **kwargs) | [
"\n Shortcut for add_route with method HEAD\n "
] |
Please provide a description of the function:def add_options(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_OPTIONS, path, handler, **kwargs) | [
"\n Shortcut for add_route with method OPTIONS\n "
] |
Please provide a description of the function:def add_get(self, path: str, handler: _WebHandler, *,
name: Optional[str]=None, allow_head: bool=True,
**kwargs: Any) -> AbstractRoute:
resource = self.add_resource(path, name=name)
if allow_head:
resource.... | [
"\n Shortcut for add_route with method GET, if allow_head is true another\n route is added allowing head requests to the same endpoint\n "
] |
Please provide a description of the function:def add_post(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_POST, path, handler, **kwargs) | [
"\n Shortcut for add_route with method POST\n "
] |
Please provide a description of the function:def add_put(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_PUT, path, handler, **kwargs) | [
"\n Shortcut for add_route with method PUT\n "
] |
Please provide a description of the function:def add_patch(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_PATCH, path, handler, **kwargs) | [
"\n Shortcut for add_route with method PATCH\n "
] |
Please provide a description of the function:def add_delete(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_DELETE, path, handler, **kwargs) | [
"\n Shortcut for add_route with method DELETE\n "
] |
Please provide a description of the function:def add_view(self, path: str, handler: AbstractView,
**kwargs: Any) -> AbstractRoute:
return self.add_route(hdrs.METH_ANY, path, handler, **kwargs) | [
"\n Shortcut for add_route with ANY methods for a class-based view\n "
] |
Please provide a description of the function:def add_routes(self, routes: Iterable[AbstractRouteDef]) -> None:
for route_def in routes:
route_def.register(self) | [
"Append routes to route table.\n\n Parameter should be a sequence of RouteDef objects.\n "
] |
Please provide a description of the function:def parse_headers(
self,
lines: List[bytes]
) -> Tuple['CIMultiDictProxy[str]',
RawHeaders,
Optional[bool],
Optional[str],
bool,
bool]:
headers, raw_header... | [
"Parses RFC 5322 headers from a stream.\n\n Line continuations are supported. Returns list of header name\n and value pairs. Header name is in upper case.\n "
] |
Please provide a description of the function:def get_extra_info(self, name: str, default: Any=None) -> Any:
conn = self._response.connection
if conn is None:
return default
transport = conn.transport
if transport is None:
return default
return tra... | [
"extra info from connection transport"
] |
Please provide a description of the function:def user_agent(style=None) -> _UserAgent:
global useragent
if (not useragent) and style:
useragent = UserAgent()
return useragent[style] if style else DEFAULT_USER_AGENT | [
"Returns an apparently legit user-agent, if not requested one of a specific\n style. Defaults to a Chrome-style User-Agent.\n "
] |
Please provide a description of the function:def raw_html(self) -> _RawHTML:
if self._html:
return self._html
else:
return etree.tostring(self.element, encoding='unicode').strip().encode(self.encoding) | [
"Bytes representation of the HTML content.\n (`learn more <http://www.diveintopython3.net/strings.html>`_).\n "
] |
Please provide a description of the function:def html(self) -> _BaseHTML:
if self._html:
return self.raw_html.decode(self.encoding, errors='replace')
else:
return etree.tostring(self.element, encoding='unicode').strip() | [
"Unicode representation of the HTML content\n (`learn more <http://www.diveintopython3.net/strings.html>`_).\n "
] |
Please provide a description of the function:def encoding(self) -> _Encoding:
if self._encoding:
return self._encoding
# Scan meta tags for charset.
if self._html:
self._encoding = html_to_unicode(self.default_encoding, self._html)[0]
# Fall back to ... | [
"The encoding string to be used, extracted from the HTML and\n :class:`HTMLResponse <HTMLResponse>` headers.\n "
] |
Please provide a description of the function:def pq(self) -> PyQuery:
if self._pq is None:
self._pq = PyQuery(self.lxml)
return self._pq | [
"`PyQuery <https://pythonhosted.org/pyquery/>`_ representation\n of the :class:`Element <Element>` or :class:`HTML <HTML>`.\n "
] |
Please provide a description of the function:def lxml(self) -> HtmlElement:
if self._lxml is None:
try:
self._lxml = soup_parse(self.html, features='html.parser')
except ValueError:
self._lxml = lxml.html.fromstring(self.raw_html)
return ... | [
"`lxml <http://lxml.de>`_ representation of the\n :class:`Element <Element>` or :class:`HTML <HTML>`.\n "
] |
Please provide a description of the function:def find(self, selector: str = "*", *, containing: _Containing = None, clean: bool = False, first: bool = False, _encoding: str = None) -> _Find:
# Convert a single containing into a list.
if isinstance(containing, str):
containing = [co... | [
"Given a CSS Selector, returns a list of\n :class:`Element <Element>` objects or a single one.\n\n :param selector: CSS Selector to use.\n :param clean: Whether or not to sanitize the found HTML of ``<script>`` and ``<style>`` tags.\n :param containing: If specified, only return elements... |
Please provide a description of the function:def xpath(self, selector: str, *, clean: bool = False, first: bool = False, _encoding: str = None) -> _XPath:
selected = self.lxml.xpath(selector)
elements = [
Element(element=selection, url=self.url, default_encoding=_encoding or self.e... | [
"Given an XPath selector, returns a list of\n :class:`Element <Element>` objects or a single one.\n\n :param selector: XPath Selector to use.\n :param clean: Whether or not to sanitize the found HTML of ``<script>`` and ``<style>`` tags.\n :param first: Whether or not to return just the ... |
Please provide a description of the function:def search_all(self, template: str) -> _Result:
return [r for r in findall(template, self.html)] | [
"Search the :class:`Element <Element>` (multiple times) for the given parse\n template.\n\n :param template: The Parse template to use.\n "
] |
Please provide a description of the function:def links(self) -> _Links:
def gen():
for link in self.find('a'):
try:
href = link.attrs['href'].strip()
if href and not (href.startswith('#') and self.skip_anchors) and not href.startswit... | [
"All found links on page, in as–is form."
] |
Please provide a description of the function:def _make_absolute(self, link):
# Parse the link with stdlib.
parsed = urlparse(link)._asdict()
# If link is relative, then join it with base_url.
if not parsed['netloc']:
return urljoin(self.base_url, link)
# L... | [
"Makes a given link absolute."
] |
Please provide a description of the function:def absolute_links(self) -> _Links:
def gen():
for link in self.links:
yield self._make_absolute(link)
return set(gen()) | [
"All found links on page, in absolute form\n (`learn more <https://www.navegabem.com/absolute-or-relative-links.html>`_).\n "
] |
Please provide a description of the function:def base_url(self) -> _URL:
# Support for <base> tag.
base = self.find('base', first=True)
if base:
result = base.attrs.get('href', '').strip()
if result:
return result
# Parse the url to sepa... | [
"The base URL for the page. Supports the ``<base>`` tag\n (`learn more <https://www.w3schools.com/tags/tag_base.asp>`_)."
] |
Please provide a description of the function:def attrs(self) -> _Attrs:
if self._attrs is None:
self._attrs = {k: v for k, v in self.element.items()}
# Split class and rel up, as there are ussually many of them:
for attr in ['class', 'rel']:
if attr ... | [
"Returns a dictionary of the attributes of the :class:`Element <Element>`\n (`learn more <https://www.w3schools.com/tags/ref_attributes.asp>`_).\n "
] |
Please provide a description of the function:def next(self, fetch: bool = False, next_symbol: _NextSymbol = DEFAULT_NEXT_SYMBOL) -> _Next:
def get_next():
candidates = self.find('a', containing=next_symbol)
for candidate in candidates:
if candidate.attrs.get('h... | [
"Attempts to find the next page, if there is one. If ``fetch``\n is ``True`` (default), returns :class:`HTML <HTML>` object of\n next page. If ``fetch`` is ``False``, simply returns the next URL.\n\n "
] |
Please provide a description of the function:async def _async_render(self, *, url: str, script: str = None, scrolldown, sleep: int, wait: float, reload, content: Optional[str], timeout: Union[float, int], keep_page: bool):
try:
page = await self.browser.newPage()
# Wait before ... | [
" Handle page creation and js rendering. Internal use for render/arender methods. "
] |
Please provide a description of the function:def render(self, retries: int = 8, script: str = None, wait: float = 0.2, scrolldown=False, sleep: int = 0, reload: bool = True, timeout: Union[float, int] = 8.0, keep_page: bool = False):
self.browser = self.session.browser # Automatically create a event ... | [
"Reloads the response in Chromium, and replaces HTML content\n with an updated version, with JavaScript executed.\n\n :param retries: The number of times to retry loading the page in Chromium.\n :param script: JavaScript to execute upon page load (optional).\n :param wait: The number of ... |
Please provide a description of the function:def response_hook(self, response, **kwargs) -> HTMLResponse:
if not response.encoding:
response.encoding = DEFAULT_ENCODING
return HTMLResponse._from_response(response, self) | [
" Change response enconding and replace it by a HTMLResponse. "
] |
Please provide a description of the function:def close(self):
if hasattr(self, "_browser"):
self.loop.run_until_complete(self._browser.close())
super().close() | [
" If a browser was created close it first. "
] |
Please provide a description of the function:def request(self, *args, **kwargs):
func = partial(super().request, *args, **kwargs)
return self.loop.run_in_executor(self.thread_pool, func) | [
" Partial original request func and run it in a thread. "
] |
Please provide a description of the function:def run(self, *coros):
tasks = [
asyncio.ensure_future(coro()) for coro in coros
]
done, _ = self.loop.run_until_complete(asyncio.wait(tasks))
return [t.result() for t in done] | [
" Pass in all the coroutines you want to run, it will wrap each one\n in a task, run it and wait for the result. Return a list with all\n results, this is returned in the same order coros are passed in. "
] |
Please provide a description of the function:def add_depth_channel(img_tensor, pad_mode):
'''
img_tensor: N, C, H, W
'''
img_tensor[:, 1] = get_depth_tensor(pad_mode)
img_tensor[:, 2] = img_tensor[:, 0] * get_depth_tensor(pad_mode) | [] |
Please provide a description of the function:def get_pre_compute(self, s):
'''
:param s: [src_sequence, batch_size, src_dim]
:return: [src_sequence, batch_size. hidden_dim]
'''
hidden_dim = self.hidden_dim
src_dim = s.get_shape().as_list()[-1]
assert src_dim is no... | [] |
Please provide a description of the function:def get_prob(self, src, tgt, mask, pre_compute, return_logits=False):
'''
:param s: [src_sequence_length, batch_size, src_dim]
:param h: [batch_size, tgt_dim] or [tgt_sequence_length, batch_size, tgt_dim]
:param mask: [src_sequence_length, bat... | [] |
Please provide a description of the function:def get_att(self, s, prob):
'''
:param s: [src_sequence_length, batch_size, src_dim]
:param prob: [src_sequence_length, batch_size]\
or [tgt_sequence_length, src_sequence_length, batch_size]
:return: [batch_size, src_dim] or [tgt_s... | [] |
Please provide a description of the function:def shape(tensor):
'''
Get shape of variable.
Return type is tuple.
'''
temp_s = tensor.get_shape()
return tuple([temp_s[i].value for i in range(0, len(temp_s))]) | [] |
Please provide a description of the function:def get_variable(name, temp_s):
'''
Get variable by name.
'''
return tf.Variable(tf.zeros(temp_s), name=name) | [] |
Please provide a description of the function:def dropout(tensor, drop_prob, is_training):
'''
Dropout except test.
'''
if not is_training:
return tensor
return tf.nn.dropout(tensor, 1.0 - drop_prob) | [] |
Please provide a description of the function:def get_elapsed(self, restart=True):
'''
Calculate time span.
'''
end = time.time()
span = end - self.__start
if restart:
self.__start = end
return span | [] |
Please provide a description of the function:def do_tta_predict(args, model, ckp_path, tta_num=4):
'''
return 18000x128x128 np array
'''
model.eval()
preds = []
meta = None
# i is tta index, 0: no change, 1: horizon flip, 2: vertical flip, 3: do both
for flip_index in range(tta_num):
... | [] |
Please provide a description of the function:def partition_dataset():
dataset = datasets.MNIST(
'./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
size = ... | [
" Partitioning MNIST "
] |
Please provide a description of the function:def average_gradients(model):
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0)
param.grad.data /= size | [
" Gradient averaging. "
] |
Please provide a description of the function:def run(params):
rank = dist.get_rank()
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
model = Net()
model = model
optimizer = optim.SGD(model.parameters(), lr=params['learning_rate'], momentum=params['momentum'])
num_batches =... | [
" Distributed Synchronous SGD Example "
] |
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['type'], layer['input'], layer['output'], layer['size'])
layer_info.is_delete = layer['is_delete']
layers.appen... | [] |
Please provide a description of the function:def set_size(self, graph_id, size):
'''
Set size.
'''
if self.graph_type == LayerType.attention.value:
if self.input[0] == graph_id:
self.size = size
if self.graph_type == LayerType.rnn.value:
se... | [] |
Please provide a description of the function:def clear_size(self):
'''
Clear size
'''
if self.graph_type == LayerType.attention.value or \
LayerType.rnn.value or LayerType.self_attention.value:
self.size = None | [] |
Please provide a description of the function:def is_topology(self, layers=None):
'''
valid the topology
'''
if layers is None:
layers = self.layers
layers_nodle = []
result = []
for i, layer in enumerate(layers):
if layer.is_delete is False... | [] |
Please provide a description of the function:def is_legal(self, layers=None):
'''
Judge whether is legal for layers
'''
if layers is None:
layers = self.layers
for layer in layers:
if layer.is_delete is False:
if len(layer.input) != layer.... | [] |
Please provide a description of the function:def mutation(self, only_add=False):
'''
Mutation for a graph
'''
types = []
if self.layer_num() < self.max_layer_num:
types.append(0)
types.append(1)
if self.layer_num() > 5 and only_add is False:
... | [] |
Please provide a description of the function:def _main_cli(self):
self.logger.info("SMAC call: %s" % (" ".join(sys.argv)))
cmd_reader = CMDReader()
args, _ = cmd_reader.read_cmd()
root_logger = logging.getLogger()
root_logger.setLevel(args.verbose_level)
logger... | [
"Main function of SMAC for CLI interface\n \n Returns\n -------\n instance\n optimizer\n "
] |
Please provide a description of the function:def update_search_space(self, search_space):
if not self.update_ss_done:
self.categorical_dict = generate_scenario(search_space)
if self.categorical_dict is None:
raise RuntimeError('categorical dict is not correctly r... | [
"TODO: this is urgly, we put all the initialization work in this method, because initialization relies\n on search space, also because update_search_space is called at the beginning.\n NOTE: updating search space is not supported.\n\n Parameters\n ----------\n search_space:\n ... |
Please provide a description of the function:def receive_trial_result(self, parameter_id, parameters, value):
reward = extract_scalar_reward(value)
if self.optimize_mode is OptimizeMode.Maximize:
reward = -reward
if parameter_id not in self.total_data:
raise Run... | [
"receive_trial_result\n \n Parameters\n ----------\n parameter_id: int\n parameter id\n parameters:\n parameters\n value:\n value\n \n Raises\n ------\n RuntimeError\n Received parameter id not in total_... |
Please provide a description of the function:def convert_loguniform_categorical(self, challenger_dict):
converted_dict = {}
for key, value in challenger_dict.items():
# convert to loguniform
if key in self.loguniform_key:
converted_dict[key] = np.exp(chal... | [
"Convert the values of type `loguniform` back to their initial range\n Also, we convert categorical:\n categorical values in search space are changed to list of numbers before,\n those original values will be changed back in this function\n \n Parameters\n ----------\n ... |
Please provide a description of the function:def generate_parameters(self, parameter_id):
if self.first_one:
init_challenger = self.smbo_solver.nni_smac_start()
self.total_data[parameter_id] = init_challenger
return self.convert_loguniform_categorical(init_challenger... | [
"generate one instance of hyperparameters\n \n Parameters\n ----------\n parameter_id: int\n parameter id\n \n Returns\n -------\n list\n new generated parameters\n "
] |
Please provide a description of the function:def generate_multiple_parameters(self, parameter_id_list):
if self.first_one:
params = []
for one_id in parameter_id_list:
init_challenger = self.smbo_solver.nni_smac_start()
self.total_data[one_id] = i... | [
"generate mutiple instances of hyperparameters\n \n Parameters\n ----------\n parameter_id_list: list\n list of parameter id\n \n Returns\n -------\n list\n list of new generated parameters\n "
] |
Please provide a description of the function:def lovasz_grad(gt_sorted):
p = len(gt_sorted)
gts = gt_sorted.sum()
intersection = gts - gt_sorted.float().cumsum(0)
union = gts + (1 - gt_sorted).float().cumsum(0)
jaccard = 1. - intersection / union
if p > 1: # cover 1-pixel case
jacca... | [
"\n Computes gradient of the Lovasz extension w.r.t sorted errors\n See Alg. 1 in paper\n "
] |
Please provide a description of the function:def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
if not per_image:
preds, labels = (preds,), (labels,)
ious = []
for pred, label in zip(preds, labels):
intersection = ((label == 1) & (pred == 1)).sum()
union = ((l... | [
"\n IoU for foreground class\n binary: 1 foreground, 0 background\n "
] |
Please provide a description of the function:def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):
if not per_image:
preds, labels = (preds,), (labels,)
ious = []
for pred, label in zip(preds, labels):
iou = []
for i in range(C):
if i != ignore: # Th... | [
"\n Array of IoU for each (non ignored) class\n "
] |
Please provide a description of the function:def lovasz_hinge(logits, labels, per_image=True, ignore=None):
if per_image:
loss = mean(lovasz_hinge_flat(*flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore))
for log, lab in zip(logits, labels))
else:
lo... | [
"\n Binary Lovasz hinge loss\n logits: [B, H, W] Variable, logits at each pixel (between -\\infty and +\\infty)\n labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)\n per_image: compute the loss per image instead of per batch\n ignore: void class id\n "
] |
Please provide a description of the function:def lovasz_hinge_flat(logits, labels):
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.
signs = 2. * labels.float() - 1.
errors = (1. - logits * Variable(signs))
errors_sorted, perm = torch.sort(... | [
"\n Binary Lovasz hinge loss\n logits: [P] Variable, logits at each prediction (between -\\infty and +\\infty)\n labels: [P] Tensor, binary ground truth labels (0 or 1)\n ignore: label to ignore\n "
] |
Please provide a description of the function:def flatten_binary_scores(scores, labels, ignore=None):
scores = scores.view(-1)
labels = labels.view(-1)
if ignore is None:
return scores, labels
valid = (labels != ignore)
vscores = scores[valid]
vlabels = labels[valid]
return vscor... | [
"\n Flattens predictions in the batch (binary case)\n Remove labels equal to 'ignore'\n "
] |
Please provide a description of the function:def binary_xloss(logits, labels, ignore=None):
logits, labels = flatten_binary_scores(logits, labels, ignore)
loss = StableBCELoss()(logits, Variable(labels.float()))
return loss | [
"\n Binary Cross entropy loss\n logits: [B, H, W] Variable, logits at each pixel (between -\\infty and +\\infty)\n labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)\n ignore: void class id\n "
] |
Please provide a description of the function:def lovasz_softmax(probas, labels, only_present=False, per_image=False, ignore=None):
if per_image:
loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), only_present=only_present)
for prob, l... | [
"\n Multi-class Lovasz-Softmax loss\n probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)\n labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)\n only_present: average only on classes present in ground truth\n per_image: compute the loss per... |
Please provide a description of the function:def lovasz_softmax_flat(probas, labels, only_present=False):
C = probas.size(1)
losses = []
for c in range(C):
fg = (labels == c).float() # foreground for class c
if only_present and fg.sum() == 0:
continue
errors = (Varia... | [
"\n Multi-class Lovasz-Softmax loss\n probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)\n labels: [P] Tensor, ground truth labels (between 0 and C - 1)\n only_present: average only on classes present in ground truth\n "
] |
Please provide a description of the function:def flatten_probas(probas, labels, ignore=None):
B, C, H, W = probas.size()
probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # B * H * W, C = P, C
labels = labels.view(-1)
if ignore is None:
return probas, labels
valid = (labels ... | [
"\n Flattens predictions in the batch\n "
] |
Please provide a description of the function:def xloss(logits, labels, ignore=None):
return F.cross_entropy(logits, Variable(labels), ignore_index=255) | [
"\n Cross entropy loss\n "
] |
Please provide a description of the function:def mean(l, ignore_nan=False, empty=0):
l = iter(l)
if ignore_nan:
l = ifilterfalse(np.isnan, l)
try:
n = 1
acc = next(l)
except StopIteration:
if empty == 'raise':
raise ValueError('Empty mean')
return... | [
"\n nanmean compatible with generators.\n "
] |
Please provide a description of the function:def main_loop(args):
'''main loop logic for trial keeper'''
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
stdout_file = open(STDOUT_FULL_PATH, 'a+')
stderr_file = open(STDERR_FULL_PATH, 'a+')
trial_keeper_syslogger = RemoteLogger(... | [] |
Please provide a description of the function:def forward(self, x):
'''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
N,C,H,W = x.size()
g = self.groups
return x.view(N,g,C/g,H,W).permute(0,2,1,3,4).contiguous().view(N,C,H,W) | [] |
Please provide a description of the function: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()... | [] |
Please provide a description of the function: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(... | [] |
Please provide a description of the function:def generate_data(path, tokenizer, char_vcb, word_vcb, is_training=False):
'''
Generate data
'''
global root_path
qp_pairs = data.load_from_file(path=path, is_training=is_training)
tokenized_sent = 0
# qp_pairs = qp_pairs[:1000]1
for qp_pair ... | [] |
Please provide a description of the function: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_to... | [] |
Please provide a description of the function: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 +=... | [] |
Please provide a description of the function:def evaluate(data_file, pred_file):
'''
Evaluate.
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version:
print('Evaluation expec... | [] |
Please provide a description of the function:def evaluate_with_predictions(data_file, predictions):
'''
Evalutate with predictions/
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version... | [] |
Please provide a description of the function:def send(command, data):
global _lock
try:
_lock.acquire()
data = data.encode('utf8')
assert len(data) < 1000000, 'Command too long'
msg = b'%b%06d%b' % (command.value, len(data), data)
logging.getLogger(__name__).debug('S... | [
"Send command to Training Service.\n command: CommandType object.\n data: string payload.\n "
] |
Please provide a description of the function:def receive():
header = _in_file.read(8)
logging.getLogger(__name__).debug('Received command, header: [%s]' % header)
if header is None or len(header) < 8:
# Pipe EOF encountered
logging.getLogger(__name__).debug('Pipe EOF encountered')
... | [
"Receive a command from Training Service.\n Returns a tuple of command (CommandType) and payload (str)\n "
] |
Please provide a description of the function:def json2space(in_x, name=ROOT):
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 _t... | [
"\n Change json to search space in hyperopt.\n\n Parameters\n ----------\n in_x : dict/list/str/int/float\n The part of json.\n name : str\n name could be ROOT, TYPE, VALUE or INDEX.\n "
] |
Please provide a description of the function:def json2parameter(in_x, parameter, name=ROOT):
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 =... | [
"\n Change json to parameters.\n "
] |
Please provide a description of the function:def _add_index(in_x, parameter):
if TYPE not in in_x: # if at the top level
out_y = dict()
for key, value in parameter.items():
out_y[key] = _add_index(in_x[key], value)
return out_y
elif isinstance(in_x, dict):
value_... | [
"\n change parameters in NNI format to parameters in hyperopt format(This function also support nested dict.).\n For example, receive parameters like:\n {'dropout_rate': 0.8, 'conv_size': 3, 'hidden_size': 512}\n Will change to format in hyperopt, like:\n {'dropout_rate': 0.8, 'conv_size': {'... |
Please provide a description of the function:def _split_index(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... | [
"\n Delete index infromation from params\n "
] |
Please provide a description of the function:def _choose_tuner(self, algorithm_name):
if algorithm_name == 'tpe':
return hp.tpe.suggest
if algorithm_name == 'random_search':
return hp.rand.suggest
if algorithm_name == 'anneal':
return hp.anneal.sugges... | [
"\n Parameters\n ----------\n algorithm_name : str\n algorithm_name includes \"tpe\", \"random_search\" and anneal\"\n "
] |
Please provide a description of the function:def update_search_space(self, search_space):
self.json = search_space
search_space_instance = json2space(self.json)
rstate = np.random.RandomState()
trials = hp.Trials()
domain = hp.Domain(None, search_space_instance,
... | [
"\n Update search space definition in tuner by search_space in parameters.\n\n Will called when first setup experiemnt or update search space in WebUI.\n\n Parameters\n ----------\n search_space : dict\n "
] |
Please provide a description of the function:def generate_parameters(self, parameter_id):
total_params = self.get_suggestion(random_search=False)
# avoid generating same parameter with concurrent trials because hyperopt doesn't support parallel mode
if total_params in self.total_data.va... | [
"\n Returns a set of trial (hyper-)parameters, as a serializable object.\n\n Parameters\n ----------\n parameter_id : int\n\n Returns\n -------\n params : dict\n "
] |
Please provide a description of the function:def receive_trial_result(self, parameter_id, parameters, value):
reward = extract_scalar_reward(value)
# restore the paramsters contains '_index'
if parameter_id not in self.total_data:
raise RuntimeError('Received parameter_id no... | [
"\n 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 value is final metrics of the trial.\n "
] |
Please provide a description of the function:def miscs_update_idxs_vals(self, miscs, idxs, vals,
assert_all_vals_used=True,
idxs_map=None):
if idxs_map is None:
idxs_map = {}
assert set(idxs.keys()) == set(vals.keys())
... | [
"\n Unpack the idxs-vals format into the list of dictionaries that is\n `misc`.\n\n Parameters\n ----------\n idxs_map : dict\n idxs_map is a dictionary of id->id mappings so that the misc['idxs'] can\n contain different numbers than the idxs argument.\n "... |
Please provide a description of the function:def get_suggestion(self, random_search=False):
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)
... | [
"get suggestion from hyperopt\n\n Parameters\n ----------\n random_search : bool\n flag to indicate random search or not (default: {False})\n\n Returns\n ----------\n total_params : dict\n parameter suggestion\n "
] |
Please provide a description of the function:def import_data(self, data):
_completed_num = 0
for trial_info in data:
logger.info("Importing data, current processing progress %s / %s" %(_completed_num, len(data)))
_completed_num += 1
if self.algorithm_name == ... | [
"Import additional data for tuning\n\n Parameters\n ----------\n data:\n a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'\n "
] |
Please provide a description of the function:def next_hyperparameter_lowest_mu(fun_prediction,
fun_prediction_args,
x_bounds, x_types,
minimize_starting_points,
minimize_constraints_fu... | [] |
Please provide a description of the function: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.m... | [] |
Please provide a description of the function:def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
max_char_length = self.cfg.max_char_length
inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
self.cfg.dropout, is_training)
... | [
"Build char embedding network for the QA model."
] |
Please provide a description of the function:def handle_report_metric_data(self, data):
if data['type'] == 'FINAL':
self._handle_final_metric_data(data)
elif data['type'] == 'PERIODICAL':
if self.assessor is not None:
self._handle_intermediate_metric_data... | [
"\n data: a dict received from nni_manager, which contains:\n - 'parameter_id': id of the trial\n - 'value': metric value reported by nni.report_final_result()\n - 'type': report type, support {'FINAL', 'PERIODICAL'}\n "
] |
Please provide a description of the function:def handle_trial_end(self, data):
trial_job_id = data['trial_job_id']
_ended_trials.add(trial_job_id)
if trial_job_id in _trial_history:
_trial_history.pop(trial_job_id)
if self.assessor is not None:
se... | [
"\n data: it has three keys: trial_job_id, event, hyper_params\n - trial_job_id: the id generated by training service\n - event: the job's state\n - hyper_params: the hyperparameters generated and returned by tuner\n "
] |
Please provide a description of the function:def _handle_final_metric_data(self, data):
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:
... | [
"Call tuner to process final results\n "
] |
Please provide a description of the function:def _handle_intermediate_metric_data(self, data):
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:
retu... | [
"Call assessor to process intermediate results\n "
] |
Please provide a description of the function:def _earlystop_notify_tuner(self, data):
_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_comma... | [
"Send last intermediate result as final result to tuner in case the\n trial is early stopped.\n "
] |
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