WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Bug Report

Current Behavior

Babel insert unnecessary polyfills in code for node in new version

Input Code

REPL or Repo link if applicable:

console.log({}.toString)

Run:

node_modules/.bin/babel test.js

Output:

// Warning about core-js
"use strict";

require("core-js/modules/es6.object.to-string");

console.log({}.toString);

Looks like regression because previously we don't have polyfills in code.

Expected behavior/code

No unnecessary polyfills

Babel Configuration (.babelrc, package.json, cli command)

{
"presets": [
[
"@babel/preset-env",
{
"targets": {
"node": "6.9.0"
},
"useBuiltIns": "usage"
}
]
]
}

Environment

Babel version(s): latest (babel-preset-env: 7.4.2)

Node/npm version: v10.15.0

OS: Ubuntu 18.10

Monorepo: no

How you are using Babel: cli

Possible Solution

No

Additional context/Screenshots

No

Issue 2:
Hello, I am using Serverless Framework and GitLab CI to deploy my react-app

I am able to build deploy from my machine, but when setting it up on GitLab CI, I got the following error:

'loose' mode configuration must be the same for both @babel/plugin-proposal-class-properties and @babel/plugin-proposal-private-methods

This error does not appear on my local machine, so I am not sure what's going on.

I also don't have plugin-proposal-private-methods anywhere in package.json, so I am not sure which babel package depends on it.

Here is my package.json

"devDependencies": { "@babel/core": "^7.6.4", "@babel/plugin-proposal-class-properties": "^7.5.5", "@babel/plugin-transform-react-jsx": "^7.3.0", "@babel/plugin-transform-runtime": "^7.6.2", "@babel/runtime": "^7.6.3", "babel-preset-nano-react-app": "^0.1.0", "cssnano": "^4.1.10", "parcel-bundler": "^1.11.0", "parcel-plugin-clean-dist": "0.0.6" }, "babel": { "presets": [ "nano-react-app" ], "plugins": [ [ "@babel/plugin-proposal-class-properties", { "loose": true } ], [ "@babel/plugin-transform-react-jsx", { "pragmaFrag": "React.Fragment" } ] ] } }

Thanks for the help in advance!

Ganda
Issue 1:
Since irow translates the integer index to a label one and then retrieves the row, in case of duplicate index multiple rows are returned.

In [34]: df = pandas.DataFrame(np.random.rand(3,3), columns=list('ABC'), index=list('aab'))

In [35]: df
Out[35]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107
b 0.475423 0.048636 0.224495

In [36]: df.irow(0)
Out[36]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107

In [37]: df.irow(1)
Out[37]:
A B C
a 0.031057 0.528825 0.694592
a 0.705572 0.122005 0.754107

In [38]: df.irow(2)
Out[38]:
A 0.475423
B 0.048636
C 0.224495
Name: b

Issue 2:
If you give a non-existent key to the subset argument it will default to the last column. For example:

In [3]: d = pd.DataFrame({'a':[1],'b':[2],'c':[np.nan]})
In [4]: len(d.dropna(subset=['x']))
Out[4]: 0

It seems the problem is in the line where dropna takes the subset:

agg_obj = self.take(ax.get_indexer_for(subset),axis=agg_axis)

Here, ax.get_indexer_for(subset) will return -1 as a sentinel value for any keys that were not found in subset, and self.take interprets the -1 as a request for the last column.
Issue 1:
Greetings! Was trying hard to find some info, but didn't, so please point if I missed.

Trying to figure out behaviour in Notification Center ("NC"), don't really understand why it works now as it is:

After checking the NC on Windows 10 and get out of it - all notifications are gone (cleared, close even for each is triggered). Why is that so? App is still running. For other apps notifications remain.

After closing the app all notifications are cleared from the NC on both OS as well. Can they persist and open app if it's not running, with some params inside, etc?

Is there an option to handle close on OS X? (f.e. to clear it inside app's Notification section as well).

Is tag not supported on Windows? Able to send several notifications with the same tag.

Is it possible to get list of app's notifications in NC?

Is it possible to clear all app's notifications programmatically (without having links to them after constructing)?

Electron version: v0.37.2

Operating system: OS X 10.11.4, Windows 10 build 10240

P.S. I'm not a native apps developer, so may not know some stuff and hope for an answer - might help others here as well. Thank you in advance.

Issue 2:
I'm using Web Notification API to show notifications in Windows. The notifications shown in Action Center are disappeared once I open Action Center and see them.

Is this a bug? Is there a way to preserve notifications in Action Center even after it's opened?

I'm using Electron 0.36.1 and Windows 10.

This is a simple example I tried:

var count = 0;
setInterval(() => {
new Notification("TEST", {body: String(count)});
++count;
}, 3000);
Issue 1:
The implementation of the non-central chi-squared distribution in scipy (scipy.stats.ncx2, scipy.special.chndtr) is not accurate for large values of the non-centrality parameter. E.g. consider

Reproducing Code example:

from scipy.stats import ncx2

print(ncx2.cdf(1200,2,1000))

print(ncx2.cdf(1500,2,1000))

print(ncx2.cdf(2000,2,1000))

print(ncx2.cdf(5000,2,1000))

which produces

0.998604279948

0.999933004449

0.999933004449

0.999933004449

instead of converging to one.

The implementation in scipy is based on Formula 26.4.25 of Abramowitz and Stegun, Handbook of Mathematical Functions (1966).

There are faster and more accurate algorithms for implementing this function, especially for large non-centrality parameter (ncp). For example, R apparently uses Ding (1992) for ncp>80. However boost states that Ding can take a long time to converge for large ncp, and recommends a different method. I am wondering if it is worth upgrading the implementation in scipy to work for large non-centrality?

Scipy/Numpy/Python version information:

import sys, scipy, numpy; print(scipy.version, numpy.version, sys.version_info)

0.18.0 1.11.1 sys.version_info(major=3, minor=5, micro=2, releaselevel='final', serial=0)

Issue 2:
I'm getting unexpected behavior from scipy.ncx2.sf. The survival function should eventually go to zero for large enough inputs, but instead I'm seeing a kind of "foot", i.e., it is bottoming out at a positive value. The following interactive I/O demonstrates the issue.

Reproducing code example:

(Pdb) v_T_norm[::50]

array([ 4.99515382e+00, 1.07617327e+01, 2.31854502e+01,

4.99515382e+01, 1.07617327e+02, 2.31854502e+02,

4.99515382e+02, 1.07617327e+03, 2.31854502e+03,

4.99515382e+03, 1.07617327e+04, 2.31854502e+04,

4.99515382e+04])

(Pdb) nu

20.0

(Pdb) lam

499.51538166556196

(Pdb) ncx2.sf(v_T_norm[::50], df=nu, nc=lam)

array([ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,

1.00000000e+00, 1.00000000e+00, 1.00000000e+00,

6.64666099e-01, 4.16325934e-05, 4.16325934e-05,

4.16325934e-05, 4.16325934e-05, 4.16325934e-05,

4.16325934e-05])
Issue 1:
abu butt opened SPR-1848 and commented

Issue originally raised in spring forums: http://forum.springframework.org/showthread.php?t=23141

After investigation this may be a bug. unable to resolve system properties.

Works if the variable is a jvm property, but not if its a system property.

Affects: 2.0 M3

Attachments:

SpringConfigTest.rar (1.61 MB)

test.props (10 bytes)

Issue Links:

#6586 Location value on PropertyPlaceholderConfigurer definition does not resolve placeholder if it is a system env ("is duplicated by")

Issue 2:
Chris Marshall opened SPR-3555 and commented

This may be a duplicate of #5571 but I cannot find any documentation in the JMX or AOP chapters about how to use cglib (I thought I already was) to ensure that a bean which is being proxied's operations are available via JMX.

Assuming that one is exporting a public method which is not on the proxied object's interfaces, is it not safe to assume that the method is safe to use directly on the underlying method? Perhaps not but it must be common to proxy for access only to certain methods (as in my case) and wish to export others on JMX (why should the former interfere with the latter?)

Anyway, it would be great if you could tell me how to use cglib to make this problem go away.

Affects: 2.0.5
Issue 1:
async function globalTeardown() {

const reportPath = path.join(__dirname, playwright-report);

const zip = new AdmZip();

zip.addLocalFolder(reportPath);

zip.writeZip('./html.zip');

}

export default globalTeardown;

In config file its- globalTeardown: './global-teardown',

Issue 2:
Basically, I want to a feature where I can upload my reporting artifacts or full 'playwright-report' directory to some storage where everyone from the team can refer it.

I am looking for the hook which will trigger once HTML report is created successfully. Currently, I am trying to achieve the same by putting the code in teardown script. But it is not working as I want.
Issue 1:
Description

I have a malloc error with TruncatedSVD.

My issue seems closed to #7626

Steps/Code to Reproduce

Code to reproduce my error:

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import decomposition
from sklearn.pipeline import make_pipeline
from sklearn.datasets import fetch_20newsgroups

twenty_train = fetch_20newsgroups(subset='train')
twenty_test = fetch_20newsgroups(subset='test')

dt_svd_clf = make_pipeline(CountVectorizer(),
decomposition.TruncatedSVD(n_components=25),
)
dt_svd_clf.fit(twenty_train.data, twenty_train.target)

python crashes with a segfault:

python(1883,0x7fff79fe5000) malloc: *** mach_vm_map(size=18446744072715628544) failed (error code=3)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
Segmentation fault: 11

I reproduce the bug all the time.

I have 8GB RAM on my computer and I have some enough RAM left when running the code.

With algorithm='arpack' it works. I can even set n_components to 250 for instance.

dt_svd_clf = make_pipeline(CountVectorizer(),
decomposition.TruncatedSVD(algorithm='arpack',n_components=25),
)

Versions

My configuration:

Darwin-15.6.0-x86_64-i386-64bit

Python 3.5.2 (default, Oct 11 2016, 15:01:29)

[GCC 4.2.1 Compatible Apple LLVM 7.3.0 (clang-703.0.31)]

NumPy 1.11.2

SciPy 0.18.1

Scikit-Learn 0.18.1

Thanks

Issue 2:
Description

I tried to use PCA with default configuration on MNIST dataset, It gave memory allocation error. The code given below worked well in the previous version of scikit-learn(0.17). The PCA class chooses the randomised svd solver for this dataset, whereas in the previous version it was just svd_solver='full'. If I give explicitly svd_solver='full', it works fine, but when I leave it to find the best solver it chooses randomized svd solver and the code gives memory allocation error when it tries to calculate linalg.lu() using Scipy.

Steps/Code to Reproduce

from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_mldata
from sklearn.decomposition import PCA

mnist = fetch_mldata('MNIST original', data_home='./data')
X_train, X_test, y_train, y_test = train_test_split(mnist.data, mnist.target, test_size=0.3, random_state=0)

pca = PCA(n_components=100)
pca.fit_transform(X_train)

Error

Python(17010,0x7fffa552c3c0) malloc: *** mach_vm_map(size=18446744066138652672) failed (error code=3)

*** error: can't allocate region

*** set a breakpoint in malloc_error_break to debug

Versions

Darwin-16.0.0-x86_64-i386-64bit

[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]

NumPy 1.11.2

SciPy 0.18.1

Scikit-Learn 0.18
Issue 1:
Do you want to request a feature or report a bug?

Bug

What is the current behavior?

(I wanna cry: )The optimization of Scope Hoisting could break core-js Array.from.

After updated to webpack v3. My code will throw the error:

Uncaught TypeError: Cannot assign to read only property 'length' of function 'function from() { [native code] }'

If the current behavior is a bug, please provide the steps to reproduce.

The error due to wrong context:

webpack use __webpack_require__.n & __webpack_require__.d to mangle exports name;

Array.from transformed to __WEBPACK_IMPORTED_MODULE_4_babel_runtime_core_js_array_from___default.a;

While calling arrayfrom.a(arraylike), the context will be function getDefault;

es6.array.from.js#L15, C will be getDefault;

#L25 new C() will return function from(arrayLike /* , mapfn = undefined, thisArg = undefined */) {...};

createProperty will make the returned function property length read only;

finally when it call result.length = index, the error Uncaught TypeError will be threw.

What is the expected behavior?

Please mention other relevant information such as the browser version, Node.js version, webpack version and Operating System.

webpack v3

Issue 2:
Bug report

What is the current behavior?

webpack.config.js

{
target: 'electron-main',
node: {
__dirname: false
},
entry: {
main: './src/main.ts'
},
}

main.ts

console.log(__dirname)

$ node main.js

ouput '/'

If the current behavior is a bug, please provide the steps to reproduce.

What is the expected behavior?

it should output the absolute path

Other relevant information:

webpack version: 4.44.1

Node.js version: 12.18.3

Operating System: macOS 10.15.6

Additional tools:
Issue 1:
Environment

Windows build number: Microsoft Windows [Version 10.0.18362.900]
PowerToys version: v0.18.2
PowerToy module for which you are reporting the bug (if applicable): Keyboard Manager

Steps to reproduce

Open Keyboard Manager

Click "Remap a shortcut"

Click "+"

Click "Type Shortcut"

Press "Ctrl (Left)" and right arrow at the shortcut

Select "Next Track" at "Mapped To"

Expected behavior

Ctrl left + right arrow should be remapped to the "Next track" key

Actual behavior

The message "Shortcut must start with a modifier key" appears

Screenshots

Issue 2:
Summary of the new feature/enhancement

In order to simplify running&debugging FancyZonesEditor, remove tmp files from Editor cmd args. Also, create mock args for debugging purposes.
Issue 1:
6l -a foo.6 used to print the assembled instructions. Now it just prints the raw bytes.
I believe this is a consequence of having no disassembler. Regardless, I miss this
feature and want it back.

Issue 2:
Please answer these questions before submitting your issue. Thanks!

What version of Go are you using (go version)?

go version go1.6 darwin/amd64

What operating system and processor architecture are you using (go env)?

GOARCH="amd64"

GOBIN=""

GOEXE=""

GOHOSTARCH="amd64"

GOHOSTOS="darwin"

GOOS="darwin"

GOPATH="/Users/jiahua/gohome"

GORACE=""

GOROOT="/usr/local/go"

GOTOOLDIR="/usr/local/go/pkg/tool/darwin_amd64"

GO15VENDOREXPERIMENT="1"

CC="clang"

GOGCCFLAGS="-fPIC -m64 -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -fno-common"

CXX="clang++"

CGO_ENABLED="1"

What did you do?

gomobile init

What did you expect to see?

no error message

What did you see instead?

gomobile: rename /Users/jiahua/gohome/pkg/gomobile/work-387114350/android-ndk-r10e/platforms/android-21/arch-arm64/usr /Users/jiahua/gohome/pkg/gomobile/android-ndk-r10e/arm64/sysroot/usr: no such file or directory
Issue 1:
Background: I've implemented most of the multi-architecture Kubernetes has today, and wrote the proposal here: https://github.com/kubernetes/community/tree/master/contributors/design-proposals/multi-platform.md

Now it's time to continue improving the multi-arch experience as well.

Tasks to do:

Deprecate armel and use armhf images instead and use GOARM=7 instead of GOARM=6

Motivation:

The only GOARM=6 board Go will support in go1.8 is the Raspberry Pi 1 which is just too slow to run newer Kubernetes versions.

Small performance improvements when using GOARM=7

The armel (http://hub.docker.com/u/armel) images are not updated as often as the armhf (http://hub.docker.com/u/armhf) images are.

Use go1.8 as fast as possible for arm and ppc64le (and of course generally as well, but that will require the "real" release)

Motivation:

Brings us a lot of mandatory fixes for arm and ppc64le

Brings us the SSA backend which is ~30% faster

We can remove the patched golang for arm and start building ppc64le binaries by default again

arm hyperkube will start working again

Even if it's beta, it's probably better than a self-patched version of go1.7

Proposal:

Use go1.8-beta1 for the arm builds already in the v1.5 release cc @saad-ali @pwittrock @jessfraz

Reenable ppc64le builds again by using go1.8betas until the stable version of go1.8 is released and release v1.6 of kubernetes for ppc64le with go1.8

Evalute s390x as a new platform

If no one loudly complains, I'm gonna take care of the PRs #37092 and #36050 and merge them in time for v1.6

Convert the essential images that are named registry/binary:version to manifest lists

TODO: Investigate rkt support for manifest lists: appc/docker2aci#193

Wait for gcr.io to roll out a v2 schema 2 registry that support manifest lists. @aronchick told me it's gonna happen mid-December.

Start building manifest lists when releasing Kubernetes (kube-apiserver, kube-scheduler, etc.)

Basically, all images will be named registry/binary-arch:version as most of them are now, but then the image without the -arch bit will be a manifest list which points to the right -arch image depending on which arch docker runs on.

Convert all other essential images to manifest lists, namely:

etcd

all kube-dns images

the dashboard image

all heapster images: first step: kubernetes-retired/heapster#1387

Convert the ingress images to multiple architectures

cc-ing involved people here:

@kubernetes/sig-testing @david-mcmahon @saad-ali @pwittrock @Pensu @ixdy @jessfraz @thockin @vishh @gajju26 @brendandburns

Issue 2:
Federation: hack/update-codegen.sh fails to generate

import_known_versions.go

namespace_expansion.go

fake_namespace_expansion.go
Issue 1:
Code Sample, a copy-pastable example if possible

import pandas as pd

df = pd.DataFrame(data=[1,2,3,3,3,3,4,4,4,5,5,5], columns=['abc'], ).sort_values('abc')
df['Rank'] = df['abc'].rank(method='dense')
df['Rank_Pct']= df['abc'].rank(pct=True, method='dense', )
df['Rank_Pct_Manual']= df['Rank'] / df['Rank'].max()

df.head()

Output:

abc Rank Rank_Pct Rank_Pct_Manual
0 1 1.0 0.083333 0.2
1 2 2.0 0.166667 0.4
2 3 3.0 0.250000 0.6
3 3 3.0 0.250000 0.6
4 3 3.0 0.250000 0.6
5 3 3.0 0.250000 0.6
6 4 4.0 0.333333 0.8
7 4 4.0 0.333333 0.8
8 4 4.0 0.333333 0.8
9 5 5.0 0.416667 1.0
10 5 5.0 0.416667 1.0
11 5 5.0 0.416667 1.0

Problem description

If you chose both the pct=True and method='dense' options of Series.rank, you don't get the expected maximum percentile of 1 if there are repeated values in the Series. This is because the function (e.g., rank_1d_float64()) always divides by the total number of elements in the Series. But in the case of the dense method, we should divide by the maximum rank value.

I'm working on a PR now.

Expected Output

I would expect the values of Rank_Pct and Rank_Pct_Manual to be the same, and that the maximum of both should be 1.

abc Rank Rank_Pct Rank_Pct_Manual
0 1 1.0 0.2 0.2
1 2 2.0 0.4 0.4
2 3 3.0 0.6 0.6
3 3 3.0 0.6 0.6
4 3 3.0 0.6 0.6
5 3 3.0 0.6 0.6
6 4 4.0 0.8 0.8
7 4 4.0 0.8 0.8
8 4 4.0 0.8 0.8
9 5 5.0 1.0 1.0
10 5 5.0 1.0 1.0
11 5 5.0 1.0 1.0

Output of pd.show_versions()

commit: None

python: 3.6.3.final.0

python-bits: 64

OS: Darwin

OS-release: 16.7.0

machine: x86_64

processor: i386

byteorder: little

LC_ALL: None

LANG: en_US.UTF-8

LOCALE: en_US.UTF-8

pandas: 0.21.0

pytest: 3.2.3

pip: 9.0.1

setuptools: 36.3.0

Cython: None

numpy: 1.13.3

scipy: 0.19.1

pyarrow: 0.7.1

xarray: None

IPython: 6.2.1

sphinx: None

patsy: None

dateutil: 2.6.1

pytz: 2017.3

blosc: None

bottleneck: None

tables: None

numexpr: None

feather: None

matplotlib: 2.0.2

openpyxl: None

xlrd: None

xlwt: None

xlsxwriter: None

lxml: None

bs4: None

html5lib: 0.999999999

sqlalchemy: 1.1.15

pymysql: None

psycopg2: None

jinja2: 2.9.6

s3fs: None

fastparquet: None

pandas_gbq: None

pandas_datareader: None

Issue 2:
I find the behavior of rank function with method = 'dense' and pct = True unexpected as it looks like, in order to calculate percentile ranks, the function is using the total number of observations instead of the number of distinct observations.

Code Sample, a copy-pastable example if possible

import pandas as pd
n_rep = 2
ts = pd.Series([1,2,3,4] * n_rep )
output = ts.rank(method = 'dense', pct = True)

Problem description

ts.rank(method = 'dense', pct = True)
Out[116]:
0 0.125
1 0.250
2 0.375
3 0.500
4 0.125
5 0.250
6 0.375
7 0.500

Expected Output

Something similar to:

pd.Series([1,2,3,4] * 2).rank(method = 'dense', pct = True) * n_rep
Out[118]:
0 0.25
1 0.50
2 0.75
3 1.00
4 0.25
5 0.50
6 0.75
7 1.00

Also, I would expected the result above to be invariant to n_rep.

i.e. I would expect a "mapping" {value -> pct_rank} that would not depend on how many times the value is repeated, while it is not the case here.
Issue 1:
I'm using python 2.7 on numpy 1.10.1 with a custom dtype implementation. Essentially the self->userloops is defined with types that do not match the specified_types. In such case numpy will hang indefinitely.

It seems like the function type_tuple_userloop_type_resolver in numpy/numpy/core/src/umath/ufunc_type_resolution.c is incorrect. It can get into an infinite loop around line 1745, which looks like:

while (funcdata != NULL) {
int *types = funcdata->arg_types;
int matched = 1;

if (n_specified == nop) {
for (j = 0; j < nop; ++j) {
if (types[j] != specified_types[j] &&
specified_types[j] != NPY_NOTYPE) {
matched = 0;
break;
}
}
} else {
if (types[nin] != specified_types[0]) {
matched = 0;
}
}
if (!matched) {
continue;
}

switch (ufunc_loop_matches(self, op,
casting, casting,
any_object, use_min_scalar,
types, NULL,
&no_castable_output, &err_src_typecode,
&err_dst_typecode)) {
/* It works */
case 1:
set_ufunc_loop_data_types(self, op,
out_dtype, types, NULL);
return 1;
/* Didn't match */
case 0:
PyErr_Format(PyExc_TypeError,
"found a user loop for ufunc '%s' "
"matching the type-tuple, "
"but the inputs and/or outputs could not be "
"cast according to the casting rule",
self->name ? self->name : "(unknown)");
return -1;
/* Error */
case -1:
return -1;
}

funcdata = funcdata->next;
}

I see that if matched = 0, then the loop does a "continue" without updating the funcdata. Thus funcdata is never null and will run forever.

Issue 2:
In relation to a bug filed on my numpy quaternion package, @eric-wieser found a bug in numpy. He reports an infinite loop in type_tuple_userloop_type_resolver. I don't actually understand that code, but I guess he's talking about the continue statement in this loop short-circuiting the funcdata = funcdata->next statement, which means the loop condition doesn't change between iterations.

Presumably, that bug needs to be fixed regardless, but (not really understanding numpy's internals) I wonder if I could have also done something in the quaternion package to avoid this problem. My package attempts to add a new dtype to numpy, so maybe it also needs to update some internal list of dtypes that numpy maintains??? If so, I welcome comments.
Issue 1:
I began the challenge for reverses in the object oriented part of Java. As soon as I began typing "function" the screen froze and I was unable to navigate away from the page, or perform any actions within the page. I can still operate outside of that page, even while the page is frozen, so I don't think it's the browser (chrome). I quit chrome, logged out and restarted my computer and the issue persists. Hoping something can be done about it so I can continue on. Having lots of fun and love your service !

Issue 2:
This issue probably exists somewhere, but adding a function below a bonfire's default code will crash the browser.

Adding a function above a Bonfire's default code doesn't cause any problems.