INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Checks the callable_ to make sure that it satisfies the given expectations. expected_args should be an iterable of Arguments in the order you expect to receive them. expect_starargs means that the function should or should not take a * args param. expect_kwargs says the callable should or should not take ** kwargs para... | def verify_callable_argspec(callable_,
expected_args=Argument.ignore,
expect_starargs=Argument.ignore,
expect_kwargs=Argument.ignore):
"""
Checks the callable_ to make sure that it satisfies the given
expectations.
expec... |
Takes a callable and returns a tuple with the list of Argument objects the name of * args and the name of ** kwargs. If * args or ** kwargs is not present it will be None. This returns a namedtuple called Argspec that has three fields named: args starargs and kwargs. | def parse_argspec(callable_):
"""
Takes a callable and returns a tuple with the list of Argument objects,
the name of *args, and the name of **kwargs.
If *args or **kwargs is not present, it will be None.
This returns a namedtuple called Argspec that has three fields named:
... |
An asset is restricted for all dts if it is in the static list. | def is_restricted(self, assets, dt):
"""
An asset is restricted for all dts if it is in the static list.
"""
if isinstance(assets, Asset):
return assets in self._restricted_set
return pd.Series(
index=pd.Index(assets),
data=vectorized_is_elemen... |
Returns whether or not an asset or iterable of assets is restricted on a dt. | def is_restricted(self, assets, dt):
"""
Returns whether or not an asset or iterable of assets is restricted
on a dt.
"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
is_restricted = partial(self._is_restricted_for_asset, dt=... |
Processes a list of splits by modifying any positions as needed. | def handle_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list
A list of splits. Each split is a tuple of (asset, ratio).
Returns
-------
int: The leftover cash from fractional... |
Given a list of dividends whose ex_dates are all the next trading day calculate and store the cash and/ or stock payments to be paid on each dividend s pay date. | def earn_dividends(self, cash_dividends, stock_dividends):
"""Given a list of dividends whose ex_dates are all the next trading
day, calculate and store the cash and/or stock payments to be paid on
each dividend's pay date.
Parameters
----------
cash_dividends : iterable... |
Returns a cash payment based on the dividends that should be paid out according to the accumulated bookkeeping of earned unpaid and stock dividends. | def pay_dividends(self, next_trading_day):
"""
Returns a cash payment based on the dividends that should be paid out
according to the accumulated bookkeeping of earned, unpaid, and stock
dividends.
"""
net_cash_payment = 0.0
try:
payments = self._unpa... |
The current status of the positions. | def stats(self):
"""The current status of the positions.
Returns
-------
stats : PositionStats
The current stats position stats.
Notes
-----
This is cached, repeated access will not recompute the stats until
the stats may have changed.
... |
Add a transaction to ledger updating the current state as needed. | def process_transaction(self, transaction):
"""Add a transaction to ledger, updating the current state as needed.
Parameters
----------
transaction : zp.Transaction
The transaction to execute.
"""
asset = transaction.asset
if isinstance(asset, Future)... |
Processes a list of splits by modifying any positions as needed. | def process_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
A list of splits. Each split is a tuple of (asset, ratio).
"""
leftover_cash = self.position_tracker.handl... |
Keep track of an order that was placed. | def process_order(self, order):
"""Keep track of an order that was placed.
Parameters
----------
order : zp.Order
The order to record.
"""
try:
dt_orders = self._orders_by_modified[order.dt]
except KeyError:
self._orders_by_mod... |
Process the commission. | def process_commission(self, commission):
"""Process the commission.
Parameters
----------
commission : zp.Event
The commission being paid.
"""
asset = commission['asset']
cost = commission['cost']
self.position_tracker.handle_commission(asse... |
Process dividends for the next session. | def process_dividends(self, next_session, asset_finder, adjustment_reader):
"""Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
well as paying out any dividends whose pay-date is the next session
"""
position_tracker = ... |
Retrieve the dict - form of all of the transactions in a given bar or for the whole simulation. | def transactions(self, dt=None):
"""Retrieve the dict-form of all of the transactions in a given bar or
for the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up transactions for. If not passed,
... |
Retrieve the dict - form of all of the orders in a given bar or for the whole simulation. | def orders(self, dt=None):
"""Retrieve the dict-form of all of the orders in a given bar or for
the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
None is explici... |
Force a computation of the current portfolio state. | def update_portfolio(self):
"""Force a computation of the current portfolio state.
"""
if not self._dirty_portfolio:
return
portfolio = self._portfolio
pt = self.position_tracker
portfolio.positions = pt.get_positions()
position_stats = pt.stats
... |
Override fields on self. account. | def override_account_fields(self,
settled_cash=not_overridden,
accrued_interest=not_overridden,
buying_power=not_overridden,
equity_with_loan=not_overridden,
to... |
Given a datashape type return the associated numpy type. Maps datashape s DateTime type to numpy s datetime64 [ ns ] dtype since the numpy datetime returned by datashape isn t supported by pipeline. | def datashape_type_to_numpy(type_):
"""
Given a datashape type, return the associated numpy type. Maps
datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
type_: datashape.coretypes.Ty... |
Creates or returns a dataset from a blaze expression. | def new_dataset(expr, missing_values, domain):
"""
Creates or returns a dataset from a blaze expression.
Parameters
----------
expr : Expr
The blaze expression representing the values.
missing_values : frozenset((name, value) pairs
Association pairs column name and missing_value... |
Validate that the expression and resources passed match up. | def _check_resources(name, expr, resources):
"""Validate that the expression and resources passed match up.
Parameters
----------
name : str
The name of the argument we are checking.
expr : Expr
The potentially bound expr.
resources
The explicitly passed resources to com... |
Check that a field is a datetime inside some measure. | def _check_datetime_field(name, measure):
"""Check that a field is a datetime inside some measure.
Parameters
----------
name : str
The name of the field to check.
measure : Record
The record to check the field of.
Raises
------
TypeError
If the field is not a d... |
Find the correct metadata expression for the expression. | def _get_metadata(field, expr, metadata_expr, no_metadata_rule):
"""Find the correct metadata expression for the expression.
Parameters
----------
field : {'deltas', 'checkpoints'}
The kind of metadata expr to lookup.
expr : Expr
The baseline expression.
metadata_expr : Expr, 'a... |
Verify that the baseline and deltas expressions have a timestamp field. | def _ensure_timestamp_field(dataset_expr, deltas, checkpoints):
"""Verify that the baseline and deltas expressions have a timestamp field.
If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
verify that it is t... |
Create a Pipeline API object from a blaze expression. | def from_blaze(expr,
deltas='auto',
checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
no_deltas_rule='warn',
no_checkpoints_rule='wa... |
Bind a Blaze expression to resources. | def bind_expression_to_resources(expr, resources):
"""
Bind a Blaze expression to resources.
Parameters
----------
expr : bz.Expr
The expression to which we want to bind resources.
resources : dict[bz.Symbol -> any]
Mapping from the loadable terms of ``expr`` to actual data reso... |
Computes a lower bound and a DataFrame checkpoints. | def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
"""
Computes a lower bound and a DataFrame checkpoints.
Parameters
----------
checkpoints : Expr
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
colnames : ite... |
Query a blaze expression in a given time range properly forward filling from values that fall before the lower date. | def ffill_query_in_range(expr,
lower,
upper,
checkpoints=None,
odo_kwargs=None,
ts_field=TS_FIELD_NAME):
"""Query a blaze expression in a given time range properly forward filling
from va... |
Explicitly map a datset to a collection of blaze expressions. | def register_dataset(self,
dataset,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a datset to a collection of blaze expressions.
Parameters
---... |
Explicitly map a single bound column to a collection of blaze expressions. The expressions need to have timestamp and as_of columns. | def register_column(self,
column,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a single bound column to a collection of blaze
expressions. The expressions n... |
Given a dict of mappings where the values are lists of OwnershipPeriod objects returns a dict with the same structure with new OwnershipPeriod objects adjusted so that the periods have no gaps. | def merge_ownership_periods(mappings):
"""
Given a dict of mappings where the values are lists of
OwnershipPeriod objects, returns a dict with the same structure with
new OwnershipPeriod objects adjusted so that the periods have no
gaps.
Orders the periods chronologically, and pushes forward th... |
Builds a dict mapping to lists of OwnershipPeriods from a db table. | def build_ownership_map(table, key_from_row, value_from_row):
"""
Builds a dict mapping to lists of OwnershipPeriods, from a db table.
"""
return _build_ownership_map_from_rows(
sa.select(table.c).execute().fetchall(),
key_from_row,
value_from_row,
) |
Builds a dict mapping group keys to maps of keys to to lists of OwnershipPeriods from a db table. | def build_grouped_ownership_map(table,
key_from_row,
value_from_row,
group_key):
"""
Builds a dict mapping group keys to maps of keys to to lists of
OwnershipPeriods, from a db table.
"""
grouped_rows = g... |
Filter out kwargs from a dictionary. | def _filter_kwargs(names, dict_):
"""Filter out kwargs from a dictionary.
Parameters
----------
names : set[str]
The names to select from ``dict_``.
dict_ : dict[str, any]
The dictionary to select from.
Returns
-------
kwargs : dict[str, any]
``dict_`` where the... |
Takes in a dict of Asset init args and converts dates to pd. Timestamps | def _convert_asset_timestamp_fields(dict_):
"""
Takes in a dict of Asset init args and converts dates to pd.Timestamps
"""
for key in _asset_timestamp_fields & viewkeys(dict_):
value = pd.Timestamp(dict_[key], tz='UTC')
dict_[key] = None if isnull(value) else value
return dict_ |
Whether or not asset was active at the time corresponding to reference_date_value. | def was_active(reference_date_value, asset):
"""
Whether or not `asset` was active at the time corresponding to
`reference_date_value`.
Parameters
----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
if `asset` was alive. ... |
Retrieve asset types for a list of sids. | def lookup_asset_types(self, sids):
"""
Retrieve asset types for a list of sids.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[sid -> str or None]
Asset types for the provided sids.
"""
found = {}
... |
Retrieve all assets in sids. | def retrieve_all(self, sids, default_none=False):
"""
Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
If False, raise `SidsNot... |
Retrieve the most recent symbol for a set of sids. | def _select_most_recent_symbols_chunk(self, sid_group):
"""Retrieve the most recent symbol for a set of sids.
Parameters
----------
sid_group : iterable[int]
The sids to lookup. The length of this sequence must be less than
or equal to SQLITE_MAX_VARIABLE_NUMBER ... |
Internal function for loading assets from a table. | def _retrieve_assets(self, sids, asset_tbl, asset_type):
"""
Internal function for loading assets from a table.
This should be the only method of `AssetFinder` that writes Assets into
self._asset_cache.
Parameters
---------
sids : iterable of int
Ass... |
Resolve a symbol to an asset object without fuzzy matching. | def _lookup_symbol_strict(self,
ownership_map,
multi_country,
symbol,
as_of_date):
"""
Resolve a symbol to an asset object without fuzzy matching.
Parameters
---------... |
Lookup an equity by symbol. | def lookup_symbol(self,
symbol,
as_of_date,
fuzzy=False,
country_code=None):
"""Lookup an equity by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
as_of_... |
Lookup a list of equities by symbol. | def lookup_symbols(self,
symbols,
as_of_date,
fuzzy=False,
country_code=None):
"""
Lookup a list of equities by symbol.
Equivalent to::
[finder.lookup_symbol(s, as_of, fuzzy) for s in symbol... |
Lookup a future contract by symbol. | def lookup_future_symbol(self, symbol):
"""Lookup a future contract by symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future contract referenced by ``symbol``.
R... |
Get the value of a supplementary field for an asset. | def get_supplementary_field(self, sid, field_name, as_of_date):
"""Get the value of a supplementary field for an asset.
Parameters
----------
sid : int
The sid of the asset to query.
field_name : str
Name of the supplementary field.
as_of_date : p... |
Convert asset_convertible to an asset. | def _lookup_generic_scalar(self,
obj,
as_of_date,
country_code,
matches,
missing):
"""
Convert asset_convertible to an asset.
On success, ap... |
Convert an object into an Asset or sequence of Assets. | def lookup_generic(self, obj, as_of_date, country_code):
"""
Convert an object into an Asset or sequence of Assets.
This method exists primarily as a convenience for implementing
user-facing APIs that can handle multiple kinds of input. It should
not be used for internal code w... |
Compute and cache a recarray of asset lifetimes. | def _compute_asset_lifetimes(self, country_codes):
"""
Compute and cache a recarray of asset lifetimes.
"""
equities_cols = self.equities.c
if country_codes:
buf = np.array(
tuple(
sa.select((
equities_cols.s... |
Compute a DataFrame representing asset lifetimes for the specified date range. | def lifetimes(self, dates, include_start_date, country_codes):
"""
Compute a DataFrame representing asset lifetimes for the specified date
range.
Parameters
----------
dates : pd.DatetimeIndex
The dates for which to compute lifetimes.
include_start_da... |
Return all of the sids for a given country. | def equities_sids_for_country_code(self, country_code):
"""Return all of the sids for a given country.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code.
Returns
-------
tuple[int]
The sids whose exchanges are in t... |
Parameters ---------- fields: list of str sid start_dt: Timestamp Beginning of the window range. end_dt: Timestamp End of the window range. sids: list of int The asset identifiers in the window. | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'sid'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of in... |
Retrieve the value at the given coordinates. | def get_value(self, continuous_future, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
... |
Get the latest minute on or before dt in which asset traded. | def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the... |
Parameters ---------- fields: list of str open high low close or volume start_dt: Timestamp Beginning of the window range. end_dt: Timestamp End of the window range. sids: list of int The asset identifiers in the window. | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the wi... |
Compute each asset s weight in the portfolio by calculating its held value divided by the total value of all positions. | def current_portfolio_weights(self):
"""
Compute each asset's weight in the portfolio by calculating its held
value divided by the total value of all positions.
Each equity's value is its price times the number of shares held. Each
futures contract's value is its unit price time... |
将微信明细中图片托管到云端，同时将html页面中的对应图片替换 | def __hosting_wechat_img(self, content_info, hosting_callback):
"""将微信明细中图片托管到云端，同时将html页面中的对应图片替换
Parameters
----------
content_info : dict 微信文章明细字典
{
'content_img_list': [], # 从微信文章解析出的原始图片列表
'content_html': '', # 从微信文章解析出文章的内容
}... |
获取公众号微信号 wechatid 的信息 | def get_gzh_info(self, wecgat_id_or_name, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""获取公众号微信号 wechatid 的信息
因为wechatid唯一确定，所以第一个就是要搜索的公众号
Parameters
----------
wecgat_id_or_name : str or unicode
wechat_id or wechat_name
unlock_ca... |
搜索 公众号 | def search_gzh(self, keyword, page=1, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""搜索 公众号
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注... |
搜索 文章 | def search_article(self, keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None,
unlock_callback=None,
identify_image_callback=None,
decode_u... |
从 公众号的最近10条群发页面 提取公众号信息 和 文章列表信息 | def get_gzh_article_by_history(self, keyword=None, url=None,
unlock_callback_sogou=None,
identify_image_callback_sogou=None,
unlock_callback_weixin=None,
identify_image_callback_we... |
获取 首页热门文章 | def get_gzh_article_by_hot(self, hot_index, page=1, unlock_callback=None, identify_image_callback=None):
"""获取 首页热门文章
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
... |
获取文章原文，避免临时链接失效 | def get_article_content(self, url, del_qqmusic=True, del_mpvoice=True, unlock_callback=None,
identify_image_callback=None, hosting_callback=None, raw=False):
"""获取文章原文，避免临时链接失效
Parameters
----------
url : str or unicode
原文链接，临时链接
raw : boo... |
获取微信搜狗搜索关键词联想 | def get_sugg(self, keyword):
"""获取微信搜狗搜索关键词联想
Parameters
----------
keyword : str or unicode
关键词
Returns
-------
list[str]
联想关键词列表
Raises
------
WechatSogouRequestsException
"""
url = 'http://w.sug... |
手动打码解锁 | def unlock_sogou_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : b... |
手动打码解锁 | def unlock_weixin_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : ... |
拼接搜索 文章 URL | def gen_search_article_url(keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None):
"""拼接搜索 文章 URL
Parameters
----------
keyword : str or unicode
搜索文字
... |
拼接搜索 公众号 URL | def gen_search_gzh_url(keyword, page=1):
"""拼接搜索 公众号 URL
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
Returns
-------
str
search_gzh_url
"""
assert isinst... |
拼接 首页热门文章 URL | def gen_hot_url(hot_index, page=1):
"""拼接 首页热门文章 URL
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
-------
str
热门文章分类的url
"""
... |
抽取lxml. etree库中elem对象中文字 | def get_first_of_element(element, sub, contype=None):
"""抽取lxml.etree库中elem对象中文字
Args:
element: lxml.etree.Element
sub: str
Returns:
elem中文字
"""
content = element.xpath(sub)
return list_or_empty(content, contype) |
获取requests库get或post返回的对象编码 | def get_encoding_from_reponse(r):
"""获取requests库get或post返回的对象编码
Args:
r: requests库get或post返回的对象
Returns:
对象编码
"""
encoding = requests.utils.get_encodings_from_content(r.text)
return encoding[0] if encoding else requests.utils.get_encoding_from_headers(r.headers) |
替换html‘" ; ’等转义内容为正常内容 | def _replace_str_html(s):
"""替换html‘"’等转义内容为正常内容
Args:
s: 文字内容
Returns:
s: 处理反转义后的文字
"""
html_str_list = [
(''', '\''),
('"', '"'),
('&', '&'),
('¥', '¥'),
('amp;', ''),
('<', '<'),
('>', '>'),
... |
从搜索公众号获得的文本 提取公众号信息 | def get_gzh_by_search(text):
"""从搜索公众号获得的文本 提取公众号信息
Parameters
----------
text : str or unicode
搜索公众号获得的文本
Returns
-------
list[dict]
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
... |
从搜索文章获得的文本 提取章列表信息 | def get_article_by_search(text):
"""从搜索文章获得的文本 提取章列表信息
Parameters
----------
text : str or unicode
搜索文章获得的文本
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '',... |
从 历史消息页的文本 提取公众号信息 | def get_gzh_info_by_history(text):
"""从 历史消息页的文本 提取公众号信息
Parameters
----------
text : str or unicode
历史消息页的文本
Returns
-------
dict
{
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introd... |
从 历史消息页的文本 提取文章列表信息 | def get_article_by_history_json(text, article_json=None):
"""从 历史消息页的文本 提取文章列表信息
Parameters
----------
text : str or unicode
历史消息页的文本
article_json : dict
历史消息页的文本 提取出来的文章json dict
Returns
-------
list[dict]
{
... |
从 首页热门搜索 提取公众号信息 和 文章列表信息 | def get_gzh_article_by_hot(text):
"""从 首页热门搜索 提取公众号信息 和 文章列表信息
Parameters
----------
text : str or unicode
首页热门搜索 页 中 某一页 的文本
Returns
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
... |
根据微信文章的临时链接获取明细 | def get_article_detail(text, del_qqmusic=True, del_voice=True):
"""根据微信文章的临时链接获取明细
1. 获取文本中所有的图片链接列表
2. 获取微信文章的html内容页面(去除标题等信息)
Parameters
----------
text : str or unicode
一篇微信文章的文本
del_qqmusic: bool
删除文章中的qq音乐
del_voice: bool
... |
Reads and decodes an image from a file object as a Numpy array. | def _decode_image(fobj, session, filename):
"""Reads and decodes an image from a file object as a Numpy array.
The SUN dataset contains images in several formats (despite the fact that
all of them have .jpg extension). Some of them are:
- BMP (RGB)
- PNG (grayscale, RGBA, RGB interlaced)
- JPEG (RGB)... |
Process image files from the dataset. | def _process_image_file(fobj, session, filename):
"""Process image files from the dataset."""
# We need to read the image files and convert them to JPEG, since some files
# actually contain GIF, PNG or BMP data (despite having a .jpg extension) and
# some encoding options that will make TF crash in general.
i... |
Yields examples. | def _generate_examples(self, archive):
"""Yields examples."""
prefix_len = len("SUN397")
with tf.Graph().as_default():
with utils.nogpu_session() as sess:
for filepath, fobj in archive:
if (filepath.endswith(".jpg") and
filepath not in _SUN397_IGNORE_IMAGES):
... |
Returns examples from parallel SGML or text files which may be gzipped. | def _parse_parallel_sentences(f1, f2):
"""Returns examples from parallel SGML or text files, which may be gzipped."""
def _parse_text(path):
"""Returns the sentences from a single text file, which may be gzipped."""
split_path = path.split(".")
if split_path[-1] == "gz":
lang = split_path[-2]
... |
Generates examples from TMX file. | def _parse_tmx(path):
"""Generates examples from TMX file."""
def _get_tuv_lang(tuv):
for k, v in tuv.items():
if k.endswith("}lang"):
return v
raise AssertionError("Language not found in `tuv` attributes.")
def _get_tuv_seg(tuv):
segs = tuv.findall("seg")
assert len(segs) == 1, "In... |
Generates examples from TSV file. | def _parse_tsv(path, language_pair=None):
"""Generates examples from TSV file."""
if language_pair is None:
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", path)
assert lang_match is not None, "Invalid TSV filename: %s" % path
l1, l2 = lang_match.groups()
else:
l1, l2 = language_pair
... |
Generates examples from Wikiheadlines dataset file. | def _parse_wikiheadlines(path):
"""Generates examples from Wikiheadlines dataset file."""
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
l1, l2 = lang_match.groups()
with tf.io.gfile.GFile(path) as f:
for line in f:... |
Generates examples from CzEng v1. 6 with optional filtering for v1. 7. | def _parse_czeng(*paths, **kwargs):
"""Generates examples from CzEng v1.6, with optional filtering for v1.7."""
filter_path = kwargs.get("filter_path", None)
if filter_path:
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
with tf.io.gfile.GFile(filter_path) as f:
bad_blocks = {
blk for b... |
Injects languages into ( potentially ) template strings. | def _inject_language(self, src, strings):
"""Injects languages into (potentially) template strings."""
if src not in self.sources:
raise ValueError("Invalid source for '{0}': {1}".format(self.name, src))
def _format_string(s):
if "{0}" in s and "{1}" and "{src}" in s:
return s.format(*so... |
Subsets that make up each split of the dataset for the language pair. | def subsets(self):
"""Subsets that make up each split of the dataset for the language pair."""
source, target = self.builder_config.language_pair
filtered_subsets = {}
for split, ss_names in self._subsets.items():
filtered_subsets[split] = []
for ss_name in ss_names:
ds = DATASET_MAP... |
Returns the examples in the raw ( text ) form. | def _generate_examples(self, split_subsets, extraction_map):
"""Returns the examples in the raw (text) form."""
source, _ = self.builder_config.language_pair
def _get_local_paths(ds, extract_dirs):
rel_paths = ds.get_path(source)
if len(extract_dirs) == 1:
extract_dirs = extract_dirs * ... |
Fetches a tfds. core. DatasetBuilder by string name. | def builder(name, **builder_init_kwargs):
"""Fetches a `tfds.core.DatasetBuilder` by string name.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `Builder... |
Loads the named dataset into a tf. data. Dataset. | def load(name,
split=None,
data_dir=None,
batch_size=1,
download=True,
as_supervised=False,
with_info=False,
builder_kwargs=None,
download_and_prepare_kwargs=None,
as_dataset_kwargs=None,
try_gcs=False):
"""Loads the named datas... |
Extract kwargs from name str. | def _dataset_name_and_kwargs_from_name_str(name_str):
"""Extract kwargs from name str."""
res = _NAME_REG.match(name_str)
if not res:
raise ValueError(_NAME_STR_ERR.format(name_str))
name = res.group("dataset_name")
kwargs = _kwargs_str_to_kwargs(res.group("kwargs"))
try:
for attr in ["config", "ver... |
Try cast to int float bool str in that order. | def _cast_to_pod(val):
"""Try cast to int, float, bool, str, in that order."""
bools = {"True": True, "False": False}
if val in bools:
return bools[val]
try:
return int(val)
except ValueError:
try:
return float(val)
except ValueError:
return tf.compat.as_text(val) |
Try importing a module with an informative error message on failure. | def _try_import(module_name):
"""Try importing a module, with an informative error message on failure."""
try:
mod = importlib.import_module(module_name)
return mod
except ImportError:
err_msg = ("Tried importing %s but failed. See setup.py extras_require. "
"The dataset you are trying ... |
Returns list from list tuple or ndarray. | def np_to_list(elem):
"""Returns list from list, tuple or ndarray."""
if isinstance(elem, list):
return elem
elif isinstance(elem, tuple):
return list(elem)
elif isinstance(elem, np.ndarray):
return list(elem)
else:
raise ValueError(
'Input elements of a sequence should be either a num... |
Transpose a nested dict [ list ] into a list [ nested dict ]. | def _transpose_dict_list(dict_list):
"""Transpose a nested dict[list] into a list[nested dict]."""
# 1. Unstack numpy arrays into list
dict_list = utils.map_nested(np_to_list, dict_list, dict_only=True)
# 2. Extract the sequence length (and ensure the length is constant for all
# elements)
length = {'value... |
See base class for details. | def get_tensor_info(self):
"""See base class for details."""
# Add the additional length dimension to every shape
def add_length_dim(tensor_info):
return feature_lib.TensorInfo(
shape=(self._length,) + tensor_info.shape,
dtype=tensor_info.dtype,
)
tensor_info = super(Se... |
See base class for details. | def get_serialized_info(self):
"""See base class for details."""
# Add the additional length dimension to every serialized features
def add_length_dim(serialized_info):
"""Add the length dimension to the serialized_info.
Args:
serialized_info: One of tf.io.FixedLenFeature, tf.io.VarLen... |
Returns SplitGenerators. | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download the full MNIST Database
filenames = {
"train_data": _MNIST_TRAIN_DATA_FILENAME,
"train_labels": _MNIST_TRAIN_LABELS_FILENAME,
"test_data": _MNIST_TEST_DATA_FILENAME,
"test_labels": _MNIST_TEST_... |
Generate MNIST examples as dicts. | def _generate_examples(self, num_examples, data_path, label_path):
"""Generate MNIST examples as dicts.
Args:
num_examples (int): The number of example.
data_path (str): Path to the data files
label_path (str): Path to the labels
Yields:
Generator yielding the next examples
"""... |
Returns SplitGenerators. | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
... |
Yields examples. | def _generate_examples(self, images_dir_path, labels_path, setid_path,
split_name):
"""Yields examples."""
with tf.io.gfile.GFile(labels_path, "rb") as f:
labels = tfds.core.lazy_imports.scipy.io.loadmat(f)["labels"][0]
with tf.io.gfile.GFile(setid_path, "rb") as f:
exam... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.