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def get_order(self, order_id):
"""Lookup an order based on the order id returned from one of the order functions. Parameters order_id : str The unique identifier... |
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj() |
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def cancel_order(self, order_param):
"""Cancel an open order. Parameters order_param : str or Order The order_id or order object to cancel. """ |
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id) |
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def register_account_control(self, control):
""" Register a new AccountControl to be checked on each bar. """ |
if self.initialized:
raise RegisterAccountControlPostInit()
self.account_controls.append(control) |
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def set_min_leverage(self, min_leverage, grace_period):
"""Set a limit on the minimum leverage of the algorithm. Parameters min_leverage : float The minimum leve... |
deadline = self.sim_params.start_session + grace_period
control = MinLeverage(min_leverage, deadline)
self.register_account_control(control) |
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def register_trading_control(self, control):
""" Register a new TradingControl to be checked prior to order calls. """ |
if self.initialized:
raise RegisterTradingControlPostInit()
self.trading_controls.append(control) |
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def set_max_order_count(self, max_count, on_error='fail'):
"""Set a limit on the number of orders that can be placed in a single day. Parameters max_count : int ... |
control = MaxOrderCount(on_error, max_count)
self.register_trading_control(control) |
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def attach_pipeline(self, pipeline, name, chunks=None, eager=True):
"""Register a pipeline to be computed at the start of each day. Parameters pipeline : Pipelin... |
if chunks is None:
# Make the first chunk smaller to get more immediate results:
# (one week, then every half year)
chunks = chain([5], repeat(126))
elif isinstance(chunks, int):
chunks = repeat(chunks)
if name in self._pipelines:
rai... |
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def _pipeline_output(self, pipeline, chunks, name):
""" Internal implementation of `pipeline_output`. """ |
today = normalize_date(self.get_datetime())
try:
data = self._pipeline_cache.get(name, today)
except KeyError:
# Calculate the next block.
data, valid_until = self.run_pipeline(
pipeline, today, next(chunks),
)
self._pi... |
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def run_pipeline(self, pipeline, start_session, chunksize):
""" Compute `pipeline`, providing values for at least `start_date`. Produces a DataFrame containing d... |
sessions = self.trading_calendar.all_sessions
# Load data starting from the previous trading day...
start_date_loc = sessions.get_loc(start_session)
# ...continuing until either the day before the simulation end, or
# until chunksize days of data have been loaded.
sim_... |
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def all_api_methods(cls):
""" Return a list of all the TradingAlgorithm API methods. """ |
return [
fn for fn in itervalues(vars(cls))
if getattr(fn, 'is_api_method', False)
] |
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def _expect_extra(expected, present, exc_unexpected, exc_missing, exc_args):
""" Checks for the presence of an extra to the argument list. Raises expections if t... |
if present:
if not expected:
raise exc_unexpected(*exc_args)
elif expected and expected is not Argument.ignore:
raise exc_missing(*exc_args) |
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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_element(assets, self._restricted_set)
) |
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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=dt)
return pd.Series(
index=pd.Index(assets),
data=vectorize(is_restricted, otypes=[bool])(assets)
) |
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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 ... |
net_cash_payment = 0.0
try:
payments = self._unpaid_dividends[next_trading_day]
# Mark these dividends as paid by dropping them from our unpaid
del self._unpaid_dividends[next_trading_day]
except KeyError:
payments = []
# representing th... |
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def stats(self):
"""The current status of the positions. Returns ------- stats : PositionStats The current stats position stats. Notes ----- This is cached, repe... |
if self._dirty_stats:
calculate_position_tracker_stats(self.positions, self._stats)
self._dirty_stats = False
return self._stats |
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def process_transaction(self, transaction):
"""Add a transaction to ledger, updating the current state as needed. Parameters transaction : zp.Transaction The tra... |
asset = transaction.asset
if isinstance(asset, Future):
try:
old_price = self._payout_last_sale_prices[asset]
except KeyError:
self._payout_last_sale_prices[asset] = transaction.price
else:
position = self.position_trac... |
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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_modified[order.dt] = OrderedDict([
(order.id, order),
])
self._orders_by_id[order.id] = order
else:
self._orders_by_id[order.id] = dt_... |
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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(asset, cost)
self._cash_flow(-cost) |
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def process_dividends(self, next_session, asset_finder, adjustment_reader):
"""Process dividends for the next session. This will earn us any dividends whose ex-d... |
position_tracker = self.position_tracker
# Earn dividends whose ex_date is the next trading day. We need to
# check if we own any of these stocks so we know to pay them out when
# the pay date comes.
held_sids = set(position_tracker.positions)
if held_sids:
... |
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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... |
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, []) |
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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, optio... |
if dt is None:
# orders by id is already flattened
return [o.to_dict() for o in itervalues(self._orders_by_id)]
return [
o.to_dict()
for o in itervalues(self._orders_by_modified.get(dt, {}))
] |
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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
portfolio.positions_value = position_value = (
position_stats.net_value
)
... |
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def override_account_fields(self, settled_cash=not_overridden, accrued_interest=not_overridden, buying_power=not_overridden, equity_with_loan=not_overridden, tota... |
# mark that the portfolio is dirty to override the fields again
self._dirty_account = True
self._account_overrides = kwargs = {
k: v for k, v in locals().items() if v is not not_overridden
}
del kwargs['self'] |
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def new_dataset(expr, missing_values, domain):
""" Creates or returns a dataset from a blaze expression. Parameters expr : Expr The blaze expression representing... |
missing_values = dict(missing_values)
class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
#... |
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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... |
if expr is None:
return
bound = expr._resources()
if not bound and resources is None:
raise ValueError('no resources provided to compute %s' % name)
if bound and resources:
raise ValueError(
'explicit and implicit resources provided to compute %s' % name,
) |
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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 ... |
if not isinstance(measure[name], (Date, DateTime)):
raise TypeError(
"'{name}' field must be a '{dt}', not: '{dshape}'".format(
name=name,
dt=DateTime(),
dshape=measure[name],
),
) |
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def _get_metadata(field, expr, metadata_expr, no_metadata_rule):
"""Find the correct metadata expression for the expression. Parameters field : {'deltas', 'check... |
if isinstance(metadata_expr, bz.Expr) or metadata_expr is None:
return metadata_expr
try:
return expr._child['_'.join(((expr._name or ''), field))]
except (ValueError, AttributeError):
if no_metadata_rule == 'raise':
raise ValueError(
"no %s table could ... |
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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... |
measure = dataset_expr.dshape.measure
if TS_FIELD_NAME not in measure.names:
dataset_expr = bz.transform(
dataset_expr,
**{TS_FIELD_NAME: dataset_expr[AD_FIELD_NAME]}
)
deltas = _ad_as_ts(deltas)
checkpoints = _ad_as_ts(checkpoints)
else:
_che... |
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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 re... |
# bind the resources into the expression
if resources is None:
resources = {}
# _subs stands for substitute. It's not actually private, blaze just
# prefixes symbol-manipulation methods with underscores to prevent
# collisions with data column names.
return expr._subs({
k: bz.... |
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def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
""" Computes a lower bound and a DataFrame checkpoints. Parameters checkpoints : E... |
if checkpoints is not None:
ts = checkpoints[TS_FIELD_NAME]
checkpoints_ts = odo(
ts[ts < lower_dt].max(),
pd.Timestamp,
**odo_kwargs
)
if pd.isnull(checkpoints_ts):
# We don't have a checkpoint for before our start date so just
... |
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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 proper... |
odo_kwargs = odo_kwargs or {}
computed_lower, materialized_checkpoints = get_materialized_checkpoints(
checkpoints,
expr.fields,
lower,
odo_kwargs,
)
pred = expr[ts_field] <= upper
if computed_lower is not None:
# only constrain the lower date if we compute... |
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def register_dataset(self, dataset, expr, deltas=None, checkpoints=None, odo_kwargs=None):
"""Explicitly map a datset to a collection of blaze expressions. Param... |
expr_data = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
)
for column in dataset.columns:
self._table_expressions[column] = expr_data |
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def register_column(self, column, expr, deltas=None, checkpoints=None, odo_kwargs=None):
"""Explicitly map a single bound column to a collection of blaze express... |
self._table_expressions[column] = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
) |
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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... |
return valmap(
lambda v: tuple(
OwnershipPeriod(
a.start,
b.start,
a.sid,
a.value,
) for a, b in sliding_window(
2,
concatv(
sorted(v),
# concat wi... |
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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,
) |
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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 OwnershipPer... |
grouped_rows = groupby(
group_key,
sa.select(table.c).execute().fetchall(),
)
return {
key: _build_ownership_map_from_rows(
rows,
key_from_row,
value_from_row,
)
for key, rows in grouped_rows.items()
} |
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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] ... |
return {k: v for k, v in dict_.items() if k in names and v is not None} |
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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_ |
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def was_active(reference_date_value, asset):
""" Whether or not `asset` was active at the time corresponding to `reference_date_value`. Parameters reference_date... |
return (
asset.start_date.value
<= reference_date_value
<= asset.end_date.value
) |
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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] Ass... |
found = {}
missing = set()
for sid in sids:
try:
found[sid] = self._asset_type_cache[sid]
except KeyError:
missing.add(sid)
if not missing:
return found
router_cols = self.asset_router.c
for assets i... |
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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 lo... |
cols = self.equity_symbol_mappings.c
# These are the columns we actually want.
data_cols = (cols.sid,) + tuple(cols[name] for name in symbol_columns)
# Also select the max of end_date so that all non-grouped fields take
# on the value associated with the max end_date. The SQLi... |
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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` t... |
# Fastpath for empty request.
if not sids:
return {}
cache = self._asset_cache
hits = {}
querying_equities = issubclass(asset_type, Equity)
filter_kwargs = (
_filter_equity_kwargs
if querying_equities else
_filter_future_... |
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def _lookup_symbol_strict(self, ownership_map, multi_country, symbol, as_of_date):
""" Resolve a symbol to an asset object without fuzzy matching. Parameters own... |
# split the symbol into the components, if there are no
# company/share class parts then share_class_symbol will be empty
company_symbol, share_class_symbol = split_delimited_symbol(symbol)
try:
owners = ownership_map[company_symbol, share_class_symbol]
assert ow... |
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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.... |
if symbol is None:
raise TypeError("Cannot lookup asset for symbol of None for "
"as of date %s." % as_of_date)
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
... |
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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, ... |
if not symbols:
return []
multi_country = country_code is None
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose... |
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def lookup_future_symbol(self, symbol):
"""Lookup a future contract by symbol. Parameters symbol : str The symbol of the desired contract. Returns ------- future... |
data = self._select_asset_by_symbol(self.futures_contracts, symbol)\
.execute().fetchone()
# If no data found, raise an exception
if not data:
raise SymbolNotFound(symbol=symbol)
return self.retrieve_asset(data['sid']) |
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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... |
try:
periods = self.equity_supplementary_map_by_sid[
field_name,
sid,
]
assert periods, 'empty periods list for %r' % (field_name, sid)
except KeyError:
raise NoValueForSid(field=field_name, sid=sid)
if not as_of_d... |
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def _lookup_generic_scalar(self, obj, as_of_date, country_code, matches, missing):
""" Convert asset_convertible to an asset. On success, append to matches. On f... |
result = self._lookup_generic_scalar_helper(
obj, as_of_date, country_code,
)
if result is not None:
matches.append(result)
else:
missing.append(obj) |
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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... |
matches = []
missing = []
# Interpret input as scalar.
if isinstance(obj, (AssetConvertible, ContinuousFuture)):
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=m... |
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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.sid,
equities_cols.start_date,
equities_cols.end_date,
)).where(... |
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def lifetimes(self, dates, include_start_date, country_codes):
""" Compute a DataFrame representing asset lifetimes for the specified date range. Parameters date... |
if isinstance(country_codes, string_types):
raise TypeError(
"Got string {!r} instead of an iterable of strings in "
"AssetFinder.lifetimes.".format(country_codes),
)
# normalize to a cache-key so that we can memoize results.
country_code... |
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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 ... |
sids = self._compute_asset_lifetimes([country_code]).sid
return tuple(sids.tolist()) |
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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``, ret... |
rf = self._roll_finders[asset.roll_style]
sid = (rf.get_contract_center(asset.root_symbol,
dt,
asset.offset))
if sid is None:
return pd.NaT
contract = rf.asset_finder.retrieve_asset(sid)
retu... |
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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. ... |
position_values = pd.Series({
asset: (
position.last_sale_price *
position.amount *
asset.price_multiplier
)
for asset, position in self.positions.items()
})
return position_values / self.portfolio_v... |
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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... |
buf = fobj.read()
image = tfds.core.lazy_imports.cv2.imdecode(
np.fromstring(buf, dtype=np.uint8), flags=3) # Note: Converts to RGB.
if image is None:
logging.warning(
"Image %s could not be decoded by OpenCV, falling back to TF", filename)
try:
image = tf.image.decode_image(buf, ch... |
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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.
image = _decode_image(fobj, session, filename=filename)
return _encode_jpeg(image) |
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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]
with tf.io.gfile.GFile(path) as f, gzip.GzipFile(fileobj=f) as g:
return g.read().split("\n"), lang
if ... |
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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, "Invalid number of segments: %d" % len(segs)
return segs[0].te... |
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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
with tf.io.gfile.GFile(path) as f:
for j, line in enumerate(f):
cols = ... |
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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:
s1, s2 = line.split("|||")
yield {
l1: s1.strip(),
l2: s2.... |
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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 blk in re.search(
r"qw{([\s\d]*)}", f.read()).groups()[0].split()
}
logging.info(
... |
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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[ss_name]
if ds.target != target or source not in ds.sources:
logging.info(
... |
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def builder(name, **builder_init_kwargs):
"""Fetches a `tfds.core.DatasetBuilder` by string name. Args: name: `str`, the registered name of the `DatasetBuilder` ... |
name, builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
builder_kwargs.update(builder_init_kwargs)
if name in _ABSTRACT_DATASET_REGISTRY:
raise DatasetNotFoundError(name, is_abstract=True)
if name in _IN_DEVELOPMENT_REGISTRY:
raise DatasetNotFoundError(name, in_development=True)
if name n... |
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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... |
name, name_builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
name_builder_kwargs.update(builder_kwargs or {})
builder_kwargs = name_builder_kwargs
# Set data_dir
if try_gcs and gcs_utils.is_dataset_on_gcs(name):
data_dir = constants.GCS_DATA_DIR
elif data_dir is None:
data_dir = consta... |
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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", "version"]:
val = res.group(attr)
if val is None:
continue
if attr in... |
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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) |
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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 to use may have additional "
"dependencies.")
utils.reraise(err_msg) |
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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 numpy array, a '
'python list or tuple. Got {}'.format(type(elem))... |
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def _generate_examples(self, num_examples, data_path, label_path):
"""Generate MNIST examples as dicts. Args: num_examples (int):
The number of example. data_pa... |
images = _extract_mnist_images(data_path, num_examples)
labels = _extract_mnist_labels(label_path, num_examples)
data = list(zip(images, labels))
# Data is shuffled automatically to distribute classes uniformly.
for image, label in data:
yield {
"image": image,
"label": l... |
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def get_dataset_feature_statistics(builder, split):
"""Calculate statistics for the specified split.""" |
statistics = statistics_pb2.DatasetFeatureStatistics()
# Make this to the best of our abilities.
schema = schema_pb2.Schema()
dataset = builder.as_dataset(split=split)
# Just computing the number of examples for now.
statistics.num_examples = 0
# Feature dictionaries.
feature_to_num_examples = coll... |
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def read_from_json(json_filename):
"""Read JSON-formatted proto into DatasetInfo proto.""" |
with tf.io.gfile.GFile(json_filename) as f:
dataset_info_json_str = f.read()
# Parse it back into a proto.
parsed_proto = json_format.Parse(dataset_info_json_str,
dataset_info_pb2.DatasetInfo())
return parsed_proto |
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def update_splits_if_different(self, split_dict):
"""Overwrite the splits if they are different from the current ones. * If splits aren't already defined or diff... |
assert isinstance(split_dict, splits_lib.SplitDict)
# If splits are already defined and identical, then we do not update
if self._splits and splits_lib.check_splits_equals(
self._splits, split_dict):
return
self._set_splits(split_dict) |
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def _compute_dynamic_properties(self, builder):
"""Update from the DatasetBuilder.""" |
# Fill other things by going over the dataset.
splits = self.splits
for split_info in utils.tqdm(
splits.values(), desc="Computing statistics...", unit=" split"):
try:
split_name = split_info.name
# Fill DatasetFeatureStatistics.
dataset_feature_statistics, schema = ge... |
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def write_to_directory(self, dataset_info_dir):
"""Write `DatasetInfo` as JSON to `dataset_info_dir`.""" |
# Save the metadata from the features (vocabulary, labels,...)
if self.features:
self.features.save_metadata(dataset_info_dir)
if self.redistribution_info.license:
with tf.io.gfile.GFile(self._license_filename(dataset_info_dir),
"w") as f:
f.write(self.redi... |
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def read_from_directory(self, dataset_info_dir):
"""Update DatasetInfo from the JSON file in `dataset_info_dir`. This function updates all the dynamically genera... |
if not dataset_info_dir:
raise ValueError(
"Calling read_from_directory with undefined dataset_info_dir.")
json_filename = self._dataset_info_filename(dataset_info_dir)
# Load the metadata from disk
parsed_proto = read_from_json(json_filename)
# Update splits
self._set_splits... |
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def initialize_from_bucket(self):
"""Initialize DatasetInfo from GCS bucket info files.""" |
# In order to support Colab, we use the HTTP GCS API to access the metadata
# files. They are copied locally and then loaded.
tmp_dir = tempfile.mkdtemp("tfds")
data_files = gcs_utils.gcs_dataset_info_files(self.full_name)
if not data_files:
return
logging.info("Loading info from GCS for ... |
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def _map_promise(map_fn, all_inputs):
"""Map the function into each element and resolve the promise.""" |
all_promises = utils.map_nested(map_fn, all_inputs) # Apply the function
res = utils.map_nested(_wait_on_promise, all_promises)
return res |
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def _handle_download_result(self, resource, tmp_dir_path, sha256, dl_size):
"""Store dled file to definitive place, write INFO file, return path.""" |
fnames = tf.io.gfile.listdir(tmp_dir_path)
if len(fnames) > 1:
raise AssertionError('More than one file in %s.' % tmp_dir_path)
original_fname = fnames[0]
tmp_path = os.path.join(tmp_dir_path, original_fname)
self._recorded_sizes_checksums[resource.url] = (dl_size, sha256)
if self._regist... |
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def _download(self, resource):
"""Download resource, returns Promise->path to downloaded file.""" |
if isinstance(resource, six.string_types):
resource = resource_lib.Resource(url=resource)
url = resource.url
if url in self._sizes_checksums:
expected_sha256 = self._sizes_checksums[url][1]
download_path = self._get_final_dl_path(url, expected_sha256)
if not self._force_download and... |
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def _extract(self, resource):
"""Extract a single archive, returns Promise->path to extraction result.""" |
if isinstance(resource, six.string_types):
resource = resource_lib.Resource(path=resource)
path = resource.path
extract_method = resource.extract_method
if extract_method == resource_lib.ExtractMethod.NO_EXTRACT:
logging.info('Skipping extraction for %s (method=NO_EXTRACT).', path)
re... |
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def _download_extract(self, resource):
"""Download-extract `Resource` or url, returns Promise->path.""" |
if isinstance(resource, six.string_types):
resource = resource_lib.Resource(url=resource)
def callback(path):
resource.path = path
return self._extract(resource)
return self._download(resource).then(callback) |
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def download_kaggle_data(self, competition_name):
"""Download data for a given Kaggle competition.""" |
with self._downloader.tqdm():
kaggle_downloader = self._downloader.kaggle_downloader(competition_name)
urls = kaggle_downloader.competition_urls
files = kaggle_downloader.competition_files
return _map_promise(self._download,
dict((f, u) for (f, u) in zip(files, url... |
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def iter_archive(self, resource):
"""Returns iterator over files within archive. **Important Note**: caller should read files as they are yielded. Reading out of... |
if isinstance(resource, six.string_types):
resource = resource_lib.Resource(path=resource)
return extractor.iter_archive(resource.path, resource.extract_method) |
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def download_and_extract(self, url_or_urls):
"""Download and extract given url_or_urls. Is roughly equivalent to: ``` extracted_paths = dl_manager.extract(dl_man... |
# Add progress bar to follow the download state
with self._downloader.tqdm():
with self._extractor.tqdm():
return _map_promise(self._download_extract, url_or_urls) |
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def manual_dir(self):
"""Returns the directory containing the manually extracted data.""" |
if not tf.io.gfile.exists(self._manual_dir):
raise AssertionError(
'Manual directory {} does not exist. Create it and download/extract '
'dataset artifacts in there.'.format(self._manual_dir))
return self._manual_dir |
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def _split_generators(self, dl_manager):
"""Return the test split of Cifar10. Args: dl_manager: download manager object. Returns: test split. """ |
path = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs={'data_dir': os.path.join(path, _DIRNAME)})
] |
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def _generate_examples(self, data_dir):
"""Generate corrupted Cifar10 test data. Apply corruptions to the raw images according to self.corruption_type. Args: dat... |
corruption = self.builder_config.corruption
severity = self.builder_config.severity
images_file = os.path.join(data_dir, _CORRUPTIONS_TO_FILENAMES[corruption])
labels_file = os.path.join(data_dir, _LABELS_FILENAME)
with tf.io.gfile.GFile(labels_file, mode='rb') as f:
labels = np.load(f)
... |
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def document_single_builder(builder):
"""Doc string for a single builder, with or without configs.""" |
mod_name = builder.__class__.__module__
cls_name = builder.__class__.__name__
mod_file = sys.modules[mod_name].__file__
if mod_file.endswith("pyc"):
mod_file = mod_file[:-1]
description_prefix = ""
if builder.builder_configs:
# Dataset with configs; document each one
config_docs = []
for... |
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def make_module_to_builder_dict(datasets=None):
"""Get all builders organized by module in nested dicts.""" |
# pylint: disable=g-long-lambda
# dict to hold tfds->image->mnist->[builders]
module_to_builder = collections.defaultdict(
lambda: collections.defaultdict(
lambda: collections.defaultdict(list)))
# pylint: enable=g-long-lambda
if datasets:
builders = [tfds.builder(name) for name in datas... |
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def _pprint_features_dict(features_dict, indent=0, add_prefix=True):
"""Pretty-print tfds.features.FeaturesDict.""" |
first_last_indent_str = " " * indent
indent_str = " " * (indent + 4)
first_line = "%s%s({" % (
first_last_indent_str if add_prefix else "",
type(features_dict).__name__,
)
lines = [first_line]
for k in sorted(list(features_dict.keys())):
v = features_dict[k]
if isinstance(v, tfds.featur... |
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def make_statistics_information(info):
"""Make statistics information table.""" |
if not info.splits.total_num_examples:
# That means that we have yet to calculate the statistics for this.
return "None computed"
stats = [(info.splits.total_num_examples, "ALL")]
for split_name, split_info in info.splits.items():
stats.append((split_info.num_examples, split_name.upper()))
# Sort ... |
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def dataset_docs_str(datasets=None):
"""Create dataset documentation string for given datasets. Args: datasets: list of datasets for which to create documentatio... |
module_to_builder = make_module_to_builder_dict(datasets)
sections = sorted(list(module_to_builder.keys()))
section_tocs = []
section_docs = []
for section in sections:
builders = tf.nest.flatten(module_to_builder[section])
builders = sorted(builders, key=lambda b: b.name)
builder_docs = [docume... |
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def schema_org(builder):
# pylint: disable=line-too-long """Builds schema.org microdata for DatasetSearch from DatasetBuilder. Markup spec: https://developers.go... |
# pylint: enable=line-too-long
properties = [
(lambda x: x.name, SCHEMA_ORG_NAME),
(lambda x: x.description, SCHEMA_ORG_DESC),
(lambda x: x.name, SCHEMA_ORG_URL),
(lambda x: (x.urls and x.urls[0]) or "", SCHEMA_ORG_SAMEAS)
]
info = builder.info
out_str = SCHEMA_ORG_PRE
for extract... |
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def disk(radius, alias_blur=0.1, dtype=np.float32):
"""Generating a Gaussian blurring kernel with disk shape. Generating a Gaussian blurring kernel with disk sha... |
if radius <= 8:
length = np.arange(-8, 8 + 1)
ksize = (3, 3)
else:
length = np.arange(-radius, radius + 1)
ksize = (5, 5)
x_axis, y_axis = np.meshgrid(length, length)
aliased_disk = np.array((x_axis**2 + y_axis**2) <= radius**2, dtype=dtype)
aliased_disk /= np.sum(aliased_disk)
# supersampl... |
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def clipped_zoom(img, zoom_factor):
"""Zoom image with clipping. Zoom the central part of the image and clip extra pixels. Args: img: numpy array, uncorrupted im... |
h = img.shape[0]
ch = int(np.ceil(h / float(zoom_factor)))
top_h = (h - ch) // 2
w = img.shape[1]
cw = int(np.ceil(w / float(zoom_factor)))
top_w = (w - cw) // 2
img = tfds.core.lazy_imports.scipy.ndimage.zoom(
img[top_h:top_h + ch, top_w:top_w + cw], (zoom_factor, zoom_factor, 1),
order=1)... |
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def plasma_fractal(mapsize=512, wibbledecay=3):
"""Generate a heightmap using diamond-square algorithm. Modification of the algorithm in https://github.com/FLHer... |
if mapsize & (mapsize - 1) != 0:
raise ValueError('mapsize must be a power of two.')
maparray = np.empty((mapsize, mapsize), dtype=np.float_)
maparray[0, 0] = 0
stepsize = mapsize
wibble = 100
def wibbledmean(array):
return array / 4 + wibble * np.random.uniform(-wibble, wibble, array.shape)
de... |
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def gaussian_noise(x, severity=1):
"""Gaussian noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. sever... |
c = [.08, .12, 0.18, 0.26, 0.38][severity - 1]
x = np.array(x) / 255.
x_clip = np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255
return around_and_astype(x_clip) |
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def shot_noise(x, severity=1):
"""Shot noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: int... |
c = [60, 25, 12, 5, 3][severity - 1]
x = np.array(x) / 255.
x_clip = np.clip(np.random.poisson(x * c) / float(c), 0, 1) * 255
return around_and_astype(x_clip) |
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def impulse_noise(x, severity=1):
"""Impulse noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severit... |
c = [.03, .06, .09, 0.17, 0.27][severity - 1]
x = tfds.core.lazy_imports.skimage.util.random_noise(
np.array(x) / 255., mode='s&p', amount=c)
x_clip = np.clip(x, 0, 1) * 255
return around_and_astype(x_clip) |
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def defocus_blur(x, severity=1):
"""Defocus blurring to images. Apply defocus blurring to images using Gaussian kernel. Args: x: numpy array, uncorrupted image, ... |
c = [(3, 0.1), (4, 0.5), (6, 0.5), (8, 0.5), (10, 0.5)][severity - 1]
x = np.array(x) / 255.
kernel = disk(radius=c[0], alias_blur=c[1])
channels = []
for d in range(3):
channels.append(tfds.core.lazy_imports.cv2.filter2D(x[:, :, d], -1, kernel))
channels = np.array(channels).transpose((1, 2, 0)) # 3x... |
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