Datasets:

semeru

/

code-text-galeras-code-summarization-3k-deduped

like 1

get_snuba_column_name

def get_snuba_column_name(name, dataset=Dataset.Events): no_conversion = {"group_id", "group_ids", "project_id", "start", "end"} if name in no_conversion: return name if not name or name.startswith("tags[") or QUOTED_LITERAL_RE.match(name): return name measurement_name = get_measurement_name(name) span_op_breakdown_name = get_span_op_breakdown_name(name) if "measurements_key" in DATASETS[dataset] and measurement_name: default = f"measurements[{measurement_name}]" elif "span_op_breakdowns_key" in DATASETS[dataset] and span_op_breakdown_name: default = f"span_op_breakdowns[{span_op_breakdown_name}]" else: default = f"tags[{name}]" return DATASETS[dataset].get(name, default)

1bcb129d69a6c4e481b950ebc5871e9c118db74f

snuba.py

perf(issue-search): optimize querying on perf issues by using hasAny on transaction.group_id (#41685) Special optimization for searching on transactions for performance issues by doing a `hasAny` check instead of arrayJoining `group_ids` column and applying a filter after. before query: ``` SELECT (arrayJoin((group_ids AS _snuba_group_ids)) AS _snuba_group_id), _snuba_group_id, (multiply(toUInt64(max((finish_ts AS _snuba_timestamp))), 1000) AS _snuba_last_seen), (ifNull(uniq(_snuba_group_id), 0) AS _snuba_total) FROM transactions_local SAMPLE 1.0 WHERE greaterOrEquals((finish_ts AS _snuba_finish_ts), toDateTime('2022-08-26T02:02:49', 'Universal')) AND less(_snuba_finish_ts, toDateTime('2022-11-24T02:03:49', 'Universal')) AND in((project_id AS _snuba_project_id), tuple(4550959674425346)) AND equals(('transaction' AS _snuba_type), 'transaction') AND in(_snuba_project_id, tuple(4550959674425346)) AND in(_snuba_group_id, (1, 2)) GROUP BY _snuba_group_id WITH TOTALS ORDER BY _snuba_last_seen DESC, _snuba_group_id ASC LIMIT 150 OFFSET 0 ``` after query: ``` SELECT (arrayJoin(arrayIntersect([1, 2], (group_ids AS _snuba_group_ids))) AS _snuba_group_id), (multiply(toUInt64(max((finish_ts AS _snuba_timestamp))), 1000) AS _snuba_last_seen), (ifNull(uniq(_snuba_group_id), 0) AS _snuba_total), _snuba_group_id FROM transactions_local SAMPLE 1.0 WHERE greaterOrEquals((finish_ts AS _snuba_finish_ts), toDateTime('2022-08-26T02:01:32', 'Universal')) AND less(_snuba_finish_ts, toDateTime('2022-11-24T02:02:32', 'Universal')) AND in((project_id AS _snuba_project_id), tuple(4550959669379074)) AND equals(hasAny(_snuba_group_ids, [1, 2]), 1) AND equals(('transaction' AS _snuba_type), 'transaction') AND in(_snuba_project_id, tuple(4550959669379074)) GROUP BY _snuba_group_id WITH TOTALS ORDER BY _snuba_last_seen DESC, _snuba_group_id ASC LIMIT 150 OFFSET 0 ```

sentry

src/sentry/utils/snuba.py

Python

https://github.com/getsentry/sentry.git

test_skip_noarchive_withtext

def test_skip_noarchive_withtext(self): parser = RasterisedDocumentParser(None) parser.parse( os.path.join(self.SAMPLE_FILES, "multi-page-digital.pdf"), "application/pdf", ) self.assertIsNone(parser.archive_path) self.assertContainsStrings( parser.get_text().lower(), ["page 1", "page 2", "page 3"], )

b3b2519bf03185aa12028fa68d3b8f8860555e6e

test_parser.py

Fixes the creation of an archive file, even if noarchive was specified

paperless-ngx

src/paperless_tesseract/tests/test_parser.py

Python

https://github.com/paperless-ngx/paperless-ngx.git

test_createcachetable_with_table_argument

def test_createcachetable_with_table_argument(self): self.drop_table() out = io.StringIO() management.call_command( "createcachetable", "test cache table", verbosity=2, stdout=out, ) self.assertEqual(out.getvalue(), "Cache table 'test cache table' created.\n")

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

Refs #33476 -- Reformatted code with Black.

django

tests/cache/tests.py

Python

https://github.com/django/django.git

get_engle_granger_two_step_cointegration_test

def get_engle_granger_two_step_cointegration_test(dependent_series, independent_series): warnings.simplefilter(action="ignore", category=FutureWarning) long_run_ols = sm.OLS(dependent_series, sm.add_constant(independent_series)) warnings.simplefilter(action="default", category=FutureWarning) long_run_ols_fit = long_run_ols.fit() c, gamma = long_run_ols_fit.params z = long_run_ols_fit.resid short_run_ols = sm.OLS(dependent_series.diff().iloc[1:], (z.shift().iloc[1:])) short_run_ols_fit = short_run_ols.fit() alpha = short_run_ols_fit.params[0] # NOTE: The p-value returned by the adfuller function assumes we do not estimate z # first, but test stationarity of an unestimated series directly. This assumption # should have limited effect for high N, however. Critical values taking this into # account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987). adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None) return c, gamma, alpha, z, adfstat, pvalue

9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b

econometrics_model.py

Here we merge all API Refactor related branches (#2236) * Update api.py * Updated forex menu * refactor ycrv command * refactor ycrv command black * refactor ecocal command * Minh changes * Adding space to test pushing * title fix ecocal df * get economic calendar annotation * fix investingcom tests * refactor index command * refactor overview command * give defaults to wsj view function args * rename date args investincom * refacto bigmac command * fix ecocal typo * refactor rtps command * alphavantage gdp * alphavantage gdp per capita * alphavantage cpi * alphavantage tyld * alphavantage inf * refactor macro command * refactor macro command w helpers * refactor treasury command * fix macro on terminal * treasury labels * refactor maturities * update treasury maturities doc strings * refactor get economic calendar finhub * refactor map command api * display map filter choices * route economy api to performance map * route economy api to performance map * display group choices on valuation command * refactor performance and valuation commands * refactor spectrum model and view * add choices to spectrum controller * delete image after view * fix model tests finviz * fix finciz view tests * refactor futures * fix some tests * fix more tests * fix controller test * refactor fred series notes * update fred notes docstring * refacto fred series ids * fix pred and qa when empty datasets * refactor fred * uncomment stuff * refacto get series data * fix some tests * set defaults on args * refactor fred yield curve * black * fix spell and remove ecocal names * fix linting * linting * pylint fix * change dangerous defaults * Working through crypto fixes (#2256) * Working through crypto fixes * Continued adding crypto stuff * Added crypto overview * Added test fixes * Added fixtures * Fixed tests * Fixed charting issue * Removed broken APIs * Final adjustments * Added test fixes * map get groups and get ycrv countries into old api * exposed econdb helper funcs * remove helpers * refactor search indices * linting * refactor arg currency * pylint from currency * Started switching crpyto ascending to ascend * Merging * Portfolio model arguements, params, and docstring * Refactored for etf commands (#2292) * Refactored for etf commands * Fixed tests * Added load command * Fixed menu * Portfolio logic fixes * Added econometrics (#2260) * Added econometrics * Fixed tests * Simplified API * Added test fixes * Added test csv * Allowed examples to be loaded * Fund refactor (#2291) * Fund refactor * Changed fund_name and fund to name * Changed ascending to ascend * Stock menu refactoring for easier API usage (#2194) * Stocks refactoring for easier API usage * Linting * Refactor newly added features * Linting * Fixing tests * Refactor common files used by stocks menu * Fixing flake8 * Fix linting and tests * Linting * Fix flake8 * refactor insider_data * refactor mentions * refactor watchlist * refactor sentiment * refactor sentiment * fix yahoofinance tests * refactor load and candle * refactor get_news and display_news * refactor stocks.ins.act * candle default matplotlib * fix yahoofinance_view tests * fix ark model tests * fix ark view tests * fix business insider model * fix business insider view * refactor csimarket model * fix tests csi market model * update dd controller * fix get suppliers tests * fix dd controller tests * fix finhub tests * fix finviz tests * fix fmp tests * fix marketwatch tests * corrected argument keywords in test_bt_model * corrected argument keywords in test_bt_view * refactor fa controller * refactor marketwatch view * refactor gov controller * fix tests fa av * fix tests elect * fix dcf tests * fix polygon tests * fix fmp tests * fix quiverquant tests * fix yahoofinance fa tests * fix more fa tests * fix insider tests * fix more tests * fix more tests * fix options tests * fix stock gov tests * fix tests test_ba_controller * fix tests for test_finviz_compare_model.py * fixed 2 tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fix final tests * fixed tests * fixed tests * Fix tests * black * forgot to black tests * fixed tests * fixed tests * fixed tests * fixed tests * flakefix * Tests + code : Stocks / Discovery * fix tests * added recorder * fixed tests * fixed tests * black * black * remove unused imports * refactor display raw * sia dicts fix * pylint * linting * remove dangerous default * fix tests * fix beta model test * black * skip screener qa test * change sector path to sectors * update tests readme * fix metric defaults * black * substitute lost ticker * defaults cpic * another round on sia * refactor cramer * reduce default tweets on sentiment * refactor yf hist, corr, volume * arkorders default * refactor income, balance, cashflow * refacto scorr, screener, getfinnhub * refactor stockgrid * ibkr refactor * another round on stockgrid * add dividens end point * refactor discovery endpoints * update docstrings with similar input * refactor messages * refactor ba * refactor regioons * refactor twitter sentiment * refactor hist * refactor regions * give default to timeframe * refactor bunch of defaults and arg names * remove leftover imports * refactor vwap * let tests run * fix tests * fix stock tests * fix stockanalysis tests * flake * MYPY * Made important changes * added fixes * Fixed big issue * Added fixes to tests * fix qa tests * fix tests * fix 1 more test * last stocks failing * fix crypto test Co-authored-by: Chavithra PARANA <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: hjoaquim <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: colin99d <[email protected]> * fix portfolio tests * change period to window * update ca docstrings * refactor get_similar_companies func * Fixed * Update CI * Update CI 2 * Update CI 3 * Update dependencies Co-authored-by: colin99d <[email protected]> Co-authored-by: Colin Delahunty <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: James Simmons <[email protected]> Co-authored-by: Theodore Aptekarev <[email protected]> Co-authored-by: minhhoang1023 <[email protected]> Co-authored-by: jose-donato <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: northern-64bit <[email protected]> Co-authored-by: hjoaquim <[email protected]>

OpenBBTerminal

openbb_terminal/econometrics/econometrics_model.py

Python

https://github.com/OpenBB-finance/OpenBBTerminal.git

naive_all_pairs_lowest_common_ancestor

def naive_all_pairs_lowest_common_ancestor(G, pairs=None): if not nx.is_directed_acyclic_graph(G): raise nx.NetworkXError("LCA only defined on directed acyclic graphs.") if len(G) == 0: raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.") ancestor_cache = {} if pairs is None: pairs = combinations_with_replacement(G, 2) for v, w in pairs: if v not in ancestor_cache: ancestor_cache[v] = nx.ancestors(G, v) ancestor_cache[v].add(v) if w not in ancestor_cache: ancestor_cache[w] = nx.ancestors(G, w) ancestor_cache[w].add(w) common_ancestors = ancestor_cache[v] & ancestor_cache[w] if common_ancestors: common_ancestor = next(iter(common_ancestors)) while True: successor = None for lower_ancestor in G.successors(common_ancestor): if lower_ancestor in common_ancestors: successor = lower_ancestor break if successor is None: break common_ancestor = successor yield ((v, w), common_ancestor) @not_implemented_for("undirected") @not_implemented_for("multigraph")

8b6c25ec952b80539a38e7a884c0fdc3fd735b28

@not_implemented_for("undirected") @not_implemented_for("multigraph")

lowest_common_ancestors.py

Improve LCA input validation (#5877) * Remove explicit checks for None nodes. The graph objects already do not allow None nodes. * Change elif to if for raising branches.

networkx

networkx/algorithms/lowest_common_ancestors.py

Python

https://github.com/networkx/networkx.git

unquote_unreserved

def unquote_unreserved(uri): parts = uri.split("%") for i in range(1, len(parts)): h = parts[i][0:2] if len(h) == 2 and h.isalnum(): try: c = chr(int(h, 16)) except ValueError: raise InvalidURL(f"Invalid percent-escape sequence: '{h}'") if c in UNRESERVED_SET: parts[i] = c + parts[i][2:] else: parts[i] = f"%{parts[i]}" else: parts[i] = f"%{parts[i]}" return "".join(parts)

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

utils.py

Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.

pipenv

pipenv/patched/pip/_vendor/requests/utils.py

Python

https://github.com/pypa/pipenv.git

is_named_tuple

def is_named_tuple(obj) -> bool: return isinstance(obj, abc.Sequence) and hasattr(obj, "_fields")

bce995817caf00ab5e82cb4cf1b540f1530cf4ea

inference.py

Fix some dosctring RT02 error (#50197)

pandas

pandas/core/dtypes/inference.py

Python

https://github.com/pandas-dev/pandas.git

_laplace_rule_timescale

def _laplace_rule_timescale(f, t, s, doit=True, **hints): r _simplify = hints.pop('simplify', True) b = Wild('b', exclude=[t]) g = WildFunction('g', nargs=1) ma1 = f.match(g) if ma1: arg = ma1[g].args[0].collect(t) ma2 = arg.match(b*t) if ma2 and ma2[b]>0: debug('_laplace_apply_rules match:') debug(' f: %s ( %s, %s )'%(f, ma1, ma2)) debug(' rule: amplitude and time scaling (1.1, 1.2)') if ma2[b]==1: if doit==True and not any(func.has(t) for func in ma1[g].atoms(AppliedUndef)): return _laplace_transform(ma1[g].func(t), t, s, simplify=_simplify) else: return LaplaceTransform(ma1[g].func(t), t, s, **hints) else: L = _laplace_apply_rules(ma1[g].func(t), t, s/ma2[b], doit=doit, **hints) try: r, p, c = L return (1/ma2[b]*r, p, c) except TypeError: return 1/ma2[b]*L return None

af443377dd48c2caf440d2b6dd76830dbe84712f

transforms.py

Moved all constant-factor calculations to `_laplace_apply_rules`

sympy

sympy/integrals/transforms.py

Python

https://github.com/sympy/sympy.git

_read

def _read(cls, path_or_buf, **kwargs): path_or_buf = cls.get_path_or_buffer(path_or_buf) if isinstance(path_or_buf, str): if not cls.file_exists(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) path_or_buf = cls.get_path(path_or_buf) elif not cls.pathlib_or_pypath(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) if not kwargs.get("lines", False): return cls.single_worker_read(path_or_buf, **kwargs) with OpenFile(path_or_buf, "rb") as f: columns = pandas.read_json(BytesIO(b"" + f.readline()), lines=True).columns kwargs["columns"] = columns empty_pd_df = pandas.DataFrame(columns=columns) with OpenFile(path_or_buf, "rb", kwargs.get("compression", "infer")) as f: partition_ids = [] index_ids = [] dtypes_ids = [] column_widths, num_splits = cls._define_metadata(empty_pd_df, columns) args = {"fname": path_or_buf, "num_splits": num_splits, **kwargs} splits = cls.partitioned_file( f, num_partitions=NPartitions.get(), ) for start, end in splits: args.update({"start": start, "end": end}) partition_id = cls.deploy(cls.parse, num_returns=num_splits + 3, **args) partition_ids.append(partition_id[:-3]) index_ids.append(partition_id[-3]) dtypes_ids.append(partition_id[-2]) # partition_id[-1] contains the columns for each partition, which will be useful # for implementing when `lines=False`. row_lengths = cls.materialize(index_ids) new_index = pandas.RangeIndex(sum(row_lengths)) dtypes = cls.get_dtypes(dtypes_ids) partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths) if isinstance(dtypes, pandas.Series): dtypes.index = columns else: dtypes = pandas.Series(dtypes, index=columns) new_frame = cls.frame_cls( np.array(partition_ids), new_index, columns, row_lengths, column_widths, dtypes=dtypes, ) new_frame.synchronize_labels(axis=0) return cls.query_compiler_cls(new_frame)

97769988a6f19e4b76f34238c97bf159ee7626a5

json_dispatcher.py

REFACTOR-#3853: interacting with Dask interface through 'DaskWrapper' class (#3854) Co-authored-by: Devin Petersohn <[email protected]> Co-authored-by: Dmitry Chigarev <[email protected]> Co-authored-by: Yaroslav Igoshev <[email protected]> Signed-off-by: Anatoly Myachev <[email protected]>

modin

modin/core/io/text/json_dispatcher.py

Python

https://github.com/modin-project/modin.git

_store

def _store(self, messages, response, remove_oldest=True, *args, **kwargs): unstored_messages = [] encoded_data = self._encode(messages) if self.max_cookie_size: # data is going to be stored eventually by SimpleCookie, which # adds its own overhead, which we must account for. cookie = SimpleCookie() # create outside the loop

9c19aff7c7561e3a82978a272ecdaad40dda5c00

cookie.py

Refs #33476 -- Reformatted code with Black.

django

django/contrib/messages/storage/cookie.py

Python

https://github.com/django/django.git

handle_out

def handle_out(out, result): if isinstance(out, tuple): if len(out) == 1: out = out[0] elif len(out) > 1: raise NotImplementedError("The out parameter is not fully supported") else: out = None # Notice, we use .__class__ as opposed to type() in order to support # object proxies see <https://github.com/dask/dask/pull/6981> if out is not None and out.__class__ != result.__class__: raise TypeError( "Mismatched types between result and out parameter. " "out=%s, result=%s" % (str(type(out)), str(type(result))) ) if isinstance(out, DataFrame): if len(out.columns) != len(result.columns): raise ValueError( "Mismatched columns count between result and out parameter. " "out=%s, result=%s" % (str(len(out.columns)), str(len(result.columns))) ) if isinstance(out, (Series, DataFrame, Scalar)): out._meta = result._meta out._name = result._name out.dask = result.dask if not isinstance(out, Scalar): out._divisions = result.divisions elif out is not None: msg = ( "The out parameter is not fully supported." " Received type %s, expected %s " % (typename(type(out)), typename(type(result))) ) raise NotImplementedError(msg) else: return result

bc43d692e8f5fc55e6d81c91053598bd89f79267

core.py

Add sanity checks to divisions setter (#8806) Adds a few sanity checks to the divisions property setter - New divisions are compatible with the existing `npartitions` - New divisions don't mix None and non-None values - New divisions are sorted (except for ordered categorical dtypes, where that's hard)

dask

dask/dataframe/core.py

Python

https://github.com/dask/dask.git

test_regressor_predict_on_arraylikes

def test_regressor_predict_on_arraylikes(): X = [[5, 1], [3, 1], [4, 3], [0, 3]] y = [2, 3, 5, 6]

742d39ca38f713027091324c4555f9b4e1b9da05

test_neighbors.py

FIX Fixes KNeighborsRegressor.predict with array-likes (#22687) Co-authored-by: Thomas J. Fan <[email protected]>

scikit-learn

sklearn/neighbors/tests/test_neighbors.py

Python

https://github.com/scikit-learn/scikit-learn.git

_send_msg

def _send_msg(self, msg): self.logger.info(f"Sending discord message: {msg}") # TODO: handle other message types if msg['type'] == RPCMessageType.EXIT_FILL: profit_ratio = msg.get('profit_ratio') open_date = msg.get('open_date').strftime('%Y-%m-%d %H:%M:%S') close_date = msg.get('close_date').strftime('%Y-%m-%d %H:%M:%S') if msg.get('close_date') else '' embeds = [{ 'title': '{} Trade: {}'.format( 'Profit' if profit_ratio > 0 else 'Loss', msg.get('pair')), 'color': (0x00FF00 if profit_ratio > 0 else 0xFF0000), 'fields': [ {'name': 'Trade ID', 'value': msg.get('id'), 'inline': True}, {'name': 'Exchange', 'value': msg.get('exchange').capitalize(), 'inline': True}, {'name': 'Pair', 'value': msg.get('pair'), 'inline': True}, {'name': 'Direction', 'value': 'Short' if msg.get('is_short') else 'Long', 'inline': True}, {'name': 'Open rate', 'value': msg.get('open_rate'), 'inline': True}, {'name': 'Close rate', 'value': msg.get('close_rate'), 'inline': True}, {'name': 'Amount', 'value': msg.get('amount'), 'inline': True}, {'name': 'Open order', 'value': msg.get('open_order_id'), 'inline': True}, {'name': 'Open date', 'value': open_date, 'inline': True}, {'name': 'Close date', 'value': close_date, 'inline': True}, {'name': 'Profit', 'value': msg.get('profit_amount'), 'inline': True}, {'name': 'Profitability', 'value': '{:.2f}%'.format(profit_ratio * 100), 'inline': True}, {'name': 'Stake currency', 'value': msg.get('stake_currency'), 'inline': True}, {'name': 'Fiat currency', 'value': msg.get('fiat_display_currency'), 'inline': True}, {'name': 'Buy Tag', 'value': msg.get('enter_tag'), 'inline': True}, {'name': 'Sell Reason', 'value': msg.get('exit_reason'), 'inline': True}, {'name': 'Strategy', 'value': self.strategy, 'inline': True}, {'name': 'Timeframe', 'value': self.timeframe, 'inline': True}, ], }] # convert all value in fields to string for discord for embed in embeds: for field in embed['fields']: field['value'] = str(field['value']) # Send the message to discord channel payload = { 'embeds': embeds, } headers = { 'Content-Type': 'application/json', } try: requests.post(self.config['discord']['webhook_url'], data=json.dumps(payload), headers=headers) except Exception as e: self.logger.error(f"Failed to send discord message: {e}")

45c47bda6000b2b57026fdedffaaa69f8fc1797e

discord.py

refactor into discord rpc module

freqtrade

freqtrade/rpc/discord.py

Python

https://github.com/freqtrade/freqtrade.git

_get_arithmetic_result_freq

def _get_arithmetic_result_freq(self, other) -> BaseOffset | None: # Adding or subtracting a Timedelta/Timestamp scalar is freq-preserving # whenever self.freq is a Tick if is_period_dtype(self.dtype): return self.freq elif not lib.is_scalar(other): return None elif isinstance(self.freq, Tick): # In these cases return self.freq return None

8f26ab7c184c7f6fc7337bfed47ac9d86ee4551f

datetimelike.py

REF: helpers to de-duplicate datetimelike arithmetic (#48844)

pandas

pandas/core/arrays/datetimelike.py

Python

https://github.com/pandas-dev/pandas.git

__reset_trigger_counts

def __reset_trigger_counts(self): for trigger_id in self.trigger_alert_counts: self.trigger_alert_counts[trigger_id] = 0 for trigger_id in self.trigger_resolve_counts: self.trigger_resolve_counts[trigger_id] = 0 self.update_alert_rule_stats()

524c8579d4d87954d993cd3fb7a29e0422058e6a

subscription_processor.py

feat(alerts): Clear counters for null subscription updates (#30831) Adds logic that clears trigger count and resolve count for crash rate alerts and metrics crash rate alerts in the case an empty subscription update is sent or if a min session count threshold is set and the count in the subscription update is lower than the min threshold

sentry

src/sentry/incidents/subscription_processor.py

Python

https://github.com/getsentry/sentry.git

wide_resnet50_2

def wide_resnet50_2(pretrained=False, **kwargs): kwargs['width'] = 64 * 2 return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)

ffcde21305c61d950a9f93e57e6180c9a9665b87

resnet.py

add disco_diffusion_ernievil_base

PaddleHub

modules/image/text_to_image/disco_diffusion_ernievil_base/vit_b_16x/ernievil2/transformers/resnet.py

Python

https://github.com/PaddlePaddle/PaddleHub.git

piecewise_linear

def piecewise_linear(t, deltas, k, m, changepoint_ts): deltas_t = (changepoint_ts[None, :] <= t[..., None]) * deltas k_t = deltas_t.sum(axis=1) + k m_t = (deltas_t * -changepoint_ts).sum(axis=1) + m return k_t * t + m_t

8fbf8ba2a5bfcdb892e8ca596e338894614000b5

forecaster.py

Speed Up Uncertainty Predictions (#2186)

prophet

python/prophet/forecaster.py

Python

https://github.com/facebook/prophet.git

test_cache_metric

def test_cache_metric(self): CACHE_NAME = "cache_metrics_test_fgjkbdfg" cache: DeferredCache[str, str] = DeferredCache(CACHE_NAME, max_entries=777) items = { x.split(b"{")[0].decode("ascii"): x.split(b" ")[1].decode("ascii") for x in filter( lambda x: b"cache_metrics_test_fgjkbdfg" in x, generate_latest(REGISTRY).split(b"\n"), ) } self.assertEqual(items["synapse_util_caches_cache_size"], "0.0") self.assertEqual(items["synapse_util_caches_cache_max_size"], "777.0") cache.prefill("1", "hi") items = { x.split(b"{")[0].decode("ascii"): x.split(b" ")[1].decode("ascii") for x in filter( lambda x: b"cache_metrics_test_fgjkbdfg" in x, generate_latest(REGISTRY).split(b"\n"), ) } self.assertEqual(items["synapse_util_caches_cache_size"], "1.0") self.assertEqual(items["synapse_util_caches_cache_max_size"], "777.0")

2bb2c32e8ed5642a5bf3ba1e8c49e10cecc88905

test_metrics.py

Avoid incrementing bg process utime/stime counters by negative durations (#14323)

synapse

tests/metrics/test_metrics.py

Python

https://github.com/matrix-org/synapse.git

test_multilabel_y_explicit_zeros

def test_multilabel_y_explicit_zeros(tmp_path): save_path = str(tmp_path / "svm_explicit_zero") rng = np.random.RandomState(42) X = rng.randn(3, 5).astype(np.float64) indptr = np.array([0, 2, 3, 6]) indices = np.array([0, 2, 2, 0, 1, 2]) # The first and last element are explicit zeros. data = np.array([0, 1, 1, 1, 1, 0]) y = sp.csr_matrix((data, indices, indptr), shape=(3, 3)) # y as a dense array would look like # [[0, 0, 1], # [0, 0, 1], # [1, 1, 0]] dump_svmlight_file(X, y, save_path, multilabel=True) _, y_load = load_svmlight_file(save_path, multilabel=True) y_true = [(2.0,), (2.0,), (0.0, 1.0)] assert y_load == y_true

2e3a0474a6195161e7e3ff02be9d1ff18591ca7b

test_svmlight_format.py

ENH Cythonize `dump_svmlight_file` (#23127) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]>

scikit-learn

sklearn/datasets/tests/test_svmlight_format.py

Python

https://github.com/scikit-learn/scikit-learn.git

handler_name

def handler_name(self) -> str: # Property to make it read only return self._handler_name

1013f84ffc67bdce04bf13eaf401ab5ced572bb0

message.py

diagrams

textual

src/textual/message.py

Python

https://github.com/Textualize/textual.git

load_sample_image

def load_sample_image(image_name): images = load_sample_images() index = None for i, filename in enumerate(images.filenames): if filename.endswith(image_name): index = i break if index is None: raise AttributeError("Cannot find sample image: %s" % image_name) return images.images[index]

9d7220b57ccac4ac12268a96281940667a4de1d8

_base.py

DOC changed some typo of _shrunk_covariance.ledoit_wolf_shrinkage (#22798) * Changed from 3rd to 1st person the 1st word and remote blanck space. * Add two periods in the content

scikit-learn

sklearn/datasets/_base.py

Python

https://github.com/scikit-learn/scikit-learn.git

test_vr_connector_with_multiple_buffers

def test_vr_connector_with_multiple_buffers(self): context_len = 5 # This view requirement simulates the use-case of a decision transformer # without reward-to-go. view_rq_dict = { # obs[t-context_len+1:t] "context_obs": ViewRequirement("obs", shift=f"-{context_len-1}:0"), # next_obs[t-context_len+1:t] "context_next_obs": ViewRequirement( "obs", shift=f"-{context_len}:1", used_for_compute_actions=False ), # act[t-context_len+1:t] "context_act": ViewRequirement( SampleBatch.ACTIONS, shift=f"-{context_len-1}:-1" ), } obs_arrs = np.arange(10)[:, None] + 1 act_arrs = (np.arange(10)[:, None] + 1) * 100 n_steps = obs_arrs.shape[0] config = PPOConfig().to_dict() ctx = ConnectorContext( view_requirements=view_rq_dict, config=config, is_policy_recurrent=True ) c = ViewRequirementAgentConnector(ctx) # keep a queue of length ctx_len of observations obs_list, act_list = [], [] for t in range(n_steps): # next state and action at time t-1 are the following timestep_data = { SampleBatch.NEXT_OBS: obs_arrs[t], SampleBatch.ACTIONS: ( np.zeros_like(act_arrs[0]) if t == 0 else act_arrs[t - 1] ), SampleBatch.T: t - 1, } data = AgentConnectorDataType(0, 1, timestep_data) processed = c([data]) sample_batch = processed[0].data.sample_batch if t == 0: obs_list.extend([obs_arrs[0] for _ in range(context_len)]) act_list.extend( [np.zeros_like(act_arrs[0]) for _ in range(context_len)] ) else: obs_list.pop(0) act_list.pop(0) obs_list.append(obs_arrs[t]) act_list.append(act_arrs[t - 1]) self.assertTrue("context_next_obs" not in sample_batch) check(sample_batch["context_obs"], np.stack(obs_list)[None]) check(sample_batch["context_act"], np.stack(act_list[:-1])[None])

5bf9d5084fe38d864c1b5075145d2e778c431a5b

test_agent.py

[RLlib] Pass input dict into action connectors, because sometimes input data is useful for adapting output actions. (#28588) Based on user feedback. Input data is sometimes useful for adapting outputs, for example action masks. Signed-off-by: Jun Gong <[email protected]>

ray

rllib/connectors/tests/test_agent.py

Python

https://github.com/ray-project/ray.git

reversed

def reversed(G): msg = ( "context manager reversed is deprecated and to be removed in 3.0." "Use G.reverse(copy=False) if G.is_directed() else G instead." ) warnings.warn(msg, DeprecationWarning) directed = G.is_directed() if directed: G._pred, G._succ = G._succ, G._pred G._adj = G._succ try: yield finally: if directed: # Reverse the reverse. G._pred, G._succ = G._succ, G._pred G._adj = G._succ

cc1db275efc709cb964ce88abbfa877798d58c10

contextmanagers.py

Minor improvements from general code readthrough (#5414) * Add deprecated directive to reversed docstring. * Add missing dep directives to shpfiles. * Remove defn of INF sentinel. * typo. * str -> comment in forloop. * STY: appropriate casing for var name.

networkx

networkx/utils/contextmanagers.py

Python

https://github.com/networkx/networkx.git

train_epoch_ch3

def train_epoch_ch3(net, train_iter, loss, updater): # Sum of training loss, sum of training accuracy, no. of examples metric = Accumulator(3) for X, y in train_iter: # Compute gradients and update parameters with tf.GradientTape() as tape: y_hat = net(X) # Keras implementations for loss takes (labels, predictions) # instead of (predictions, labels) that users might implement # in this book, e.g. `cross_entropy` that we implemented above if isinstance(loss, tf.keras.losses.Loss): l = loss(y, y_hat) else: l = loss(y_hat, y) if isinstance(updater, tf.keras.optimizers.Optimizer): params = net.trainable_variables grads = tape.gradient(l, params) updater.apply_gradients(zip(grads, params)) else: updater(X.shape[0], tape.gradient(l, updater.params)) # Keras loss by default returns the average loss in a batch l_sum = l * float(tf.size(y)) if isinstance( loss, tf.keras.losses.Loss) else tf.reduce_sum(l) metric.add(l_sum, accuracy(y_hat, y), tf.size(y)) # Return training loss and training accuracy return metric[0] / metric[2], metric[1] / metric[2]

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

tensorflow.py

[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: （“bert.mall”）->（“bert.small”） (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯，将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2，num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <[email protected]> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <[email protected]> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <[email protected]> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <[email protected]> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: Aston Zhang <[email protected]> * 重复语句 (#1188) Co-authored-by: Aston Zhang <[email protected]> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <[email protected]> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <[email protected]> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <[email protected]> Co-authored-by: zhou201505013 <[email protected]> Co-authored-by: Xinwei Liu <[email protected]> Co-authored-by: Anirudh Dagar <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: hugo_han <[email protected]> Co-authored-by: gyro永不抽风 <[email protected]> Co-authored-by: CanChengZheng <[email protected]> Co-authored-by: linlin <[email protected]> Co-authored-by: iuk <[email protected]> Co-authored-by: yoos <[email protected]> Co-authored-by: Mr. Justice Lawrence John Wargrave <[email protected]> Co-authored-by: Chiyuan Fu <[email protected]> Co-authored-by: Sunhuashan <[email protected]> Co-authored-by: Haiker Sun <[email protected]> Co-authored-by: Ming Liu <[email protected]> Co-authored-by: goldmermaid <[email protected]> Co-authored-by: silenceZheng66 <[email protected]> Co-authored-by: Wenchao Yan <[email protected]> Co-authored-by: Kiki2049 <[email protected]> Co-authored-by: Krahets <[email protected]> Co-authored-by: friedmainfunction <[email protected]> Co-authored-by: Jameson <[email protected]> Co-authored-by: P. Yao <[email protected]> Co-authored-by: Yulv-git <[email protected]> Co-authored-by: Liu,Xiao <[email protected]> Co-authored-by: YIN, Gang <[email protected]> Co-authored-by: Joe-HZ <[email protected]> Co-authored-by: lybloveyou <[email protected]> Co-authored-by: VigourJiang <[email protected]> Co-authored-by: zxhd863943427 <[email protected]> Co-authored-by: LYF <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: xiaotinghe <[email protected]> Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Holly-Max <[email protected]> Co-authored-by: HinGwenWoong <[email protected]> Co-authored-by: Shuai Zhang <[email protected]>

d2l-zh

d2l/tensorflow.py

Python

https://github.com/d2l-ai/d2l-zh.git

_laplace_apply_rules

def _laplace_apply_rules(f, t, s, doit=True, **hints): k, func = f.as_independent(t, as_Add=False) simple_rules = _laplace_build_rules(t, s) for t_dom, s_dom, check, plane, prep in simple_rules: ma = prep(func).match(t_dom) if ma: debug('_laplace_apply_rules match:') debug(' f: %s'%(func,)) debug(' rule: %s o---o %s'%(t_dom, s_dom)) try: debug(' try %s'%(check,)) c = check.xreplace(ma) debug(' check %s -> %s'%(check, c)) if c==True: return _laplace_cr(k*s_dom.xreplace(ma), plane.xreplace(ma), S.true, **hints) except Exception: debug('_laplace_apply_rules did not match.') if f.has(DiracDelta): return None prog_rules = [_laplace_rule_timescale, _laplace_rule_heaviside, _laplace_rule_exp, _laplace_rule_trig, _laplace_rule_diff] for p_rule in prog_rules: LT = p_rule(func, t, s, doit=doit, **hints) if LT is not None: try: r, p, c = LT return (k*r, p, c) except TypeError: return k*LT return LT return None

af443377dd48c2caf440d2b6dd76830dbe84712f

transforms.py

Moved all constant-factor calculations to `_laplace_apply_rules`

sympy

sympy/integrals/transforms.py

Python

https://github.com/sympy/sympy.git

_object2proto

def _object2proto(self) -> SMPCActionSeqBatchMessage_PB: return SMPCActionSeqBatchMessage_PB( smpc_actions=list(map(lambda x: serialize(x), self.smpc_actions)), address=serialize(self.address), msg_id=serialize(self.id), )

e272ed2fa4c58e0a89e273a3e85da7d13a85e04c

smpc_action_seq_batch_message.py

[syft.core.node.common.action] Change syft import absolute -> relative

PySyft

packages/syft/src/syft/core/node/common/action/smpc_action_seq_batch_message.py

Python

https://github.com/OpenMined/PySyft.git

postprocessing

def postprocessing(data): if type_to_string(type(data)) == "torch.Tensor": try: import torch from torchvision import transforms # By default Torch tensors are displayed as images. To display them as JSON, # the user can simply convert them to numpy arrays. transformer = transforms.ToPILImage() return transformer(torch.squeeze(data)) except ModuleNotFoundError: logger.warning( "Module `torchvision` isn't installed, unable to process torch tensor." ) return data return data

203253321d34543aa25483803ebc21e3903679b6

gradio_visualize_graph.py

[serve] Add additional features to DAG visualization with Gradio (#28246)

ray

python/ray/serve/experimental/gradio_visualize_graph.py

Python

https://github.com/ray-project/ray.git

get_anchor_yield_reserve

def get_anchor_yield_reserve() -> pd.DataFrame: df = get_history_asset_from_terra_address( address="terra1tmnqgvg567ypvsvk6rwsga3srp7e3lg6u0elp8" ) return df

72b0a9f1ee8b91ad9fd9e76d80d2ccab51ee6d21

terraengineer_model.py

Next release : reports on steroids (#2349) * fix gov tests * refactor insider * new virtual path extraction * removed some symbol default params as they're considered critical * little adjustments * portfolio refactor * merge API factory * add helpers, stocks, crypto, forex * minor forex changes * include forex api paths * add 2 missing forex funcs * portfolio brokers refactor * display help on api func call * add econometrics virtual paths to api * add api unit test * fixed report for the new api * minor portfolio refactorings * added gdapps * anchor_yield path * some more crypto path fixes * small change * fixed wrong param * minor fixes * wip - inital commit for forex report * add bw as a model, we'll get better solution afterwards * added ema with dummy model as it adds great functionality to the report * minor fixes * wip - added functions to forex report * add feedparser news path * add new virtual paths to api * adding commands to equity report * revert to old paths, new ones were breaking * Add in very basic ETF report * Add candle chart to ETF report * add etf load * allow use of candle without data * add raw to candle * added forex report * ongoing equity report * equity report change * fix some portfolio bugs and add docstrings * include portfolio paths and coin class * add crypto paths * change event dates to str * starting economy report * window for limit * equity report and refactor newsapi * add helper to api * update on economy report * equity report * update economy report * refactor some docstrings * change maturities helper * refactor newsapi * refactor futures command * add some sauce to ycrv plot * black * update report * refactor alphavantage * refactor wsj * update economy report * ycrv tenor * map avaiable_indices * map economy helpers * fix econdb docstring * add plots on economy report * minor fixes * wip - crypto report * update economy report * added same default args as view * added view to explicity use chart=True when suing the api * adjustments - removed rich tables to use only df * final version economy report * change report name * equity report for review * linting * add etf symbols endpoint * incorporate feedback economy report * fix reports launch by adding tag to economy report * fix equity bug * remove analyst name * fix * fix news * make links hyperlinks for equity * click links * fixed arg name * improved news * small improves * Fix light terminal stylesheet that would prevent using it in notebooks (#2473) * improved report * run reports in installer * fix #2209 * minor ycrv refactoring * refactor portfolio/holdv virtual path * refactor benchmark trades * fix events args * adapt economy report to changes * fix portfolio controller bug * holdv refactor * refactor perf command * start portfolio report * remove perf view * refactor holp * add textwrap3 to poetry (doesn't solve the error) * fix equity after merge * add some rolling commands * fix equity after save button * improved crypto report, plus minor fixes * minor fixes on the reports * add maxdd and distr * refactor qa * var command * refactor qa expected shortfall * add es command * add es command * fix qa percentile bug * fix economy rendering * refactor qa omega * add om command * add summary command * add dret command * add mret command * add yret command * add metrics * add allocs to report * remove bro and po commands, add later * fixed some tests * adjustments to crypto report * Fix docstring for VSCode Added a note about installing Jupyter PowerToys extension for optimal API usage in Jupyter VSCode, in the API_README.md. * minor adjustment * remove nft calendar model virtual paths * Add in Portfolio report * fix external axes portfolio view * Update portfolio report with rolling plots * Details for ETF and Portfolio * fix economy report * change analyst to openbb * floppy * fixed unmatched axis in reports * Speed up tests * fix file and load on po * get_news output * add some po paths * Add integration tests for Reports menu * refactor maxsharpe * open maxsharpe * open minrisk * open maxutil * open maxret * Added fixes * black * remove useless views * Fixed small issue * refactor ef * open ef api * portfolio optimization report * Added fixes * unblock api loading * add more endpoints * update po report * unblock api loading * update po report * expose herc * expose property endpoint * Added fixes * More api fixes * flake8 * Fixed some mypy * news api model * flake8 * mypy fix * mypy * black * pylint * fix tests * markdown * markdown * Added fixes * fix economy report * merge * fix economy report * remove empty notebook * expose nco * remove jupyter notebook * expose plot endpoint * remove po report, just used for tests * api v paths plot * remove api_old * change loading msg Co-authored-by: montezdesousa <[email protected]> Co-authored-by: hjoaquim <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: Om Gupta <[email protected]> Co-authored-by: minhhoang1023 <[email protected]> Co-authored-by: JerBouma <[email protected]> Co-authored-by: Theodore Aptekarev <[email protected]> Co-authored-by: Om Gupta <[email protected]> Co-authored-by: Diogo Sousa <[email protected]> Co-authored-by: Colin Delahunty <[email protected]> Co-authored-by: northern-64bit <[email protected]> Co-authored-by: colin99d <[email protected]> Co-authored-by: Minh Hoang <[email protected]>

OpenBBTerminal

openbb_terminal/cryptocurrency/defi/terraengineer_model.py

Python

https://github.com/OpenBB-finance/OpenBBTerminal.git

disable_telemetry

def disable_telemetry(): os.environ[HAYSTACK_TELEMETRY_ENABLED] = "False" logger.info("Telemetry has been disabled.")

ac5617e757e9ace6f30b7291686d9dbbc339f433

telemetry.py

Add basic telemetry features (#2314) * add basic telemetry features * change pipeline_config to _component_config * Update Documentation & Code Style * add super().__init__() calls to error classes * make posthog mock work with python 3.7 * Update Documentation & Code Style * update link to docs web page * log exceptions, send event for raised HaystackErrors, refactor Path(CONFIG_PATH) * add comment on send_event in BaseComponent.init() and fix mypy * mock NonPrivateParameters and fix pylint undefined-variable * Update Documentation & Code Style * check model path contains multiple / * add test for writing to file * add test for en-/disable telemetry * Update Documentation & Code Style * merge file deletion methods and ignore pylint global statement * Update Documentation & Code Style * set env variable in demo to activate telemetry * fix mock of HAYSTACK_TELEMETRY_ENABLED * fix mypy and linter * add CI as env variable to execution contexts * remove threading, add test for custom error event * Update Documentation & Code Style * simplify config/log file deletion * add test for final event being sent * force writing config file in test * make test compatible with python 3.7 * switch to posthog production server * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

haystack

haystack/telemetry.py

Python

https://github.com/deepset-ai/haystack.git

cyclic_reduction

def cyclic_reduction(self, removed=False): word = self.copy() g = self.group.identity while not word.is_cyclically_reduced(): exp1 = abs(word.exponent_syllable(0)) exp2 = abs(word.exponent_syllable(-1)) exp = min(exp1, exp2) start = word[0]**abs(exp) end = word[-1]**abs(exp) word = start**-1*word*end**-1 g = g*start if removed: return word, g return word

498015021131af4dbb07eb110e5badaba8250c7b

free_groups.py

Updated import locations

sympy

sympy/combinatorics/free_groups.py

Python

https://github.com/sympy/sympy.git

clear_bpbynumber

def clear_bpbynumber(self, arg): try: bp = self.get_bpbynumber(arg) except ValueError as err: return str(err) bp.deleteMe() self._prune_breaks(bp.file, bp.line) return None

8198943edd73a363c266633e1aa5b2a9e9c9f526

bdb.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/bdb.py

Python

https://github.com/XX-net/XX-Net.git

test_mapped_dag_pre_existing_tis

def test_mapped_dag_pre_existing_tis(self, dag_maker, session): from airflow.decorators import task from airflow.operators.python import PythonOperator list_result = [[1], [2], [{'a': 'b'}]]

de3bf06863714c6c02ad0c7f5999c961b382bf4a

test_backfill_job.py

Ensure that BackfillJob re-runs existing mapped task instances (#22952) * Ensure that BackfillJob re-runs existing mapped task instances `expand_mapped_task` only returns _new_ TaskInstances, so if a backfill job was run for a dag that already existed the mapped task would never be executed. * Fix tests for changed interface This interface also isn't the best, and we could probably do with refactoring it. Not now though

airflow

tests/jobs/test_backfill_job.py

Python

https://github.com/apache/airflow.git

_collate_rejected

def _collate_rejected(self, pair, row): # It could be fun to enable hyperopt mode to write # a loss function to reduce rejected signals if (self.config.get('export', 'none') == 'signals' and self.dataprovider.runmode == RunMode.BACKTEST): if pair not in self.rejected_dict: self.rejected_dict[pair] = [] self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])

5a4e99b413f84f818662cc3012819db76aec47c1

backtesting.py

Add support for collating and analysing rejected trades in backtest

freqtrade

freqtrade/optimize/backtesting.py

Python

https://github.com/freqtrade/freqtrade.git

not_found

def not_found(error): return ( render_template( 'airflow/not_found.html', hostname=get_hostname() if conf.getboolean('webserver', 'EXPOSE_HOSTNAME', fallback=True) else 'redact', ), 404, )

e29543ec00c0a3eae7a789cb49350499c3b584c2

views.py

Use `get_hostname` instead of `socket.getfqdn` (#24260) We allow users to configure a different function to determine the hostname, so we should use that consistently when we need the hostname.

airflow

airflow/www/views.py

Python

https://github.com/apache/airflow.git

test_http_proxy_relative_request_no_host_header

def test_http_proxy_relative_request_no_host_header(tctx): assert ( Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=False) >> DataReceived(tctx.client, b"GET / HTTP/1.1\r\n\r\n") << SendData(tctx.client, BytesMatching(b"400 Bad Request.+" b"HTTP request has no host header, destination unknown.")) << CloseConnection(tctx.client) )

56eea20f6389b751d38079fb09b29237a0d2b262

test_http.py

tutils: add BytesMatching placeholder

mitmproxy

test/mitmproxy/proxy/layers/http/test_http.py

Python

https://github.com/mitmproxy/mitmproxy.git

project

def project(x): return torch.cat([torch.sqrt(1.0 + torch.sum(x * x, 1, keepdim=True)), x], 1)

04eb35c234de59d4a47848a4f85cb5be3c58c24e

hyperbolic.py

Added prototypical decoder

flair

flair/models/sandbox/prototypical_decoder/distance/hyperbolic.py

Python

https://github.com/flairNLP/flair.git

_create_method

def _create_method(cls, op, coerce_to_dtype=True, result_dtype=None): # NOTE(Clark): This overrides, but coerce_to_dtype, result_dtype might # not be needed

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

tensor_extension.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

python/ray/data/extensions/tensor_extension.py

Python

https://github.com/ray-project/ray.git

_collect_tcl_modules

def _collect_tcl_modules(tcl_root): # Obtain Tcl major version. tcl_major_version = tcl_version.split('.')[0] modules_dirname = f"tcl{tcl_major_version}" modules_path = os.path.join(tcl_root, '..', modules_dirname) if not os.path.isdir(modules_path): logger.warning('Tcl modules directory %s does not exist.', modules_path) return [] return Tree(modules_path, prefix=modules_dirname)

2b2559af1c7790596e7b2040f48e56baef608f9d

tcl_tk.py

hookutils: tcl/tk: port to PyInstaller.isolated framework

pyinstaller

PyInstaller/utils/hooks/tcl_tk.py

Python

https://github.com/pyinstaller/pyinstaller.git

policy_scope

def policy_scope(policy): old_policy = _global_policy try: set_global_policy(policy) yield finally: set_global_policy(old_policy)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

policy.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/mixed_precision/policy.py

Python

https://github.com/keras-team/keras.git

directed_edge_swap

def directed_edge_swap(G, *, nswap=1, max_tries=100, seed=None): if nswap > max_tries: raise nx.NetworkXError("Number of swaps > number of tries allowed.") if len(G) < 4: raise nx.NetworkXError("DiGraph has fewer than four nodes.") if len(G.edges) < 3: raise nx.NetworkXError("DiGraph has fewer than 3 edges") # Instead of choosing uniformly at random from a generated edge list, # this algorithm chooses nonuniformly from the set of nodes with # probability weighted by degree. tries = 0 swapcount = 0 keys, degrees = zip(*G.degree()) # keys, degree cdf = nx.utils.cumulative_distribution(degrees) # cdf of degree discrete_sequence = nx.utils.discrete_sequence while swapcount < nswap: # choose source node index from discrete distribution start_index = discrete_sequence(1, cdistribution=cdf, seed=seed)[0] start = keys[start_index] tries += 1 if tries > max_tries: msg = f"Maximum number of swap attempts ({tries}) exceeded before desired swaps achieved ({nswap})." raise nx.NetworkXAlgorithmError(msg) # If the given node doesn't have any out edges, then there isn't anything to swap if G.out_degree(start) == 0: continue second = seed.choice(list(G.succ[start])) if start == second: continue if G.out_degree(second) == 0: continue third = seed.choice(list(G.succ[second])) if second == third: continue if G.out_degree(third) == 0: continue fourth = seed.choice(list(G.succ[third])) if third == fourth: continue if ( third not in G.succ[start] and fourth not in G.succ[second] and second not in G.succ[third] ): # Swap nodes G.add_edge(start, third) G.add_edge(third, second) G.add_edge(second, fourth) G.remove_edge(start, second) G.remove_edge(second, third) G.remove_edge(third, fourth) swapcount += 1 return G @py_random_state(3)

247231f0154badc4a07b6a4ceb40148ea18f264b

@py_random_state(3)

swap.py

Bug fix in swap: directed_edge_swap and double_edge_swap (#6149) * raise exception if graph has no edges and test for that * Simplify code: raise exception if G has less than 3 edges * add correction * Solved bug in double_edge_swap and added tests for that. Also updated the doc entry * Update networkx/algorithms/swap.py * Added some final suggestions * add merge suggestions Co-authored-by: Ross Barnowski <[email protected]>

networkx

networkx/algorithms/swap.py

Python

https://github.com/networkx/networkx.git

open

def open(fp, mode="r"): if mode != "r": raise ValueError("bad mode") try: return GdImageFile(fp) except SyntaxError as e: raise UnidentifiedImageError("cannot identify this image file") from e

965df6df6f806887fca3f7b5c6dd6c70a20e803e

GdImageFile.py

Add missing paramters to docstrings

Pillow

src/PIL/GdImageFile.py

Python

https://github.com/python-pillow/Pillow.git

_make_relation_requests

def _make_relation_requests(self) -> Tuple[List[str], JsonDict]: # Request the relations of the event. channel = self.make_request( "GET", f"/_matrix/client/unstable/rooms/{self.room}/relations/{self.parent_id}", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) event_ids = [ev["event_id"] for ev in channel.json_body["chunk"]] # Fetch the bundled aggregations of the event. channel = self.make_request( "GET", f"/_matrix/client/unstable/rooms/{self.room}/event/{self.parent_id}", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) bundled_relations = channel.json_body["unsigned"].get("m.relations", {}) return event_ids, bundled_relations

f63bedef07360216a8de71dc38f00f1aea503903

test_relations.py

Invalidate caches when an event with a relation is redacted. (#12121) The caches for the target of the relation must be cleared so that the bundled aggregations are re-calculated after the redaction is processed.

synapse

tests/rest/client/test_relations.py

Python

https://github.com/matrix-org/synapse.git

at_time

def at_time(self, time, asof=False, axis=None): # noqa: PR01, RT01, D200 axis = self._get_axis_number(axis) idx = self.index if axis == 0 else self.columns indexer = pandas.Series(index=idx).at_time(time, asof=asof).index return self.loc[indexer] if axis == 0 else self.loc[:, indexer]

605efa618e7994681f57b11d04d417f353ef8d50

base.py

DOCS-#3099: Fix `BasePandasDataSet` docstrings warnings (#4333) Co-authored-by: Yaroslav Igoshev <[email protected]> Signed-off-by: Alexander Myskov <[email protected]>

modin

modin/pandas/base.py

Python

https://github.com/modin-project/modin.git

_check_fields

def _check_fields(self, obj): if obj.fields is None: return [] elif not isinstance(obj.fields, (list, tuple)): return must_be("a list or tuple", option="fields", obj=obj, id="admin.E004") elif obj.fieldsets: return [ checks.Error( "Both 'fieldsets' and 'fields' are specified.", obj=obj.__class__, id="admin.E005", ) ] fields = flatten(obj.fields) if len(fields) != len(set(fields)): return [ checks.Error( "The value of 'fields' contains duplicate field(s).", obj=obj.__class__, id="admin.E006", ) ] return list( chain.from_iterable( self._check_field_spec(obj, field_name, "fields") for field_name in obj.fields ) )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

checks.py

Refs #33476 -- Reformatted code with Black.

django

django/contrib/admin/checks.py

Python

https://github.com/django/django.git

register_for_auto_class

def register_for_auto_class(cls, auto_class="TFAutoModel"): if not isinstance(auto_class, str): auto_class = auto_class.__name__ import transformers.models.auto as auto_module if not hasattr(auto_module, auto_class): raise ValueError(f"{auto_class} is not a valid auto class.") cls._auto_class = auto_class

811c4c9f79758235762b4f70ffae00deae494fb1

modeling_tf_utils.py

fix bug: register_for_auto_class should be defined on TFPreTrainedModel instead of TFSequenceSummary (#18607)

transformers

src/transformers/modeling_tf_utils.py

Python

https://github.com/huggingface/transformers.git

get

def get(self, request): model = self.queryset.model content_type = ContentType.objects.get_for_model(model) if self.filterset: self.queryset = self.filterset(request.GET, self.queryset).qs # Compile a dictionary indicating which permissions are available to the current user for this model permissions = {} for action in ('add', 'change', 'delete', 'view'): perm_name = get_permission_for_model(model, action) permissions[action] = request.user.has_perm(perm_name) if 'export' in request.GET: # Export the current table view if request.GET['export'] == 'table': table = self.get_table(request, permissions) columns = [name for name, _ in table.selected_columns] return self.export_table(table, columns) # Render an ExportTemplate elif request.GET['export']: template = get_object_or_404(ExportTemplate, content_type=content_type, name=request.GET['export']) return self.export_template(template, request) # Check for YAML export support on the model elif hasattr(model, 'to_yaml'): response = HttpResponse(self.export_yaml(), content_type='text/yaml') filename = 'netbox_{}.yaml'.format(self.queryset.model._meta.verbose_name_plural) response['Content-Disposition'] = 'attachment; filename="{}"'.format(filename) return response # Fall back to default table/YAML export else: table = self.get_table(request, permissions) return self.export_table(table) # Render the objects table table = self.get_table(request, permissions) configure_table(table, request) # If this is an HTMX request, return only the rendered table HTML if is_htmx(request): return render(request, 'htmx/table.html', { 'table': table, }) context = { 'content_type': content_type, 'table': table, 'permissions': permissions, 'action_buttons': self.action_buttons, 'filter_form': self.filterset_form(request.GET, label_suffix='') if self.filterset_form else None, } context.update(self.get_extra_context(request)) return render(request, self.template_name, context) # # Export methods #

54834c47f8870e7faabcd847c3270da0bd3d2884

bulk_views.py

Refactor generic views; add plugins dev documentation

netbox

netbox/netbox/views/generic/bulk_views.py

Python

https://github.com/netbox-community/netbox.git

load_audio

def load_audio(file_path): x, sr = torchaudio.load(file_path) assert (x > 1).sum() + (x < -1).sum() == 0 return x, sr

00c7600103ee34ac50506af88f1b34b713f849e7

vits.py

Update Vits model API

TTS

TTS/tts/models/vits.py

Python

https://github.com/coqui-ai/TTS.git

state

def state(self) -> Mapping[str, Any]: if self._cursor_value: return { self.cursor_field: self._cursor_value, "include_deleted": self._include_deleted, } return {}

a3aae8017a0a40ff2006e2567f71dccb04c997a5

base_streams.py

🎉 🎉 Source FB Marketing: performance and reliability fixes (#9805) * Facebook Marketing performance improvement * add comments and little refactoring * fix integration tests with the new config * improve job status handling, limit concurrency to 10 * fix campaign jobs, refactor manager * big refactoring of async jobs, support random order of slices * update source _read_incremental to hook new state logic * fix issues with timeout * remove debugging and clean up, improve retry logic * merge changes from #8234 * fix call super _read_increment * generalize batch execution, add use_batch flag * improve coverage, do some refactoring of spec * update test, remove overrides of source * add split by AdSet * add smaller insights * fix end_date < start_date case * add account_id to PK * add notes * fix new streams * fix reversed incremental stream * update spec.json for SAT * upgrade CDK and bump version Co-authored-by: Dmytro Rezchykov <[email protected]> Co-authored-by: Eugene Kulak <[email protected]>

airbyte

airbyte-integrations/connectors/source-facebook-marketing/source_facebook_marketing/streams/base_streams.py

Python

https://github.com/airbytehq/airbyte.git

create_nested_bom

def create_nested_bom(tree, prefix="_Test bom "): def create_items(bom_tree): for item_code, subtree in bom_tree.items(): bom_item_code = prefix + item_code if not frappe.db.exists("Item", bom_item_code): frappe.get_doc(doctype="Item", item_code=bom_item_code, item_group="_Test Item Group").insert() create_items(subtree) create_items(tree) def dfs(tree, node): for node_name, subtree in tree.items(): if node_name == node: return subtree else: result = dfs(subtree, node) if result is not None: return result order_of_creating_bom = reversed(level_order_traversal(tree)) for item in order_of_creating_bom: child_items = dfs(tree, item) if child_items: bom_item_code = prefix + item bom = frappe.get_doc(doctype="BOM", item=bom_item_code) for child_item in child_items.keys(): bom.append("items", {"item_code": prefix + child_item}) bom.company = "_Test Company" bom.currency = "INR" bom.insert() bom.submit() return bom # parent bom is last bom

8dff4d66a4a13def61276c2b46cbf0984c469819

test_bom.py

fix: bom valuation - handle lack of LPP (#30454)

erpnext

erpnext/manufacturing/doctype/bom/test_bom.py

Python

https://github.com/frappe/erpnext.git

_make_historical_mat_time

def _make_historical_mat_time(deltas, changepoints_t, t_time, n_row=1, single_diff=None): if single_diff is None: single_diff = np.diff(t_time).mean() prev_time = np.arange(0, 1 + single_diff, single_diff) idxs = [] for changepoint in changepoints_t: idxs.append(np.where(prev_time > changepoint)[0][0]) prev_deltas = np.zeros(len(prev_time)) prev_deltas[idxs] = deltas prev_deltas = np.repeat(prev_deltas.reshape(1, -1), n_row, axis=0) return prev_deltas, prev_time

8fbf8ba2a5bfcdb892e8ca596e338894614000b5

forecaster.py

Speed Up Uncertainty Predictions (#2186)

prophet

python/prophet/forecaster.py

Python

https://github.com/facebook/prophet.git

test_lm_generate_gpt2_xla

def test_lm_generate_gpt2_xla(self): model = TFGPT2LMHeadModel.from_pretrained("gpt2") input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog # fmt: off expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290] # fmt: on xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)

cd4c5c90605b2e23879fcca484f7079b0fc0c361

test_modeling_tf_gpt2.py

TF XLA greedy generation (#15786) * First attempt at TF XLA generation * Fix comments * Update XLA greedy generate with direct XLA calls * Support attention mask, prepare_inputs_for_generation no longer hardcoded for greedy * Handle position_ids correctly * make xla generate work for non xla case * force using xla generate * refactor * more fixes * finish cleaning * finish * finish * clean gpt2 tests * add gpt2 tests * correct more cases * up * finish * finish * more fixes * flake 8 stuff * final rag fix * Update src/transformers/models/rag/modeling_tf_rag.py * finish t5 as well * finish * Update src/transformers/generation_utils.py Co-authored-by: Patrick von Platen <[email protected]>

transformers

tests/gpt2/test_modeling_tf_gpt2.py

Python

https://github.com/huggingface/transformers.git

Error

def Error(filename, linenum, category, confidence, message): if _ShouldPrintError(category, confidence, linenum): _cpplint_state.IncrementErrorCount(category) if _cpplint_state.output_format == 'vs7': sys.stderr.write('%s(%s): %s [%s] [%d]\n' % ( filename, linenum, message, category, confidence)) elif _cpplint_state.output_format == 'eclipse': sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' % ( filename, linenum, message, category, confidence)) else: sys.stderr.write('%s:%s: %s [%s] [%d]\n' % ( filename, linenum, message, category, confidence)) # Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard. _RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile( r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)') # Matches strings. Escape codes should already be removed by ESCAPES. _RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES = re.compile(r'"[^"]*"') # Matches characters. Escape codes should already be removed by ESCAPES. _RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES = re.compile(r"'.'") # Matches multi-line C++ comments. # This RE is a little bit more complicated than one might expect, because we # have to take care of space removals tools so we can handle comments inside # statements better. # The current rule is: We only clear spaces from both sides when we're at the # end of the line. Otherwise, we try to remove spaces from the right side, # if this doesn't work we try on left side but only if there's a non-character # on the right. _RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile( r, re.VERBOSE)

cc4d0564756ca067516f71718a3d135996525909

cpp_lint.py

Balanced joint maximum mean discrepancy for deep transfer learning

transferlearning

code/deep/BJMMD/caffe/scripts/cpp_lint.py

Python

https://github.com/jindongwang/transferlearning.git

write_gexf

def write_gexf(G, path, encoding="utf-8", prettyprint=True, version="1.2draft"): writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version) writer.add_graph(G) writer.write(path)

54e36acb36c75e09bc53dfcb81c73386b82a20c9

gexf.py

Update gexf website link in documentation (#5275) Hi, we've recently put the GEXF website again into its own domain http://gexf.net/ so this documentation should be updated. Thanks!

networkx

networkx/readwrite/gexf.py

Python

https://github.com/networkx/networkx.git

test_user_supplied_base_job_with_labels

def test_user_supplied_base_job_with_labels(self): manifest = KubernetesJob( command=["echo", "hello"], job={ "apiVersion": "batch/v1", "kind": "Job", "metadata": {"labels": {"my-custom-label": "sweet"}}, "spec": { "template": { "spec": { "parallelism": 1, "completions": 1, "restartPolicy": "Never", "containers": [ { "name": "prefect-job", "env": [], } ], } } }, }, ).build_job() assert manifest["metadata"]["labels"] == { # the labels provided in the user's job base "my-custom-label": "sweet", }

daddc2985f0cba6c6e0ae3903232cbca155e7e91

test_kubernetes_job.py

Port KubernetesFlowRunner tests to KubernetesJob tests

prefect

tests/infrastructure/test_kubernetes_job.py

Python

https://github.com/PrefectHQ/prefect.git

require_torch_bf16_gpu

def require_torch_bf16_gpu(test_case): return unittest.skipUnless( is_torch_bf16_gpu_available(), "test requires torch>=1.10, using Ampere GPU or newer arch with cuda>=11.0", )(test_case)

a2d34b7c040723b92823310e3b8fd66874c9d667

testing_utils.py

deprecate is_torch_bf16_available (#17738) * deprecate is_torch_bf16_available * address suggestions

transformers

src/transformers/testing_utils.py

Python

https://github.com/huggingface/transformers.git

test_enable_disable_conflict_with_config

def test_enable_disable_conflict_with_config(): nlp = English() nlp.add_pipe("tagger") nlp.add_pipe("senter") nlp.add_pipe("sentencizer") with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) # Expected to fail, as config and arguments conflict. with pytest.raises(ValueError): spacy.load( tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}} ) # Expected to succeed without warning due to the lack of a conflicting config option. spacy.load(tmp_dir, enable=["tagger"]) # Expected to succeed with a warning, as disable=[] should override the config setting. with pytest.warns(UserWarning): spacy.load( tmp_dir, enable=["tagger"], disable=[], config={"nlp": {"disabled": ["senter"]}}, )

aea16719be04d4d6ab889cd20fe0e323b2c7ffee

test_pipe_methods.py

Simplify and clarify enable/disable behavior of spacy.load() (#11459) * Change enable/disable behavior so that arguments take precedence over config options. Extend error message on conflict. Add warning message in case of overwriting config option with arguments. * Fix tests in test_serialize_pipeline.py to reflect changes to handling of enable/disable. * Fix type issue. * Move comment. * Move comment. * Issue UserWarning instead of printing wasabi message. Adjust test. * Added pytest.warns(UserWarning) for expected warning to fix tests. * Update warning message. * Move type handling out of fetch_pipes_status(). * Add global variable for default value. Use id() to determine whether used values are default value. * Fix default value for disable. * Rename DEFAULT_PIPE_STATUS to _DEFAULT_EMPTY_PIPES.

spaCy

spacy/tests/pipeline/test_pipe_methods.py

Python

https://github.com/explosion/spaCy.git

broadcast_object_list

def broadcast_object_list(object_list, src=0, group=None, device=None): if _rank_not_in_group(group): _warn_not_in_group("broadcast_object_list") return my_rank = get_rank() # Serialize object_list elements to tensors on src rank. if my_rank == src: tensor_list, size_list = zip(*[_object_to_tensor(obj) for obj in object_list]) object_sizes_tensor = torch.cat(size_list) else: object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long) # Current device selection. # To preserve backwards compatibility, ``device`` is default to ``None`` # in which case we run current logic of device selection, i.e. # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the # case it is not ``None`` we move the size and object tensors to be # broadcasted to this device. is_nccl_backend = _check_for_nccl_backend(group) current_device = None if device is not None: if is_nccl_backend and device.type != "cuda": raise ValueError("device type must be cuda for nccl backend") current_device = device else: current_device = torch.device("cpu") if is_nccl_backend: # See note about using torch.cuda.current_device() here in # docstring. We cannot simply use my_rank since rank == device is # not necessarily true. current_device = torch.device("cuda", torch.cuda.current_device()) if is_nccl_backend: object_sizes_tensor = object_sizes_tensor.to(current_device) # Broadcast object sizes broadcast(object_sizes_tensor, src=src, group=group) # Concatenate and broadcast serialized object tensors if my_rank == src: object_tensor = torch.cat(tensor_list) else: object_tensor = torch.empty( torch.sum(object_sizes_tensor).item(), # type: ignore[arg-type] dtype=torch.uint8, ) if is_nccl_backend: object_tensor = object_tensor.to(current_device) broadcast(object_tensor, src=src, group=group) # Deserialize objects using their stored sizes. offset = 0 if my_rank != src: for i, obj_size in enumerate(object_sizes_tensor): obj_view = object_tensor[offset : offset + obj_size] obj_view = obj_view.type(torch.uint8) if obj_view.device != torch.device("cpu"): obj_view = obj_view.cpu() offset += obj_size object_list[i] = _tensor_to_object(obj_view, obj_size)

e1e43c4e710389a3fcf54cd7f3537336e21d3ae5

distributed_c10d.py

Prevent sum overflow in broadcast_object_list (#70605) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70605 broadcast_object_list casted the sum of all object lengths to int from long causing overflows. Test Plan: Add a Tensor with >2GB storage requirement (in distributed_test.py) to object broadcast. This Tensor is only added if test are running at Meta as github tests will oom. Without fix the length will overflow and the program will request a negative sized Tensor: ``` RuntimeError: Trying to create tensor with negative dimension -2147482417: [-2147482417] ``` With fix it will pass the test. Test used on server with GPUs: buck test mode/dev-nosan //caffe2/test/distributed:distributed_nccl_spawn --local -- broadcast_object buck test mode/dev-nosan //caffe2/test/distributed:distributed_gloo_spawn --local -- broadcast_object Reviewed By: r-barnes Differential Revision: D33405741 fbshipit-source-id: 972165f8297b3f5d475636e6127ed4a49adacab1

pytorch

torch/distributed/distributed_c10d.py

Python

https://github.com/pytorch/pytorch.git

forward

def forward(self, data, optimizer=None, return_loss=False, **kwargs): batch_inputs, batch_data_samples = self.preprocss_data(data) if torch.onnx.is_in_onnx_export(): # TODO: Delete assert len(batch_inputs) == 1 return self.onnx_export(batch_inputs, batch_data_samples) if return_loss: losses = self.forward_train(batch_inputs, batch_data_samples, **kwargs) loss, log_vars = self._parse_losses(losses) outputs = dict( loss=loss, log_vars=log_vars, num_samples=len(batch_data_samples)) return outputs else: # TODO: refactor and support aug test later assert isinstance(data[0]['inputs'], torch.Tensor), \ 'Only support simple test currently. Aug-test is ' \ 'not supported yet' return self.forward_simple_test(batch_inputs, batch_data_samples, **kwargs)

924c381a78eb70cede198e042ef34e038e05c15a

base.py

Modify RetinaNet model interface

mmdetection

mmdet/models/detectors/base.py

Python

https://github.com/open-mmlab/mmdetection.git

mode

def mode(a, axis=0, nan_policy='propagate'): a, axis = _chk_asarray(a, axis) if a.size == 0: return ModeResult(np.array([]), np.array([])) contains_nan, nan_policy = _contains_nan(a, nan_policy) if contains_nan and nan_policy == 'omit': a = ma.masked_invalid(a) return mstats_basic.mode(a, axis) if a.dtype == object and np.nan in set(a.ravel()): # Fall back to a slower method since np.unique does not work with NaN scores = set(np.ravel(a)) # get ALL unique values testshape = list(a.shape) testshape[axis] = 1 oldmostfreq = np.zeros(testshape, dtype=a.dtype) oldcounts = np.zeros(testshape, dtype=int) for score in scores: template = (a == score) counts = np.sum(template, axis, keepdims=True) mostfrequent = np.where(counts > oldcounts, score, oldmostfreq) oldcounts = np.maximum(counts, oldcounts) oldmostfreq = mostfrequent return ModeResult(mostfrequent, oldcounts)

7438fe5edfb565ff341fa6ab054461fcdd504aa2

_stats_py.py

MAINT: stats: mode: fix negative axis issue with np.moveaxis instead of custom code (#15421)

scipy

scipy/stats/_stats_py.py

Python

https://github.com/scipy/scipy.git

ascents

def ascents(self): a = self.array_form pos = [i for i in range(len(a) - 1) if a[i] < a[i + 1]] return pos

498015021131af4dbb07eb110e5badaba8250c7b

permutations.py

Updated import locations

sympy

sympy/combinatorics/permutations.py

Python

https://github.com/sympy/sympy.git

_orbit

def _orbit(degree, generators, alpha, action='tuples'): r if not hasattr(alpha, '__getitem__'): alpha = [alpha] gens = [x._array_form for x in generators] if len(alpha) == 1 or action == 'union': orb = alpha used = [False]*degree for el in alpha: used[el] = True for b in orb: for gen in gens: temp = gen[b] if used[temp] == False: orb.append(temp) used[temp] = True return set(orb) elif action == 'tuples': alpha = tuple(alpha) orb = [alpha] used = {alpha} for b in orb: for gen in gens: temp = tuple([gen[x] for x in b]) if temp not in used: orb.append(temp) used.add(temp) return set(orb) elif action == 'sets': alpha = frozenset(alpha) orb = [alpha] used = {alpha} for b in orb: for gen in gens: temp = frozenset([gen[x] for x in b]) if temp not in used: orb.append(temp) used.add(temp) return {tuple(x) for x in orb}

498015021131af4dbb07eb110e5badaba8250c7b

perm_groups.py

Updated import locations

sympy

sympy/combinatorics/perm_groups.py

Python

https://github.com/sympy/sympy.git

_obj_reference_counts

def _obj_reference_counts(self): self._maybe_create_attribute( "_obj_reference_counts_dict", object_identity.ObjectIdentityDictionary(), ) return self._obj_reference_counts_dict

84afc5193d38057e2e2badf9c889ea87d80d8fbf

base_layer.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/engine/base_layer.py

Python

https://github.com/keras-team/keras.git

on_idle

async def on_idle(self) -> None: if self._require_styles_update: await self.post_message(messages.StylesUpdated(self)) self._require_styles_update = False

ada31e68de9065d5d407b16868fe1a2ddb88c548

app.py

docstring

textual

src/textual/app.py

Python

https://github.com/Textualize/textual.git

unset_outlier_removal

def unset_outlier_removal(self): if self.ft_params.get('use_SVM_to_remove_outliers', False): self.ft_params.update({'use_SVM_to_remove_outliers': False}) logger.warning('User tried to use SVM with RL. Deactivating SVM.') if self.ft_params.get('use_DBSCAN_to_remove_outliers', False): self.ft_params.update({'use_SVM_to_remove_outliers': False}) logger.warning('User tried to use DBSCAN with RL. Deactivating DBSCAN.') if self.freqai_info['data_split_parameters'].get('shuffle', False): self.freqai_info['data_split_parameters'].update('shuffle', False) logger.warning('User tried to shuffle training data. Setting shuffle to False')

8aac644009dd7a8ab8f006594b547abddad5aca9

BaseReinforcementLearningModel.py

add tests. add guardrails.

freqtrade

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

Python

https://github.com/freqtrade/freqtrade.git

log_has_when

def log_has_when(line, logs, when): return any(line == message.message for message in logs.get_records(when))

866a5649588c77ec686ba65e581d1306d8ea3751

conftest.py

update emc start/shutdown, initial emc tests

freqtrade

tests/conftest.py

Python

https://github.com/freqtrade/freqtrade.git

bcoo_dot_general_sampled

def bcoo_dot_general_sampled(A, B, indices, *, dimension_numbers): (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers cdims = (api_util._ensure_index_tuple(lhs_contract), api_util._ensure_index_tuple(rhs_contract)) bdims = (api_util._ensure_index_tuple(lhs_batch), api_util._ensure_index_tuple(rhs_batch)) return bcoo_dot_general_sampled_p.bind(A, B, indices, dimension_numbers=(cdims, bdims)) @bcoo_dot_general_sampled_p.def_impl

3184dd65a222354bffa2466d9a375162f5649132

@bcoo_dot_general_sampled_p.def_impl

bcoo.py

[sparse] Update docstrings for bcoo primitives. PiperOrigin-RevId: 438685829

jax

jax/experimental/sparse/bcoo.py

Python

https://github.com/google/jax.git

setup_args

def setup_args(parser=None) -> ParlaiParser: if parser is None: parser = ParlaiParser(True, True, 'Train a model') train = parser.add_argument_group('Training Loop Arguments') train.add_argument( '-et', '--evaltask', help='task to use for valid/test (defaults to the one used for training)', ) train.add_argument( '--final-extra-opt', type=str, default='', help="A '.opt' file that is used for final eval. Useful for setting skip-generation to false. 'datatype' must be included as part of the opt.", ) train.add_argument( '--eval-batchsize', type=int, hidden=True, help='Eval time batch size (defaults to same as -bs)', ) train.add_argument( '--eval-dynamic-batching', # FIXME: see https://github.com/facebookresearch/ParlAI/issues/3367 default=None, type='nonestr', choices={None, 'off', 'full', 'batchsort'}, help=( 'Set dynamic batching at evaluation time. Set to off for ' 'train-only dynamic batching. Set to none (default) to use same ' 'setting as --dynamic-batching.' ), ) train.add_argument( '--num-workers', default=0, type=int, help='Number of background workers (training only)', ) train.add_argument('--display-examples', type='bool', default=False, hidden=True) train.add_argument('-eps', '--num-epochs', type=float, default=-1) train.add_argument('-ttim', '--max-train-time', type=float, default=-1) train.add_argument( '-tstep', '--max-train-steps', '--max-lr-steps', type=int, default=-1, help='End training after n model updates', ) train.add_argument('-ltim', '--log-every-n-secs', type=float, default=-1) train.add_argument( '-lstep', '--log-every-n-steps', type=int, default=50, help='Log every n training steps', ) train.add_argument( '-vtim', '--validation-every-n-secs', type=float, default=-1, help='Validate every n seconds. Saves model to model_file ' '(if set) whenever best val metric is found', ) train.add_argument( '-vstep', '--validation-every-n-steps', type=int, default=-1, help='Validate every n training steps. Saves model to model_file ' '(if set) whenever best val metric is found', ) train.add_argument( '-stim', '--save-every-n-secs', type=float, default=-1, help='Saves the model to model_file.checkpoint after ' 'every n seconds (default -1, never).', ) train.add_argument( '-sval', '--save-after-valid', type='bool', default=False, help='Saves the model to model_file.checkpoint after ' 'every validation (default %(default)s).', ) train.add_argument( '-veps', '--validation-every-n-epochs', type=float, default=-1, help='Validate every n epochs. Saves model to model_file ' '(if set) whenever best val metric is found', ) train.add_argument( '-vme', '--validation-max-exs', type=int, default=-1, hidden=True, help='max examples to use during validation (default -1 uses all)', ) train.add_argument( '--short-final-eval', default=False, hidden=True, type='bool', help='If true, obeys --validation-max-exs in the final ' 'validation and test evaluations.', ) train.add_argument( '-vp', '--validation-patience', type=int, default=10, help=( 'number of iterations of validation where result' ' does not improve before we stop training' ), ) train.add_argument( '-vmt', '--validation-metric', default='accuracy', help='key into report table for selecting best validation', ) train.add_argument( '-vmm', '--validation-metric-mode', type=str, choices=['max', 'min'], help='the direction in which to optimize the validation metric, i.e. maximize or minimize', ) train.add_argument( '-vcut', '--validation-cutoff', type=float, default=1.0, hidden=True, help='value at which training will stop if exceeded by metric', ) train.add_argument( '-lfc', '--load-from-checkpoint', type='bool', default=True, hidden=True, help='load model from checkpoint if available', ) train.add_argument( '-vshare', '--validation-share-agent', default=False, hidden=True, help='use a shared copy of the agent for validation. ' 'this will eventually default to True, but ' 'currently defaults to False.', ) train.add_argument( '-mcs', '--metrics', type=str, default='default', help='list of metrics to show/compute, e.g. all, default,' 'or give a list split by , like ' 'ppl,f1,accuracy,hits@1,rouge,bleu' 'the rouge metrics will be computed as rouge-1, rouge-2 and rouge-l', ) train.add_argument( '-micro', '--aggregate-micro', type='bool', default=False, help='Report micro-averaged metrics instead of macro averaged metrics.', recommended=False, ) train.add_argument( '--world-logs', type=str, default='', help='Saves a jsonl file of the world logs.' 'Set to the empty string to not save at all.', ) train.add_argument( '--save-format', type=str, default='conversations', choices=['conversations', 'parlai'], ) WorldLogger.add_cmdline_args(parser, partial_opt=None) TensorboardLogger.add_cmdline_args(parser, partial_opt=None) WandbLogger.add_cmdline_args(parser, partial_opt=None) parser = setup_dict_args(parser) return parser

7c2b199d0b315c9016072897d849811cfc8a5073

train_model.py

Add WorldLogger to train_model script. (#4369) * Add WorldLogger to train_model ParlAI script. * Fix test_save_multiple_world_logs in test_train_model.py

ParlAI

parlai/scripts/train_model.py

Python

https://github.com/facebookresearch/ParlAI.git

test_messages_filter_not_labels

def test_messages_filter_not_labels(self) -> None: self._send_labelled_messages_in_room() token = "s0_0_0_0_0_0_0_0_0" channel = self.make_request( "GET", "/rooms/%s/messages?access_token=%s&from=%s&filter=%s" % (self.room_id, self.tok, token, json.dumps(self.FILTER_NOT_LABELS)), ) events = channel.json_body["chunk"] self.assertEqual(len(events), 4, [event["content"] for event in events]) self.assertEqual(events[0]["content"]["body"], "without label", events[0]) self.assertEqual(events[1]["content"]["body"], "without label", events[1]) self.assertEqual(events[2]["content"]["body"], "with wrong label", events[2]) self.assertEqual( events[3]["content"]["body"], "with two wrong labels", events[3] )

2ffaf30803f93273a4d8a65c9e6c3110c8433488

test_rooms.py

Add type hints to `tests/rest/client` (#12108) * Add type hints to `tests/rest/client` * newsfile * fix imports * add `test_account.py` * Remove one type hint in `test_report_event.py` * change `on_create_room` to `async` * update new functions in `test_third_party_rules.py` * Add `test_filter.py` * add `test_rooms.py` * change to `assertEquals` to `assertEqual` * lint

synapse

tests/rest/client/test_rooms.py

Python

https://github.com/matrix-org/synapse.git

json

def json(self, body): import streamlit as st if isinstance(body, LazySessionState): body = body.to_dict() if not isinstance(body, str): try: body = json.dumps(body, default=repr) except TypeError as err: st.warning( "Warning: this data structure was not fully serializable as " "JSON due to one or more unexpected keys. (Error was: %s)" % err ) body = json.dumps(body, skipkeys=True, default=repr) json_proto = JsonProto() json_proto.body = body return self.dg._enqueue("json", json_proto)

72703b38029f9358a0ec7ca5ed875a6b438ece19

json.py

Replace static apps with live Cloud apps (#4317) Co-authored-by: kajarenc <[email protected]>

streamlit

lib/streamlit/elements/json.py

Python

https://github.com/streamlit/streamlit.git

set_kill_child_on_death_win32

def set_kill_child_on_death_win32(child_proc): if isinstance(child_proc, subprocess.Popen): child_proc = child_proc._handle assert isinstance(child_proc, subprocess.Handle) if detect_fate_sharing_support_win32(): if not win32_AssignProcessToJobObject(win32_job, int(child_proc)): import ctypes raise OSError(ctypes.get_last_error(), "AssignProcessToJobObject() failed") else: assert False, "AssignProcessToJobObject used despite being unavailable"

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

utils.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

python/ray/_private/utils.py

Python

https://github.com/ray-project/ray.git

unflatten_superdims

def unflatten_superdims(assignment): def check(cond): if cond: return raise NotImplementedError("Failed to convert OpSharding into a ShardingSpec. " "Please open a bug report!") flat_assignment = np.asarray(assignment, dtype=np.int64) check(flat_assignment[0] == 0) dims = [] while flat_assignment.size > 1: stride = flat_assignment[1] for i in range(len(flat_assignment)): if flat_assignment[i] != i * stride: break else: # After this loop i should point to an "element after the sequence", so # we have to increment it if the whole array is a strided sequence. i += 1 size = i dims.append((size, stride)) assert size > 1 # Ensure progress flat_assignment = flat_assignment[::size] return dims

4b587fa1f0049db5366fd04812ab940d80a71a22

pjit.py

Move `pjit.py` to `jax/_src` in preparation for merging the `jit` and `pjit` frontend APIs PiperOrigin-RevId: 495944279

jax

jax/_src/pjit.py

Python

https://github.com/google/jax.git

_get_storage_by_url

def _get_storage_by_url(self, url, storage_objects): from io_storages.models import get_storage_classes for storage_object in storage_objects: # check url is string because task can have int, float, dict, list # and 'can_resolve_url' will fail if isinstance(url, str) and storage_object.can_resolve_url(url): return storage_object

6293c3226e3713bdae678603d6c1300e09c41448

models.py

fix: DEV-1476: Resolving performance for project storages (#1910) * Fix: DEV-1476: Resolving performance for project storages * Rewrite cache * Remove cache completely

label-studio

label_studio/tasks/models.py

Python

https://github.com/heartexlabs/label-studio.git

swap_memory

def swap_memory(): mem = cext.virtual_mem() total_phys = mem[0] free_phys = mem[1] total_system = mem[2] free_system = mem[3] # Despite the name PageFile refers to total system memory here # thus physical memory values need to be subtracted to get swap values total = total_system - total_phys free = min(total, free_system - free_phys) used = total - free percent = usage_percent(used, total, round_=1) return _common.sswap(total, used, free, percent, 0, 0) # ===================================================================== # --- disk # ===================================================================== disk_io_counters = cext.disk_io_counters

471b19d2aa799cd73bded23379e864dd35bec2b6

_pswindows.py

Fix typos

psutil

psutil/_pswindows.py

Python

https://github.com/giampaolo/psutil.git

test_default_disabling_entity

async def test_default_disabling_entity(hass, create_registrations, webhook_client): webhook_id = create_registrations[1]["webhook_id"] webhook_url = f"/api/webhook/{webhook_id}" reg_resp = await webhook_client.post( webhook_url, json={ "type": "register_sensor", "data": { "name": "Battery State", "type": "sensor", "unique_id": "battery_state", "default_disabled": True, }, }, ) assert reg_resp.status == HTTPStatus.CREATED json = await reg_resp.json() assert json == {"success": True} await hass.async_block_till_done() entity = hass.states.get("sensor.test_1_battery_state") assert entity is None assert ( er.async_get(hass).async_get("sensor.test_1_battery_state").disabled_by == er.RegistryEntryDisabler.INTEGRATION )

539ce7ff0e9d9bc59cd8f028f245c09f802c89cb

test_sensor.py

Allow mobile app to disable entities by default (#71562)

core

tests/components/mobile_app/test_sensor.py

Python

https://github.com/home-assistant/core.git

RGS

def RGS(self): rgs = {} partition = self.partition for i, part in enumerate(partition): for j in part: rgs[j] = i return tuple([rgs[i] for i in sorted( [i for p in partition for i in p], key=default_sort_key)])

498015021131af4dbb07eb110e5badaba8250c7b

partitions.py

Updated import locations

sympy

sympy/combinatorics/partitions.py

Python

https://github.com/sympy/sympy.git

pause_time

def pause_time(self) -> int | None: pause_time = self._status.get("timer_target") if pause_time is not None: return wilight_to_hass_pause_time(pause_time) return pause_time

34984a8af8efc5ef6d1d204404c517e7f7c2d1bb

switch.py

Add switch to wilight (#62873) * Created switch.py and support * updated support.py * test for wilight switch * Update for Test * Updated test_switch.py * Trigger service with index * Updated support.py and switch.py * Updated support.py * Updated switch.py as PR#63614 * Updated switch.py * add type hints * Updated support.py * Updated switch.py * Updated switch.py and services.yaml * Updated pywilight * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update ci.yaml * Update ci.yaml * Updated as pywilight Renamed Device as PyWiLightDevice in pywilight. * Updated as pywilight Renamed Device as PyWiLightDevice in pywilight. * Updated as pywilight Renamed Device as PyWiLightDevice in pywilight. * Updated as pywilight Renamed Device as PyWiLightDevice in pywilight. * Update switch.py * Update homeassistant/components/wilight/support.py Co-authored-by: Martin Hjelmare <[email protected]> * Update support.py * Update switch.py * Update support.py * Update support.py * Update switch.py * Update switch.py * Update services.yaml * Update switch.py * Update services.yaml * Update switch.py * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update homeassistant/components/wilight/switch.py Co-authored-by: Martin Hjelmare <[email protected]> * Update switch.py * Update switch.py * Update switch.py * Update test_switch.py * Update test_switch.py * Update test_switch.py * Decrease exception scope * Clean up Co-authored-by: Martin Hjelmare <[email protected]>

core

homeassistant/components/wilight/switch.py

Python

https://github.com/home-assistant/core.git

set_param_recursive

def set_param_recursive(pipeline_steps, parameter, value): for (_, obj) in pipeline_steps: recursive_attrs = ["steps", "transformer_list", "estimators"] for attr in recursive_attrs: if hasattr(obj, attr): set_param_recursive(getattr(obj, attr), parameter, value) if hasattr(obj, "estimator"): # nested estimator est = getattr(obj, "estimator") if hasattr(est, parameter): setattr(est, parameter, value) if hasattr(obj, parameter): setattr(obj, parameter, value)

388616b6247ca4ea8de4e2f340d6206aee523541

export_utils.py

Revert "Deployed 7ccda9a with MkDocs version: 1.3.0" This reverts commit bd9629c40e01241766197119b581a99409b07068.

tpot

tpot/export_utils.py

Python

https://github.com/EpistasisLab/tpot.git

embedding_dim

def embedding_dim(self) -> int: emedding_dim = self.model.get_sentence_embedding_dimension() if not emedding_dim: logger.warning( "Can't find the output embedding dimensions for '%s'. Some checks will not run as intended.", self.model_name_or_path, ) return emedding_dim

101d2bc86cab29f5e93cccd58730c5932acf8ca3

sentence_transformers.py

feat: `MultiModalRetriever` (#2891) * Adding Data2VecVision and Data2VecText to the supported models and adapt Tokenizers accordingly * content_types * Splitting classes into respective folders * small changes * Fix EOF * eof * black * API * EOF * whitespace * api * improve multimodal similarity processor * tokenizer -> feature extractor * Making feature vectors come out of the feature extractor in the similarity head * embed_queries is now self-sufficient * couple trivial errors * Implemented separate language model classes for multimodal inference * Document embedding seems to work * removing batch_encode_plus, is deprecated anyway * Realized the base Data2Vec models are not trained on retrieval tasks * Issue with the generated embeddings * Add batching * Try to fit CLIP in * Stub of CLIP integration * Retrieval goes through but returns noise only * Still working on the scores * Introduce temporary adapter for CLIP models * Image retrieval now works with sentence-transformers * Tidying up the code * Refactoring is now functional * Add MPNet to the supported sentence transformers models * Remove unused classes * pylint * docs * docs * Remove the method renaming * mpyp first pass * docs * tutorial * schema * mypy * Move devices setup into get_model * more mypy * mypy * pylint * Move a few params in HaystackModel's init * make feature extractor work with squadprocessor * fix feature_extractor_kwargs forwarding * Forgotten part of the fix * Revert unrelated ES change * Revert unrelated memdocstore changes * comment * Small corrections * mypy and pylint * mypy * typo * mypy * Refactor the call * mypy * Do not make FARMReader use the new FeatureExtractor * mypy * Detach DPR tests from FeatureExtractor too * Detach processor tests too * Add end2end marker * extract end2end feature extractor tests * temporary disable feature extraction tests * Introduce end2end tests for tokenizer tests * pylint * Fix model loading from folder in FeatureExtractor * working o n end2end * end2end keeps failing * Restructuring retriever tests * Restructuring retriever tests * remove covert_dataset_to_dataloader * remove comment * Better check sentence-transformers models * Use embed_meta_fields properly * rename passage into document * Embedding dims can't be found * Add check for models that support it * pylint * Split all retriever tests into suites, running mostly on InMemory only * fix mypy * fix tfidf test * fix weaviate tests * Parallelize on every docstore * Fix schema and specify modality in base retriever suite * tests * Add first image tests * remove comment * Revert to simpler tests * Update docs/_src/api/api/primitives.md Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Update haystack/modeling/model/multimodal/__init__.py Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * get_args * mypy * Update haystack/modeling/model/multimodal/__init__.py * Update haystack/modeling/model/multimodal/base.py * Update haystack/modeling/model/multimodal/base.py Co-authored-by: Agnieszka Marzec <[email protected]> * Update haystack/modeling/model/multimodal/sentence_transformers.py * Update haystack/modeling/model/multimodal/sentence_transformers.py Co-authored-by: Agnieszka Marzec <[email protected]> * Update haystack/modeling/model/multimodal/transformers.py * Update haystack/modeling/model/multimodal/transformers.py Co-authored-by: Agnieszka Marzec <[email protected]> * Update haystack/modeling/model/multimodal/transformers.py Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Apply suggestions from code review Co-authored-by: Agnieszka Marzec <[email protected]> * Update haystack/nodes/retriever/multimodal/retriever.py Co-authored-by: Agnieszka Marzec <[email protected]> * mypy * mypy * removing more ContentTypes * more contentypes * pylint * add to __init__ * revert end2end workflow for now * missing integration markers * Update haystack/nodes/retriever/multimodal/embedder.py Co-authored-by: bogdankostic <[email protected]> * review feedback, removing HaystackImageTransformerModel * review feedback part 2 * mypy & pylint * mypy * mypy * fix multimodal docs also for Pinecone * add note on internal constants * Fix pinecone write_documents * schemas * keep support for sentence-transformers only * fix pinecone test * schemas * fix pinecone again * temporarily disable some tests, need to understand if they're still relevant Co-authored-by: Agnieszka Marzec <[email protected]> Co-authored-by: bogdankostic <[email protected]>

haystack

haystack/modeling/model/multimodal/sentence_transformers.py

Python

https://github.com/deepset-ai/haystack.git

blobprotovector_str_to_arraylist

def blobprotovector_str_to_arraylist(str): vec = caffe_pb2.BlobProtoVector() vec.ParseFromString(str) return [blobproto_to_array(blob) for blob in vec.blobs]

cc4d0564756ca067516f71718a3d135996525909

io.py

Balanced joint maximum mean discrepancy for deep transfer learning

transferlearning

code/deep/BJMMD/caffe/python/caffe/io.py

Python

https://github.com/jindongwang/transferlearning.git

dag_bag_ext

def dag_bag_ext(): clear_db_runs() dag_bag = DagBag(dag_folder=DEV_NULL, include_examples=False) dag_0 = DAG("dag_0", start_date=DEFAULT_DATE, schedule_interval=None) task_a_0 = EmptyOperator(task_id="task_a_0", dag=dag_0) task_b_0 = ExternalTaskMarker( task_id="task_b_0", external_dag_id="dag_1", external_task_id="task_a_1", recursion_depth=3, dag=dag_0 ) task_a_0 >> task_b_0 dag_1 = DAG("dag_1", start_date=DEFAULT_DATE, schedule_interval=None) task_a_1 = ExternalTaskSensor( task_id="task_a_1", external_dag_id=dag_0.dag_id, external_task_id=task_b_0.task_id, dag=dag_1 ) task_b_1 = ExternalTaskMarker( task_id="task_b_1", external_dag_id="dag_2", external_task_id="task_a_2", recursion_depth=2, dag=dag_1 ) task_a_1 >> task_b_1 dag_2 = DAG("dag_2", start_date=DEFAULT_DATE, schedule_interval=None) task_a_2 = ExternalTaskSensor( task_id="task_a_2", external_dag_id=dag_1.dag_id, external_task_id=task_b_1.task_id, dag=dag_2 ) task_b_2 = ExternalTaskMarker( task_id="task_b_2", external_dag_id="dag_3", external_task_id="task_a_3", recursion_depth=1, dag=dag_2 ) task_a_2 >> task_b_2 dag_3 = DAG("dag_3", start_date=DEFAULT_DATE, schedule_interval=None) task_a_3 = ExternalTaskSensor( task_id="task_a_3", external_dag_id=dag_2.dag_id, external_task_id=task_b_2.task_id, dag=dag_3 ) task_b_3 = EmptyOperator(task_id="task_b_3", dag=dag_3) task_a_3 >> task_b_3 for dag in [dag_0, dag_1, dag_2, dag_3]: dag_bag.bag_dag(dag=dag, root_dag=dag) yield dag_bag clear_db_runs() @pytest.fixture

49e336ae0302b386a2f47269a6d13988382d975f

@pytest.fixture

test_external_task_sensor.py

Replace usage of `DummyOperator` with `EmptyOperator` (#22974) * Replace usage of `DummyOperator` with `EmptyOperator`

airflow

tests/sensors/test_external_task_sensor.py

Python

https://github.com/apache/airflow.git

clone_model

def clone_model(model, input_tensors=None, clone_function=None): with serialization.DisableSharedObjectScope(): if clone_function is None: clone_function = _clone_layer if isinstance(model, Sequential): return _clone_sequential_model( model, input_tensors=input_tensors, layer_fn=clone_function ) else: return _clone_functional_model( model, input_tensors=input_tensors, layer_fn=clone_function ) # "Clone" a subclassed model by resetting all of the attributes.

c269e3cd8fed713fb54d2971319df0bfe6e1bf10

cloning.py

Move serialization-related logic in utils/generic_utils.py to saving/legacy/serialization.py. PiperOrigin-RevId: 479688207

keras

keras/models/cloning.py

Python

https://github.com/keras-team/keras.git

update_billed_amount_based_on_so

def update_billed_amount_based_on_so(so_detail, update_modified=True): # Billed against Sales Order directly billed_against_so = frappe.db.sql(, so_detail) billed_against_so = billed_against_so and billed_against_so[0][0] or 0 # Get all Delivery Note Item rows against the Sales Order Item row dn_details = frappe.db.sql(, so_detail, as_dict=1) updated_dn = [] for dnd in dn_details: billed_amt_agianst_dn = 0 # If delivered against Sales Invoice if dnd.si_detail: billed_amt_agianst_dn = flt(dnd.amount) billed_against_so -= billed_amt_agianst_dn else: # Get billed amount directly against Delivery Note billed_amt_agianst_dn = frappe.db.sql(, dnd.name) billed_amt_agianst_dn = billed_amt_agianst_dn and billed_amt_agianst_dn[0][0] or 0 # Distribute billed amount directly against SO between DNs based on FIFO if billed_against_so and billed_amt_agianst_dn < dnd.amount: if dnd.returned_qty: pending_to_bill = flt(dnd.amount) * (dnd.stock_qty - dnd.returned_qty) / dnd.stock_qty else: pending_to_bill = flt(dnd.amount) pending_to_bill -= billed_amt_agianst_dn if pending_to_bill <= billed_against_so: billed_amt_agianst_dn += pending_to_bill billed_against_so -= pending_to_bill else: billed_amt_agianst_dn += billed_against_so billed_against_so = 0 frappe.db.set_value("Delivery Note Item", dnd.name, "billed_amt", billed_amt_agianst_dn, update_modified=update_modified) updated_dn.append(dnd.parent) return updated_dn

b50036c04a116b2a3aa1784daf161a2f618765a8

delivery_note.py

fix: consider returned_qty while updating billed_amt (cherry picked from commit 63aaa1e357280b24c537a502a479f7bb7a6654e4)

erpnext

erpnext/stock/doctype/delivery_note/delivery_note.py

Python

https://github.com/frappe/erpnext.git

floyd_warshall_numpy

def floyd_warshall_numpy(G, nodelist=None, weight="weight"): import numpy as np if nodelist is not None: if not (len(nodelist) == len(G) == len(set(nodelist))): raise nx.NetworkXError( "nodelist must contain every node in G with no repeats." "If you wanted a subgraph of G use G.subgraph(nodelist)" ) # To handle cases when an edge has weight=0, we must make sure that # nonedges are not given the value 0 as well. A = nx.to_numpy_array( G, nodelist, multigraph_weight=min, weight=weight, nonedge=np.inf ) n, m = A.shape np.fill_diagonal(A, 0) # diagonal elements should be zero for i in range(n): # The second term has the same shape as A due to broadcasting A = np.minimum(A, A[i, :][np.newaxis, :] + A[:, i][:, np.newaxis]) return A

930121ffb89d01077b9888abbd5e810a7e5e16a4

dense.py

More numpy.matrix cleanups for NX2.7 (#5319) * Fix return type in docstring of internal function. * Rm explicit mention of numpy matrix from class docstrings. * Fix return type of floyd_warshall_numpy docstring. * Remove mention of numpy matrix from code comment. * Fix simrank similarity internal docstring. * Rm explicit mention of matrix in favor of 2D array. * Update to_networkx_graph array exception wording. * Remove extraneous mention of numpy matrix. * Don't print array in exception message.

networkx

networkx/algorithms/shortest_paths/dense.py

Python

https://github.com/networkx/networkx.git

__getattr__

def __getattr__(cls, name): if _is_dunder(name): raise AttributeError(name) try: return cls._member_map_[name] except KeyError: raise AttributeError(name) from None

8198943edd73a363c266633e1aa5b2a9e9c9f526

enum.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/enum.py

Python

https://github.com/XX-net/XX-Net.git

index_subdirectory

def index_subdirectory(directory, class_indices, follow_links, formats): dirname = os.path.basename(directory) valid_files = iter_valid_files(directory, follow_links, formats) labels = [] filenames = [] for root, fname in valid_files: labels.append(class_indices[dirname]) absolute_path = tf.io.gfile.join(root, fname) relative_path = tf.io.gfile.join( dirname, os.path.relpath(absolute_path, directory)) filenames.append(relative_path) return filenames, labels

b3f1f645238c93ec3b283f64f73a02df3aea6b53

dataset_utils.py

use tf.io.gfile instead of os.path to support cloud paths. PiperOrigin-RevId: 430975400

keras

keras/preprocessing/dataset_utils.py

Python

https://github.com/keras-team/keras.git

test_loading_race_condition

async def test_loading_race_condition(hass): store = auth_store.AuthStore(hass) with patch( "homeassistant.helpers.entity_registry.async_get" ) as mock_ent_registry, patch( "homeassistant.helpers.device_registry.async_get" ) as mock_dev_registry, patch( "homeassistant.helpers.storage.Store.async_load", return_value=None ) as mock_load: results = await asyncio.gather(store.async_get_users(), store.async_get_users()) mock_ent_registry.assert_called_once_with(hass) mock_dev_registry.assert_called_once_with(hass) mock_load.assert_called_once_with() assert results[0] == results[1]

69cc6ab5f1d58adc586c3b300a4f7f0cde2cd0c2

test_auth_store.py

Clean up accessing entity_registry.async_get_registry helper via hass (#72005)

core

tests/auth/test_auth_store.py

Python

https://github.com/home-assistant/core.git

test_fcos_head_loss

def test_fcos_head_loss(self): s = 256 img_metas = [{ 'img_shape': (s, s, 3), 'scale_factor': 1, }] fcos_head = FCOSHead( num_classes=4, in_channels=1, feat_channels=1, stacked_convs=1, norm_cfg=None) # Fcos head expects a multiple levels of features per image feats = ( torch.rand(1, 1, s // stride[1], s // stride[0]) for stride in fcos_head.prior_generator.strides) cls_scores, bbox_preds, centernesses = fcos_head.forward(feats) # Test that empty ground truth encourages the network to # predict background gt_instances = InstanceData() gt_instances.bboxes = torch.empty((0, 4)) gt_instances.labels = torch.LongTensor([]) empty_gt_losses = fcos_head.loss(cls_scores, bbox_preds, centernesses, [gt_instances], img_metas) # When there is no truth, the cls loss should be nonzero but # box loss and centerness loss should be zero empty_cls_loss = empty_gt_losses['loss_cls'].item() empty_box_loss = empty_gt_losses['loss_bbox'].item() empty_ctr_loss = empty_gt_losses['loss_centerness'].item() self.assertGreater(empty_cls_loss, 0, 'cls loss should be non-zero') self.assertEqual( empty_box_loss, 0, 'there should be no box loss when there are no true boxes') self.assertEqual( empty_ctr_loss, 0, 'there should be no centerness loss when there are no true boxes') # When truth is non-empty then all cls, box loss and centerness loss # should be nonzero for random inputs gt_instances = InstanceData() gt_instances.bboxes = torch.Tensor( [[23.6667, 23.8757, 238.6326, 151.8874]]) gt_instances.labels = torch.LongTensor([2]) one_gt_losses = fcos_head.loss(cls_scores, bbox_preds, centernesses, [gt_instances], img_metas) onegt_cls_loss = one_gt_losses['loss_cls'].item() onegt_box_loss = one_gt_losses['loss_bbox'].item() onegt_ctr_loss = one_gt_losses['loss_centerness'].item() self.assertGreater(onegt_cls_loss, 0, 'cls loss should be non-zero') self.assertGreater(onegt_box_loss, 0, 'box loss should be non-zero') self.assertGreater(onegt_ctr_loss, 0, 'centerness loss should be non-zero') # Test the `center_sampling` works fine. fcos_head.center_sampling = True ctrsamp_losses = fcos_head.loss(cls_scores, bbox_preds, centernesses, [gt_instances], img_metas) ctrsamp_cls_loss = ctrsamp_losses['loss_cls'].item() ctrsamp_box_loss = ctrsamp_losses['loss_bbox'].item() ctrsamp_ctr_loss = ctrsamp_losses['loss_centerness'].item() self.assertGreater(ctrsamp_cls_loss, 0, 'cls loss should be non-zero') self.assertGreater(ctrsamp_box_loss, 0, 'box loss should be non-zero') self.assertGreater(ctrsamp_ctr_loss, 0, 'centerness loss should be non-zero') # Test the `norm_on_bbox` works fine. fcos_head.norm_on_bbox = True normbox_losses = fcos_head.loss(cls_scores, bbox_preds, centernesses, [gt_instances], img_metas) normbox_cls_loss = normbox_losses['loss_cls'].item() normbox_box_loss = normbox_losses['loss_bbox'].item() normbox_ctr_loss = normbox_losses['loss_centerness'].item() self.assertGreater(normbox_cls_loss, 0, 'cls loss should be non-zero') self.assertGreater(normbox_box_loss, 0, 'box loss should be non-zero') self.assertGreater(normbox_ctr_loss, 0, 'centerness loss should be non-zero')

015f8a9bafe808fbe3db673d629f126a804a9207

test_fcos_head.py

Refactor interface of base dense free head and fcos head

mmdetection

tests/test_models/test_dense_heads/test_fcos_head.py

Python

https://github.com/open-mmlab/mmdetection.git

test_cancellation_while_holding_read_lock

def test_cancellation_while_holding_read_lock(self): rwlock = ReadWriteLock() key = "key" # 1. A reader takes the lock and blocks. reader_d, _, _ = self._start_blocking_reader(rwlock, key, "read completed") # 2. A writer waits for the reader to complete. writer_d, _ = self._start_nonblocking_writer(rwlock, key, "write completed") self.assertFalse(writer_d.called) # 3. The reader is cancelled. reader_d.cancel() self.failureResultOf(reader_d, CancelledError) # 4. The writer should take the lock and complete. self.assertTrue( writer_d.called, "Writer is stuck waiting for a cancelled reader" ) self.assertEqual("write completed", self.successResultOf(writer_d))

605d161d7d585847fd1bb98d14d5281daeac8e86

test_rwlock.py

Add cancellation support to `ReadWriteLock` (#12120) Also convert `ReadWriteLock` to use async context managers. Signed-off-by: Sean Quah <[email protected]>

synapse

tests/util/test_rwlock.py

Python

https://github.com/matrix-org/synapse.git

description_of

def description_of(lines, name="stdin"): u = UniversalDetector() for line in lines: line = bytearray(line) u.feed(line) # shortcut out of the loop to save reading further - particularly useful if we read a BOM. if u.done: break u.close() result = u.result if result["encoding"]: return f'{name}: {result["encoding"]} with confidence {result["confidence"]}' return f"{name}: no result"

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

chardetect.py

Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.

pipenv

pipenv/patched/pip/_vendor/chardet/cli/chardetect.py

Python

https://github.com/pypa/pipenv.git

load

def load(self, loader): loader.add_option( "connection_strategy", str, "eager", "Determine when server connections should be established. When set to lazy, mitmproxy " "tries to defer establishing an upstream connection as long as possible. This makes it possible to " "use server replay while being offline. When set to eager, mitmproxy can detect protocols with " "server-side greetings, as well as accurately mirror TLS ALPN negotiation.", choices=("eager", "lazy"), ) loader.add_option( "stream_large_bodies", Optional[str], None, , ) loader.add_option( "body_size_limit", Optional[str], None, , ) loader.add_option( "keep_host_header", bool, False, , ) loader.add_option( "proxy_debug", bool, False, "Enable debug logs in the proxy core.", ) loader.add_option( "normalize_outbound_headers", bool, True, , ) loader.add_option( "validate_inbound_headers", bool, True, , ) loader.add_option( "connect_addr", Optional[str], None, , )

42ccc85b6f1881d92b55e411ba9719f26459b720

proxyserver.py

[quic] work on eventify layers

mitmproxy

mitmproxy/addons/proxyserver.py

Python

https://github.com/mitmproxy/mitmproxy.git

parse_etags

def parse_etags(etag_str): if etag_str.strip() == "*": return ["*"] else: # Parse each ETag individually, and return any that are valid. etag_matches = (ETAG_MATCH.match(etag.strip()) for etag in etag_str.split(",")) return [match[1] for match in etag_matches if match]

9c19aff7c7561e3a82978a272ecdaad40dda5c00

http.py

Refs #33476 -- Reformatted code with Black.

django

django/utils/http.py

Python

https://github.com/django/django.git

print_

def print_(*args, **kwargs): fp = kwargs.pop("file", sys.stdout) if fp is None: return

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

six.py

upd; format

transferlearning

.venv/lib/python3.8/site-packages/pip/_vendor/six.py

Python

https://github.com/jindongwang/transferlearning.git

_solve_least_squares

def _solve_least_squares(M, rhs, method='CH'): if method == 'CH': return M.cholesky_solve(rhs) elif method == 'QR': return M.QRsolve(rhs) elif method == 'LDL': return M.LDLsolve(rhs) elif method == 'PINV': return M.pinv_solve(rhs) else: t = M.H return (t * M).solve(t * rhs, method=method)

59d22b6bb7287613d598611027f640d068ca5748

solvers.py

Moved imports to higher level

sympy

sympy/matrices/solvers.py

Python

https://github.com/sympy/sympy.git

get_bound_panel

def get_bound_panel(self, instance=None, request=None, form=None): if self.model is None: raise ImproperlyConfigured( "%s.bind_to_model(model) must be called before get_bound_panel" % type(self).__name__ ) if not issubclass(self.BoundPanel, EditHandler.BoundPanel): raise ImproperlyConfigured( "%s.BoundPanel must be a subclass of EditHandler.BoundPanel" % type(self).__name__ ) return self.BoundPanel( panel=self, instance=instance, request=request, form=form )

37784643e9207380abaed3c5720dbbcaf69b473b

panels.py

API docs for Panel

wagtail

wagtail/admin/panels.py

Python

https://github.com/wagtail/wagtail.git

ols_ridge_dataset

def ols_ridge_dataset(global_random_seed, request): # Make larger dim more than double as big as the smaller one. # This helps when constructing singular matrices like (X, X). if request.param == "long": n_samples, n_features = 12, 4 else: n_samples, n_features = 4, 12 k = min(n_samples, n_features) rng = np.random.RandomState(global_random_seed) X = make_low_rank_matrix( n_samples=n_samples, n_features=n_features, effective_rank=k ) X[:, -1] = 1 # last columns acts as intercept U, s, Vt = linalg.svd(X) assert np.all(s) > 1e-3 # to be sure U1, U2 = U[:, :k], U[:, k:] Vt1, _ = Vt[:k, :], Vt[k:, :] if request.param == "long": # Add a term that vanishes in the product X'y coef_ols = rng.uniform(low=-10, high=10, size=n_features) y = X @ coef_ols y += U2 @ rng.normal(size=n_samples - n_features) ** 2 else: y = rng.uniform(low=-10, high=10, size=n_samples) # w = X'(XX')^-1 y = V s^-1 U' y coef_ols = Vt1.T @ np.diag(1 / s) @ U1.T @ y # Add penalty alpha * ||coef||_2^2 for alpha=1 and solve via normal equations. # Note that the problem is well conditioned such that we get accurate results. alpha = 1 d = alpha * np.identity(n_features) d[-1, -1] = 0 # intercept gets no penalty coef_ridge = linalg.solve(X.T @ X + d, X.T @ y) # To be sure R_OLS = y - X @ coef_ols R_Ridge = y - X @ coef_ridge assert np.linalg.norm(R_OLS) < np.linalg.norm(R_Ridge) return X, y, coef_ols, coef_ridge @pytest.mark.parametrize("solver", SOLVERS) @pytest.mark.parametrize("fit_intercept", [True, False])

6528e14085d059f9d0c94f93378e7e3c0b967f27

@pytest.mark.parametrize("solver", SOLVERS) @pytest.mark.parametrize("fit_intercept", [True, False])

test_ridge.py

TST tight and clean tests for Ridge (#22910) * MNT replace pinvh by solve * DOC more info for svd solver * TST rewrite test_ridge * MNT remove test_ridge_singular * MNT restructure into several tests * MNT remove test_toy_ridge_object * MNT remove test_ridge_sparse_svd This is tested in test_ridge_fit_intercept_sparse_error. * TST exclude cholesky from singular problem * CLN two fixes * MNT parametrize test_ridge_sample_weights * MNT restructure test_ridge_sample_weights * CLN tighten tolerance for sag solver * CLN try to fix saga tolerance * CLN make test_ridge_sample_weights nicer * MNT remove test_ridge_regression_sample_weights * MNT rename to test_ridge_regression_sample_weights * CLN make test_ridge_regression_unpenalized pass for all random seeds * CLN make tests pass for all random seeds * DOC fix typos * TST skip cholesky for singular problems * MNT move up test_ridge_regression_sample_weights * CLN set skip reason as comment

scikit-learn

sklearn/linear_model/tests/test_ridge.py

Python

https://github.com/scikit-learn/scikit-learn.git

get_supplier_details

def get_supplier_details(suppliers): supplier_details = {} for supp in frappe.db.sql( % ", ".join(["%s"] * len(suppliers)), tuple(suppliers), as_dict=1, ): supplier_details.setdefault(supp.name, supp.supplier_group) return supplier_details

494bd9ef78313436f0424b918f200dab8fc7c20b

purchase_register.py

style: format code with black

erpnext

erpnext/accounts/report/purchase_register/purchase_register.py

Python

https://github.com/frappe/erpnext.git

test_get_whois_admin

def test_get_whois_admin(self) -> None: channel = self.make_request( "GET", self.url, access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertEqual(self.other_user, channel.json_body["user_id"]) self.assertIn("devices", channel.json_body)

901b264c0c88f39cbfb8b2229e0dc57968882658

test_user.py

Add type hints to `tests/rest/admin` (#11851)

synapse

tests/rest/admin/test_user.py

Python

https://github.com/matrix-org/synapse.git

compute_configs

def compute_configs(organization_id=None, project_id=None, public_key=None): from sentry.models import Project, ProjectKey validate_args(organization_id, project_id, public_key) configs = {} if organization_id: # We want to re-compute all projects in an organization, instead of simply # removing the configs and rely on relay requests to lazily re-compute them. This # is done because we do want want to delete project configs in `invalidate_project_config` # which might cause the key to disappear and trigger the task again. Without this behavior # it could be possible that refrequent invalidations cause the task to take excessive time # to complete. projects = list(Project.objects.filter(organization_id=organization_id)) for key in ProjectKey.objects.filter(project__in=projects): # If we find the config in the cache it means it was active. As such we want to # recalculate it. If the config was not there at all, we leave it and avoid the # cost of re-computation. if projectconfig_cache.get(key.public_key) is not None: configs[key.public_key] = compute_projectkey_config(key) elif project_id: for key in ProjectKey.objects.filter(project_id=project_id): configs[key.public_key] = compute_projectkey_config(key) elif public_key: try: key = ProjectKey.objects.get(public_key=public_key) except ProjectKey.DoesNotExist: # The invalidation task was triggered for a deletion and the # ProjectKey should be deleted from the cache. # # This used to delete the cache entry instead of disabling it. The # reason for that was to work around a bug in our model signal # handlers that sent off the invalidation tasks before the DB # transaction was committed, causing us to write stale caches. That # bug was fixed in https://github.com/getsentry/sentry/pull/35671 configs[public_key] = {"disabled": True} else: configs[public_key] = compute_projectkey_config(key) else: raise TypeError("One of the arguments must not be None") return configs

d2fdbaf39e38bacf1fc5924b3feeb46f26de2171

relay.py

feat(relay): Eagerly recalculate project options in compute_configs (#36059)