ng doc not working

🐞 Bug report
Command (mark with an x)

[ ] new
[ ] build
[ ] serve
[ ] test
[ ] e2e
[ ] generate
[ ] add
[ ] update
[ ] lint
[ ] extract-i18n
[ ] run
[ ] config
[ ] help
[ ] version
[x] doc

Is this a regression?
Yes, the previous version in which this bug was not present was: I don't know.

N/A

Yes, I can reproduce this. I'll investigate further.

Thanks @gkalpak.

@gkalpak, I can now take this as I have a Windows environment for testing.

Oh, cool :smiley:
It'll be a couple of days before I can get to this, so happy for you to take it over 🚀


[RFC] (UN)STABLE flag keywords

STABLE or UNSTABLE flag keywords (just like TODOs) could be useful in docs.

Can you show the example?

@MakeNowJust according to https://github.com/crystal-lang/crystal/issues/5051#issuecomment-332681116, some methods could be marked UNSTABLE

Yeah, so when all methods have "stable" flag we can prepare for 1.0 😅

@vladfaust According to what? There's nothing in that comment that would suggest it.
Also, TODO and others were meant to be used as a doc flags (FIXME: xxx), not keywords.

So, I have to rewrite my comment 😅
when all TODO and FIXME flags are solved, we can prepare for 1.0
Currently there is:

50 TODO:
5 FIXME:
57 NOTE:

Crystal still have some great work to do for improving the language 💪

@Sija,

Also, TODO and others were meant to be used as a doc flags (FIXME: xxx), not keywords.

From documentation:

The supported flag keywords are:

If a method is stable and we 100% know we wouldn't change it, we can add STABLE to it. Visa versa, using of #future, for example, is not recommended because it's likely to change, so we could mark it UNSTABLE.

@faustinoaq, in my opinion 1.0 means no any UNSTABLE flags if speaking of this issue :blush:
Also NOTE doesn't mean something wrong, it's just a note.

Also NOTE doesn't mean something wrong, it's just a note.

Yeah, you're right! 😅

To sum it up, I believe that we need UNSTABLE keyword with optional description of why is it unstable. Developers will try to avoid unstable entities or keep in my that it might change with next release:
# UNSTABLE: Windows support is likely to have strong changes prior to 1.0
class WindowsSupport
end

# Returns Fiber
# UNSTABLE: Experimental feature
def future
end

Are these flags useful in the longrun? Or outside the compiler?
I don't really see much benefit from it. Things, that need to be done for 1.0 are not just APIs and this is tracked and managed outside the source code anyway, so why bother? And it's not very binding: Maybe a method was flagged STABLE but it turns out, it needs a refactoring for reasons not considered before.


Mention stackoverflow tags or channels of help on /README.md

I often got stuck after discovering issues in firestore and I found no mention of stackoverflow tags or slack channels of communication or twitter user/bots on the PHP github repo -> Firestore folder.
Maybe there could be section called Where to find help?
ref internal: b/218990878

We already mention this on the documentation site though but not in README:
http://googleapis.github.io/google-cloud-php/

https://github.com/googleapis/google-cloud-ruby already has a section to lead users to SO.


Update developer guide with use cases

Codecov Report

Merging #43 (fe40904) into master (a8c4367) will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master      #43   +/-   ##
=========================================
  Coverage     72.10%   72.10%
  Complexity      399      399
=========================================
  Files            70       70
  Lines          1233     1233
  Branches        125      125
=========================================
  Hits            889      889
  Misses          312      312
  Partials         32       32

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.


Pusher error: cURL error 52: Empty reply from server

Pusher error: cURL error 52: Empty reply from server (see https://curl.haxx.se/libcurl/c/libcurl-errors.html) for http://<IP_ADDRESS>:6001/apps/xxxx/events?auth_key=fxxxxxxx&auth_timestamp=1664888557&auth_version=1.0&body_md5=35bed2c8dba4db8c3cb04b34a4cf1551&auth_signature=xxxxxxxx.

What is your current domain? Do you work on local or on a server? @developcreativo

I have the same issue but on local and server

I have the same issue on local server

I have the same issue both on local and server

I switched to Soketi its works pefectly

Had something similar when running on https.
Got a workaround. Basically tell Guzzlehttp to use https on localhost and ignore ssl

set PUSHER_APP_SCHEME=https

set in broadcasting.php 'client_options' => ['verify' => false,]

eg:
'connections' => [
 'pusher' => [
     'driver' => 'pusher',
     'key' => env('PUSHER_APP_KEY'),
     'secret' => env('PUSHER_APP_SECRET'),
     'app_id' => env('PUSHER_APP_ID'),
     'options' => [
         'cluster' => env('PUSHER_APP_CLUSTER'),
         'encrypted' => env('PUSHER_APP_ENCRYPTED', true),
         'host' => env('PUSHER_APP_HOST', '<IP_ADDRESS>'),
         'port' => env('PUSHER_APP_PORT', 6001),
         'scheme' => env('PUSHER_APP_SCHEME', 'http'),
         'useTLS' => env('PUSHER_APP_SCHEME', 'http') === 'https',
         'curl_options' => [
             CURLOPT_SSL_VERIFYHOST => 0,
             CURLOPT_SSL_VERIFYPEER => 0,
         ],
     ],
     'client_options' => [
         // Guzzle client options: https://docs.guzzlephp.org/en/stable/request-options.html
         'verify' => false,
     ],
 ],

do we need use guzzle in controller ?

Had something similar when running on https. Got a workaround. Basically tell Guzzlehttp to use https on localhost and ignore ssl

set PUSHER_APP_SCHEME=https
set in broadcasting.php 'client_options' => ['verify' => false,]
eg:
'connections' => [ 'pusher' => [
     'driver' => 'pusher',
     'key' => env('PUSHER_APP_KEY'),
     'secret' => env('PUSHER_APP_SECRET'),
     'app_id' => env('PUSHER_APP_ID'),
     'options' => [
         'cluster' => env('PUSHER_APP_CLUSTER'),
         'encrypted' => env('PUSHER_APP_ENCRYPTED', true),
         'host' => env('PUSHER_APP_HOST', '<IP_ADDRESS>'),
         'port' => env('PUSHER_APP_PORT', 6001),
         'scheme' => env('PUSHER_APP_SCHEME', 'http'),
         'useTLS' => env('PUSHER_APP_SCHEME', 'http') === 'https',
         'curl_options' => [
             CURLOPT_SSL_VERIFYHOST => 0,
             CURLOPT_SSL_VERIFYPEER => 0,
         ],
     ],
     'client_options' => [
         // Guzzle client options: https://docs.guzzlephp.org/en/stable/request-options.html
         'verify' => false,
     ],
 ],

And how to use guzzle in controller?

Had something similar when running on https. Got a workaround. Basically tell Guzzlehttp to use https on localhost and ignore ssl

set PUSHER_APP_SCHEME=https
set in broadcasting.php 'client_options' => ['verify' => false,]
eg:
'connections' => [ 'pusher' => [
     'driver' => 'pusher',
     'key' => env('PUSHER_APP_KEY'),
     'secret' => env('PUSHER_APP_SECRET'),
     'app_id' => env('PUSHER_APP_ID'),
     'options' => [
         'cluster' => env('PUSHER_APP_CLUSTER'),
         'encrypted' => env('PUSHER_APP_ENCRYPTED', true),
         'host' => env('PUSHER_APP_HOST', '<IP_ADDRESS>'),
         'port' => env('PUSHER_APP_PORT', 6001),
         'scheme' => env('PUSHER_APP_SCHEME', 'http'),
         'useTLS' => env('PUSHER_APP_SCHEME', 'http') === 'https',
         'curl_options' => [
             CURLOPT_SSL_VERIFYHOST => 0,
             CURLOPT_SSL_VERIFYPEER => 0,
         ],
     ],
     'client_options' => [
         // Guzzle client options: https://docs.guzzlephp.org/en/stable/request-options.html
         'verify' => false,
     ],
 ],

And how to use guzzle in controller?

This is only a setting change. Everything else works as instructed in websocket instructions.

I have same error but above soultion is not working

got the same only on vps
Pusher error: cURL error 52: Empty reply from server
and
Pusher error: cURL error 52: Send failure: Broken pipe

https://github.com/beyondcode/laravel-websockets/issues/879#issuecomment-1528775633
I was struggling from week and at last above solution worked for me.
@bubaololo you can try the same.


Improve the EI documentation on how to configure secured proxy services

Description
How to configure secure proxy services is not mentioned in the Working with Proxy Servers section of the official documentation [1]. Anyway this has been documented here [2].
Wouldn’t it be better to add a reference here [1] as well, to avoid the confusions ?
[1] https://ei.docs.wso2.com/en/latest/micro-integrator/setup/configuring_proxy_servers/
[2] https://apim.docs.wso2.com/en/latest/reference/config-catalog-mi/#http-secured-proxy-profile:~:text=secured proxy profile-,¶,-[[transport.http.secured_proxy_profile
Steps to reproduce

The steps to be followed to reproduce the issue.

Affected version(s)

The versions that are affected by the issue.

Related issue(s) (optional)

Any related issues such as sub tasks and issues reported in other repositories (e.g., component repositories), similar problems, etc.

Suggested label(s) (optional)

Optional comma-separated list of suggested labels. Non committers can’t assign labels to issues, and thereby, this will help issue creators who are not a committer to suggest possible labels.

Suggested assignee(s) (optional)

Optional comma-separated list of suggested team members who should attend the issue. Non committers can’t assign issues to assignees, and thereby, this will help issue creators who are not a committer to suggest possible assignees.

This is available in the MI documentation. We need to add this to the EI documentation.

Do we still maintain EI Documentation?

No, if this is there in MI docs, we can close this.


Modify Type Definitions and improve env example clarity

I have updated the type definitions for channel, message_type and pin_type for better intellisense.
I have also added something to the env.example file to make it more clear

These changes are definitely more type safe.
I'll go ahead and merge them.


Updated SUPPORTED.md with new configuration

Added MSI GS63VR 7RF Stealth Pro running Ubuntu 18.04 LTS with libusb 2:1.0.21-2. Kernel version 4.15.0-20-generic.

Thanks!


Move from Bolt.new to Local Version

Is your feature request related to a problem? Please describe:
Many of us will find this version, when our token is finished.
Describe the solution you'd like:
There should be a clear and easy transition option, where we can download our bolt.new project, and continue working on local version which is created by you.
Either we can upload the project on github, and then download from github to local version to continue.

#27


New rule proposal: Check if this.refs.someRef properties are actually defined

I had a few occasions myself that some of my components had typo's or missing reference attributes in components causing crashes.
const Hello = React.createClass({
  onFocus() {
    const scrollResponder = this.refs.scrollView.getScrollResponder();
    ...
  }

  render() {
     return <ScrollList />
  }
});

Just a heads up... I remember reading (but can't seem to find) a mention that this.refs is eventually going to be phased out by the React team. I believe the reasoning is that ref= is basically an escape hatch that breaks encapsulation and its use should be minimized. To make the code simpler, they'll phase out ref="thing" going into this.refs.thing and instead only support the function-based ref={c => this.thing = c} style. I can't seem to find the github issue where this was discussed, but I remember reading it in the past few months.
A better rule might be to disallow use of this.refs.

Good point, I'll do some research and if I come across the discussion you mentioned, I'll reference it here.

"React also supports using a string (instead of a callback) as a ref prop on any component, although this approach is mostly legacy at this point." https://facebook.github.io/react/docs/more-about-refs.html so indeed a rule to prevent using this.refs would make more sense.


Resource handling

We started to work on resource handling.  A very first idea is here: https://github.com/Open-CMSIS-Pack/devtools/blob/main/tools/projmgr/docs/Manual/Overview.md#cmsis-zone-integration

Great, rather than doing something specific for CMSIS-Zone can we say that we open the door for a solution (rather than a project because to me we are talking here about system configuration) to have several system configuration files ?

resources partitioning => CMSIS-Zone
system clock => vendor dependent : no format defined
pinout => vendor dependent : no format defined

But at least we have a placeholder to attach these settings to a solution.

The documentation is now reworked and the resource handling is here https://github.com/Open-CMSIS-Pack/devtools/blob/main/tools/projmgr/docs/Manual/YML-Format.md#resource-management
It is not completely spelled out yet, but closer to what we need finally.  When time permits we can discuss it today.

rzone file format: no strong opinion about YML or JSON but we have some missing items to suggest
Handlebars : this is a "YES" for me
Feeding Handlebars with JSON is easier in my opinion because you "see" the data object processed by the templating engine directly

Even when the topic RZone has not been addressed yet, I'm closing this issue.
I believe we need to review resource management later this year in context with enhancements to linker script handling and the MPU support that is now available with CMSIS version 6 and the RTX implementation.


remove Deploying your own instance with terraform link in README

Describe the bug
In the README.md, line 256 has a link for "Deploying your own instance with terraform" that does not go anywhere within the README.
To Reproduce
Steps to reproduce the behavior:

Go to 'README.md'
Scroll down to line 254
Alternatively, you can jump to section: Running the Frontend Server in your own GCP project

Expected behavior
The link should anchor the README to the corresponding header, but it could have been deleted at some point.
Screenshots

Device (please complete the following information):

Device: Macbook Pro
OS: Ventura 13.4.1
Browser: Firefox
Version 115.0.2

@kccrtv it looks like in our code it used a capital D in the word deploying-, but for the link to work it needs to be lower-cased. i confirmed that this link is working https://github.com/SatcherInstitute/health-equity-tracker#deploying-your-own-instance-with-terraform
Want to make your first PR and make this fix? 🟩 🟩 🟩 🟩


The experimental results of ETTH1 cannot be reproduced

Hi thank you very much for your work. When training the ETTH1 dataset, with the same hyperparameters as yours, the mse and mae I get are 0.3594 and 0.3923, respectively, which is a bit large compared to the data 0.311 and 0.348 in your paper, can you tell me if it is still There are parameters that need to be modified, thank you! Below is my log.
Args in experiment:
Namespace(INN=1, RIN=False, batch_size=4, c_out=7, checkpoints='exp/ETT_checkpoints/', cols=None, concat_len=0, data='ETTh1', data_path='ETTh1.csv', dec_in=7, detail_freq='h', devices='0', dilation=1, dropout=0.5, embed='timeF', enc_in=7, evaluate=False, features='M', freq='h', gpu=0, groups=1, hidden_size=4.0, inverse=False, itr=0, kernel=5, label_len=24, lastWeight=1.0, levels=3, loss='mae', lr=0.009, lradj=1, model='SCINet', model_name='etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3', num_workers=0, patience=15, positionalEcoding=False, pred_len=24, resume=False, root_path='./datasets/ETT-small/', save=False, seq_len=48, single_step=0, single_step_output_One=0, stacks=1, target='OT', train_epochs=100, use_amp=False, use_gpu=True, use_multi_gpu=False, window_size=12)
SCINet(
(blocks1): EncoderTree(
(SCINet_Tree): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(7, 28, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(28, 7, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
)
)
)
(projection1): Conv1d(48, 24, kernel_size=(1,), stride=(1,), bias=False)
)

start training : SCINet_ETTh1_ftM_sl48_ll24_pl24_lr0.009_bs4_hid4.0_s1_l3_dp0.5_invFalse_itr0>>>>>>>>>>>>>>>>>>>>>>>>>>
train 8569
val 2857
test 2857
exp/ETT_checkpoints/SCINet_ETTh1_ftM_sl48_ll24_pl24_lr0.009_bs4_hid4.0_s1_l3_dp0.5_invFalse_itr0
iters: 100, epoch: 1 | loss: 0.3651017
speed: 0.0295s/iter; left time: 6318.7703s
iters: 200, epoch: 1 | loss: 0.3505008
speed: 0.0279s/iter; left time: 5976.3270s
iters: 300, epoch: 1 | loss: 0.3929793
speed: 0.0279s/iter; left time: 5962.0694s
iters: 400, epoch: 1 | loss: 0.3301361
speed: 0.0279s/iter; left time: 5966.3788s
iters: 500, epoch: 1 | loss: 0.3228126
speed: 0.0282s/iter; left time: 6027.5348s
iters: 600, epoch: 1 | loss: 0.3718488
speed: 0.0283s/iter; left time: 6044.8674s
iters: 700, epoch: 1 | loss: 0.3676443
speed: 0.0282s/iter; left time: 6011.5804s
iters: 800, epoch: 1 | loss: 0.3627875
speed: 0.0282s/iter; left time: 6025.8960s
iters: 900, epoch: 1 | loss: 0.4119731
speed: 0.0279s/iter; left time: 5941.4842s
iters: 1000, epoch: 1 | loss: 0.3445457
speed: 0.0278s/iter; left time: 5927.6358s
iters: 1100, epoch: 1 | loss: 0.3446910
speed: 0.0279s/iter; left time: 5936.7592s
iters: 1200, epoch: 1 | loss: 0.2979367
speed: 0.0277s/iter; left time: 5891.3081s
iters: 1300, epoch: 1 | loss: 0.3462140
speed: 0.0275s/iter; left time: 5860.2575s
iters: 1400, epoch: 1 | loss: 0.5417344
speed: 0.0276s/iter; left time: 5875.8505s
iters: 1500, epoch: 1 | loss: 0.4214476
speed: 0.0278s/iter; left time: 5909.9664s
iters: 1600, epoch: 1 | loss: 0.2875453
speed: 0.0278s/iter; left time: 5902.4232s
iters: 1700, epoch: 1 | loss: 0.4202584
speed: 0.0278s/iter; left time: 5899.5329s
iters: 1800, epoch: 1 | loss: 0.3184803
speed: 0.0278s/iter; left time: 5903.0943s
iters: 1900, epoch: 1 | loss: 0.4054892
speed: 0.0278s/iter; left time: 5910.5245s
iters: 2000, epoch: 1 | loss: 0.3641047
speed: 0.0279s/iter; left time: 5925.2561s
iters: 2100, epoch: 1 | loss: 0.2887170
speed: 0.0284s/iter; left time: 6015.2713s
Epoch: 1 cost time: 59.9717230796814
--------start to validate-----------
normed mse:0.4301, mae:0.4408, rmse:0.6558, mape:6.1262, mspe:25663.3134, corr:0.8366
denormed mse:7.0964, mae:1.5289, rmse:2.6639, mape:inf, mspe:inf, corr:0.8366
--------start to test-----------
normed mse:0.3644, mae:0.4027, rmse:0.6037, mape:8.4118, mspe:29899.2508, corr:0.7235
denormed mse:7.4835, mae:1.4891, rmse:2.7356, mape:inf, mspe:inf, corr:0.7235
Epoch: 1, Steps: 2142 | Train Loss: 0.3791955 valid Loss: 0.4408401 Test Loss: 0.4026500
Validation loss decreased (inf --> 0.440840).  Saving model ...
Updating learning rate to 0.008549999999999999
iters: 100, epoch: 2 | loss: 0.4033299
speed: 0.1533s/iter; left time: 32495.7200s
iters: 200, epoch: 2 | loss: 0.4220750
speed: 0.0276s/iter; left time: 5842.5447s
iters: 300, epoch: 2 | loss: 0.3310422
speed: 0.0275s/iter; left time: 5828.7353s
iters: 400, epoch: 2 | loss: 0.3571222
speed: 0.0277s/iter; left time: 5853.6155s
iters: 500, epoch: 2 | loss: 0.3545492
speed: 0.0280s/iter; left time: 5915.4474s
iters: 600, epoch: 2 | loss: 0.2830701
speed: 0.0280s/iter; left time: 5922.0331s
iters: 700, epoch: 2 | loss: 0.3858727
speed: 0.0280s/iter; left time: 5917.8075s
iters: 800, epoch: 2 | loss: 0.3042993
speed: 0.0280s/iter; left time: 5922.7572s
iters: 900, epoch: 2 | loss: 0.3451869
speed: 0.0278s/iter; left time: 5873.5665s
iters: 1000, epoch: 2 | loss: 0.2723933
speed: 0.0277s/iter; left time: 5853.1839s
iters: 1100, epoch: 2 | loss: 0.4236742
speed: 0.0275s/iter; left time: 5806.4575s
iters: 1200, epoch: 2 | loss: 0.4081782
speed: 0.0276s/iter; left time: 5812.7159s
iters: 1300, epoch: 2 | loss: 0.3291027
speed: 0.0278s/iter; left time: 5868.3560s
iters: 1400, epoch: 2 | loss: 0.4038144
speed: 0.0280s/iter; left time: 5888.6294s
iters: 1500, epoch: 2 | loss: 0.3101422
speed: 0.0279s/iter; left time: 5884.1583s
iters: 1600, epoch: 2 | loss: 0.3712929
speed: 0.0279s/iter; left time: 5879.8941s
iters: 1700, epoch: 2 | loss: 0.3459564
speed: 0.0281s/iter; left time: 5904.1190s
iters: 1800, epoch: 2 | loss: 0.3104483
speed: 0.0281s/iter; left time: 5911.8671s
iters: 1900, epoch: 2 | loss: 0.3965688
speed: 0.0275s/iter; left time: 5785.9043s
iters: 2000, epoch: 2 | loss: 0.4188831
speed: 0.0275s/iter; left time: 5772.6210s
iters: 2100, epoch: 2 | loss: 0.3976253
speed: 0.0275s/iter; left time: 5780.8356s
Epoch: 2 cost time: 59.54820132255554
--------start to validate-----------
normed mse:0.4192, mae:0.4341, rmse:0.6475, mape:5.8769, mspe:24098.6398, corr:0.8388
denormed mse:6.6228, mae:1.4725, rmse:2.5735, mape:inf, mspe:inf, corr:0.8388
--------start to test-----------
normed mse:0.3648, mae:0.4016, rmse:0.6040, mape:7.6375, mspe:23761.7005, corr:0.7234
denormed mse:6.9325, mae:1.4331, rmse:2.6330, mape:inf, mspe:inf, corr:0.7234
Epoch: 2, Steps: 2142 | Train Loss: 0.3706865 valid Loss: 0.4340943 Test Loss: 0.4016285
Validation loss decreased (0.440840 --> 0.434094).  Saving model ...
Updating learning rate to 0.0081225
iters: 100, epoch: 3 | loss: 0.3006243
speed: 0.1515s/iter; left time: 31777.6295s
iters: 200, epoch: 3 | loss: 0.3596947
speed: 0.0278s/iter; left time: 5825.7000s
iters: 300, epoch: 3 | loss: 0.2566329
speed: 0.0276s/iter; left time: 5794.7708s
iters: 400, epoch: 3 | loss: 0.3511219
speed: 0.0276s/iter; left time: 5783.3121s
iters: 500, epoch: 3 | loss: 0.3138544
speed: 0.0278s/iter; left time: 5827.6712s
iters: 600, epoch: 3 | loss: 0.2947013
speed: 0.0285s/iter; left time: 5969.1809s
iters: 700, epoch: 3 | loss: 0.3902800
speed: 0.0284s/iter; left time: 5944.6223s
iters: 800, epoch: 3 | loss: 0.3481478
speed: 0.0278s/iter; left time: 5813.9536s
iters: 900, epoch: 3 | loss: 0.4417644
speed: 0.0278s/iter; left time: 5802.4645s
iters: 1000, epoch: 3 | loss: 0.4901435
speed: 0.0278s/iter; left time: 5805.0026s
iters: 1100, epoch: 3 | loss: 0.3575970
speed: 0.0278s/iter; left time: 5809.4056s
iters: 1200, epoch: 3 | loss: 0.3082275
speed: 0.0278s/iter; left time: 5800.3406s
iters: 1300, epoch: 3 | loss: 0.3474898
speed: 0.0278s/iter; left time: 5796.6274s
iters: 1400, epoch: 3 | loss: 0.3873351
speed: 0.0278s/iter; left time: 5801.9911s
iters: 1500, epoch: 3 | loss: 0.3985312
speed: 0.0278s/iter; left time: 5796.4244s
iters: 1600, epoch: 3 | loss: 0.3473285
speed: 0.0279s/iter; left time: 5813.9807s
iters: 1700, epoch: 3 | loss: 0.2953809
speed: 0.0280s/iter; left time: 5837.9339s
iters: 1800, epoch: 3 | loss: 0.2939656
speed: 0.0280s/iter; left time: 5836.6911s
iters: 1900, epoch: 3 | loss: 0.3971220
speed: 0.0280s/iter; left time: 5832.4716s
iters: 2000, epoch: 3 | loss: 0.3230156
speed: 0.0280s/iter; left time: 5817.5040s
iters: 2100, epoch: 3 | loss: 0.3292217
speed: 0.0280s/iter; left time: 5815.1257s
Epoch: 3 cost time: 59.7959942817688
--------start to validate-----------
normed mse:0.4106, mae:0.4224, rmse:0.6408, mape:5.8930, mspe:23804.0522, corr:0.8409
denormed mse:6.5745, mae:1.4459, rmse:2.5641, mape:inf, mspe:inf, corr:0.8409
--------start to test-----------
normed mse:0.3594, mae:0.3923, rmse:0.5995, mape:7.5035, mspe:24301.3721, corr:0.7205
denormed mse:6.9649, mae:1.4154, rmse:2.6391, mape:inf, mspe:inf, corr:0.7205
Epoch: 3, Steps: 2142 | Train Loss: 0.3675621 valid Loss: 0.4224180 Test Loss: 0.3923225
Validation loss decreased (0.434094 --> 0.422418).  Saving model ...
Updating learning rate to 0.007716374999999998
iters: 100, epoch: 4 | loss: 0.4576252
speed: 0.1493s/iter; left time: 30998.9868s
iters: 200, epoch: 4 | loss: 0.4031994
speed: 0.0281s/iter; left time: 5830.8540s
iters: 300, epoch: 4 | loss: 0.4278273
speed: 0.0286s/iter; left time: 5938.6052s
iters: 400, epoch: 4 | loss: 0.3313950
speed: 0.0280s/iter; left time: 5805.4616s
iters: 500, epoch: 4 | loss: 0.2830721
speed: 0.0288s/iter; left time: 5969.7668s
iters: 600, epoch: 4 | loss: 0.3394617
speed: 0.0287s/iter; left time: 5953.4050s
iters: 700, epoch: 4 | loss: 0.3362619
speed: 0.0291s/iter; left time: 6016.4815s
iters: 800, epoch: 4 | loss: 0.2766969
speed: 0.0287s/iter; left time: 5939.6163s
iters: 900, epoch: 4 | loss: 0.3397340
speed: 0.0290s/iter; left time: 5996.2590s
iters: 1000, epoch: 4 | loss: 0.3242672
speed: 0.0282s/iter; left time: 5828.7358s
iters: 1100, epoch: 4 | loss: 0.3496055
speed: 0.0284s/iter; left time: 5871.3363s
iters: 1200, epoch: 4 | loss: 0.3492430
speed: 0.0283s/iter; left time: 5851.4826s
iters: 1300, epoch: 4 | loss: 0.3514314
speed: 0.0280s/iter; left time: 5772.2568s
iters: 1400, epoch: 4 | loss: 0.2979621
speed: 0.0294s/iter; left time: 6071.8731s
iters: 1500, epoch: 4 | loss: 0.3437521
speed: 0.0289s/iter; left time: 5961.6856s
iters: 1600, epoch: 4 | loss: 0.2574955
speed: 0.0282s/iter; left time: 5816.3523s
iters: 1700, epoch: 4 | loss: 0.3036486
speed: 0.0276s/iter; left time: 5679.0905s
iters: 1800, epoch: 4 | loss: 0.3060672
speed: 0.0289s/iter; left time: 5955.5722s
iters: 1900, epoch: 4 | loss: 0.3665306
speed: 0.0283s/iter; left time: 5833.5155s
iters: 2000, epoch: 4 | loss: 0.3999368
speed: 0.0280s/iter; left time: 5760.6245s
iters: 2100, epoch: 4 | loss: 0.3088425
speed: 0.0280s/iter; left time: 5756.6908s
Epoch: 4 cost time: 60.87839937210083
--------start to validate-----------
normed mse:0.4124, mae:0.4279, rmse:0.6422, mape:5.9884, mspe:24114.0258, corr:0.8404
denormed mse:6.6824, mae:1.4731, rmse:2.5850, mape:inf, mspe:inf, corr:0.8404
--------start to test-----------
normed mse:0.3601, mae:0.3963, rmse:0.6001, mape:8.4487, mspe:28741.5909, corr:0.7119
denormed mse:7.3766, mae:1.4553, rmse:2.7160, mape:inf, mspe:inf, corr:0.7119
Epoch: 4, Steps: 2142 | Train Loss: 0.3661055 valid Loss: 0.4279180 Test Loss: 0.3962910
EarlyStopping counter: 1 out of 15
Updating learning rate to 0.007330556249999998
iters: 100, epoch: 5 | loss: 0.3216497
speed: 0.1513s/iter; left time: 31094.1250s
iters: 200, epoch: 5 | loss: 0.3528404
speed: 0.0277s/iter; left time: 5697.3340s
iters: 300, epoch: 5 | loss: 0.3661552
speed: 0.0283s/iter; left time: 5803.2182s
iters: 400, epoch: 5 | loss: 0.3049476
speed: 0.0284s/iter; left time: 5837.1130s
iters: 500, epoch: 5 | loss: 0.4457057
speed: 0.0287s/iter; left time: 5893.7462s
iters: 600, epoch: 5 | loss: 0.3698492
speed: 0.0292s/iter; left time: 5985.3711s
iters: 700, epoch: 5 | loss: 0.3416702
speed: 0.0286s/iter; left time: 5867.2302s
iters: 800, epoch: 5 | loss: 0.3614202
speed: 0.0292s/iter; left time: 5972.0056s
iters: 900, epoch: 5 | loss: 0.3991942
speed: 0.0286s/iter; left time: 5851.8310s
iters: 1000, epoch: 5 | loss: 0.2818972
speed: 0.0281s/iter; left time: 5748.2781s
iters: 1100, epoch: 5 | loss: 0.3370062
speed: 0.0281s/iter; left time: 5757.3880s
iters: 1200, epoch: 5 | loss: 0.3217392
speed: 0.0289s/iter; left time: 5908.1417s
iters: 1300, epoch: 5 | loss: 0.2573744
speed: 0.0295s/iter; left time: 6031.2498s
iters: 1400, epoch: 5 | loss: 0.4832221
speed: 0.0283s/iter; left time: 5770.3668s
iters: 1500, epoch: 5 | loss: 0.3038020
speed: 0.0281s/iter; left time: 5735.7644s
iters: 1600, epoch: 5 | loss: 0.4502504
speed: 0.0285s/iter; left time: 5810.1176s
iters: 1700, epoch: 5 | loss: 0.3126244
speed: 0.0289s/iter; left time: 5901.8960s
iters: 1800, epoch: 5 | loss: 0.3371652
speed: 0.0291s/iter; left time: 5924.0287s
iters: 1900, epoch: 5 | loss: 0.3134840
speed: 0.0287s/iter; left time: 5837.1210s
iters: 2000, epoch: 5 | loss: 0.3738162
speed: 0.0286s/iter; left time: 5818.7893s
iters: 2100, epoch: 5 | loss: 0.4856545
speed: 0.0282s/iter; left time: 5739.0360s
Epoch: 5 cost time: 61.17092418670654
--------start to validate-----------
normed mse:0.4397, mae:0.4497, rmse:0.6631, mape:5.5498, mspe:20281.5202, corr:0.8386
denormed mse:6.8925, mae:1.5190, rmse:2.6254, mape:inf, mspe:inf, corr:0.8386
--------start to test-----------
normed mse:0.4051, mae:0.4371, rmse:0.6365, mape:7.2726, mspe:20975.8068, corr:0.7116
denormed mse:8.7119, mae:1.6950, rmse:2.9516, mape:inf, mspe:inf, corr:0.7116
Epoch: 5, Steps: 2142 | Train Loss: 0.3653860 valid Loss: 0.4497384 Test Loss: 0.4371418
EarlyStopping counter: 2 out of 15
Updating learning rate to 0.006964028437499998
iters: 100, epoch: 6 | loss: 0.4057341
speed: 0.1540s/iter; left time: 31326.4922s
iters: 200, epoch: 6 | loss: 0.4183176
speed: 0.0283s/iter; left time: 5756.8585s
iters: 300, epoch: 6 | loss: 0.3801117
speed: 0.0279s/iter; left time: 5662.7553s
iters: 400, epoch: 6 | loss: 0.3694860
speed: 0.0283s/iter; left time: 5748.9176s
iters: 500, epoch: 6 | loss: 0.4518154
speed: 0.0295s/iter; left time: 5995.1529s
iters: 600, epoch: 6 | loss: 0.3504744
speed: 0.0284s/iter; left time: 5764.1939s
iters: 700, epoch: 6 | loss: 0.3283683
speed: 0.0277s/iter; left time: 5621.3028s
iters: 800, epoch: 6 | loss: 0.3374281
speed: 0.0282s/iter; left time: 5724.4058s
iters: 900, epoch: 6 | loss: 0.4623635
speed: 0.0289s/iter; left time: 5860.4811s
iters: 1000, epoch: 6 | loss: 0.4638884
speed: 0.0292s/iter; left time: 5912.6509s
iters: 1100, epoch: 6 | loss: 0.3457366
speed: 0.0294s/iter; left time: 5960.2961s
iters: 1200, epoch: 6 | loss: 0.3443884
speed: 0.0295s/iter; left time: 5971.9475s
iters: 1300, epoch: 6 | loss: 0.3005659
speed: 0.0295s/iter; left time: 5963.8961s
iters: 1400, epoch: 6 | loss: 0.3177886
speed: 0.0295s/iter; left time: 5965.3368s
iters: 1500, epoch: 6 | loss: 0.4176153
speed: 0.0291s/iter; left time: 5885.1216s
iters: 1600, epoch: 6 | loss: 0.3041800
speed: 0.0283s/iter; left time: 5714.1515s
iters: 1700, epoch: 6 | loss: 0.4047523
speed: 0.0283s/iter; left time: 5708.8059s
iters: 1800, epoch: 6 | loss: 0.4489512
speed: 0.0283s/iter; left time: 5717.4653s
iters: 1900, epoch: 6 | loss: 0.3522519
speed: 0.0282s/iter; left time: 5684.8170s
iters: 2000, epoch: 6 | loss: 0.2812566
speed: 0.0289s/iter; left time: 5819.9594s
iters: 2100, epoch: 6 | loss: 0.2865458
speed: 0.0279s/iter; left time: 5614.1825s
Epoch: 6 cost time: 61.42661213874817
--------start to validate-----------
normed mse:0.4341, mae:0.4399, rmse:0.6588, mape:6.3993, mspe:29826.0129, corr:0.8372
denormed mse:7.1085, mae:1.5216, rmse:2.6662, mape:inf, mspe:inf, corr:0.8372
--------start to test-----------
normed mse:0.3834, mae:0.4100, rmse:0.6192, mape:7.4289, mspe:23121.1416, corr:0.7140
denormed mse:7.5249, mae:1.4937, rmse:2.7431, mape:inf, mspe:inf, corr:0.7140
Epoch: 6, Steps: 2142 | Train Loss: 0.3628683 valid Loss: 0.4399267 Test Loss: 0.4100347
EarlyStopping counter: 3 out of 15
Updating learning rate to 0.006615827015624997
iters: 100, epoch: 7 | loss: 0.2912202
speed: 0.1505s/iter; left time: 30285.9419s
iters: 200, epoch: 7 | loss: 0.4448686
speed: 0.0276s/iter; left time: 5547.5923s
iters: 300, epoch: 7 | loss: 0.3186685
speed: 0.0276s/iter; left time: 5557.9615s
iters: 400, epoch: 7 | loss: 0.3669732
speed: 0.0276s/iter; left time: 5550.2082s
iters: 500, epoch: 7 | loss: 0.3279809
speed: 0.0278s/iter; left time: 5585.8188s
iters: 600, epoch: 7 | loss: 0.3229183
speed: 0.0277s/iter; left time: 5565.8671s
iters: 700, epoch: 7 | loss: 0.3569002
speed: 0.0280s/iter; left time: 5610.5575s
iters: 800, epoch: 7 | loss: 0.2938167
speed: 0.0280s/iter; left time: 5606.9675s
iters: 900, epoch: 7 | loss: 0.3984652
speed: 0.0284s/iter; left time: 5685.5962s
iters: 1000, epoch: 7 | loss: 0.3540559
speed: 0.0282s/iter; left time: 5648.0905s
iters: 1100, epoch: 7 | loss: 0.3409511
speed: 0.0283s/iter; left time: 5663.9862s
iters: 1200, epoch: 7 | loss: 0.4427322
speed: 0.0282s/iter; left time: 5638.7087s
iters: 1300, epoch: 7 | loss: 0.2954441
speed: 0.0282s/iter; left time: 5640.2222s
iters: 1400, epoch: 7 | loss: 0.3650829
speed: 0.0282s/iter; left time: 5638.8868s
iters: 1500, epoch: 7 | loss: 0.3450136
speed: 0.0282s/iter; left time: 5637.7205s
iters: 1600, epoch: 7 | loss: 0.2694797
speed: 0.0282s/iter; left time: 5632.1659s
iters: 1700, epoch: 7 | loss: 0.2669967
speed: 0.0282s/iter; left time: 5629.5071s
iters: 1800, epoch: 7 | loss: 0.3467433
speed: 0.0282s/iter; left time: 5630.7933s
iters: 1900, epoch: 7 | loss: 0.3424510
speed: 0.0279s/iter; left time: 5556.7276s
iters: 2000, epoch: 7 | loss: 0.3744892
speed: 0.0279s/iter; left time: 5559.5775s
iters: 2100, epoch: 7 | loss: 0.3527800
speed: 0.0278s/iter; left time: 5548.8207s
Epoch: 7 cost time: 59.94628930091858
--------start to validate-----------
normed mse:0.4303, mae:0.4424, rmse:0.6560, mape:5.9782, mspe:24160.5354, corr:0.8376
denormed mse:7.0387, mae:1.5203, rmse:2.6531, mape:inf, mspe:inf, corr:0.8376
--------start to test-----------
normed mse:0.3878, mae:0.4161, rmse:0.6227, mape:7.4371, mspe:22721.2904, corr:0.7129
denormed mse:7.5770, mae:1.5209, rmse:2.7526, mape:inf, mspe:inf, corr:0.7129
Epoch: 7, Steps: 2142 | Train Loss: 0.3609556 valid Loss: 0.4423997 Test Loss: 0.4160987
EarlyStopping counter: 4 out of 15
Updating learning rate to 0.006285035664843747
iters: 100, epoch: 8 | loss: 0.3686014
speed: 0.1510s/iter; left time: 30068.4973s
iters: 200, epoch: 8 | loss: 0.3988787
speed: 0.0279s/iter; left time: 5556.8577s
iters: 300, epoch: 8 | loss: 0.2740081
speed: 0.0279s/iter; left time: 5549.9116s
iters: 400, epoch: 8 | loss: 0.2599104
speed: 0.0277s/iter; left time: 5513.6021s
iters: 500, epoch: 8 | loss: 0.3056968
speed: 0.0277s/iter; left time: 5509.8173s
iters: 600, epoch: 8 | loss: 0.4316751
speed: 0.0279s/iter; left time: 5533.8005s
iters: 700, epoch: 8 | loss: 0.3636506
speed: 0.0280s/iter; left time: 5548.2945s
iters: 800, epoch: 8 | loss: 0.3442728
speed: 0.0279s/iter; left time: 5543.6797s
iters: 900, epoch: 8 | loss: 0.3701980
speed: 0.0278s/iter; left time: 5512.7161s
iters: 1000, epoch: 8 | loss: 0.4252179
speed: 0.0280s/iter; left time: 5541.9490s
iters: 1100, epoch: 8 | loss: 0.3149583
speed: 0.0277s/iter; left time: 5481.9597s
iters: 1200, epoch: 8 | loss: 0.3407968
speed: 0.0276s/iter; left time: 5473.4407s
iters: 1300, epoch: 8 | loss: 0.3287254
speed: 0.0275s/iter; left time: 5448.2962s
iters: 1400, epoch: 8 | loss: 0.2965896
speed: 0.0275s/iter; left time: 5445.9206s
iters: 1500, epoch: 8 | loss: 0.3934681
speed: 0.0275s/iter; left time: 5443.9127s
iters: 1600, epoch: 8 | loss: 0.4411677
speed: 0.0277s/iter; left time: 5470.0075s
iters: 1700, epoch: 8 | loss: 0.4214006
speed: 0.0276s/iter; left time: 5455.5593s
iters: 1800, epoch: 8 | loss: 0.2960534
speed: 0.0275s/iter; left time: 5427.0669s
iters: 1900, epoch: 8 | loss: 0.3062829
speed: 0.0274s/iter; left time: 5415.3308s
iters: 2000, epoch: 8 | loss: 0.3225315
speed: 0.0275s/iter; left time: 5427.1049s
iters: 2100, epoch: 8 | loss: 0.3319520
speed: 0.0275s/iter; left time: 5426.2641s
Epoch: 8 cost time: 59.34956979751587
--------start to validate-----------
normed mse:0.4230, mae:0.4322, rmse:0.6504, mape:6.2235, mspe:25939.0239, corr:0.8386
denormed mse:6.7046, mae:1.4756, rmse:2.5893, mape:inf, mspe:inf, corr:0.8386
--------start to test-----------
normed mse:0.3464, mae:0.3819, rmse:0.5885, mape:8.2333, mspe:31089.5456, corr:0.7259
denormed mse:6.9707, mae:1.3995, rmse:2.6402, mape:inf, mspe:inf, corr:0.7259
Epoch: 8, Steps: 2142 | Train Loss: 0.3605087 valid Loss: 0.4322181 Test Loss: 0.3819402
EarlyStopping counter: 5 out of 15
Updating learning rate to 0.0059707838816015595
iters: 100, epoch: 9 | loss: 0.4236137
speed: 0.1485s/iter; left time: 29246.6871s
iters: 200, epoch: 9 | loss: 0.3181653
speed: 0.0280s/iter; left time: 5515.5740s
iters: 300, epoch: 9 | loss: 0.3890515
speed: 0.0278s/iter; left time: 5469.9286s
iters: 400, epoch: 9 | loss: 0.3365726
speed: 0.0279s/iter; left time: 5485.1154s
iters: 500, epoch: 9 | loss: 0.3757693
speed: 0.0278s/iter; left time: 5455.4438s
iters: 600, epoch: 9 | loss: 0.3484147
speed: 0.0278s/iter; left time: 5453.4754s
iters: 700, epoch: 9 | loss: 0.4239825
speed: 0.0278s/iter; left time: 5460.2592s
iters: 800, epoch: 9 | loss: 0.2945142
speed: 0.0278s/iter; left time: 5457.7710s
iters: 900, epoch: 9 | loss: 0.4924962
speed: 0.0278s/iter; left time: 5456.6360s
iters: 1000, epoch: 9 | loss: 0.2940640
speed: 0.0279s/iter; left time: 5467.9393s
iters: 1100, epoch: 9 | loss: 0.3161500
speed: 0.0279s/iter; left time: 5475.9595s
iters: 1200, epoch: 9 | loss: 0.3189687
speed: 0.0278s/iter; left time: 5446.5077s
iters: 1300, epoch: 9 | loss: 0.3449011
speed: 0.0281s/iter; left time: 5510.5618s
iters: 1400, epoch: 9 | loss: 0.3816228
speed: 0.0283s/iter; left time: 5529.3776s
iters: 1500, epoch: 9 | loss: 0.4140366
speed: 0.0282s/iter; left time: 5518.1599s
iters: 1600, epoch: 9 | loss: 0.3251882
speed: 0.0282s/iter; left time: 5515.2035s
iters: 1700, epoch: 9 | loss: 0.3331461
speed: 0.0280s/iter; left time: 5475.0170s
iters: 1800, epoch: 9 | loss: 0.4283416
speed: 0.0278s/iter; left time: 5429.8488s
iters: 1900, epoch: 9 | loss: 0.3325794
speed: 0.0278s/iter; left time: 5420.5551s
iters: 2000, epoch: 9 | loss: 0.3564896
speed: 0.0278s/iter; left time: 5425.4002s
iters: 2100, epoch: 9 | loss: 0.3499347
speed: 0.0278s/iter; left time: 5425.6226s
Epoch: 9 cost time: 59.779510259628296
--------start to validate-----------
normed mse:0.4228, mae:0.4387, rmse:0.6502, mape:6.3290, mspe:29452.3138, corr:0.8390
denormed mse:6.6619, mae:1.4925, rmse:2.5811, mape:inf, mspe:inf, corr:0.8390
--------start to test-----------
normed mse:0.3737, mae:0.4073, rmse:0.6113, mape:7.3629, mspe:21858.6990, corr:0.7157
denormed mse:7.2940, mae:1.4876, rmse:2.7007, mape:inf, mspe:inf, corr:0.7157
Epoch: 9, Steps: 2142 | Train Loss: 0.3616719 valid Loss: 0.4386964 Test Loss: 0.4073216
EarlyStopping counter: 6 out of 15
Updating learning rate to 0.0056722446875214815
iters: 100, epoch: 10 | loss: 0.2900885
speed: 0.1486s/iter; left time: 28951.5411s
iters: 200, epoch: 10 | loss: 0.3973311
speed: 0.0275s/iter; left time: 5350.2237s
iters: 300, epoch: 10 | loss: 0.3671376
speed: 0.0276s/iter; left time: 5379.2409s
iters: 400, epoch: 10 | loss: 0.3689273
speed: 0.0280s/iter; left time: 5437.8581s
iters: 500, epoch: 10 | loss: 0.3674505
speed: 0.0280s/iter; left time: 5438.4738s
iters: 600, epoch: 10 | loss: 0.3244604
speed: 0.0278s/iter; left time: 5397.7057s
iters: 700, epoch: 10 | loss: 0.3428321
speed: 0.0279s/iter; left time: 5417.2417s
iters: 800, epoch: 10 | loss: 0.2774915
speed: 0.0276s/iter; left time: 5354.6267s
iters: 900, epoch: 10 | loss: 0.5094953
speed: 0.0276s/iter; left time: 5350.0250s
iters: 1000, epoch: 10 | loss: 0.3412493
speed: 0.0280s/iter; left time: 5430.0755s
iters: 1100, epoch: 10 | loss: 0.3945991
speed: 0.0275s/iter; left time: 5338.2592s
iters: 1200, epoch: 10 | loss: 0.3750900
speed: 0.0275s/iter; left time: 5336.3502s
iters: 1300, epoch: 10 | loss: 0.4605041
speed: 0.0275s/iter; left time: 5332.7286s
iters: 1400, epoch: 10 | loss: 0.4066358
speed: 0.0276s/iter; left time: 5334.1538s
iters: 1500, epoch: 10 | loss: 0.3515254
speed: 0.0275s/iter; left time: 5328.1034s
iters: 1600, epoch: 10 | loss: 0.5100784
speed: 0.0276s/iter; left time: 5332.2505s
iters: 1700, epoch: 10 | loss: 0.4475189
speed: 0.0278s/iter; left time: 5367.5615s
iters: 1800, epoch: 10 | loss: 0.3756194
speed: 0.0280s/iter; left time: 5404.4193s
iters: 1900, epoch: 10 | loss: 0.3614157
speed: 0.0282s/iter; left time: 5435.4784s
iters: 2000, epoch: 10 | loss: 0.4440518
speed: 0.0288s/iter; left time: 5560.7261s
iters: 2100, epoch: 10 | loss: 0.3380460
speed: 0.0285s/iter; left time: 5492.9167s
Epoch: 10 cost time: 59.5790855884552
--------start to validate-----------
normed mse:0.4307, mae:0.4398, rmse:0.6563, mape:6.2669, mspe:29055.0190, corr:0.8386
denormed mse:6.8327, mae:1.5033, rmse:2.6139, mape:inf, mspe:inf, corr:0.8386
--------start to test-----------
normed mse:0.3560, mae:0.3932, rmse:0.5967, mape:7.4114, mspe:24304.7538, corr:0.7213
denormed mse:7.0991, mae:1.4397, rmse:2.6644, mape:inf, mspe:inf, corr:0.7213
Epoch: 10, Steps: 2142 | Train Loss: 0.3579478 valid Loss: 0.4398302 Test Loss: 0.3932088
EarlyStopping counter: 7 out of 15
Updating learning rate to 0.0053886324531454075
iters: 100, epoch: 11 | loss: 0.4010978
speed: 0.1514s/iter; left time: 29166.4668s
iters: 200, epoch: 11 | loss: 0.3511622
speed: 0.0281s/iter; left time: 5411.7574s
iters: 300, epoch: 11 | loss: 0.3938432
speed: 0.0281s/iter; left time: 5401.9186s
iters: 400, epoch: 11 | loss: 0.3107118
speed: 0.0280s/iter; left time: 5395.8313s
iters: 500, epoch: 11 | loss: 0.3301463
speed: 0.0282s/iter; left time: 5427.8730s
iters: 600, epoch: 11 | loss: 0.3875788
speed: 0.0280s/iter; left time: 5389.4144s
iters: 700, epoch: 11 | loss: 0.3090936
speed: 0.0280s/iter; left time: 5383.3654s
iters: 800, epoch: 11 | loss: 0.3182698
speed: 0.0278s/iter; left time: 5345.9382s
iters: 900, epoch: 11 | loss: 0.3655111
speed: 0.0278s/iter; left time: 5331.0441s
iters: 1000, epoch: 11 | loss: 0.2847968
speed: 0.0278s/iter; left time: 5331.1720s
iters: 1100, epoch: 11 | loss: 0.2750012
speed: 0.0282s/iter; left time: 5414.7571s
iters: 1200, epoch: 11 | loss: 0.4303603
speed: 0.0281s/iter; left time: 5391.7382s
iters: 1300, epoch: 11 | loss: 0.2917214
speed: 0.0284s/iter; left time: 5436.3090s
iters: 1400, epoch: 11 | loss: 0.3828793
speed: 0.0280s/iter; left time: 5349.8228s
iters: 1500, epoch: 11 | loss: 0.3677426
speed: 0.0279s/iter; left time: 5340.5779s
iters: 1600, epoch: 11 | loss: 0.2885067
speed: 0.0279s/iter; left time: 5336.3839s
iters: 1700, epoch: 11 | loss: 0.3390763
speed: 0.0280s/iter; left time: 5350.4018s
iters: 1800, epoch: 11 | loss: 0.2992726
speed: 0.0280s/iter; left time: 5343.0944s
iters: 1900, epoch: 11 | loss: 0.3509193
speed: 0.0280s/iter; left time: 5341.6770s
iters: 2000, epoch: 11 | loss: 0.3269296
speed: 0.0280s/iter; left time: 5348.3123s
iters: 2100, epoch: 11 | loss: 0.6610678
speed: 0.0284s/iter; left time: 5410.5713s
Epoch: 11 cost time: 60.09651231765747
--------start to validate-----------
normed mse:0.4126, mae:0.4290, rmse:0.6424, mape:6.2955, mspe:28839.2656, corr:0.8417
denormed mse:6.6323, mae:1.4699, rmse:2.5753, mape:inf, mspe:inf, corr:0.8417
--------start to test-----------
normed mse:0.3609, mae:0.3942, rmse:0.6007, mape:8.2736, mspe:29474.0744, corr:0.7162
denormed mse:6.8909, mae:1.4109, rmse:2.6250, mape:inf, mspe:inf, corr:0.7162
Epoch: 11, Steps: 2142 | Train Loss: 0.3590887 valid Loss: 0.4289519 Test Loss: 0.3941905
EarlyStopping counter: 8 out of 15
Updating learning rate to 0.0051192008304881366
iters: 100, epoch: 12 | loss: 0.3217768
speed: 0.1514s/iter; left time: 28853.4659s
iters: 200, epoch: 12 | loss: 0.3381207
speed: 0.0283s/iter; left time: 5385.6346s
iters: 300, epoch: 12 | loss: 0.3095023
speed: 0.0283s/iter; left time: 5377.6092s
iters: 400, epoch: 12 | loss: 0.3355850
speed: 0.0282s/iter; left time: 5361.3733s
iters: 500, epoch: 12 | loss: 0.4180435
speed: 0.0282s/iter; left time: 5361.3226s
iters: 600, epoch: 12 | loss: 0.2757073
speed: 0.0282s/iter; left time: 5365.0668s
iters: 700, epoch: 12 | loss: 0.3483370
speed: 0.0282s/iter; left time: 5347.4459s
iters: 800, epoch: 12 | loss: 0.2809200
speed: 0.0279s/iter; left time: 5294.3898s
iters: 900, epoch: 12 | loss: 0.3577588
speed: 0.0279s/iter; left time: 5287.8842s
iters: 1000, epoch: 12 | loss: 0.3402998
speed: 0.0285s/iter; left time: 5411.8483s
iters: 1100, epoch: 12 | loss: 0.3226743
speed: 0.0283s/iter; left time: 5354.6314s
iters: 1200, epoch: 12 | loss: 0.3679209
speed: 0.0280s/iter; left time: 5307.4323s
iters: 1300, epoch: 12 | loss: 0.4554799
speed: 0.0280s/iter; left time: 5307.0615s
iters: 1400, epoch: 12 | loss: 0.4203964
speed: 0.0282s/iter; left time: 5334.6971s
iters: 1500, epoch: 12 | loss: 0.3709324
speed: 0.0278s/iter; left time: 5256.8723s
iters: 1600, epoch: 12 | loss: 0.4153709
speed: 0.0282s/iter; left time: 5327.5631s
iters: 1700, epoch: 12 | loss: 0.3395702
speed: 0.0284s/iter; left time: 5367.3621s
iters: 1800, epoch: 12 | loss: 0.3653106
speed: 0.0275s/iter; left time: 5200.3756s
iters: 1900, epoch: 12 | loss: 0.3667592
speed: 0.0276s/iter; left time: 5202.0497s
iters: 2000, epoch: 12 | loss: 0.3408771
speed: 0.0277s/iter; left time: 5229.9565s
iters: 2100, epoch: 12 | loss: 0.3763930
speed: 0.0279s/iter; left time: 5266.1365s
Epoch: 12 cost time: 60.12066292762756
--------start to validate-----------
normed mse:0.4283, mae:0.4382, rmse:0.6545, mape:6.6969, mspe:32954.8826, corr:0.8367
denormed mse:6.7505, mae:1.4951, rmse:2.5982, mape:inf, mspe:inf, corr:0.8367
--------start to test-----------
normed mse:0.3669, mae:0.3961, rmse:0.6057, mape:7.2348, mspe:22615.7119, corr:0.7106
denormed mse:6.8549, mae:1.3993, rmse:2.6182, mape:inf, mspe:inf, corr:0.7106
Epoch: 12, Steps: 2142 | Train Loss: 0.3563409 valid Loss: 0.4382461 Test Loss: 0.3960557
EarlyStopping counter: 9 out of 15
Updating learning rate to 0.00486324078896373
iters: 100, epoch: 13 | loss: 0.4522523
speed: 0.1505s/iter; left time: 28353.4317s
iters: 200, epoch: 13 | loss: 0.3278935
speed: 0.0280s/iter; left time: 5268.7788s
iters: 300, epoch: 13 | loss: 0.3633406
speed: 0.0279s/iter; left time: 5256.5203s
iters: 400, epoch: 13 | loss: 0.4294580
speed: 0.0279s/iter; left time: 5251.6118s
iters: 500, epoch: 13 | loss: 0.3454820
speed: 0.0280s/iter; left time: 5257.8887s
iters: 600, epoch: 13 | loss: 0.3645260
speed: 0.0280s/iter; left time: 5261.4420s
iters: 700, epoch: 13 | loss: 0.3417323
speed: 0.0280s/iter; left time: 5260.9822s
iters: 800, epoch: 13 | loss: 0.3129990
speed: 0.0280s/iter; left time: 5255.6694s
iters: 900, epoch: 13 | loss: 0.2905439
speed: 0.0280s/iter; left time: 5252.0826s
iters: 1000, epoch: 13 | loss: 0.2850313
speed: 0.0281s/iter; left time: 5259.5704s
iters: 1100, epoch: 13 | loss: 0.3172965
speed: 0.0280s/iter; left time: 5246.1584s
iters: 1200, epoch: 13 | loss: 0.3209522
speed: 0.0280s/iter; left time: 5246.7961s
iters: 1300, epoch: 13 | loss: 0.3128294
speed: 0.0280s/iter; left time: 5249.5419s
iters: 1400, epoch: 13 | loss: 0.3069360
speed: 0.0280s/iter; left time: 5244.6790s
iters: 1500, epoch: 13 | loss: 0.2832389
speed: 0.0280s/iter; left time: 5245.1639s
iters: 1600, epoch: 13 | loss: 0.3491664
speed: 0.0279s/iter; left time: 5205.3280s
iters: 1700, epoch: 13 | loss: 0.3340007
speed: 0.0284s/iter; left time: 5309.2219s
iters: 1800, epoch: 13 | loss: 0.3344918
speed: 0.0285s/iter; left time: 5313.6044s
iters: 1900, epoch: 13 | loss: 0.3484406
speed: 0.0285s/iter; left time: 5308.9329s
iters: 2000, epoch: 13 | loss: 0.2734198
speed: 0.0284s/iter; left time: 5304.1585s
iters: 2100, epoch: 13 | loss: 0.3701983
speed: 0.0279s/iter; left time: 5199.2803s
Epoch: 13 cost time: 60.11739897727966
--------start to validate-----------
normed mse:0.4303, mae:0.4390, rmse:0.6560, mape:6.6884, mspe:34114.8247, corr:0.8375
denormed mse:6.7497, mae:1.4995, rmse:2.5980, mape:inf, mspe:inf, corr:0.8375
--------start to test-----------
normed mse:0.3589, mae:0.3925, rmse:0.5991, mape:7.5275, mspe:25406.8618, corr:0.7192
denormed mse:6.9260, mae:1.4134, rmse:2.6317, mape:inf, mspe:inf, corr:0.7192
Epoch: 13, Steps: 2142 | Train Loss: 0.3540810 valid Loss: 0.4389815 Test Loss: 0.3925090
EarlyStopping counter: 10 out of 15
Updating learning rate to 0.004620078749515543
iters: 100, epoch: 14 | loss: 0.3438686
speed: 0.1479s/iter; left time: 27544.2964s
iters: 200, epoch: 14 | loss: 0.2884781
speed: 0.0280s/iter; left time: 5210.4980s
iters: 300, epoch: 14 | loss: 0.3126722
speed: 0.0280s/iter; left time: 5203.0928s
iters: 400, epoch: 14 | loss: 0.3271807
speed: 0.0280s/iter; left time: 5200.1641s
iters: 500, epoch: 14 | loss: 0.4116562
speed: 0.0280s/iter; left time: 5199.4813s
iters: 600, epoch: 14 | loss: 0.3995543
speed: 0.0281s/iter; left time: 5212.3402s
iters: 700, epoch: 14 | loss: 0.3520363
speed: 0.0282s/iter; left time: 5228.8119s
iters: 800, epoch: 14 | loss: 0.3702521
speed: 0.0280s/iter; left time: 5202.5050s
iters: 900, epoch: 14 | loss: 0.3815005
speed: 0.0281s/iter; left time: 5220.2957s
iters: 1000, epoch: 14 | loss: 0.3172183
speed: 0.0281s/iter; left time: 5214.7825s
iters: 1100, epoch: 14 | loss: 0.2710787
speed: 0.0280s/iter; left time: 5182.3521s
iters: 1200, epoch: 14 | loss: 0.3369085
speed: 0.0281s/iter; left time: 5194.1470s
iters: 1300, epoch: 14 | loss: 0.3464781
speed: 0.0281s/iter; left time: 5205.2410s
iters: 1400, epoch: 14 | loss: 0.3249996
speed: 0.0280s/iter; left time: 5172.7727s
iters: 1500, epoch: 14 | loss: 0.3748113
speed: 0.0279s/iter; left time: 5164.6070s
iters: 1600, epoch: 14 | loss: 0.3545220
speed: 0.0282s/iter; left time: 5205.9071s
iters: 1700, epoch: 14 | loss: 0.3138350
speed: 0.0283s/iter; left time: 5226.5786s
iters: 1800, epoch: 14 | loss: 0.2359443
speed: 0.0279s/iter; left time: 5154.5516s
iters: 1900, epoch: 14 | loss: 0.3059377
speed: 0.0280s/iter; left time: 5166.2721s
iters: 2000, epoch: 14 | loss: 0.4641337
speed: 0.0275s/iter; left time: 5076.5863s
iters: 2100, epoch: 14 | loss: 0.5113470
speed: 0.0275s/iter; left time: 5072.0486s
Epoch: 14 cost time: 59.94197106361389
--------start to validate-----------
normed mse:0.4293, mae:0.4387, rmse:0.6552, mape:6.3817, mspe:29298.9563, corr:0.8369
denormed mse:6.6472, mae:1.4853, rmse:2.5782, mape:inf, mspe:inf, corr:0.8369
--------start to test-----------
normed mse:0.3625, mae:0.3948, rmse:0.6021, mape:7.2433, mspe:23448.7457, corr:0.7215
denormed mse:6.9705, mae:1.4218, rmse:2.6402, mape:inf, mspe:inf, corr:0.7215
Epoch: 14, Steps: 2142 | Train Loss: 0.3525640 valid Loss: 0.4387257 Test Loss: 0.3948280
EarlyStopping counter: 11 out of 15
Updating learning rate to 0.004389074812039766
iters: 100, epoch: 15 | loss: 0.2835026
speed: 0.1466s/iter; left time: 26997.5798s
iters: 200, epoch: 15 | loss: 0.3711831
speed: 0.0277s/iter; left time: 5104.0032s
iters: 300, epoch: 15 | loss: 0.2705855
speed: 0.0280s/iter; left time: 5143.2221s
iters: 400, epoch: 15 | loss: 0.3280960
speed: 0.0280s/iter; left time: 5146.2398s
iters: 500, epoch: 15 | loss: 0.3160923
speed: 0.0294s/iter; left time: 5406.9974s
iters: 600, epoch: 15 | loss: 0.3436387
speed: 0.0289s/iter; left time: 5302.4576s
iters: 700, epoch: 15 | loss: 0.3115176
speed: 0.0288s/iter; left time: 5280.9901s
iters: 800, epoch: 15 | loss: 0.3346707
speed: 0.0284s/iter; left time: 5212.5078s
iters: 900, epoch: 15 | loss: 0.3677571
speed: 0.0286s/iter; left time: 5244.5566s
iters: 1000, epoch: 15 | loss: 0.4072943
speed: 0.0284s/iter; left time: 5203.1259s
iters: 1100, epoch: 15 | loss: 0.3804372
speed: 0.0280s/iter; left time: 5128.9054s
iters: 1200, epoch: 15 | loss: 0.3443121
speed: 0.0279s/iter; left time: 5112.3673s
iters: 1300, epoch: 15 | loss: 0.3637579
speed: 0.0283s/iter; left time: 5176.0714s
iters: 1400, epoch: 15 | loss: 0.3499659
speed: 0.0286s/iter; left time: 5227.1227s
iters: 1500, epoch: 15 | loss: 0.5039499
speed: 0.0291s/iter; left time: 5310.4525s
iters: 1600, epoch: 15 | loss: 0.4006560
speed: 0.0283s/iter; left time: 5170.5931s
iters: 1700, epoch: 15 | loss: 0.3319346
speed: 0.0296s/iter; left time: 5400.1587s
iters: 1800, epoch: 15 | loss: 0.2939537
speed: 0.0289s/iter; left time: 5280.7841s
iters: 1900, epoch: 15 | loss: 0.3131467
speed: 0.0288s/iter; left time: 5247.6471s
iters: 2000, epoch: 15 | loss: 0.3423584
speed: 0.0282s/iter; left time: 5135.6943s
iters: 2100, epoch: 15 | loss: 0.3027259
speed: 0.0293s/iter; left time: 5336.4273s
Epoch: 15 cost time: 61.06135439872742
--------start to validate-----------
normed mse:0.4270, mae:0.4357, rmse:0.6535, mape:6.2969, mspe:29361.8301, corr:0.8387
denormed mse:6.7744, mae:1.4934, rmse:2.6028, mape:inf, mspe:inf, corr:0.8387
--------start to test-----------
normed mse:0.3801, mae:0.4064, rmse:0.6165, mape:7.6775, mspe:24234.3983, corr:0.7222
denormed mse:7.0129, mae:1.4326, rmse:2.6482, mape:inf, mspe:inf, corr:0.7222
Epoch: 15, Steps: 2142 | Train Loss: 0.3506703 valid Loss: 0.4357405 Test Loss: 0.4064314
EarlyStopping counter: 12 out of 15
Updating learning rate to 0.004169621071437777
iters: 100, epoch: 16 | loss: 0.4510111
speed: 0.1491s/iter; left time: 27125.3628s
iters: 200, epoch: 16 | loss: 0.2692741
speed: 0.0277s/iter; left time: 5035.8793s
iters: 300, epoch: 16 | loss: 0.3289906
speed: 0.0276s/iter; left time: 5009.9902s
iters: 400, epoch: 16 | loss: 0.3797024
speed: 0.0279s/iter; left time: 5068.8114s
iters: 500, epoch: 16 | loss: 0.3341546
speed: 0.0279s/iter; left time: 5071.4988s
iters: 600, epoch: 16 | loss: 0.3495839
speed: 0.0279s/iter; left time: 5062.5783s
iters: 700, epoch: 16 | loss: 0.4357414
speed: 0.0279s/iter; left time: 5067.4511s
iters: 800, epoch: 16 | loss: 0.3485725
speed: 0.0282s/iter; left time: 5117.5560s
iters: 900, epoch: 16 | loss: 0.3322166
speed: 0.0280s/iter; left time: 5073.7118s
iters: 1000, epoch: 16 | loss: 0.4298945
speed: 0.0278s/iter; left time: 5034.2741s
iters: 1100, epoch: 16 | loss: 0.3356522
speed: 0.0277s/iter; left time: 5019.3092s
iters: 1200, epoch: 16 | loss: 0.3022777
speed: 0.0278s/iter; left time: 5030.0439s
iters: 1300, epoch: 16 | loss: 0.5145088
speed: 0.0279s/iter; left time: 5043.8716s
iters: 1400, epoch: 16 | loss: 0.3351660
speed: 0.0280s/iter; left time: 5053.5629s
iters: 1500, epoch: 16 | loss: 0.2188999
speed: 0.0280s/iter; left time: 5056.2751s
iters: 1600, epoch: 16 | loss: 0.3333143
speed: 0.0280s/iter; left time: 5047.3125s
iters: 1700, epoch: 16 | loss: 0.3249144
speed: 0.0280s/iter; left time: 5042.0314s
iters: 1800, epoch: 16 | loss: 0.3542661
speed: 0.0286s/iter; left time: 5164.6782s
iters: 1900, epoch: 16 | loss: 0.2972439
speed: 0.0282s/iter; left time: 5072.0900s
iters: 2000, epoch: 16 | loss: 0.5136545
speed: 0.0280s/iter; left time: 5043.7353s
iters: 2100, epoch: 16 | loss: 0.4824405
speed: 0.0276s/iter; left time: 4965.6085s
Epoch: 16 cost time: 59.80357551574707
--------start to validate-----------
normed mse:0.4162, mae:0.4301, rmse:0.6451, mape:6.6630, mspe:33023.5124, corr:0.8392
denormed mse:6.6257, mae:1.4663, rmse:2.5741, mape:inf, mspe:inf, corr:0.8392
--------start to test-----------
normed mse:0.3918, mae:0.4191, rmse:0.6259, mape:8.4547, mspe:26315.2061, corr:0.7104
denormed mse:6.8745, mae:1.4420, rmse:2.6219, mape:inf, mspe:inf, corr:0.7104
Epoch: 16, Steps: 2142 | Train Loss: 0.3491714 valid Loss: 0.4301337 Test Loss: 0.4191123
EarlyStopping counter: 13 out of 15
Updating learning rate to 0.003961140017865888
iters: 100, epoch: 17 | loss: 0.3080508
speed: 0.1480s/iter; left time: 26622.6370s
iters: 200, epoch: 17 | loss: 0.3300118
speed: 0.0282s/iter; left time: 5071.8839s
iters: 300, epoch: 17 | loss: 0.3696486
speed: 0.0283s/iter; left time: 5076.6316s
iters: 400, epoch: 17 | loss: 0.3103400
speed: 0.0280s/iter; left time: 5030.2413s
iters: 500, epoch: 17 | loss: 0.2892926
speed: 0.0276s/iter; left time: 4947.6185s
iters: 600, epoch: 17 | loss: 0.2658455
speed: 0.0277s/iter; left time: 4960.8161s
iters: 700, epoch: 17 | loss: 0.3430028
speed: 0.0279s/iter; left time: 5003.9050s
iters: 800, epoch: 17 | loss: 0.4180345
speed: 0.0280s/iter; left time: 5014.6019s
iters: 900, epoch: 17 | loss: 0.3565879
speed: 0.0281s/iter; left time: 5037.1669s
iters: 1000, epoch: 17 | loss: 0.3849063
speed: 0.0282s/iter; left time: 5042.6895s
iters: 1100, epoch: 17 | loss: 0.3202542
speed: 0.0282s/iter; left time: 5041.5153s
iters: 1200, epoch: 17 | loss: 0.2963192
speed: 0.0280s/iter; left time: 5011.3633s
iters: 1300, epoch: 17 | loss: 0.2859437
speed: 0.0281s/iter; left time: 5014.8229s
iters: 1400, epoch: 17 | loss: 0.2909003
speed: 0.0280s/iter; left time: 5007.0248s
iters: 1500, epoch: 17 | loss: 0.3638932
speed: 0.0281s/iter; left time: 5008.2811s
iters: 1600, epoch: 17 | loss: 0.3561967
speed: 0.0280s/iter; left time: 4997.1358s
iters: 1700, epoch: 17 | loss: 0.5093152
speed: 0.0277s/iter; left time: 4935.8631s
iters: 1800, epoch: 17 | loss: 0.2677069
speed: 0.0276s/iter; left time: 4913.5956s
iters: 1900, epoch: 17 | loss: 0.3010083
speed: 0.0276s/iter; left time: 4913.4009s
iters: 2000, epoch: 17 | loss: 0.2638042
speed: 0.0276s/iter; left time: 4907.3877s
iters: 2100, epoch: 17 | loss: 0.4300926
speed: 0.0276s/iter; left time: 4903.8334s
Epoch: 17 cost time: 59.81618595123291
--------start to validate-----------
normed mse:0.4280, mae:0.4377, rmse:0.6542, mape:5.5515, mspe:21868.2175, corr:0.8367
denormed mse:6.6570, mae:1.4786, rmse:2.5801, mape:inf, mspe:inf, corr:0.8367
--------start to test-----------
normed mse:0.3892, mae:0.4184, rmse:0.6239, mape:7.2614, mspe:21184.3688, corr:0.7183
denormed mse:7.3174, mae:1.5167, rmse:2.7051, mape:inf, mspe:inf, corr:0.7183
Epoch: 17, Steps: 2142 | Train Loss: 0.3478450 valid Loss: 0.4376619 Test Loss: 0.4184305
EarlyStopping counter: 14 out of 15
Updating learning rate to 0.0037630830169725934
iters: 100, epoch: 18 | loss: 0.3493851
speed: 0.1469s/iter; left time: 26103.3502s
iters: 200, epoch: 18 | loss: 0.3936039
speed: 0.0283s/iter; left time: 5019.1979s
iters: 300, epoch: 18 | loss: 0.3425503
speed: 0.0282s/iter; left time: 5013.9808s
iters: 400, epoch: 18 | loss: 0.4523201
speed: 0.0283s/iter; left time: 5015.3901s
iters: 500, epoch: 18 | loss: 0.4523338
speed: 0.0282s/iter; left time: 5007.3683s
iters: 600, epoch: 18 | loss: 0.3734182
speed: 0.0276s/iter; left time: 4890.5614s
iters: 700, epoch: 18 | loss: 0.4001013
speed: 0.0276s/iter; left time: 4883.5716s
iters: 800, epoch: 18 | loss: 0.3639956
speed: 0.0276s/iter; left time: 4889.4474s
iters: 900, epoch: 18 | loss: 0.3252257
speed: 0.0281s/iter; left time: 4966.6539s
iters: 1000, epoch: 18 | loss: 0.2928127
speed: 0.0276s/iter; left time: 4877.2264s
iters: 1100, epoch: 18 | loss: 0.3182337
speed: 0.0277s/iter; left time: 4897.8872s
iters: 1200, epoch: 18 | loss: 0.2747648
speed: 0.0278s/iter; left time: 4904.7353s
iters: 1300, epoch: 18 | loss: 0.3395426
speed: 0.0278s/iter; left time: 4898.0745s
iters: 1400, epoch: 18 | loss: 0.3047990
speed: 0.0277s/iter; left time: 4893.5173s
iters: 1500, epoch: 18 | loss: 0.4318493
speed: 0.0280s/iter; left time: 4939.3328s
iters: 1600, epoch: 18 | loss: 0.4360686
speed: 0.0283s/iter; left time: 4994.3697s
iters: 1700, epoch: 18 | loss: 0.3741941
speed: 0.0280s/iter; left time: 4925.0932s
iters: 1800, epoch: 18 | loss: 0.3823816
speed: 0.0278s/iter; left time: 4887.5877s
iters: 1900, epoch: 18 | loss: 0.3651931
speed: 0.0277s/iter; left time: 4874.1016s
iters: 2000, epoch: 18 | loss: 0.2952438
speed: 0.0280s/iter; left time: 4914.5200s
iters: 2100, epoch: 18 | loss: 0.2951702
speed: 0.0279s/iter; left time: 4908.1618s
Epoch: 18 cost time: 59.75731587409973
--------start to validate-----------
normed mse:0.4196, mae:0.4308, rmse:0.6478, mape:5.8832, mspe:25092.2845, corr:0.8388
denormed mse:6.5500, mae:1.4567, rmse:2.5593, mape:inf, mspe:inf, corr:0.8388
--------start to test-----------
normed mse:0.3768, mae:0.4091, rmse:0.6138, mape:7.3974, mspe:21079.9626, corr:0.7191
denormed mse:7.0757, mae:1.4669, rmse:2.6600, mape:inf, mspe:inf, corr:0.7191
Epoch: 18, Steps: 2142 | Train Loss: 0.3477751 valid Loss: 0.4308268 Test Loss: 0.4091339
EarlyStopping counter: 15 out of 15
Early stopping
save model in  exp/ETT_checkpoints/SCINet_ETTh1_ftM_sl48_ll24_pl24_lr0.009_bs4_hid4.0_s1_l3_dp0.5_invFalse_itr0/ETTh124.bin
testing : SCINet_ETTh1_ftM_sl48_ll24_pl24_lr0.009_bs4_hid4.0_s1_l3_dp0.5_invFalse_itr0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
test 2857
normed mse:0.3594, mae:0.3923, rmse:0.5995, mape:7.5035, mspe:24301.3721, corr:0.7205
TTTT denormed mse:6.9649, mae:1.4154, rmse:2.6391, mape:inf, mspe:inf, corr:0.7205
Final mean normed mse:0.3594,mae:0.3923,denormed mse:6.9649,mae:1.4154

For readability, we have refactored the code which can affect the final result. After finetuning, this version of code can achieve MSE 0.341 and  MAE 0.379 on etth1 input 48 output 24 setting. The latest version of paper will be released soon.

If there is no further query. We will close this issue.
Thanks for your attention.


NacosRegistry 是否支持  access-key secret-key 鉴权访问

Your question
describe your question clearly
Your scenes
describe your use scenes (why need this feature)
Your advice
describe the advice or solution you'd like
Environment

SOFARPC version:
JVM version (e.g. java -version):
OS version (e.g. uname -a):
Maven version:
IDE version:

朱立焕收到，谢谢


support preferences for specifying colors

allow setting the colors via applet preferences

actually instead of preferences, just use i3's bar_config and grab the colors that way.

colors are now taken from i3. See README on how to use i3's config file to set the colors of the applet


Can I use vllm for this to speed up inference?

as title

Yes you can — vllm speeds are significantly better!


Possibility of webdriver's background work

Hi, Danp2!
First of all, I'd like to appreciate your effort for this nice library.
I'm now developing a simple automation tool by using chrome webdriver, and everything is almost ok.
But the chrome always take the focus to itself while processing action, so I feel uncomfortable when I want to do some other works while running the script.
I found that the built-in functions of IE has no problem like this.
So my question is,
Is possible to make the chrome work in background like _IE functions?
I look forward to your reply.
Thank you.

Hi @nika90426,
Have you tried to determine which commands are causing the focus change? Does it also occur if you test with FIrefox or Edge?
Depending on your needs, you may be able to solve this by running the browser in "headless" mode.
Dan

I know "headless" mode is one of the solutions, but its not what I exactly want.
Let me explain what I want.
For example, suppose there exists two tabs in chrome browser.
And I want to do some actions on currently not focused tab.
So when I call _WD_Attach() function to find the not focused tab, chrome automatically take focus to itself and switch to the tab.
But when I use _IEAttach(), there is no such like this problem.
_IEAttach() can find the specific tab without any focusing action, and even _IENavigate() can navigate to url without focusing the tab.
I want to make chrome exactly work like _IEAttach() and _IENavigate().
I wonder if its possible by using this library.
I still haven't tested with Firefox or Edge, so I don't know what will happen on Firefox or Edge.
Nika

_WD_Attach cycles through the available tabs until it finds a match. Are you calling this function regularly? If so, why?

Yes, I'm calling _WD_Attach regularly.
The main html page spends long time for complete loading, so I created two tab (first - main page, second - other page) and made each tab to do their own work.
For this, I should have to use _WD_Attach regularly.
And chrome takes focus to itself and repeats switching between two tabs.
Finally,

I cannot do other works on my PC while running the script. (due to chrome's focusing)
I want chrome to always focus the main page's tab and to make other tab work in background, but it looks impossible now.

Can I solve these two problems?

It would make more sense to do the following --

Save the result from calling _WD_Attach
Use _WD_Window and this "handle" to switch directly to the desired tab

You may want to rethink your design. Doesn't chromedriver support multiple active sessions? If so, you may be able to have a separate session established for each tab and then just use the appropriate SessionID for the desired target.

@nika90426 Is this something that you still want kept open?


[9.4-stable] backport: Carry media type through download verify.

This PR is a backport of PR #3706.
It stores the Blob media type as a part of the verified status filename so it can be restored after reboot.

All checks are green! It has been a while since I saw all the eden tests to pass...


Introduce command/script generating the next tag for a project following YYYY.CW.patch schema

AC:

Shows the last two tags for informative reason
Validates the working directory and exits if it's not a git-repository.
Outputs the new tag

CW means calendar week. Example: 2021.48.0
Additional note: This tag schema should not be used for composer packages which will be required by other. Use it for website projects for example where you only need/want a basic but informative tag history.

Rejected for now. It's a nice to have tool, but I have a lot of other things on my plate.

In a different project I came to the step, at which I am in need of a shell script. So, I am re-opening this issue.

[ ] Include fetching tags from remote
[ ] Show info/reminder: Before creating a release, make sure the branches are up to date
[ ] Show last 2 tags

Following can be used to get started:
Get the amount of releases in this calender week:
git tag | grep -c $(date +"%Y.%-U")

Show tags:
git tag | grep $(date +"%Y.%-U")

Show new tag:
releaseNumber=$(git tag | grep -c $(date +"%Y.%-U"))
releaseVersion=$(date +"%Y.%-U.$releaseNumber")
echo "Going to make the tag: $releaseVersion"

Braindump:
echo ""
echo "This week releases:"
git tag | grep $(date +"%Y.%-U")

count=$(git tag | grep -c $(date +"%Y.%-U"))
echo "> count: $count"

echo ""
releaseVersion=$(date +"%Y.%-U.$count")
echo "New tag would be: $releaseVersion"


Feature/csv output

Preliminary support for generating a .csv file output from a .fit file. This .csv is formatted to be human-readable if loaded from Excel.

@dtcooper circling back on this PR, merge?


'save-as-root.saveFile' not found

I just installed the extension to work with a Linux Server, but when I try to save I get the following:
The command "Save as Root" extension results in an error:
"command 'save-as-root.save file' not found"
Does anybody get this error? I searched for an Issue and find none...

same issue, using mac and linux

Hi, I just checked in mi mac and found that the package.json of the
extension is ok, but still not working. It stopped working about 2 weeks
ago, could it be a problem with a vscode update?
[image: image.png]
El mar, 16 abr 2024 a las 16:01, yy0931 @.***>)
escribió:

Could you check keybindings.json (
https://code.visualstudio.com/docs/getstarted/keybindings#_advanced-customization)
or any files within the ".vscode/" folder, if present, for the string
"save-as-root.save file"?
The correct command name is "save-as-root.saveFile", not "save-as-root.save
file". Since I couldn't find any occurrence of "save-as-root.save file"
in the source code, I think your configuration might be the cause.
https://github.com/yy0931/save-as-root/blob/3ca28afee8c8820ed09f464d8f57597d4640061c/package.json#L36
—
Reply to this email directly, view it on GitHub
https://github.com/yy0931/save-as-root/issues/24#issuecomment-2059916106,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AHRDX7ZQMFODVYOZ3P2G6JLY5WGSLAVCNFSM6AAAAABFX3XTMCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANJZHEYTMMJQGY
.
You are receiving this because you commented.Message ID:
@.***>

--
Juan Pablo Duque Arango
Coordinador de Desarrollo
Cel:<PHONE_NUMBER>


kubectl scale fails with cryptic error if it can't fetch parent resource

What happened:
I ran command:
$ kubectl scale nodepools my-pool --replicas=3
error: no objects passed to scale

But my user actually didn't have get nodepools permission.
This error is very cryptic and prompted me to try things like specifying resource as RESOURCE/NAME, RESOURCE NAME and move --replicas before positional args --but that wasn't the issue.
Only when I added -v=7, I realized I actually have a permission issue:
Response Body: {"kind":"Status","apiVersion":"v1","metadata":
{},"status":"Failure","message":"nodepools.example.com \"my-pool\"
is forbidden: User \"new-user\" cannot get resource \"nodepools\"
in API group \"example.com\" at the cluster scope","reason":"Forbidden",
"details":{"name":"my-pool","group":"example.com","kind":"nodepools"},"code":403}

error: no objects passed to scale

What you expected to happen:
Tell me that I have the permission issue, don't mislead me that I didn't specify a resource.
How to reproduce it (as minimally and precisely as possible):
Just try kubectl scale on a resource that you don't have permission to get.
Anything else we need to know?:
Environment:

Kubernetes client and server versions (use kubectl version): kubectl v1.31.1
Cloud provider or hardware configuration: k8s kind v1.27.3
OS (e.g: cat /etc/os-release): macOS

It seems that you want to use custom CRD resources to implement the scale function. Does your CRD implement subresources similar to ?//+kubebuilder:subresource:scale:specpath=.spec.replicas,statuspath=.status.replicas,selectorpath=.status.selector
FYI: https://github.com/kubernetes-sigs/kubebuilder/discussions/3486

Yes I already have a scale subresource. kubectl still makes a GET query to the resource first. I will send a full request log.

Check the error you returned, it seems to be printed here in the source code. It seems that the resource was not obtained?
https://github.com/kubernetes/kubernetes/blob/7b28a115ba04651bc31aa1d7089abbd67ec5c067/staging/src/k8s.io/kubectl/pkg/cmd/scale/scale.go#L231-L233

I think the ContinueOnError() might be the problem here.

Why ContinueOnError is  the problem? I am a little confused

The expected behavior is that the kubectl makes a POST /scale call, right? It shouldn't need a GET call on the parent resource I think?

kubectl scale kpool ei4-k8s-intdemo-system --replicas=0 -v 8
...
GET https://<IP_ADDRESS>:63505/apis/compute.linkedin.com/v1alpha1/kubernetespools/ei4-k8s-intdemo-system
Response Body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"kubernetespools.compute.linkedin.com
\"ei4-k8s-intdemo-system\" is forbidden: User \"new-user\" cannot get resource \"kubernetespools\" in API group \"compute.linkedin.com\" at the cluster
scope","reason":"Forbidden","details":{"name":"ei4-k8s-intdemo-system","group":"compute.linkedin.com","kind":"kubernetespools"},"code":403}

error: no objects passed to scale

Even if scale command needs to GET the resource, and it can't due to a permission issue, the error it should be printing is "why it can't GET the resource". So the bug is valid.

The scale.go clearly ignores the errors returned from resourcebuilder. I was wrong in saying ContinueOnError() is the problem, but this snippet seems to be the issue:
https://github.com/kubernetes/kubectl/blob/v0.31.1/pkg/cmd/scale/scale.go#L208-L212
infoErr is not checked until the very end --yet the infos variable is used despite being wrong/incomplete in case of error.
If I locally fix the code to check if infoErr != nil { return infoErr } right after this snippet, kubectl now works more like what I expected and prints the permission error:
go run ./cmd/kubectl scale kubernetespool.compute.linkedin.com/ei4.k8s-0.e2e-testing --replicas=3 -v 7
...
Error from server (Forbidden): kubernetespools.compute.linkedin.com "ei4.k8s-0.e2e-testing" is forbidden: User "new-user" cannot get resource "kubernetespools" in API group "compute.linkedin.com" at the cluster scope

Even despite this, I don't think the behavior is fully correct. I don't think kubectl scale should need access on the parent resource, as the /scale subresource has its own GET/POST/PATCH APIs that can work standalone, and has its own RBACs.

In my opinion it is better to wrap this error https://github.com/kubernetes/kubectl/blob/0315be426ca25e6554f1c9089534b62ce12254d4/pkg/cmd/scale/scale.go#L232 with the error gotten from resourcebuilder.
/triage accepted
/priority backlog

/assign
ok, I will handle this


Is dataset iterative or not?

Hi
I want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?
could you provide me with example how I can use datasets as iterative datasets?
thanks

Hello !
Could you give more details ?
If you mean iter through one dataset then yes, Dataset object does implement the __iter__ method so you can use
for example in dataset:
    # do something

If you want to iter through several datasets you can first concatenate them
from datasets import concatenate_datasets

new_dataset = concatenate_datasets([dataset1, dataset2])

Let me know if this helps !

Hi Huggingface/Datasets team,
I want to use the datasets inside Seq2SeqDataset here
https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py
and there I need to return back each line from the datasets and I am not
sure how to access each line and implement this?
It seems it also has get_item attribute? so I was not sure if this is
iterative dataset? or if this is non-iterable datasets?
thanks.
On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest<EMAIL_ADDRESS>wrote:

Hello !
Could you give more details ?
If you mean iter through one dataset then yes, Dataset object does
implement the iter method so you can use
for example in dataset:
# do something
If you want to iter through several datasets you can first concatenate them
from datasets import concatenate_datasets
new_dataset = concatenate_datasets([dataset1, dataset2])
Let me know if this helps !
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/huggingface/datasets/issues/815#issuecomment-723881199,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA
.

could you tell me please if datasets also has getitem any idea on how
to integrate it with Seq2SeqDataset is appreciated thanks
On Mon, Nov 9, 2020 at 10:22 AM Rabeeh Karimi Mahabadi<EMAIL_ADDRESS>wrote:

Hi Huggingface/Datasets team,
I want to use the datasets inside Seq2SeqDataset here
https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py
and there I need to return back each line from the datasets and I am not
sure how to access each line and implement this?
It seems it also has get_item attribute? so I was not sure if this is
iterative dataset? or if this is non-iterable datasets?
thanks.
On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest<EMAIL_ADDRESS>wrote:

Hello !
Could you give more details ?
If you mean iter through one dataset then yes, Dataset object does
implement the iter method so you can use
for example in dataset:
# do something
If you want to iter through several datasets you can first concatenate
them
from datasets import concatenate_datasets
new_dataset = concatenate_datasets([dataset1, dataset2])
Let me know if this helps !
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/huggingface/datasets/issues/815#issuecomment-723881199,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA
.

datasets.Dataset objects implement indeed __getitem__. It returns a dictionary with one field per column.
We've not added the integration of the datasets library for the seq2seq utilities yet. The current seq2seq utilities are based on text files.
However as soon as you have a datasets.Dataset with columns "tgt_texts" (str), "src_texts" (str), and "id" (int) you should be able to implement your own Seq2SeqDataset class that wraps your dataset object. Does that make sense ?

Hi
I am sorry for asking it multiple times but I am not getting the dataloader
type, could you confirm if the dataset library returns back an iterable
type dataloader or a mapping type one where one has access to getitem,
in the former case, one can iterate with iter, and how I can configure
it to return the data back as the iterative type? I am dealing with
large-scale datasets and  I do not want to bring all in memory
thanks for your help
Best regards
Rabeeh
On Mon, Nov 9, 2020 at 11:17 AM Quentin Lhoest<EMAIL_ADDRESS>wrote:

datasets.Dataset objects implement indeed getitem. It returns a
dictionary with one field per column.
We've not added the integration of the datasets library for the seq2seq
utilities yet. The current seq2seq utilities are based on text files.
However as soon as you have a datasets.Dataset with columns "tgt_texts"
(str), "src_texts" (str), and "id" (int) you should be able to implement
your own Seq2SeqDataset class that wraps your dataset object. Does that
make sense ?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/huggingface/datasets/issues/815#issuecomment-723915556,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARPXHHYOC22EM7F666BZSOTSO66R3ANCNFSM4TPB7OWA
.

datasets.Dataset objects are both iterative and mapping types: it has both __iter__ and __getitem__
For example you can do
for example in dataset:
    # do something

or
for i in range(len(dataset)):
    example = dataset[i]
    # do something

When you do that, one and only one example is loaded into memory at a time.

Hi there,
Here is what I am trying, this is not working for me in map-style datasets, could you please tell me how to use datasets with being able to access _getitem ? could you assist me please correcting this example? I need map-style datasets which is formed from concatenation of two datasets from your library. thanks
import datasets
dataset1 = load_dataset("squad", split="train[:10]")
dataset1 = dataset1.map(lambda example: {"src_texts": "question: {0} context: {1} ".format(
    example["question"], example["context"]),
    "tgt_texts": example["answers"]["text"][0]}, remove_columns=dataset1.column_names)
dataset2 = load_dataset("imdb", split="train[:10]")
dataset2 = dataset2.map(lambda example: {"src_texts": "imdb: " + example["text"],
            "tgt_texts": str(example["label"])}, remove_columns=dataset2.column_names)
train_dataset = datasets.concatenate_datasets([dataset1, dataset2])
train_dataset.set_format(type='torch', columns=['src_texts', 'tgt_texts'])
dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
for id, batch in enumerate(dataloader):
    print(batch)

closed since I found this response on the issue https://github.com/huggingface/datasets/issues/469


Japanese gives an error

When running tesseract cmd line with Japanese (latest language file - tried both tessdata and tessdata_best) this error message is returned: read_params_file: parameter not found: textord_tabfind_vertical_horizontal_mix
Tesseract version: latest (2cc46fa6d456f9b49bc8a70036fa989a9c3baf50), build via vcpkg install tesseract:x64-windows --HEAD

Is it possible that some deprecated parameters are still there in config files?

Yes.
That parameter was removed in #1418

It looks like we should add a testcase which simply loads all languages.

@Shreeshrii: thx for the info,tessdata_fast does not crash :)

According to my tests, jpn.traineddata and jpn_vert.traineddata` in tessdata are the only files which need to be fixed. This is addressed by https://github.com/tesseract-ocr/tessdata/pull/92.
All other languages and also the files from tessdata_best and tessdata_fast look good.

@stweil, what did you look for? Just textord_tabfind_vertical_horizontal_mix
or all removed parameters?

I first looked for all parameters used in tessdata and checked whether they still exist in tesseract git master. In addition I looked for textord_tabfind_vertical_horizontal_mix (which was the only problem reported from the first test) in tessdata_best and tessdata_fast.
Finally, I also checked all parameters in tesseract/tessdata (no problem found):
cat $(ls tessdata/*configs/*|grep -v Make)|sort|uniq|grep -v '^#'|sed 's/[[:space:]].*//'|uniq|grep ^[a-z] >/tmp/parameters.txt
for p in $(cat /tmp/parameters.txt|sort|uniq); do git grep -q $p || echo $p not found; done

@stweil I found the issue still exists in the latest tessdata_best.

You are right. See https://github.com/tesseract-ocr/tessdata_best/pull/28.


Add character set and collation to column spec

Context
This change allows to define CHARACTER SET and COLLATE for an individual column. This is useful if you want certain values to be stored in ascii rather than utf8.
Caveats

This change will apply to MySQL only. This approach can be replicated for other SQL databases.
Writing a unit test requires a PR to db-meta have it return the character set and collation. Updating the documentation requires a PR to english-docs. It is surprisingly high friction to contribute to this library due to how fragmented it is :(

db-meta is irrelevant and you can just do it without. the docs in english-docs are only for db-migrate not for submodules, that is why they became independent, b/c they are. Drivers can have their own percs and should, but they also need their own docs of course in that case.


[18/10/2022]: Backend API [feat] - Social signin with Google

Sub issue for #5
Description
Social signin feature with google
Which task are you working on?

[ ] Add social signin feature with Google

Who want to work on this issue?

If this issue has been addressed, can we close this issue?


Create patch for Python 3.13 series

Allows compiling for Python 3.13, at least on Linux targets

This allowed 3.13.0 to compile locally on my x86-64 NixOS system.


[Android][Accessibility] [Input validation errors]

Platform
What platform is your issue or question related to?

Android

Author or host
Microsoft Teams
Version of SDK
SDK version 2.1.0 (ACv1.3)
Details

When there are errors on the cards after pressing submit action, failure to submit due to errors is not called to users. User also not aware if there are multiple errors on the card. An expected callout could be "There are errors on the card” a generic message, or “There are x error/errors on the card”.

While the card is read out, all the errors are read out as if it were regular card content. People with any form of visual impairment may not realize that it is an error message displayed on the card.

Link to video for #1 and #2

When the choiceset/toggle input is not the first input field with the error, no highlight (eg: red highlight in other input types) is applied to the input field. Only the associated error is displayed. Could we confirm if this is the expected behavior.

@almedina-ms assigning to you for tackling once we get clarity from the Teams side on expectation from the Accessibility SMEs here. @jwoo-msft  FYI.


val results is differnet with test results (val_dataset = test_dataset ).

I found that the result of the training phase differs from the results of the testing phase. (val_dataloader = test_dataloader)
I use dist_train.sh for training.
similar question: https://github.com/open-mmlab/mmdetection/issues/3573， and the answer point out “the "BN's means and stds are different in different GPU cards, during training evaluation, different GPU thus inference with different BN's means and stds."
How can I save the parameters of the BN during dist_training and reproduce the results of the training phase?

@MingboHong The performance difference should be very small. How much of a difference do you currently have?

@MingboHong The performance difference should be very small. How much of a difference do you currently have?

Detector:yolov3_d53_8xb8-ms-608-273e_coco.py


45 update cart appearance

I meant to make a PR to my own fork. I am just experimenting with the store. Closed.


feat(java-sdk): optimize java sdk(non reactor version)

What this PR does:
optimize java sdk(non reactor version)

add more methods for sdk interfaces
grpc client support connection pool pattern
more defensive programming.For example, add exceptionHandler for grpc server,add more argument checking.

Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:
Does this PR introduce a user-facing change?:

Working on unit tests


LOC CHECKIN | Microsoft/vstest master | 20200207

Description
Please add a meaningful description for this change.
Ensure the PR has required unit tests.
Related issue
Kindly link any related issues. E.g. Fixes #xyz.

@v-zbsail this has conflicts, could you solve them or open a new PR please?

@nohwnd These changes were superseded by newer PR #2383   https://github.com/microsoft/vstest/pull/2383
You can abandon this one.


HintAssist.FloatingScale and ComboBox

Hi,
is there a way to get FloatingScale work with ComboBoxes?

I believe this is related to issue #547. I do not believe the MaterialDesignTheme.ComboBox.xaml wpf:SmartHint is binding to the appropriate properties on the TemplatedParent. The FloatingOffset is also not working due to this.

Also #988

Resolved by #899


Corrected dimensions of twitter icon in README.md

With new dimensions

Request two ppl to review in Twitter group.


Deprecated Account RUS fields can't be deleted due to Field Set availability

The Account fields npe01__FirstDonationDate__c, npe01__LastDonationDate__c, npe01__LifetimeDonationHistory_Amount__c, and npe01__LifetimeDonationHistory_Number__c show a delete option in the field list, but cannot actually be deleted because they are available to the packaged BDE List FS field set.

Hey @tet3 - I think you want this in the Cumulus repo? Or is this specifically because of how cci is generating your destructive changes?

Doh! I shouldn't log bugs in the middle of the night while working on something else. Closing,  and I'll repost in the right repo.


[unreachable_subsetting] should not flag characters part of Google Fonts Latin core

Observed behaviour
The check says
The following codepoints supported by the font are not covered by any subsets defined in the font
U+02D8 BREVE: try adding one of: yi, canadian-aboriginal
U+02D9 DOT ABOVE: try adding one of: yi, canadian-aboriginal
U+02DB OGONEK: try adding one of: yi, canadian-aboriginal
U+0302 COMBINING CIRCUMFLEX ACCENT: try adding one of: tifinagh, math, cherokee, coptic
U+0306 COMBINING BREVE: try adding one of: tifinagh, old-permic
U+0307 COMBINING DOT ABOVE: try adding one of: hebrew, old-permic, math, todhri, canadian-aboriginal, tai-le, duployan, coptic, malayalam, syriac, tifinagh
U+030A COMBINING RING ABOVE: try adding one of: duployan, syriac
U+030B COMBINING DOUBLE ACUTE ACCENT: try adding one of: osage, cherokee
U+030C COMBINING CARON: try adding one of: cherokee, tai-le
U+0326 COMBINING COMMA BELOW: try adding math
U+0327 COMBINING CEDILLA: try adding math
U+0328 COMBINING OGONEK: not included in any glyphset definition
U+0331 COMBINING MACRON BELOW: try adding one of: caucasian-albanian, sunuwar, syriac, gothic, thai, tifinagh, cherokee
Or you can add the above codepoints to one of the subsets supported by the font: devanagari, latin, latin-ext
Expected behaviour
As these charcters are part of GF Latin Core they should not be flagged
Resources and steps needed to reproduce
font Samaano https://github.com/mitradranirban/samaano-fonts
FontBakery version  0.12.10
OS Ubuntu 22.04
Python version, 3.10.12

This is not a bug. There are two different concepts here which need to be separated:

A glyphset is set of glyphs which designers use to organise the glyphs that they are drawing. GF Latin Core is a glyphset.
A subset is a set of characters which the Google Fonts front-ends uses to split the font into various sub fonts for CSS delivery.

The glyph circumflexcomb is in GF Latin Core, so designers should design it. But sadly U+0302 is not in the latin subset served by the Google Fonts front end. (There are long and complicated discussions about why this is the case. We know it's not ideal.) So a warning that a glyph in the font will not be served by the GF front end is a useful thing to have.


Update README.md

how to find generated credentials in the logs
adding default passwords security policy

Thank you for submitting a contribution to Apache NiFi.
Please provide a short description of the PR here:
Description of PR
Enables X functionality; fixes bug NIFI-YYYY.
In order to streamline the review of the contribution we ask you
to ensure the following steps have been taken:
For all changes:

[ ] Is there a JIRA ticket associated with this PR? Is it referenced
in the commit message?

[ ] Does your PR title start with NIFI-XXXX where XXXX is the JIRA number you are trying to resolve? Pay particular attention to the hyphen "-" character.

[ ] Has your PR been rebased against the latest commit within the target branch (typically main)?

[ ] Is your initial contribution a single, squashed commit? Additional commits in response to PR reviewer feedback should be made on this branch and pushed to allow change tracking. Do not squash or use --force when pushing to allow for clean monitoring of changes.

For code changes:

[ ] Have you ensured that the full suite of tests is executed via mvn -Pcontrib-check clean install at the root nifi folder?
[ ] Have you written or updated unit tests to verify your changes?
[ ] Have you verified that the full build is successful on JDK 8?
[ ] Have you verified that the full build is successful on JDK 11?
[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?
[ ] If applicable, have you updated the LICENSE file, including the main LICENSE file under nifi-assembly?
[ ] If applicable, have you updated the NOTICE file, including the main NOTICE file found under nifi-assembly?
[ ] If adding new Properties, have you added .displayName in addition to .name (programmatic access) for each of the new properties?

For documentation related changes:

[ ] Have you ensured that format looks appropriate for the output in which it is rendered?

Note:
Please ensure that once the PR is submitted, you check GitHub Actions CI for build issues and submit an update to your PR as soon as possible.

Thanks for the pull request @laurentedel! As noted in the Pull Request checklist, each change requires an associated Jira issue number, so please open an issue in the Apache NiFi Jira board. You can also join the Developers mailing list and request Jira contributor access so that you can assign the issue to yourself. After opening the Jira issue, you can update this pull request to include the Jira number.

This commit aims to resolve NIFI-9367 for Docker image usage details and clarification

Thank you for all your very valuable suggestions @exceptionfactory !