Node 12 - What Are The New Improvements And Features?

Node has released it's version 12 in late April (codename Erbium) which brings improved support for ECMAScript modules. It implements phase 2 of the plan that was released late last year as it's Node v12 (this is as even-numbered version) which comes under Long term support start from October 2019 & will get support till 2022 it also packed with V8 engine which is maintained by Google so will receive updates in future as well.

Node 12 New Features

Node.js 12 features include faster startup and better default heap limits, updates to V8, llhttp, TLS, new features including diagnostic report, bundled heap dump capability and updates to Worker Threads, N-API and ES6 module support and more let's check what's new in Node js 12.

V8 Engine Updated to V8 7.4

As always a new version of the V8 JavaScript engine brings performance tweaks and improvements to Node. It was initially running on V8 .7.2 (updated in Dec 18) and eventually upgrade to 7.6 in April which included notable improvements with V8 7.4 are performance updates for faster JavaScript execution, better memory management, and broadened ECMAScript syntax support.

Major Features of V8 version 7.4

JIT-less V8

Version 7.4 now allows JavaScript execution without allocating executable memory at runtime it relies heavily on the ability to allocate and modify executable memory at runtime in its the default configuration & creating executable memory at runtime is part of what makes V8 fast.

WebAssembly Threads/Atomics shipped

WebAssembly Threads/Atomics are now enabled on non-Android operating systems which unlocks the usage of multiple cores on a user’s machine via WebAssembly, enabling new, computation-heavy use cases on the web.

Faster calls with arguments mismatch

JavaScript engines must provide a way to get to the actual parameters. In V8 this is done via arguments adaption, which provides the actual parameters in case of under- or over-application. In the latest version, V8 now completely skips arguments adaption, reducing call overhead by up to 60%.

Check out More of the v8.7.4 New Features Below:

Improved native accessor performance
Parser performance
Bytecode flushing in memory
Bytecode dead basic block elimination
JavaScript language features include Private class fields
Hashbang grammar
V8 API

ES6 Module Support

The import/export syntax has become the favorite module syntax for developers since its standardization in ES6, and the Node team has been working on it to enable natively. Mostly all major browsers support ECMAScript modules via

Types of import from ES modules files for phase 3 that will work with all built-in Node packages:

// default exports
import module from 'module'

// named exports
import { namedExport } from 'module'

// namespace exports
import * as module from 'module'

Phase 3 for ECAM Script Modules in Node 12 which correspond to a path to the stability of the modules and the plan is to remove the --experimental-modules flag into LTS version.

Private Class Fields

Node V 12 has shipped with private class fields These variables are only accessible within the class itself and can't be accessed externally & if you try to access it outside of the class, you will receive a syntax error and private class field is initialized with the # symbol.

Improved Startup Performance

Node.js 12 feature brings an updated experimental version of support for ES6 modules. It will generate the code cache for built-in libraries before build time and embed it as a binary. The main thread is able to use this code cache and gives a 30% speedup in startup time for the main thread.

New compiler and platform minimums

Node 12 takes the full advantage of newer compiler optimizations and security enhancements. The codebase requires a minimum of GCC 6 and glibc 2.17 on platforms other than macOS and Windows. The increment in minimum compiler and libc requirements also increments minimums in supported platforms. Node binary uses a new toolchain minimum which provides compile-time performance and security enhancements. Windows minimums remain the same as Node.js version 11, requiring at least Windows 7, 2008 R2 or 2012 R2 and a minimum compiler of Visual Studio 2017 and Mac users needing to compile Node will require a minimum of Xcode 8.

TLS 1.3 and Security

Node added TLS 1.3 support which increases security and reduced the latency. TLS has been a major update and it actively being integrated across the web. TLS 1.0 and 1.1 have been disabled by default, and the crypto library has removed deprecated functions. With TLS 1.3, Node.js apps have increased end-user privacy & also improved the performance of requests by reducing the time required for the HTTPS handshake.

Heap Dump

Node.js 12 brings integrated heap dump capability out of the box. Previously the default V8 heap sizes were 700MB for 32-bit systems & 1400MB for 64-bit systems, Now it will automatically determine the heap size that it does not use more than the allowed resources.

Native Modules N-API Improvements

N-API provide ABI compatibility across various Node.js versions and also across different Node VMs - allowing N-API enabled native modules to just work across different versions. Node 12 added a feature to improved support for N-API in combination with worker threads.

Diagnostic Reports

Node.js 12 includes an experimental feature named Diagnostic report that allows developers to create a report on demand that will be triggered by specific events.

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Node 12 - What Are The New Improvements And Features?

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Chromium-net-errors: Chromium Network Errors for Node.js

Chromium Network Errors

Provides Chromium network errors found in net_error_list.h as custom error classes that can be conveniently used in Node.js, Electron apps and browsers.

The errors correspond to the error codes that are provided in Electron's did-fail-load events of the WebContents class and the webview tag.

Features

No dependencies.
100% test coverage.
ES6 build with import and export, and a CommonJS build. Your bundler can use the ES6 modules if it supports the "module" or "jsnext:main" directives in the package.json.
Daily cron-triggered checks for updates on net_error_list.h on Travis CI to always get the most up-to-date list of errors.

Installation

npm install chromium-net-errors --save

import * as chromiumNetErrors from 'chromium-net-errors';
// or
const chromiumNetErrors = require('chromium-net-errors');

Example Use in Electron

import { app, BrowserWindow } from 'electron';
import * as chromiumNetErrors from 'chromium-net-errors';

app.on('ready', () => {
  const win = new BrowserWindow({
    width: 800,
    height: 600,
  });

  win.webContents.on('did-fail-load', (event) => {
    try {
      const Err = chromiumNetErrors.getErrorByCode(event.errorCode);
      throw new Err();
    } catch (err) {
      if (err instanceof chromiumNetErrors.NameNotResolvedError) {
        console.error(`The name '${event.validatedURL}' could not be resolved:\n  ${err.message}`);
      } else {
        console.error(`Something went wrong while loading ${event.validatedURL}`);
      }
    }
  });

  win.loadURL('http://blablanotexist.com');
});

Usage

import * as chromiumNetErrors from 'chromium-net-errors';

Create New Errors

const err = new chromiumNetErrors.ConnectionTimedOutError();

console.log(err instanceof Error);
// true
console.log(err instanceof chromiumNetErrors.ChromiumNetError);
// true
console.log(err instanceof chromiumNetErrors.ConnectionTimedOutError);
// true

function thrower() {
  throw new chromiumNetErrors.ConnectionTimedOutError();
}

try {
  thrower();
} catch (err) {
  console.log(err instanceof Error);
  // true
  console.log(err instanceof chromiumNetErrors.ChromiumNetError);
  // true
  console.log(err instanceof chromiumNetErrors.ConnectionTimedOutError);
  // true
}

Get Error by errorCode

Get the class of an error by its errorCode.

const Err = chromiumNetErrors.getErrorByCode(-201);
const err = new Err();

console.log(err instanceof chromiumNetErrors.CertDateInvalidError);
// true

console.log(err.isCertificateError());
// true

console.log(err.type); 
// 'certificate'

console.log(err.message);
// The server responded with a certificate that, by our clock, appears to
// either not yet be valid or to have expired. This could mean:
// 
// 1. An attacker is presenting an old certificate for which they have
// managed to obtain the private key.
// 
// 2. The server is misconfigured and is not presenting a valid cert.
// 
// 3. Our clock is wrong.

Get Error by errorDescription

Get the class of an error by its errorDescription.

const Err = chromiumNetErrors.getErrorByDescription('CERT_DATE_INVALID');
const err = new Err();

console.log(err instanceof chromiumNetErrors.CertDateInvalidError);
// true

console.log(err.isCertificateError());
// true

console.log(err.type); 
// 'certificate'

console.log(err.message);
// The server responded with a certificate that, by our clock, appears to
// either not yet be valid or to have expired. This could mean:
// 
// 1. An attacker is presenting an old certificate for which they have
// managed to obtain the private key.
// 
// 2. The server is misconfigured and is not presenting a valid cert.
// 
// 3. Our clock is wrong.

Get All Errors

Get an array of all possible errors.

console.log(chromiumNetErrors.getErrors());

// [ { name: 'IoPendingError',
//     code: -1,
//     description: 'IO_PENDING',
//     type: 'system',
//     message: 'An asynchronous IO operation is not yet complete.  This usually does not\nindicate a fatal error.  Typically this error will be generated as a\nnotification to wait for some external notification that the IO operation\nfinally completed.' },
//   { name: 'FailedError',
//     code: -2,
//     description: 'FAILED',
//     type: 'system',
//     message: 'A generic failure occurred.' },
//   { name: 'AbortedError',
//     code: -3,
//     description: 'ABORTED',
//     type: 'system',
//     message: 'An operation was aborted (due to user action).' },
//   { name: 'InvalidArgumentError',
//     code: -4,
//     description: 'INVALID_ARGUMENT',
//     type: 'system',
//     message: 'An argument to the function is incorrect.' },
//   { name: 'InvalidHandleError',
//     code: -5,
//     description: 'INVALID_HANDLE',
//     type: 'system',
//     message: 'The handle or file descriptor is invalid.' },
//   ...
// ]

List of Errors

IoPendingError

An asynchronous IO operation is not yet complete. This usually does not indicate a fatal error. Typically this error will be generated as a notification to wait for some external notification that the IO operation finally completed.

Name: IoPendingError
Code: -1
Description: IO_PENDING
Type: system

const err = new chromiumNetErrors.IoPendingError();
// or
const Err = chromiumNetErrors.getErrorByCode(-1);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('IO_PENDING');
const err = new Err();

FailedError

A generic failure occurred.

Name: FailedError
Code: -2
Description: FAILED
Type: system

const err = new chromiumNetErrors.FailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-2);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FAILED');
const err = new Err();

AbortedError

An operation was aborted (due to user action).

Name: AbortedError
Code: -3
Description: ABORTED
Type: system

const err = new chromiumNetErrors.AbortedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-3);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ABORTED');
const err = new Err();

InvalidArgumentError

An argument to the function is incorrect.

Name: InvalidArgumentError
Code: -4
Description: INVALID_ARGUMENT
Type: system

const err = new chromiumNetErrors.InvalidArgumentError();
// or
const Err = chromiumNetErrors.getErrorByCode(-4);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_ARGUMENT');
const err = new Err();

InvalidHandleError

The handle or file descriptor is invalid.

Name: InvalidHandleError
Code: -5
Description: INVALID_HANDLE
Type: system

const err = new chromiumNetErrors.InvalidHandleError();
// or
const Err = chromiumNetErrors.getErrorByCode(-5);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_HANDLE');
const err = new Err();

FileNotFoundError

The file or directory cannot be found.

Name: FileNotFoundError
Code: -6
Description: FILE_NOT_FOUND
Type: system

const err = new chromiumNetErrors.FileNotFoundError();
// or
const Err = chromiumNetErrors.getErrorByCode(-6);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_NOT_FOUND');
const err = new Err();

TimedOutError

An operation timed out.

Name: TimedOutError
Code: -7
Description: TIMED_OUT
Type: system

const err = new chromiumNetErrors.TimedOutError();
// or
const Err = chromiumNetErrors.getErrorByCode(-7);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TIMED_OUT');
const err = new Err();

FileTooBigError

The file is too large.

Name: FileTooBigError
Code: -8
Description: FILE_TOO_BIG
Type: system

const err = new chromiumNetErrors.FileTooBigError();
// or
const Err = chromiumNetErrors.getErrorByCode(-8);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_TOO_BIG');
const err = new Err();

UnexpectedError

An unexpected error. This may be caused by a programming mistake or an invalid assumption.

Name: UnexpectedError
Code: -9
Description: UNEXPECTED
Type: system

const err = new chromiumNetErrors.UnexpectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-9);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNEXPECTED');
const err = new Err();

AccessDeniedError

Permission to access a resource, other than the network, was denied.

Name: AccessDeniedError
Code: -10
Description: ACCESS_DENIED
Type: system

const err = new chromiumNetErrors.AccessDeniedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-10);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ACCESS_DENIED');
const err = new Err();

NotImplementedError

The operation failed because of unimplemented functionality.

Name: NotImplementedError
Code: -11
Description: NOT_IMPLEMENTED
Type: system

const err = new chromiumNetErrors.NotImplementedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-11);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NOT_IMPLEMENTED');
const err = new Err();

InsufficientResourcesError

There were not enough resources to complete the operation.

Name: InsufficientResourcesError
Code: -12
Description: INSUFFICIENT_RESOURCES
Type: system

const err = new chromiumNetErrors.InsufficientResourcesError();
// or
const Err = chromiumNetErrors.getErrorByCode(-12);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INSUFFICIENT_RESOURCES');
const err = new Err();

OutOfMemoryError

Memory allocation failed.

Name: OutOfMemoryError
Code: -13
Description: OUT_OF_MEMORY
Type: system

const err = new chromiumNetErrors.OutOfMemoryError();
// or
const Err = chromiumNetErrors.getErrorByCode(-13);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('OUT_OF_MEMORY');
const err = new Err();

UploadFileChangedError

The file upload failed because the file's modification time was different from the expectation.

Name: UploadFileChangedError
Code: -14
Description: UPLOAD_FILE_CHANGED
Type: system

const err = new chromiumNetErrors.UploadFileChangedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-14);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UPLOAD_FILE_CHANGED');
const err = new Err();

SocketNotConnectedError

The socket is not connected.

Name: SocketNotConnectedError
Code: -15
Description: SOCKET_NOT_CONNECTED
Type: system

const err = new chromiumNetErrors.SocketNotConnectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-15);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_NOT_CONNECTED');
const err = new Err();

FileExistsError

The file already exists.

Name: FileExistsError
Code: -16
Description: FILE_EXISTS
Type: system

const err = new chromiumNetErrors.FileExistsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-16);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_EXISTS');
const err = new Err();

FilePathTooLongError

The path or file name is too long.

Name: FilePathTooLongError
Code: -17
Description: FILE_PATH_TOO_LONG
Type: system

const err = new chromiumNetErrors.FilePathTooLongError();
// or
const Err = chromiumNetErrors.getErrorByCode(-17);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_PATH_TOO_LONG');
const err = new Err();

FileNoSpaceError

Not enough room left on the disk.

Name: FileNoSpaceError
Code: -18
Description: FILE_NO_SPACE
Type: system

const err = new chromiumNetErrors.FileNoSpaceError();
// or
const Err = chromiumNetErrors.getErrorByCode(-18);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_NO_SPACE');
const err = new Err();

FileVirusInfectedError

The file has a virus.

Name: FileVirusInfectedError
Code: -19
Description: FILE_VIRUS_INFECTED
Type: system

const err = new chromiumNetErrors.FileVirusInfectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-19);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FILE_VIRUS_INFECTED');
const err = new Err();

BlockedByClientError

The client chose to block the request.

Name: BlockedByClientError
Code: -20
Description: BLOCKED_BY_CLIENT
Type: system

const err = new chromiumNetErrors.BlockedByClientError();
// or
const Err = chromiumNetErrors.getErrorByCode(-20);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BLOCKED_BY_CLIENT');
const err = new Err();

NetworkChangedError

The network changed.

Name: NetworkChangedError
Code: -21
Description: NETWORK_CHANGED
Type: system

const err = new chromiumNetErrors.NetworkChangedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-21);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NETWORK_CHANGED');
const err = new Err();

BlockedByAdministratorError

The request was blocked by the URL block list configured by the domain administrator.

Name: BlockedByAdministratorError
Code: -22
Description: BLOCKED_BY_ADMINISTRATOR
Type: system

const err = new chromiumNetErrors.BlockedByAdministratorError();
// or
const Err = chromiumNetErrors.getErrorByCode(-22);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BLOCKED_BY_ADMINISTRATOR');
const err = new Err();

SocketIsConnectedError

The socket is already connected.

Name: SocketIsConnectedError
Code: -23
Description: SOCKET_IS_CONNECTED
Type: system

const err = new chromiumNetErrors.SocketIsConnectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-23);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_IS_CONNECTED');
const err = new Err();

BlockedEnrollmentCheckPendingError

The request was blocked because the forced reenrollment check is still pending. This error can only occur on ChromeOS. The error can be emitted by code in chrome/browser/policy/policy_helpers.cc.

Name: BlockedEnrollmentCheckPendingError
Code: -24
Description: BLOCKED_ENROLLMENT_CHECK_PENDING
Type: system

const err = new chromiumNetErrors.BlockedEnrollmentCheckPendingError();
// or
const Err = chromiumNetErrors.getErrorByCode(-24);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BLOCKED_ENROLLMENT_CHECK_PENDING');
const err = new Err();

UploadStreamRewindNotSupportedError

The upload failed because the upload stream needed to be re-read, due to a retry or a redirect, but the upload stream doesn't support that operation.

Name: UploadStreamRewindNotSupportedError
Code: -25
Description: UPLOAD_STREAM_REWIND_NOT_SUPPORTED
Type: system

const err = new chromiumNetErrors.UploadStreamRewindNotSupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-25);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UPLOAD_STREAM_REWIND_NOT_SUPPORTED');
const err = new Err();

ContextShutDownError

The request failed because the URLRequestContext is shutting down, or has been shut down.

Name: ContextShutDownError
Code: -26
Description: CONTEXT_SHUT_DOWN
Type: system

const err = new chromiumNetErrors.ContextShutDownError();
// or
const Err = chromiumNetErrors.getErrorByCode(-26);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONTEXT_SHUT_DOWN');
const err = new Err();

BlockedByResponseError

The request failed because the response was delivered along with requirements which are not met ('X-Frame-Options' and 'Content-Security-Policy' ancestor checks and 'Cross-Origin-Resource-Policy', for instance).

Name: BlockedByResponseError
Code: -27
Description: BLOCKED_BY_RESPONSE
Type: system

const err = new chromiumNetErrors.BlockedByResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-27);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BLOCKED_BY_RESPONSE');
const err = new Err();

CleartextNotPermittedError

The request was blocked by system policy disallowing some or all cleartext requests. Used for NetworkSecurityPolicy on Android.

Name: CleartextNotPermittedError
Code: -29
Description: CLEARTEXT_NOT_PERMITTED
Type: system

const err = new chromiumNetErrors.CleartextNotPermittedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-29);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CLEARTEXT_NOT_PERMITTED');
const err = new Err();

BlockedByCspError

The request was blocked by a Content Security Policy

Name: BlockedByCspError
Code: -30
Description: BLOCKED_BY_CSP
Type: system

const err = new chromiumNetErrors.BlockedByCspError();
// or
const Err = chromiumNetErrors.getErrorByCode(-30);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BLOCKED_BY_CSP');
const err = new Err();

H2OrQuicRequiredError

The request was blocked because of no H/2 or QUIC session.

Name: H2OrQuicRequiredError
Code: -31
Description: H2_OR_QUIC_REQUIRED
Type: system

const err = new chromiumNetErrors.H2OrQuicRequiredError();
// or
const Err = chromiumNetErrors.getErrorByCode(-31);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('H2_OR_QUIC_REQUIRED');
const err = new Err();

ConnectionClosedError

A connection was closed (corresponding to a TCP FIN).

Name: ConnectionClosedError
Code: -100
Description: CONNECTION_CLOSED
Type: connection

const err = new chromiumNetErrors.ConnectionClosedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-100);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_CLOSED');
const err = new Err();

ConnectionResetError

A connection was reset (corresponding to a TCP RST).

Name: ConnectionResetError
Code: -101
Description: CONNECTION_RESET
Type: connection

const err = new chromiumNetErrors.ConnectionResetError();
// or
const Err = chromiumNetErrors.getErrorByCode(-101);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_RESET');
const err = new Err();

ConnectionRefusedError

A connection attempt was refused.

Name: ConnectionRefusedError
Code: -102
Description: CONNECTION_REFUSED
Type: connection

const err = new chromiumNetErrors.ConnectionRefusedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-102);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_REFUSED');
const err = new Err();

ConnectionAbortedError

A connection timed out as a result of not receiving an ACK for data sent. This can include a FIN packet that did not get ACK'd.

Name: ConnectionAbortedError
Code: -103
Description: CONNECTION_ABORTED
Type: connection

const err = new chromiumNetErrors.ConnectionAbortedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-103);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_ABORTED');
const err = new Err();

ConnectionFailedError

A connection attempt failed.

Name: ConnectionFailedError
Code: -104
Description: CONNECTION_FAILED
Type: connection

const err = new chromiumNetErrors.ConnectionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-104);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_FAILED');
const err = new Err();

NameNotResolvedError

The host name could not be resolved.

Name: NameNotResolvedError
Code: -105
Description: NAME_NOT_RESOLVED
Type: connection

const err = new chromiumNetErrors.NameNotResolvedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-105);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NAME_NOT_RESOLVED');
const err = new Err();

InternetDisconnectedError

The Internet connection has been lost.

Name: InternetDisconnectedError
Code: -106
Description: INTERNET_DISCONNECTED
Type: connection

const err = new chromiumNetErrors.InternetDisconnectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-106);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INTERNET_DISCONNECTED');
const err = new Err();

SslProtocolError

An SSL protocol error occurred.

Name: SslProtocolError
Code: -107
Description: SSL_PROTOCOL_ERROR
Type: connection

const err = new chromiumNetErrors.SslProtocolError();
// or
const Err = chromiumNetErrors.getErrorByCode(-107);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_PROTOCOL_ERROR');
const err = new Err();

AddressInvalidError

The IP address or port number is invalid (e.g., cannot connect to the IP address 0 or the port 0).

Name: AddressInvalidError
Code: -108
Description: ADDRESS_INVALID
Type: connection

const err = new chromiumNetErrors.AddressInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-108);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ADDRESS_INVALID');
const err = new Err();

AddressUnreachableError

The IP address is unreachable. This usually means that there is no route to the specified host or network.

Name: AddressUnreachableError
Code: -109
Description: ADDRESS_UNREACHABLE
Type: connection

const err = new chromiumNetErrors.AddressUnreachableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-109);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ADDRESS_UNREACHABLE');
const err = new Err();

SslClientAuthCertNeededError

The server requested a client certificate for SSL client authentication.

Name: SslClientAuthCertNeededError
Code: -110
Description: SSL_CLIENT_AUTH_CERT_NEEDED
Type: connection

const err = new chromiumNetErrors.SslClientAuthCertNeededError();
// or
const Err = chromiumNetErrors.getErrorByCode(-110);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_CERT_NEEDED');
const err = new Err();

TunnelConnectionFailedError

A tunnel connection through the proxy could not be established.

Name: TunnelConnectionFailedError
Code: -111
Description: TUNNEL_CONNECTION_FAILED
Type: connection

const err = new chromiumNetErrors.TunnelConnectionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-111);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TUNNEL_CONNECTION_FAILED');
const err = new Err();

NoSslVersionsEnabledError

No SSL protocol versions are enabled.

Name: NoSslVersionsEnabledError
Code: -112
Description: NO_SSL_VERSIONS_ENABLED
Type: connection

const err = new chromiumNetErrors.NoSslVersionsEnabledError();
// or
const Err = chromiumNetErrors.getErrorByCode(-112);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NO_SSL_VERSIONS_ENABLED');
const err = new Err();

SslVersionOrCipherMismatchError

The client and server don't support a common SSL protocol version or cipher suite.

Name: SslVersionOrCipherMismatchError
Code: -113
Description: SSL_VERSION_OR_CIPHER_MISMATCH
Type: connection

const err = new chromiumNetErrors.SslVersionOrCipherMismatchError();
// or
const Err = chromiumNetErrors.getErrorByCode(-113);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_VERSION_OR_CIPHER_MISMATCH');
const err = new Err();

SslRenegotiationRequestedError

The server requested a renegotiation (rehandshake).

Name: SslRenegotiationRequestedError
Code: -114
Description: SSL_RENEGOTIATION_REQUESTED
Type: connection

const err = new chromiumNetErrors.SslRenegotiationRequestedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-114);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_RENEGOTIATION_REQUESTED');
const err = new Err();

ProxyAuthUnsupportedError

The proxy requested authentication (for tunnel establishment) with an unsupported method.

Name: ProxyAuthUnsupportedError
Code: -115
Description: PROXY_AUTH_UNSUPPORTED
Type: connection

const err = new chromiumNetErrors.ProxyAuthUnsupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-115);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_AUTH_UNSUPPORTED');
const err = new Err();

CertErrorInSslRenegotiationError

During SSL renegotiation (rehandshake), the server sent a certificate with an error.
Note: this error is not in the -2xx range so that it won't be handled as a certificate error.

Name: CertErrorInSslRenegotiationError
Code: -116
Description: CERT_ERROR_IN_SSL_RENEGOTIATION
Type: connection

const err = new chromiumNetErrors.CertErrorInSslRenegotiationError();
// or
const Err = chromiumNetErrors.getErrorByCode(-116);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_ERROR_IN_SSL_RENEGOTIATION');
const err = new Err();

BadSslClientAuthCertError

The SSL handshake failed because of a bad or missing client certificate.

Name: BadSslClientAuthCertError
Code: -117
Description: BAD_SSL_CLIENT_AUTH_CERT
Type: connection

const err = new chromiumNetErrors.BadSslClientAuthCertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-117);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('BAD_SSL_CLIENT_AUTH_CERT');
const err = new Err();

ConnectionTimedOutError

A connection attempt timed out.

Name: ConnectionTimedOutError
Code: -118
Description: CONNECTION_TIMED_OUT
Type: connection

const err = new chromiumNetErrors.ConnectionTimedOutError();
// or
const Err = chromiumNetErrors.getErrorByCode(-118);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONNECTION_TIMED_OUT');
const err = new Err();

HostResolverQueueTooLargeError

There are too many pending DNS resolves, so a request in the queue was aborted.

Name: HostResolverQueueTooLargeError
Code: -119
Description: HOST_RESOLVER_QUEUE_TOO_LARGE
Type: connection

const err = new chromiumNetErrors.HostResolverQueueTooLargeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-119);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HOST_RESOLVER_QUEUE_TOO_LARGE');
const err = new Err();

SocksConnectionFailedError

Failed establishing a connection to the SOCKS proxy server for a target host.

Name: SocksConnectionFailedError
Code: -120
Description: SOCKS_CONNECTION_FAILED
Type: connection

const err = new chromiumNetErrors.SocksConnectionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-120);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKS_CONNECTION_FAILED');
const err = new Err();

SocksConnectionHostUnreachableError

The SOCKS proxy server failed establishing connection to the target host because that host is unreachable.

Name: SocksConnectionHostUnreachableError
Code: -121
Description: SOCKS_CONNECTION_HOST_UNREACHABLE
Type: connection

const err = new chromiumNetErrors.SocksConnectionHostUnreachableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-121);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKS_CONNECTION_HOST_UNREACHABLE');
const err = new Err();

AlpnNegotiationFailedError

The request to negotiate an alternate protocol failed.

Name: AlpnNegotiationFailedError
Code: -122
Description: ALPN_NEGOTIATION_FAILED
Type: connection

const err = new chromiumNetErrors.AlpnNegotiationFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-122);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ALPN_NEGOTIATION_FAILED');
const err = new Err();

SslNoRenegotiationError

The peer sent an SSL no_renegotiation alert message.

Name: SslNoRenegotiationError
Code: -123
Description: SSL_NO_RENEGOTIATION
Type: connection

const err = new chromiumNetErrors.SslNoRenegotiationError();
// or
const Err = chromiumNetErrors.getErrorByCode(-123);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_NO_RENEGOTIATION');
const err = new Err();

WinsockUnexpectedWrittenBytesError

Winsock sometimes reports more data written than passed. This is probably due to a broken LSP.

Name: WinsockUnexpectedWrittenBytesError
Code: -124
Description: WINSOCK_UNEXPECTED_WRITTEN_BYTES
Type: connection

const err = new chromiumNetErrors.WinsockUnexpectedWrittenBytesError();
// or
const Err = chromiumNetErrors.getErrorByCode(-124);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('WINSOCK_UNEXPECTED_WRITTEN_BYTES');
const err = new Err();

SslDecompressionFailureAlertError

An SSL peer sent us a fatal decompression_failure alert. This typically occurs when a peer selects DEFLATE compression in the mistaken belief that it supports it.

Name: SslDecompressionFailureAlertError
Code: -125
Description: SSL_DECOMPRESSION_FAILURE_ALERT
Type: connection

const err = new chromiumNetErrors.SslDecompressionFailureAlertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-125);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_DECOMPRESSION_FAILURE_ALERT');
const err = new Err();

SslBadRecordMacAlertError

An SSL peer sent us a fatal bad_record_mac alert. This has been observed from servers with buggy DEFLATE support.

Name: SslBadRecordMacAlertError
Code: -126
Description: SSL_BAD_RECORD_MAC_ALERT
Type: connection

const err = new chromiumNetErrors.SslBadRecordMacAlertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-126);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_BAD_RECORD_MAC_ALERT');
const err = new Err();

ProxyAuthRequestedError

The proxy requested authentication (for tunnel establishment).

Name: ProxyAuthRequestedError
Code: -127
Description: PROXY_AUTH_REQUESTED
Type: connection

const err = new chromiumNetErrors.ProxyAuthRequestedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-127);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_AUTH_REQUESTED');
const err = new Err();

ProxyConnectionFailedError

Could not create a connection to the proxy server. An error occurred either in resolving its name, or in connecting a socket to it. Note that this does NOT include failures during the actual "CONNECT" method of an HTTP proxy.

Name: ProxyConnectionFailedError
Code: -130
Description: PROXY_CONNECTION_FAILED
Type: connection

const err = new chromiumNetErrors.ProxyConnectionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-130);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_CONNECTION_FAILED');
const err = new Err();

MandatoryProxyConfigurationFailedError

A mandatory proxy configuration could not be used. Currently this means that a mandatory PAC script could not be fetched, parsed or executed.

Name: MandatoryProxyConfigurationFailedError
Code: -131
Description: MANDATORY_PROXY_CONFIGURATION_FAILED
Type: connection

const err = new chromiumNetErrors.MandatoryProxyConfigurationFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-131);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('MANDATORY_PROXY_CONFIGURATION_FAILED');
const err = new Err();

PreconnectMaxSocketLimitError

We've hit the max socket limit for the socket pool while preconnecting. We don't bother trying to preconnect more sockets.

Name: PreconnectMaxSocketLimitError
Code: -133
Description: PRECONNECT_MAX_SOCKET_LIMIT
Type: connection

const err = new chromiumNetErrors.PreconnectMaxSocketLimitError();
// or
const Err = chromiumNetErrors.getErrorByCode(-133);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PRECONNECT_MAX_SOCKET_LIMIT');
const err = new Err();

SslClientAuthPrivateKeyAccessDeniedError

The permission to use the SSL client certificate's private key was denied.

Name: SslClientAuthPrivateKeyAccessDeniedError
Code: -134
Description: SSL_CLIENT_AUTH_PRIVATE_KEY_ACCESS_DENIED
Type: connection

const err = new chromiumNetErrors.SslClientAuthPrivateKeyAccessDeniedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-134);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_PRIVATE_KEY_ACCESS_DENIED');
const err = new Err();

SslClientAuthCertNoPrivateKeyError

The SSL client certificate has no private key.

Name: SslClientAuthCertNoPrivateKeyError
Code: -135
Description: SSL_CLIENT_AUTH_CERT_NO_PRIVATE_KEY
Type: connection

const err = new chromiumNetErrors.SslClientAuthCertNoPrivateKeyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-135);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_CERT_NO_PRIVATE_KEY');
const err = new Err();

ProxyCertificateInvalidError

The certificate presented by the HTTPS Proxy was invalid.

Name: ProxyCertificateInvalidError
Code: -136
Description: PROXY_CERTIFICATE_INVALID
Type: connection

const err = new chromiumNetErrors.ProxyCertificateInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-136);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_CERTIFICATE_INVALID');
const err = new Err();

NameResolutionFailedError

An error occurred when trying to do a name resolution (DNS).

Name: NameResolutionFailedError
Code: -137
Description: NAME_RESOLUTION_FAILED
Type: connection

const err = new chromiumNetErrors.NameResolutionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-137);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NAME_RESOLUTION_FAILED');
const err = new Err();

NetworkAccessDeniedError

Permission to access the network was denied. This is used to distinguish errors that were most likely caused by a firewall from other access denied errors. See also ERR_ACCESS_DENIED.

Name: NetworkAccessDeniedError
Code: -138
Description: NETWORK_ACCESS_DENIED
Type: connection

const err = new chromiumNetErrors.NetworkAccessDeniedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-138);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NETWORK_ACCESS_DENIED');
const err = new Err();

TemporarilyThrottledError

The request throttler module cancelled this request to avoid DDOS.

Name: TemporarilyThrottledError
Code: -139
Description: TEMPORARILY_THROTTLED
Type: connection

const err = new chromiumNetErrors.TemporarilyThrottledError();
// or
const Err = chromiumNetErrors.getErrorByCode(-139);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TEMPORARILY_THROTTLED');
const err = new Err();

HttpsProxyTunnelResponseRedirectError

A request to create an SSL tunnel connection through the HTTPS proxy received a 302 (temporary redirect) response. The response body might include a description of why the request failed.
TODO(https://crbug.com/928551): This is deprecated and should not be used by new code.

Name: HttpsProxyTunnelResponseRedirectError
Code: -140
Description: HTTPS_PROXY_TUNNEL_RESPONSE_REDIRECT
Type: connection

const err = new chromiumNetErrors.HttpsProxyTunnelResponseRedirectError();
// or
const Err = chromiumNetErrors.getErrorByCode(-140);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTPS_PROXY_TUNNEL_RESPONSE_REDIRECT');
const err = new Err();

SslClientAuthSignatureFailedError

We were unable to sign the CertificateVerify data of an SSL client auth handshake with the client certificate's private key.
Possible causes for this include the user implicitly or explicitly denying access to the private key, the private key may not be valid for signing, the key may be relying on a cached handle which is no longer valid, or the CSP won't allow arbitrary data to be signed.

Name: SslClientAuthSignatureFailedError
Code: -141
Description: SSL_CLIENT_AUTH_SIGNATURE_FAILED
Type: connection

const err = new chromiumNetErrors.SslClientAuthSignatureFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-141);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_SIGNATURE_FAILED');
const err = new Err();

MsgTooBigError

The message was too large for the transport. (for example a UDP message which exceeds size threshold).

Name: MsgTooBigError
Code: -142
Description: MSG_TOO_BIG
Type: connection

const err = new chromiumNetErrors.MsgTooBigError();
// or
const Err = chromiumNetErrors.getErrorByCode(-142);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('MSG_TOO_BIG');
const err = new Err();

WsProtocolError

Websocket protocol error. Indicates that we are terminating the connection due to a malformed frame or other protocol violation.

Name: WsProtocolError
Code: -145
Description: WS_PROTOCOL_ERROR
Type: connection

const err = new chromiumNetErrors.WsProtocolError();
// or
const Err = chromiumNetErrors.getErrorByCode(-145);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('WS_PROTOCOL_ERROR');
const err = new Err();

AddressInUseError

Returned when attempting to bind an address that is already in use.

Name: AddressInUseError
Code: -147
Description: ADDRESS_IN_USE
Type: connection

const err = new chromiumNetErrors.AddressInUseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-147);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ADDRESS_IN_USE');
const err = new Err();

SslHandshakeNotCompletedError

An operation failed because the SSL handshake has not completed.

Name: SslHandshakeNotCompletedError
Code: -148
Description: SSL_HANDSHAKE_NOT_COMPLETED
Type: connection

const err = new chromiumNetErrors.SslHandshakeNotCompletedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-148);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_HANDSHAKE_NOT_COMPLETED');
const err = new Err();

SslBadPeerPublicKeyError

SSL peer's public key is invalid.

Name: SslBadPeerPublicKeyError
Code: -149
Description: SSL_BAD_PEER_PUBLIC_KEY
Type: connection

const err = new chromiumNetErrors.SslBadPeerPublicKeyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-149);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_BAD_PEER_PUBLIC_KEY');
const err = new Err();

SslPinnedKeyNotInCertChainError

The certificate didn't match the built-in public key pins for the host name. The pins are set in net/http/transport_security_state.cc and require that one of a set of public keys exist on the path from the leaf to the root.

Name: SslPinnedKeyNotInCertChainError
Code: -150
Description: SSL_PINNED_KEY_NOT_IN_CERT_CHAIN
Type: connection

const err = new chromiumNetErrors.SslPinnedKeyNotInCertChainError();
// or
const Err = chromiumNetErrors.getErrorByCode(-150);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_PINNED_KEY_NOT_IN_CERT_CHAIN');
const err = new Err();

ClientAuthCertTypeUnsupportedError

Server request for client certificate did not contain any types we support.

Name: ClientAuthCertTypeUnsupportedError
Code: -151
Description: CLIENT_AUTH_CERT_TYPE_UNSUPPORTED
Type: connection

const err = new chromiumNetErrors.ClientAuthCertTypeUnsupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-151);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CLIENT_AUTH_CERT_TYPE_UNSUPPORTED');
const err = new Err();

SslDecryptErrorAlertError

An SSL peer sent us a fatal decrypt_error alert. This typically occurs when a peer could not correctly verify a signature (in CertificateVerify or ServerKeyExchange) or validate a Finished message.

Name: SslDecryptErrorAlertError
Code: -153
Description: SSL_DECRYPT_ERROR_ALERT
Type: connection

const err = new chromiumNetErrors.SslDecryptErrorAlertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-153);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_DECRYPT_ERROR_ALERT');
const err = new Err();

WsThrottleQueueTooLargeError

There are too many pending WebSocketJob instances, so the new job was not pushed to the queue.

Name: WsThrottleQueueTooLargeError
Code: -154
Description: WS_THROTTLE_QUEUE_TOO_LARGE
Type: connection

const err = new chromiumNetErrors.WsThrottleQueueTooLargeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-154);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('WS_THROTTLE_QUEUE_TOO_LARGE');
const err = new Err();

SslServerCertChangedError

The SSL server certificate changed in a renegotiation.

Name: SslServerCertChangedError
Code: -156
Description: SSL_SERVER_CERT_CHANGED
Type: connection

const err = new chromiumNetErrors.SslServerCertChangedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-156);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_SERVER_CERT_CHANGED');
const err = new Err();

SslUnrecognizedNameAlertError

The SSL server sent us a fatal unrecognized_name alert.

Name: SslUnrecognizedNameAlertError
Code: -159
Description: SSL_UNRECOGNIZED_NAME_ALERT
Type: connection

const err = new chromiumNetErrors.SslUnrecognizedNameAlertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-159);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_UNRECOGNIZED_NAME_ALERT');
const err = new Err();

SocketSetReceiveBufferSizeError

Failed to set the socket's receive buffer size as requested.

Name: SocketSetReceiveBufferSizeError
Code: -160
Description: SOCKET_SET_RECEIVE_BUFFER_SIZE_ERROR
Type: connection

const err = new chromiumNetErrors.SocketSetReceiveBufferSizeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-160);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_SET_RECEIVE_BUFFER_SIZE_ERROR');
const err = new Err();

SocketSetSendBufferSizeError

Failed to set the socket's send buffer size as requested.

Name: SocketSetSendBufferSizeError
Code: -161
Description: SOCKET_SET_SEND_BUFFER_SIZE_ERROR
Type: connection

const err = new chromiumNetErrors.SocketSetSendBufferSizeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-161);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_SET_SEND_BUFFER_SIZE_ERROR');
const err = new Err();

SocketReceiveBufferSizeUnchangeableError

Failed to set the socket's receive buffer size as requested, despite success return code from setsockopt.

Name: SocketReceiveBufferSizeUnchangeableError
Code: -162
Description: SOCKET_RECEIVE_BUFFER_SIZE_UNCHANGEABLE
Type: connection

const err = new chromiumNetErrors.SocketReceiveBufferSizeUnchangeableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-162);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_RECEIVE_BUFFER_SIZE_UNCHANGEABLE');
const err = new Err();

SocketSendBufferSizeUnchangeableError

Failed to set the socket's send buffer size as requested, despite success return code from setsockopt.

Name: SocketSendBufferSizeUnchangeableError
Code: -163
Description: SOCKET_SEND_BUFFER_SIZE_UNCHANGEABLE
Type: connection

const err = new chromiumNetErrors.SocketSendBufferSizeUnchangeableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-163);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SOCKET_SEND_BUFFER_SIZE_UNCHANGEABLE');
const err = new Err();

SslClientAuthCertBadFormatError

Failed to import a client certificate from the platform store into the SSL library.

Name: SslClientAuthCertBadFormatError
Code: -164
Description: SSL_CLIENT_AUTH_CERT_BAD_FORMAT
Type: connection

const err = new chromiumNetErrors.SslClientAuthCertBadFormatError();
// or
const Err = chromiumNetErrors.getErrorByCode(-164);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_CERT_BAD_FORMAT');
const err = new Err();

IcannNameCollisionError

Resolving a hostname to an IP address list included the IPv4 address "127.0.53.53". This is a special IP address which ICANN has recommended to indicate there was a name collision, and alert admins to a potential problem.

Name: IcannNameCollisionError
Code: -166
Description: ICANN_NAME_COLLISION
Type: connection

const err = new chromiumNetErrors.IcannNameCollisionError();
// or
const Err = chromiumNetErrors.getErrorByCode(-166);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ICANN_NAME_COLLISION');
const err = new Err();

SslServerCertBadFormatError

The SSL server presented a certificate which could not be decoded. This is not a certificate error code as no X509Certificate object is available. This error is fatal.

Name: SslServerCertBadFormatError
Code: -167
Description: SSL_SERVER_CERT_BAD_FORMAT
Type: connection

const err = new chromiumNetErrors.SslServerCertBadFormatError();
// or
const Err = chromiumNetErrors.getErrorByCode(-167);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_SERVER_CERT_BAD_FORMAT');
const err = new Err();

CtSthParsingFailedError

Certificate Transparency: Received a signed tree head that failed to parse.

Name: CtSthParsingFailedError
Code: -168
Description: CT_STH_PARSING_FAILED
Type: connection

const err = new chromiumNetErrors.CtSthParsingFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-168);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CT_STH_PARSING_FAILED');
const err = new Err();

CtSthIncompleteError

Certificate Transparency: Received a signed tree head whose JSON parsing was OK but was missing some of the fields.

Name: CtSthIncompleteError
Code: -169
Description: CT_STH_INCOMPLETE
Type: connection

const err = new chromiumNetErrors.CtSthIncompleteError();
// or
const Err = chromiumNetErrors.getErrorByCode(-169);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CT_STH_INCOMPLETE');
const err = new Err();

UnableToReuseConnectionForProxyAuthError

The attempt to reuse a connection to send proxy auth credentials failed before the AuthController was used to generate credentials. The caller should reuse the controller with a new connection. This error is only used internally by the network stack.

Name: UnableToReuseConnectionForProxyAuthError
Code: -170
Description: UNABLE_TO_REUSE_CONNECTION_FOR_PROXY_AUTH
Type: connection

const err = new chromiumNetErrors.UnableToReuseConnectionForProxyAuthError();
// or
const Err = chromiumNetErrors.getErrorByCode(-170);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNABLE_TO_REUSE_CONNECTION_FOR_PROXY_AUTH');
const err = new Err();

CtConsistencyProofParsingFailedError

Certificate Transparency: Failed to parse the received consistency proof.

Name: CtConsistencyProofParsingFailedError
Code: -171
Description: CT_CONSISTENCY_PROOF_PARSING_FAILED
Type: connection

const err = new chromiumNetErrors.CtConsistencyProofParsingFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-171);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CT_CONSISTENCY_PROOF_PARSING_FAILED');
const err = new Err();

SslObsoleteCipherError

The SSL server required an unsupported cipher suite that has since been removed. This error will temporarily be signaled on a fallback for one or two releases immediately following a cipher suite's removal, after which the fallback will be removed.

Name: SslObsoleteCipherError
Code: -172
Description: SSL_OBSOLETE_CIPHER
Type: connection

const err = new chromiumNetErrors.SslObsoleteCipherError();
// or
const Err = chromiumNetErrors.getErrorByCode(-172);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_OBSOLETE_CIPHER');
const err = new Err();

WsUpgradeError

When a WebSocket handshake is done successfully and the connection has been upgraded, the URLRequest is cancelled with this error code.

Name: WsUpgradeError
Code: -173
Description: WS_UPGRADE
Type: connection

const err = new chromiumNetErrors.WsUpgradeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-173);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('WS_UPGRADE');
const err = new Err();

ReadIfReadyNotImplementedError

Socket ReadIfReady support is not implemented. This error should not be user visible, because the normal Read() method is used as a fallback.

Name: ReadIfReadyNotImplementedError
Code: -174
Description: READ_IF_READY_NOT_IMPLEMENTED
Type: connection

const err = new chromiumNetErrors.ReadIfReadyNotImplementedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-174);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('READ_IF_READY_NOT_IMPLEMENTED');
const err = new Err();

NoBufferSpaceError

No socket buffer space is available.

Name: NoBufferSpaceError
Code: -176
Description: NO_BUFFER_SPACE
Type: connection

const err = new chromiumNetErrors.NoBufferSpaceError();
// or
const Err = chromiumNetErrors.getErrorByCode(-176);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NO_BUFFER_SPACE');
const err = new Err();

SslClientAuthNoCommonAlgorithmsError

There were no common signature algorithms between our client certificate private key and the server's preferences.

Name: SslClientAuthNoCommonAlgorithmsError
Code: -177
Description: SSL_CLIENT_AUTH_NO_COMMON_ALGORITHMS
Type: connection

const err = new chromiumNetErrors.SslClientAuthNoCommonAlgorithmsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-177);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_CLIENT_AUTH_NO_COMMON_ALGORITHMS');
const err = new Err();

EarlyDataRejectedError

TLS 1.3 early data was rejected by the server. This will be received before any data is returned from the socket. The request should be retried with early data disabled.

Name: EarlyDataRejectedError
Code: -178
Description: EARLY_DATA_REJECTED
Type: connection

const err = new chromiumNetErrors.EarlyDataRejectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-178);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('EARLY_DATA_REJECTED');
const err = new Err();

WrongVersionOnEarlyDataError

TLS 1.3 early data was offered, but the server responded with TLS 1.2 or earlier. This is an internal error code to account for a backwards-compatibility issue with early data and TLS 1.2. It will be received before any data is returned from the socket. The request should be retried with early data disabled.
See https://tools.ietf.org/html/rfc8446#appendix-D.3 for details.

Name: WrongVersionOnEarlyDataError
Code: -179
Description: WRONG_VERSION_ON_EARLY_DATA
Type: connection

const err = new chromiumNetErrors.WrongVersionOnEarlyDataError();
// or
const Err = chromiumNetErrors.getErrorByCode(-179);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('WRONG_VERSION_ON_EARLY_DATA');
const err = new Err();

Tls13DowngradeDetectedError

TLS 1.3 was enabled, but a lower version was negotiated and the server returned a value indicating it supported TLS 1.3. This is part of a security check in TLS 1.3, but it may also indicate the user is behind a buggy TLS-terminating proxy which implemented TLS 1.2 incorrectly. (See https://crbug.com/boringssl/226.)

Name: Tls13DowngradeDetectedError
Code: -180
Description: TLS13_DOWNGRADE_DETECTED
Type: connection

const err = new chromiumNetErrors.Tls13DowngradeDetectedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-180);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TLS13_DOWNGRADE_DETECTED');
const err = new Err();

SslKeyUsageIncompatibleError

The server's certificate has a keyUsage extension incompatible with the negotiated TLS key exchange method.

Name: SslKeyUsageIncompatibleError
Code: -181
Description: SSL_KEY_USAGE_INCOMPATIBLE
Type: connection

const err = new chromiumNetErrors.SslKeyUsageIncompatibleError();
// or
const Err = chromiumNetErrors.getErrorByCode(-181);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_KEY_USAGE_INCOMPATIBLE');
const err = new Err();

CertCommonNameInvalidError

The server responded with a certificate whose common name did not match the host name. This could mean:
An attacker has redirected our traffic to their server and is presenting a certificate for which they know the private key.
The server is misconfigured and responding with the wrong cert.
The user is on a wireless network and is being redirected to the network's login page.
The OS has used a DNS search suffix and the server doesn't have a certificate for the abbreviated name in the address bar.

Name: CertCommonNameInvalidError
Code: -200
Description: CERT_COMMON_NAME_INVALID
Type: certificate

const err = new chromiumNetErrors.CertCommonNameInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-200);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_COMMON_NAME_INVALID');
const err = new Err();

CertDateInvalidError

The server responded with a certificate that, by our clock, appears to either not yet be valid or to have expired. This could mean:
An attacker is presenting an old certificate for which they have managed to obtain the private key.
The server is misconfigured and is not presenting a valid cert.
Our clock is wrong.

Name: CertDateInvalidError
Code: -201
Description: CERT_DATE_INVALID
Type: certificate

const err = new chromiumNetErrors.CertDateInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-201);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_DATE_INVALID');
const err = new Err();

CertAuthorityInvalidError

The server responded with a certificate that is signed by an authority we don't trust. The could mean:
An attacker has substituted the real certificate for a cert that contains their public key and is signed by their cousin.
The server operator has a legitimate certificate from a CA we don't know about, but should trust.
The server is presenting a self-signed certificate, providing no defense against active attackers (but foiling passive attackers).

Name: CertAuthorityInvalidError
Code: -202
Description: CERT_AUTHORITY_INVALID
Type: certificate

const err = new chromiumNetErrors.CertAuthorityInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-202);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_AUTHORITY_INVALID');
const err = new Err();

CertContainsErrorsError

The server responded with a certificate that contains errors. This error is not recoverable.
MSDN describes this error as follows: "The SSL certificate contains errors." NOTE: It's unclear how this differs from ERR_CERT_INVALID. For consistency, use that code instead of this one from now on.

Name: CertContainsErrorsError
Code: -203
Description: CERT_CONTAINS_ERRORS
Type: certificate

const err = new chromiumNetErrors.CertContainsErrorsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-203);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_CONTAINS_ERRORS');
const err = new Err();

CertNoRevocationMechanismError

The certificate has no mechanism for determining if it is revoked. In effect, this certificate cannot be revoked.

Name: CertNoRevocationMechanismError
Code: -204
Description: CERT_NO_REVOCATION_MECHANISM
Type: certificate

const err = new chromiumNetErrors.CertNoRevocationMechanismError();
// or
const Err = chromiumNetErrors.getErrorByCode(-204);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_NO_REVOCATION_MECHANISM');
const err = new Err();

CertUnableToCheckRevocationError

Revocation information for the security certificate for this site is not available. This could mean:
An attacker has compromised the private key in the certificate and is blocking our attempt to find out that the cert was revoked.
The certificate is unrevoked, but the revocation server is busy or unavailable.

Name: CertUnableToCheckRevocationError
Code: -205
Description: CERT_UNABLE_TO_CHECK_REVOCATION
Type: certificate

const err = new chromiumNetErrors.CertUnableToCheckRevocationError();
// or
const Err = chromiumNetErrors.getErrorByCode(-205);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_UNABLE_TO_CHECK_REVOCATION');
const err = new Err();

CertRevokedError

The server responded with a certificate has been revoked. We have the capability to ignore this error, but it is probably not the thing to do.

Name: CertRevokedError
Code: -206
Description: CERT_REVOKED
Type: certificate

const err = new chromiumNetErrors.CertRevokedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-206);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_REVOKED');
const err = new Err();

CertInvalidError

The server responded with a certificate that is invalid. This error is not recoverable.
MSDN describes this error as follows: "The SSL certificate is invalid."

Name: CertInvalidError
Code: -207
Description: CERT_INVALID
Type: certificate

const err = new chromiumNetErrors.CertInvalidError();
// or
const Err = chromiumNetErrors.getErrorByCode(-207);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_INVALID');
const err = new Err();

CertWeakSignatureAlgorithmError

The server responded with a certificate that is signed using a weak signature algorithm.

Name: CertWeakSignatureAlgorithmError
Code: -208
Description: CERT_WEAK_SIGNATURE_ALGORITHM
Type: certificate

const err = new chromiumNetErrors.CertWeakSignatureAlgorithmError();
// or
const Err = chromiumNetErrors.getErrorByCode(-208);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_WEAK_SIGNATURE_ALGORITHM');
const err = new Err();

CertNonUniqueNameError

The host name specified in the certificate is not unique.

Name: CertNonUniqueNameError
Code: -210
Description: CERT_NON_UNIQUE_NAME
Type: certificate

const err = new chromiumNetErrors.CertNonUniqueNameError();
// or
const Err = chromiumNetErrors.getErrorByCode(-210);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_NON_UNIQUE_NAME');
const err = new Err();

CertWeakKeyError

The server responded with a certificate that contains a weak key (e.g. a too-small RSA key).

Name: CertWeakKeyError
Code: -211
Description: CERT_WEAK_KEY
Type: certificate

const err = new chromiumNetErrors.CertWeakKeyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-211);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_WEAK_KEY');
const err = new Err();

CertNameConstraintViolationError

The certificate claimed DNS names that are in violation of name constraints.

Name: CertNameConstraintViolationError
Code: -212
Description: CERT_NAME_CONSTRAINT_VIOLATION
Type: certificate

const err = new chromiumNetErrors.CertNameConstraintViolationError();
// or
const Err = chromiumNetErrors.getErrorByCode(-212);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_NAME_CONSTRAINT_VIOLATION');
const err = new Err();

CertValidityTooLongError

The certificate's validity period is too long.

Name: CertValidityTooLongError
Code: -213
Description: CERT_VALIDITY_TOO_LONG
Type: certificate

const err = new chromiumNetErrors.CertValidityTooLongError();
// or
const Err = chromiumNetErrors.getErrorByCode(-213);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_VALIDITY_TOO_LONG');
const err = new Err();

CertificateTransparencyRequiredError

Certificate Transparency was required for this connection, but the server did not provide CT information that complied with the policy.

Name: CertificateTransparencyRequiredError
Code: -214
Description: CERTIFICATE_TRANSPARENCY_REQUIRED
Type: certificate

const err = new chromiumNetErrors.CertificateTransparencyRequiredError();
// or
const Err = chromiumNetErrors.getErrorByCode(-214);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERTIFICATE_TRANSPARENCY_REQUIRED');
const err = new Err();

CertSymantecLegacyError

The certificate chained to a legacy Symantec root that is no longer trusted. https://g.co/chrome/symantecpkicerts

Name: CertSymantecLegacyError
Code: -215
Description: CERT_SYMANTEC_LEGACY
Type: certificate

const err = new chromiumNetErrors.CertSymantecLegacyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-215);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_SYMANTEC_LEGACY');
const err = new Err();

CertKnownInterceptionBlockedError

The certificate is known to be used for interception by an entity other the device owner.

Name: CertKnownInterceptionBlockedError
Code: -217
Description: CERT_KNOWN_INTERCEPTION_BLOCKED
Type: certificate

const err = new chromiumNetErrors.CertKnownInterceptionBlockedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-217);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_KNOWN_INTERCEPTION_BLOCKED');
const err = new Err();

SslObsoleteVersionError

The connection uses an obsolete version of SSL/TLS.

Name: SslObsoleteVersionError
Code: -218
Description: SSL_OBSOLETE_VERSION
Type: certificate

const err = new chromiumNetErrors.SslObsoleteVersionError();
// or
const Err = chromiumNetErrors.getErrorByCode(-218);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SSL_OBSOLETE_VERSION');
const err = new Err();

CertEndError

The value immediately past the last certificate error code.

Name: CertEndError
Code: -219
Description: CERT_END
Type: certificate

const err = new chromiumNetErrors.CertEndError();
// or
const Err = chromiumNetErrors.getErrorByCode(-219);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_END');
const err = new Err();

InvalidUrlError

The URL is invalid.

Name: InvalidUrlError
Code: -300
Description: INVALID_URL
Type: http

const err = new chromiumNetErrors.InvalidUrlError();
// or
const Err = chromiumNetErrors.getErrorByCode(-300);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_URL');
const err = new Err();

DisallowedUrlSchemeError

The scheme of the URL is disallowed.

Name: DisallowedUrlSchemeError
Code: -301
Description: DISALLOWED_URL_SCHEME
Type: http

const err = new chromiumNetErrors.DisallowedUrlSchemeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-301);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DISALLOWED_URL_SCHEME');
const err = new Err();

UnknownUrlSchemeError

The scheme of the URL is unknown.

Name: UnknownUrlSchemeError
Code: -302
Description: UNKNOWN_URL_SCHEME
Type: http

const err = new chromiumNetErrors.UnknownUrlSchemeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-302);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNKNOWN_URL_SCHEME');
const err = new Err();

InvalidRedirectError

Attempting to load an URL resulted in a redirect to an invalid URL.

Name: InvalidRedirectError
Code: -303
Description: INVALID_REDIRECT
Type: http

const err = new chromiumNetErrors.InvalidRedirectError();
// or
const Err = chromiumNetErrors.getErrorByCode(-303);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_REDIRECT');
const err = new Err();

TooManyRedirectsError

Attempting to load an URL resulted in too many redirects.

Name: TooManyRedirectsError
Code: -310
Description: TOO_MANY_REDIRECTS
Type: http

const err = new chromiumNetErrors.TooManyRedirectsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-310);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TOO_MANY_REDIRECTS');
const err = new Err();

UnsafeRedirectError

Attempting to load an URL resulted in an unsafe redirect (e.g., a redirect to file:// is considered unsafe).

Name: UnsafeRedirectError
Code: -311
Description: UNSAFE_REDIRECT
Type: http

const err = new chromiumNetErrors.UnsafeRedirectError();
// or
const Err = chromiumNetErrors.getErrorByCode(-311);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNSAFE_REDIRECT');
const err = new Err();

UnsafePortError

Attempting to load an URL with an unsafe port number. These are port numbers that correspond to services, which are not robust to spurious input that may be constructed as a result of an allowed web construct (e.g., HTTP looks a lot like SMTP, so form submission to port 25 is denied).

Name: UnsafePortError
Code: -312
Description: UNSAFE_PORT
Type: http

const err = new chromiumNetErrors.UnsafePortError();
// or
const Err = chromiumNetErrors.getErrorByCode(-312);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNSAFE_PORT');
const err = new Err();

InvalidResponseError

The server's response was invalid.

Name: InvalidResponseError
Code: -320
Description: INVALID_RESPONSE
Type: http

const err = new chromiumNetErrors.InvalidResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-320);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_RESPONSE');
const err = new Err();

InvalidChunkedEncodingError

Error in chunked transfer encoding.

Name: InvalidChunkedEncodingError
Code: -321
Description: INVALID_CHUNKED_ENCODING
Type: http

const err = new chromiumNetErrors.InvalidChunkedEncodingError();
// or
const Err = chromiumNetErrors.getErrorByCode(-321);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_CHUNKED_ENCODING');
const err = new Err();

MethodNotSupportedError

The server did not support the request method.

Name: MethodNotSupportedError
Code: -322
Description: METHOD_NOT_SUPPORTED
Type: http

const err = new chromiumNetErrors.MethodNotSupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-322);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('METHOD_NOT_SUPPORTED');
const err = new Err();

UnexpectedProxyAuthError

The response was 407 (Proxy Authentication Required), yet we did not send the request to a proxy.

Name: UnexpectedProxyAuthError
Code: -323
Description: UNEXPECTED_PROXY_AUTH
Type: http

const err = new chromiumNetErrors.UnexpectedProxyAuthError();
// or
const Err = chromiumNetErrors.getErrorByCode(-323);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNEXPECTED_PROXY_AUTH');
const err = new Err();

EmptyResponseError

The server closed the connection without sending any data.

Name: EmptyResponseError
Code: -324
Description: EMPTY_RESPONSE
Type: http

const err = new chromiumNetErrors.EmptyResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-324);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('EMPTY_RESPONSE');
const err = new Err();

ResponseHeadersTooBigError

The headers section of the response is too large.

Name: ResponseHeadersTooBigError
Code: -325
Description: RESPONSE_HEADERS_TOO_BIG
Type: http

const err = new chromiumNetErrors.ResponseHeadersTooBigError();
// or
const Err = chromiumNetErrors.getErrorByCode(-325);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_HEADERS_TOO_BIG');
const err = new Err();

PacScriptFailedError

The evaluation of the PAC script failed.

Name: PacScriptFailedError
Code: -327
Description: PAC_SCRIPT_FAILED
Type: http

const err = new chromiumNetErrors.PacScriptFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-327);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PAC_SCRIPT_FAILED');
const err = new Err();

RequestRangeNotSatisfiableError

The response was 416 (Requested range not satisfiable) and the server cannot satisfy the range requested.

Name: RequestRangeNotSatisfiableError
Code: -328
Description: REQUEST_RANGE_NOT_SATISFIABLE
Type: http

const err = new chromiumNetErrors.RequestRangeNotSatisfiableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-328);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('REQUEST_RANGE_NOT_SATISFIABLE');
const err = new Err();

MalformedIdentityError

The identity used for authentication is invalid.

Name: MalformedIdentityError
Code: -329
Description: MALFORMED_IDENTITY
Type: http

const err = new chromiumNetErrors.MalformedIdentityError();
// or
const Err = chromiumNetErrors.getErrorByCode(-329);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('MALFORMED_IDENTITY');
const err = new Err();

ContentDecodingFailedError

Content decoding of the response body failed.

Name: ContentDecodingFailedError
Code: -330
Description: CONTENT_DECODING_FAILED
Type: http

const err = new chromiumNetErrors.ContentDecodingFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-330);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONTENT_DECODING_FAILED');
const err = new Err();

NetworkIoSuspendedError

An operation could not be completed because all network IO is suspended.

Name: NetworkIoSuspendedError
Code: -331
Description: NETWORK_IO_SUSPENDED
Type: http

const err = new chromiumNetErrors.NetworkIoSuspendedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-331);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NETWORK_IO_SUSPENDED');
const err = new Err();

SynReplyNotReceivedError

FLIP data received without receiving a SYN_REPLY on the stream.

Name: SynReplyNotReceivedError
Code: -332
Description: SYN_REPLY_NOT_RECEIVED
Type: http

const err = new chromiumNetErrors.SynReplyNotReceivedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-332);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SYN_REPLY_NOT_RECEIVED');
const err = new Err();

EncodingConversionFailedError

Converting the response to target encoding failed.

Name: EncodingConversionFailedError
Code: -333
Description: ENCODING_CONVERSION_FAILED
Type: http

const err = new chromiumNetErrors.EncodingConversionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-333);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ENCODING_CONVERSION_FAILED');
const err = new Err();

UnrecognizedFtpDirectoryListingFormatError

The server sent an FTP directory listing in a format we do not understand.

Name: UnrecognizedFtpDirectoryListingFormatError
Code: -334
Description: UNRECOGNIZED_FTP_DIRECTORY_LISTING_FORMAT
Type: http

const err = new chromiumNetErrors.UnrecognizedFtpDirectoryListingFormatError();
// or
const Err = chromiumNetErrors.getErrorByCode(-334);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNRECOGNIZED_FTP_DIRECTORY_LISTING_FORMAT');
const err = new Err();

NoSupportedProxiesError

There are no supported proxies in the provided list.

Name: NoSupportedProxiesError
Code: -336
Description: NO_SUPPORTED_PROXIES
Type: http

const err = new chromiumNetErrors.NoSupportedProxiesError();
// or
const Err = chromiumNetErrors.getErrorByCode(-336);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NO_SUPPORTED_PROXIES');
const err = new Err();

Http2ProtocolError

There is an HTTP/2 protocol error.

Name: Http2ProtocolError
Code: -337
Description: HTTP2_PROTOCOL_ERROR
Type: http

const err = new chromiumNetErrors.Http2ProtocolError();
// or
const Err = chromiumNetErrors.getErrorByCode(-337);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_PROTOCOL_ERROR');
const err = new Err();

InvalidAuthCredentialsError

Credentials could not be established during HTTP Authentication.

Name: InvalidAuthCredentialsError
Code: -338
Description: INVALID_AUTH_CREDENTIALS
Type: http

const err = new chromiumNetErrors.InvalidAuthCredentialsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-338);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_AUTH_CREDENTIALS');
const err = new Err();

UnsupportedAuthSchemeError

An HTTP Authentication scheme was tried which is not supported on this machine.

Name: UnsupportedAuthSchemeError
Code: -339
Description: UNSUPPORTED_AUTH_SCHEME
Type: http

const err = new chromiumNetErrors.UnsupportedAuthSchemeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-339);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNSUPPORTED_AUTH_SCHEME');
const err = new Err();

EncodingDetectionFailedError

Detecting the encoding of the response failed.

Name: EncodingDetectionFailedError
Code: -340
Description: ENCODING_DETECTION_FAILED
Type: http

const err = new chromiumNetErrors.EncodingDetectionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-340);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ENCODING_DETECTION_FAILED');
const err = new Err();

MissingAuthCredentialsError

(GSSAPI) No Kerberos credentials were available during HTTP Authentication.

Name: MissingAuthCredentialsError
Code: -341
Description: MISSING_AUTH_CREDENTIALS
Type: http

const err = new chromiumNetErrors.MissingAuthCredentialsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-341);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('MISSING_AUTH_CREDENTIALS');
const err = new Err();

UnexpectedSecurityLibraryStatusError

An unexpected, but documented, SSPI or GSSAPI status code was returned.

Name: UnexpectedSecurityLibraryStatusError
Code: -342
Description: UNEXPECTED_SECURITY_LIBRARY_STATUS
Type: http

const err = new chromiumNetErrors.UnexpectedSecurityLibraryStatusError();
// or
const Err = chromiumNetErrors.getErrorByCode(-342);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNEXPECTED_SECURITY_LIBRARY_STATUS');
const err = new Err();

MisconfiguredAuthEnvironmentError

The environment was not set up correctly for authentication (for example, no KDC could be found or the principal is unknown.

Name: MisconfiguredAuthEnvironmentError
Code: -343
Description: MISCONFIGURED_AUTH_ENVIRONMENT
Type: http

const err = new chromiumNetErrors.MisconfiguredAuthEnvironmentError();
// or
const Err = chromiumNetErrors.getErrorByCode(-343);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('MISCONFIGURED_AUTH_ENVIRONMENT');
const err = new Err();

UndocumentedSecurityLibraryStatusError

An undocumented SSPI or GSSAPI status code was returned.

Name: UndocumentedSecurityLibraryStatusError
Code: -344
Description: UNDOCUMENTED_SECURITY_LIBRARY_STATUS
Type: http

const err = new chromiumNetErrors.UndocumentedSecurityLibraryStatusError();
// or
const Err = chromiumNetErrors.getErrorByCode(-344);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('UNDOCUMENTED_SECURITY_LIBRARY_STATUS');
const err = new Err();

ResponseBodyTooBigToDrainError

The HTTP response was too big to drain.

Name: ResponseBodyTooBigToDrainError
Code: -345
Description: RESPONSE_BODY_TOO_BIG_TO_DRAIN
Type: http

const err = new chromiumNetErrors.ResponseBodyTooBigToDrainError();
// or
const Err = chromiumNetErrors.getErrorByCode(-345);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_BODY_TOO_BIG_TO_DRAIN');
const err = new Err();

ResponseHeadersMultipleContentLengthError

The HTTP response contained multiple distinct Content-Length headers.

Name: ResponseHeadersMultipleContentLengthError
Code: -346
Description: RESPONSE_HEADERS_MULTIPLE_CONTENT_LENGTH
Type: http

const err = new chromiumNetErrors.ResponseHeadersMultipleContentLengthError();
// or
const Err = chromiumNetErrors.getErrorByCode(-346);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_HEADERS_MULTIPLE_CONTENT_LENGTH');
const err = new Err();

IncompleteHttp2HeadersError

HTTP/2 headers have been received, but not all of them - status or version headers are missing, so we're expecting additional frames to complete them.

Name: IncompleteHttp2HeadersError
Code: -347
Description: INCOMPLETE_HTTP2_HEADERS
Type: http

const err = new chromiumNetErrors.IncompleteHttp2HeadersError();
// or
const Err = chromiumNetErrors.getErrorByCode(-347);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INCOMPLETE_HTTP2_HEADERS');
const err = new Err();

PacNotInDhcpError

No PAC URL configuration could be retrieved from DHCP. This can indicate either a failure to retrieve the DHCP configuration, or that there was no PAC URL configured in DHCP.

Name: PacNotInDhcpError
Code: -348
Description: PAC_NOT_IN_DHCP
Type: http

const err = new chromiumNetErrors.PacNotInDhcpError();
// or
const Err = chromiumNetErrors.getErrorByCode(-348);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PAC_NOT_IN_DHCP');
const err = new Err();

ResponseHeadersMultipleContentDispositionError

The HTTP response contained multiple Content-Disposition headers.

Name: ResponseHeadersMultipleContentDispositionError
Code: -349
Description: RESPONSE_HEADERS_MULTIPLE_CONTENT_DISPOSITION
Type: http

const err = new chromiumNetErrors.ResponseHeadersMultipleContentDispositionError();
// or
const Err = chromiumNetErrors.getErrorByCode(-349);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_HEADERS_MULTIPLE_CONTENT_DISPOSITION');
const err = new Err();

ResponseHeadersMultipleLocationError

The HTTP response contained multiple Location headers.

Name: ResponseHeadersMultipleLocationError
Code: -350
Description: RESPONSE_HEADERS_MULTIPLE_LOCATION
Type: http

const err = new chromiumNetErrors.ResponseHeadersMultipleLocationError();
// or
const Err = chromiumNetErrors.getErrorByCode(-350);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_HEADERS_MULTIPLE_LOCATION');
const err = new Err();

Http2ServerRefusedStreamError

HTTP/2 server refused the request without processing, and sent either a GOAWAY frame with error code NO_ERROR and Last-Stream-ID lower than the stream id corresponding to the request indicating that this request has not been processed yet, or a RST_STREAM frame with error code REFUSED_STREAM. Client MAY retry (on a different connection). See RFC7540 Section 8.1.4.

Name: Http2ServerRefusedStreamError
Code: -351
Description: HTTP2_SERVER_REFUSED_STREAM
Type: http

const err = new chromiumNetErrors.Http2ServerRefusedStreamError();
// or
const Err = chromiumNetErrors.getErrorByCode(-351);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_SERVER_REFUSED_STREAM');
const err = new Err();

Http2PingFailedError

HTTP/2 server didn't respond to the PING message.

Name: Http2PingFailedError
Code: -352
Description: HTTP2_PING_FAILED
Type: http

const err = new chromiumNetErrors.Http2PingFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-352);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_PING_FAILED');
const err = new Err();

ContentLengthMismatchError

The HTTP response body transferred fewer bytes than were advertised by the Content-Length header when the connection is closed.

Name: ContentLengthMismatchError
Code: -354
Description: CONTENT_LENGTH_MISMATCH
Type: http

const err = new chromiumNetErrors.ContentLengthMismatchError();
// or
const Err = chromiumNetErrors.getErrorByCode(-354);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONTENT_LENGTH_MISMATCH');
const err = new Err();

IncompleteChunkedEncodingError

The HTTP response body is transferred with Chunked-Encoding, but the terminating zero-length chunk was never sent when the connection is closed.

Name: IncompleteChunkedEncodingError
Code: -355
Description: INCOMPLETE_CHUNKED_ENCODING
Type: http

const err = new chromiumNetErrors.IncompleteChunkedEncodingError();
// or
const Err = chromiumNetErrors.getErrorByCode(-355);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INCOMPLETE_CHUNKED_ENCODING');
const err = new Err();

QuicProtocolError

There is a QUIC protocol error.

Name: QuicProtocolError
Code: -356
Description: QUIC_PROTOCOL_ERROR
Type: http

const err = new chromiumNetErrors.QuicProtocolError();
// or
const Err = chromiumNetErrors.getErrorByCode(-356);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('QUIC_PROTOCOL_ERROR');
const err = new Err();

ResponseHeadersTruncatedError

The HTTP headers were truncated by an EOF.

Name: ResponseHeadersTruncatedError
Code: -357
Description: RESPONSE_HEADERS_TRUNCATED
Type: http

const err = new chromiumNetErrors.ResponseHeadersTruncatedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-357);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('RESPONSE_HEADERS_TRUNCATED');
const err = new Err();

QuicHandshakeFailedError

The QUIC crytpo handshake failed. This means that the server was unable to read any requests sent, so they may be resent.

Name: QuicHandshakeFailedError
Code: -358
Description: QUIC_HANDSHAKE_FAILED
Type: http

const err = new chromiumNetErrors.QuicHandshakeFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-358);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('QUIC_HANDSHAKE_FAILED');
const err = new Err();

Http2InadequateTransportSecurityError

Transport security is inadequate for the HTTP/2 version.

Name: Http2InadequateTransportSecurityError
Code: -360
Description: HTTP2_INADEQUATE_TRANSPORT_SECURITY
Type: http

const err = new chromiumNetErrors.Http2InadequateTransportSecurityError();
// or
const Err = chromiumNetErrors.getErrorByCode(-360);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_INADEQUATE_TRANSPORT_SECURITY');
const err = new Err();

Http2FlowControlError

The peer violated HTTP/2 flow control.

Name: Http2FlowControlError
Code: -361
Description: HTTP2_FLOW_CONTROL_ERROR
Type: http

const err = new chromiumNetErrors.Http2FlowControlError();
// or
const Err = chromiumNetErrors.getErrorByCode(-361);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_FLOW_CONTROL_ERROR');
const err = new Err();

Http2FrameSizeError

The peer sent an improperly sized HTTP/2 frame.

Name: Http2FrameSizeError
Code: -362
Description: HTTP2_FRAME_SIZE_ERROR
Type: http

const err = new chromiumNetErrors.Http2FrameSizeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-362);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_FRAME_SIZE_ERROR');
const err = new Err();

Http2CompressionError

Decoding or encoding of compressed HTTP/2 headers failed.

Name: Http2CompressionError
Code: -363
Description: HTTP2_COMPRESSION_ERROR
Type: http

const err = new chromiumNetErrors.Http2CompressionError();
// or
const Err = chromiumNetErrors.getErrorByCode(-363);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_COMPRESSION_ERROR');
const err = new Err();

ProxyAuthRequestedWithNoConnectionError

Proxy Auth Requested without a valid Client Socket Handle.

Name: ProxyAuthRequestedWithNoConnectionError
Code: -364
Description: PROXY_AUTH_REQUESTED_WITH_NO_CONNECTION
Type: http

const err = new chromiumNetErrors.ProxyAuthRequestedWithNoConnectionError();
// or
const Err = chromiumNetErrors.getErrorByCode(-364);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_AUTH_REQUESTED_WITH_NO_CONNECTION');
const err = new Err();

Http_1_1RequiredError

HTTP_1_1_REQUIRED error code received on HTTP/2 session.

Name: Http_1_1RequiredError
Code: -365
Description: HTTP_1_1_REQUIRED
Type: http

const err = new chromiumNetErrors.Http_1_1RequiredError();
// or
const Err = chromiumNetErrors.getErrorByCode(-365);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP_1_1_REQUIRED');
const err = new Err();

ProxyHttp_1_1RequiredError

HTTP_1_1_REQUIRED error code received on HTTP/2 session to proxy.

Name: ProxyHttp_1_1RequiredError
Code: -366
Description: PROXY_HTTP_1_1_REQUIRED
Type: http

const err = new chromiumNetErrors.ProxyHttp_1_1RequiredError();
// or
const Err = chromiumNetErrors.getErrorByCode(-366);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PROXY_HTTP_1_1_REQUIRED');
const err = new Err();

PacScriptTerminatedError

The PAC script terminated fatally and must be reloaded.

Name: PacScriptTerminatedError
Code: -367
Description: PAC_SCRIPT_TERMINATED
Type: http

const err = new chromiumNetErrors.PacScriptTerminatedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-367);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PAC_SCRIPT_TERMINATED');
const err = new Err();

InvalidHttpResponseError

The server was expected to return an HTTP/1.x response, but did not. Rather than treat it as HTTP/0.9, this error is returned.

Name: InvalidHttpResponseError
Code: -370
Description: INVALID_HTTP_RESPONSE
Type: http

const err = new chromiumNetErrors.InvalidHttpResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-370);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_HTTP_RESPONSE');
const err = new Err();

ContentDecodingInitFailedError

Initializing content decoding failed.

Name: ContentDecodingInitFailedError
Code: -371
Description: CONTENT_DECODING_INIT_FAILED
Type: http

const err = new chromiumNetErrors.ContentDecodingInitFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-371);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CONTENT_DECODING_INIT_FAILED');
const err = new Err();

Http2RstStreamNoErrorReceivedError

Received HTTP/2 RST_STREAM frame with NO_ERROR error code. This error should be handled internally by HTTP/2 code, and should not make it above the SpdyStream layer.

Name: Http2RstStreamNoErrorReceivedError
Code: -372
Description: HTTP2_RST_STREAM_NO_ERROR_RECEIVED
Type: http

const err = new chromiumNetErrors.Http2RstStreamNoErrorReceivedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-372);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_RST_STREAM_NO_ERROR_RECEIVED');
const err = new Err();

Http2PushedStreamNotAvailableError

The pushed stream claimed by the request is no longer available.

Name: Http2PushedStreamNotAvailableError
Code: -373
Description: HTTP2_PUSHED_STREAM_NOT_AVAILABLE
Type: http

const err = new chromiumNetErrors.Http2PushedStreamNotAvailableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-373);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_PUSHED_STREAM_NOT_AVAILABLE');
const err = new Err();

Http2ClaimedPushedStreamResetByServerError

A pushed stream was claimed and later reset by the server. When this happens, the request should be retried.

Name: Http2ClaimedPushedStreamResetByServerError
Code: -374
Description: HTTP2_CLAIMED_PUSHED_STREAM_RESET_BY_SERVER
Type: http

const err = new chromiumNetErrors.Http2ClaimedPushedStreamResetByServerError();
// or
const Err = chromiumNetErrors.getErrorByCode(-374);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_CLAIMED_PUSHED_STREAM_RESET_BY_SERVER');
const err = new Err();

TooManyRetriesError

An HTTP transaction was retried too many times due for authentication or invalid certificates. This may be due to a bug in the net stack that would otherwise infinite loop, or if the server or proxy continually requests fresh credentials or presents a fresh invalid certificate.

Name: TooManyRetriesError
Code: -375
Description: TOO_MANY_RETRIES
Type: http

const err = new chromiumNetErrors.TooManyRetriesError();
// or
const Err = chromiumNetErrors.getErrorByCode(-375);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TOO_MANY_RETRIES');
const err = new Err();

Http2StreamClosedError

Received an HTTP/2 frame on a closed stream.

Name: Http2StreamClosedError
Code: -376
Description: HTTP2_STREAM_CLOSED
Type: http

const err = new chromiumNetErrors.Http2StreamClosedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-376);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_STREAM_CLOSED');
const err = new Err();

Http2ClientRefusedStreamError

Client is refusing an HTTP/2 stream.

Name: Http2ClientRefusedStreamError
Code: -377
Description: HTTP2_CLIENT_REFUSED_STREAM
Type: http

const err = new chromiumNetErrors.Http2ClientRefusedStreamError();
// or
const Err = chromiumNetErrors.getErrorByCode(-377);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_CLIENT_REFUSED_STREAM');
const err = new Err();

Http2PushedResponseDoesNotMatchError

A pushed HTTP/2 stream was claimed by a request based on matching URL and request headers, but the pushed response headers do not match the request.

Name: Http2PushedResponseDoesNotMatchError
Code: -378
Description: HTTP2_PUSHED_RESPONSE_DOES_NOT_MATCH
Type: http

const err = new chromiumNetErrors.Http2PushedResponseDoesNotMatchError();
// or
const Err = chromiumNetErrors.getErrorByCode(-378);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP2_PUSHED_RESPONSE_DOES_NOT_MATCH');
const err = new Err();

HttpResponseCodeFailureError

The server returned a non-2xx HTTP response code.
Not that this error is only used by certain APIs that interpret the HTTP response itself. URLRequest for instance just passes most non-2xx response back as success.

Name: HttpResponseCodeFailureError
Code: -379
Description: HTTP_RESPONSE_CODE_FAILURE
Type: http

const err = new chromiumNetErrors.HttpResponseCodeFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-379);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('HTTP_RESPONSE_CODE_FAILURE');
const err = new Err();

QuicCertRootNotKnownError

The certificate presented on a QUIC connection does not chain to a known root and the origin connected to is not on a list of domains where unknown roots are allowed.

Name: QuicCertRootNotKnownError
Code: -380
Description: QUIC_CERT_ROOT_NOT_KNOWN
Type: http

const err = new chromiumNetErrors.QuicCertRootNotKnownError();
// or
const Err = chromiumNetErrors.getErrorByCode(-380);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('QUIC_CERT_ROOT_NOT_KNOWN');
const err = new Err();

QuicGoawayRequestCanBeRetriedError

A GOAWAY frame has been received indicating that the request has not been processed and is therefore safe to retry on a different connection.

Name: QuicGoawayRequestCanBeRetriedError
Code: -381
Description: QUIC_GOAWAY_REQUEST_CAN_BE_RETRIED
Type: http

const err = new chromiumNetErrors.QuicGoawayRequestCanBeRetriedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-381);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('QUIC_GOAWAY_REQUEST_CAN_BE_RETRIED');
const err = new Err();

CacheMissError

The cache does not have the requested entry.

Name: CacheMissError
Code: -400
Description: CACHE_MISS
Type: cache

const err = new chromiumNetErrors.CacheMissError();
// or
const Err = chromiumNetErrors.getErrorByCode(-400);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_MISS');
const err = new Err();

CacheReadFailureError

Unable to read from the disk cache.

Name: CacheReadFailureError
Code: -401
Description: CACHE_READ_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheReadFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-401);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_READ_FAILURE');
const err = new Err();

CacheWriteFailureError

Unable to write to the disk cache.

Name: CacheWriteFailureError
Code: -402
Description: CACHE_WRITE_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheWriteFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-402);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_WRITE_FAILURE');
const err = new Err();

CacheOperationNotSupportedError

The operation is not supported for this entry.

Name: CacheOperationNotSupportedError
Code: -403
Description: CACHE_OPERATION_NOT_SUPPORTED
Type: cache

const err = new chromiumNetErrors.CacheOperationNotSupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-403);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_OPERATION_NOT_SUPPORTED');
const err = new Err();

CacheOpenFailureError

The disk cache is unable to open this entry.

Name: CacheOpenFailureError
Code: -404
Description: CACHE_OPEN_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheOpenFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-404);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_OPEN_FAILURE');
const err = new Err();

CacheCreateFailureError

The disk cache is unable to create this entry.

Name: CacheCreateFailureError
Code: -405
Description: CACHE_CREATE_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheCreateFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-405);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_CREATE_FAILURE');
const err = new Err();

CacheRaceError

Multiple transactions are racing to create disk cache entries. This is an internal error returned from the HttpCache to the HttpCacheTransaction that tells the transaction to restart the entry-creation logic because the state of the cache has changed.

Name: CacheRaceError
Code: -406
Description: CACHE_RACE
Type: cache

const err = new chromiumNetErrors.CacheRaceError();
// or
const Err = chromiumNetErrors.getErrorByCode(-406);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_RACE');
const err = new Err();

CacheChecksumReadFailureError

The cache was unable to read a checksum record on an entry. This can be returned from attempts to read from the cache. It is an internal error, returned by the SimpleCache backend, but not by any URLRequest methods or members.

Name: CacheChecksumReadFailureError
Code: -407
Description: CACHE_CHECKSUM_READ_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheChecksumReadFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-407);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_CHECKSUM_READ_FAILURE');
const err = new Err();

CacheChecksumMismatchError

The cache found an entry with an invalid checksum. This can be returned from attempts to read from the cache. It is an internal error, returned by the SimpleCache backend, but not by any URLRequest methods or members.

Name: CacheChecksumMismatchError
Code: -408
Description: CACHE_CHECKSUM_MISMATCH
Type: cache

const err = new chromiumNetErrors.CacheChecksumMismatchError();
// or
const Err = chromiumNetErrors.getErrorByCode(-408);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_CHECKSUM_MISMATCH');
const err = new Err();

CacheLockTimeoutError

Internal error code for the HTTP cache. The cache lock timeout has fired.

Name: CacheLockTimeoutError
Code: -409
Description: CACHE_LOCK_TIMEOUT
Type: cache

const err = new chromiumNetErrors.CacheLockTimeoutError();
// or
const Err = chromiumNetErrors.getErrorByCode(-409);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_LOCK_TIMEOUT');
const err = new Err();

CacheAuthFailureAfterReadError

Received a challenge after the transaction has read some data, and the credentials aren't available. There isn't a way to get them at that point.

Name: CacheAuthFailureAfterReadError
Code: -410
Description: CACHE_AUTH_FAILURE_AFTER_READ
Type: cache

const err = new chromiumNetErrors.CacheAuthFailureAfterReadError();
// or
const Err = chromiumNetErrors.getErrorByCode(-410);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_AUTH_FAILURE_AFTER_READ');
const err = new Err();

CacheEntryNotSuitableError

Internal not-quite error code for the HTTP cache. In-memory hints suggest that the cache entry would not have been useable with the transaction's current configuration (e.g. load flags, mode, etc.)

Name: CacheEntryNotSuitableError
Code: -411
Description: CACHE_ENTRY_NOT_SUITABLE
Type: cache

const err = new chromiumNetErrors.CacheEntryNotSuitableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-411);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_ENTRY_NOT_SUITABLE');
const err = new Err();

CacheDoomFailureError

The disk cache is unable to doom this entry.

Name: CacheDoomFailureError
Code: -412
Description: CACHE_DOOM_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheDoomFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-412);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_DOOM_FAILURE');
const err = new Err();

CacheOpenOrCreateFailureError

The disk cache is unable to open or create this entry.

Name: CacheOpenOrCreateFailureError
Code: -413
Description: CACHE_OPEN_OR_CREATE_FAILURE
Type: cache

const err = new chromiumNetErrors.CacheOpenOrCreateFailureError();
// or
const Err = chromiumNetErrors.getErrorByCode(-413);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CACHE_OPEN_OR_CREATE_FAILURE');
const err = new Err();

InsecureResponseError

The server's response was insecure (e.g. there was a cert error).

Name: InsecureResponseError
Code: -501
Description: INSECURE_RESPONSE
Type: unknown

const err = new chromiumNetErrors.InsecureResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-501);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INSECURE_RESPONSE');
const err = new Err();

NoPrivateKeyForCertError

An attempt to import a client certificate failed, as the user's key database lacked a corresponding private key.

Name: NoPrivateKeyForCertError
Code: -502
Description: NO_PRIVATE_KEY_FOR_CERT
Type: unknown

const err = new chromiumNetErrors.NoPrivateKeyForCertError();
// or
const Err = chromiumNetErrors.getErrorByCode(-502);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('NO_PRIVATE_KEY_FOR_CERT');
const err = new Err();

AddUserCertFailedError

An error adding a certificate to the OS certificate database.

Name: AddUserCertFailedError
Code: -503
Description: ADD_USER_CERT_FAILED
Type: unknown

const err = new chromiumNetErrors.AddUserCertFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-503);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('ADD_USER_CERT_FAILED');
const err = new Err();

InvalidSignedExchangeError

An error occurred while handling a signed exchange.

Name: InvalidSignedExchangeError
Code: -504
Description: INVALID_SIGNED_EXCHANGE
Type: unknown

const err = new chromiumNetErrors.InvalidSignedExchangeError();
// or
const Err = chromiumNetErrors.getErrorByCode(-504);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_SIGNED_EXCHANGE');
const err = new Err();

InvalidWebBundleError

An error occurred while handling a Web Bundle source.

Name: InvalidWebBundleError
Code: -505
Description: INVALID_WEB_BUNDLE
Type: unknown

const err = new chromiumNetErrors.InvalidWebBundleError();
// or
const Err = chromiumNetErrors.getErrorByCode(-505);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('INVALID_WEB_BUNDLE');
const err = new Err();

TrustTokenOperationFailedError

A Trust Tokens protocol operation-executing request failed for one of a number of reasons (precondition failure, internal error, bad response).

Name: TrustTokenOperationFailedError
Code: -506
Description: TRUST_TOKEN_OPERATION_FAILED
Type: unknown

const err = new chromiumNetErrors.TrustTokenOperationFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-506);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TRUST_TOKEN_OPERATION_FAILED');
const err = new Err();

TrustTokenOperationSuccessWithoutSendingRequestError

When handling a Trust Tokens protocol operation-executing request, the system was able to execute the request's Trust Tokens operation without sending the request to its destination: for instance, the results could have been present in a local cache (for redemption) or the operation could have been diverted to a local provider (for "platform-provided" issuance).

Name: TrustTokenOperationSuccessWithoutSendingRequestError
Code: -507
Description: TRUST_TOKEN_OPERATION_SUCCESS_WITHOUT_SENDING_REQUEST
Type: unknown

const err = new chromiumNetErrors.TrustTokenOperationSuccessWithoutSendingRequestError();
// or
const Err = chromiumNetErrors.getErrorByCode(-507);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('TRUST_TOKEN_OPERATION_SUCCESS_WITHOUT_SENDING_REQUEST');
const err = new Err();

FtpFailedError

A generic error for failed FTP control connection command. If possible, please use or add a more specific error code.

Name: FtpFailedError
Code: -601
Description: FTP_FAILED
Type: ftp

const err = new chromiumNetErrors.FtpFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-601);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_FAILED');
const err = new Err();

FtpServiceUnavailableError

The server cannot fulfill the request at this point. This is a temporary error. FTP response code 421.

Name: FtpServiceUnavailableError
Code: -602
Description: FTP_SERVICE_UNAVAILABLE
Type: ftp

const err = new chromiumNetErrors.FtpServiceUnavailableError();
// or
const Err = chromiumNetErrors.getErrorByCode(-602);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_SERVICE_UNAVAILABLE');
const err = new Err();

FtpTransferAbortedError

The server has aborted the transfer. FTP response code 426.

Name: FtpTransferAbortedError
Code: -603
Description: FTP_TRANSFER_ABORTED
Type: ftp

const err = new chromiumNetErrors.FtpTransferAbortedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-603);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_TRANSFER_ABORTED');
const err = new Err();

FtpFileBusyError

The file is busy, or some other temporary error condition on opening the file. FTP response code 450.

Name: FtpFileBusyError
Code: -604
Description: FTP_FILE_BUSY
Type: ftp

const err = new chromiumNetErrors.FtpFileBusyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-604);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_FILE_BUSY');
const err = new Err();

FtpSyntaxError

Server rejected our command because of syntax errors. FTP response codes 500, 501.

Name: FtpSyntaxError
Code: -605
Description: FTP_SYNTAX_ERROR
Type: ftp

const err = new chromiumNetErrors.FtpSyntaxError();
// or
const Err = chromiumNetErrors.getErrorByCode(-605);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_SYNTAX_ERROR');
const err = new Err();

FtpCommandNotSupportedError

Server does not support the command we issued. FTP response codes 502, 504.

Name: FtpCommandNotSupportedError
Code: -606
Description: FTP_COMMAND_NOT_SUPPORTED
Type: ftp

const err = new chromiumNetErrors.FtpCommandNotSupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-606);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_COMMAND_NOT_SUPPORTED');
const err = new Err();

FtpBadCommandSequenceError

Server rejected our command because we didn't issue the commands in right order. FTP response code 503.

Name: FtpBadCommandSequenceError
Code: -607
Description: FTP_BAD_COMMAND_SEQUENCE
Type: ftp

const err = new chromiumNetErrors.FtpBadCommandSequenceError();
// or
const Err = chromiumNetErrors.getErrorByCode(-607);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('FTP_BAD_COMMAND_SEQUENCE');
const err = new Err();

Pkcs12ImportBadPasswordError

PKCS #12 import failed due to incorrect password.

Name: Pkcs12ImportBadPasswordError
Code: -701
Description: PKCS12_IMPORT_BAD_PASSWORD
Type: certificate-manager

const err = new chromiumNetErrors.Pkcs12ImportBadPasswordError();
// or
const Err = chromiumNetErrors.getErrorByCode(-701);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PKCS12_IMPORT_BAD_PASSWORD');
const err = new Err();

Pkcs12ImportFailedError

PKCS #12 import failed due to other error.

Name: Pkcs12ImportFailedError
Code: -702
Description: PKCS12_IMPORT_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.Pkcs12ImportFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-702);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PKCS12_IMPORT_FAILED');
const err = new Err();

ImportCaCertNotCaError

CA import failed - not a CA cert.

Name: ImportCaCertNotCaError
Code: -703
Description: IMPORT_CA_CERT_NOT_CA
Type: certificate-manager

const err = new chromiumNetErrors.ImportCaCertNotCaError();
// or
const Err = chromiumNetErrors.getErrorByCode(-703);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('IMPORT_CA_CERT_NOT_CA');
const err = new Err();

ImportCertAlreadyExistsError

Import failed - certificate already exists in database. Note it's a little weird this is an error but reimporting a PKCS12 is ok (no-op). That's how Mozilla does it, though.

Name: ImportCertAlreadyExistsError
Code: -704
Description: IMPORT_CERT_ALREADY_EXISTS
Type: certificate-manager

const err = new chromiumNetErrors.ImportCertAlreadyExistsError();
// or
const Err = chromiumNetErrors.getErrorByCode(-704);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('IMPORT_CERT_ALREADY_EXISTS');
const err = new Err();

ImportCaCertFailedError

CA import failed due to some other error.

Name: ImportCaCertFailedError
Code: -705
Description: IMPORT_CA_CERT_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.ImportCaCertFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-705);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('IMPORT_CA_CERT_FAILED');
const err = new Err();

ImportServerCertFailedError

Server certificate import failed due to some internal error.

Name: ImportServerCertFailedError
Code: -706
Description: IMPORT_SERVER_CERT_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.ImportServerCertFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-706);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('IMPORT_SERVER_CERT_FAILED');
const err = new Err();

Pkcs12ImportInvalidMacError

PKCS #12 import failed due to invalid MAC.

Name: Pkcs12ImportInvalidMacError
Code: -707
Description: PKCS12_IMPORT_INVALID_MAC
Type: certificate-manager

const err = new chromiumNetErrors.Pkcs12ImportInvalidMacError();
// or
const Err = chromiumNetErrors.getErrorByCode(-707);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PKCS12_IMPORT_INVALID_MAC');
const err = new Err();

Pkcs12ImportInvalidFileError

PKCS #12 import failed due to invalid/corrupt file.

Name: Pkcs12ImportInvalidFileError
Code: -708
Description: PKCS12_IMPORT_INVALID_FILE
Type: certificate-manager

const err = new chromiumNetErrors.Pkcs12ImportInvalidFileError();
// or
const Err = chromiumNetErrors.getErrorByCode(-708);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PKCS12_IMPORT_INVALID_FILE');
const err = new Err();

Pkcs12ImportUnsupportedError

PKCS #12 import failed due to unsupported features.

Name: Pkcs12ImportUnsupportedError
Code: -709
Description: PKCS12_IMPORT_UNSUPPORTED
Type: certificate-manager

const err = new chromiumNetErrors.Pkcs12ImportUnsupportedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-709);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PKCS12_IMPORT_UNSUPPORTED');
const err = new Err();

KeyGenerationFailedError

Key generation failed.

Name: KeyGenerationFailedError
Code: -710
Description: KEY_GENERATION_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.KeyGenerationFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-710);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('KEY_GENERATION_FAILED');
const err = new Err();

PrivateKeyExportFailedError

Failure to export private key.

Name: PrivateKeyExportFailedError
Code: -712
Description: PRIVATE_KEY_EXPORT_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.PrivateKeyExportFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-712);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('PRIVATE_KEY_EXPORT_FAILED');
const err = new Err();

SelfSignedCertGenerationFailedError

Self-signed certificate generation failed.

Name: SelfSignedCertGenerationFailedError
Code: -713
Description: SELF_SIGNED_CERT_GENERATION_FAILED
Type: certificate-manager

const err = new chromiumNetErrors.SelfSignedCertGenerationFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-713);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('SELF_SIGNED_CERT_GENERATION_FAILED');
const err = new Err();

CertDatabaseChangedError

The certificate database changed in some way.

Name: CertDatabaseChangedError
Code: -714
Description: CERT_DATABASE_CHANGED
Type: certificate-manager

const err = new chromiumNetErrors.CertDatabaseChangedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-714);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('CERT_DATABASE_CHANGED');
const err = new Err();

DnsMalformedResponseError

DNS resolver received a malformed response.

Name: DnsMalformedResponseError
Code: -800
Description: DNS_MALFORMED_RESPONSE
Type: dns

const err = new chromiumNetErrors.DnsMalformedResponseError();
// or
const Err = chromiumNetErrors.getErrorByCode(-800);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_MALFORMED_RESPONSE');
const err = new Err();

DnsServerRequiresTcpError

DNS server requires TCP

Name: DnsServerRequiresTcpError
Code: -801
Description: DNS_SERVER_REQUIRES_TCP
Type: dns

const err = new chromiumNetErrors.DnsServerRequiresTcpError();
// or
const Err = chromiumNetErrors.getErrorByCode(-801);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_SERVER_REQUIRES_TCP');
const err = new Err();

DnsServerFailedError

DNS server failed. This error is returned for all of the following error conditions: 1 - Format error - The name server was unable to interpret the query. 2 - Server failure - The name server was unable to process this query due to a problem with the name server. 4 - Not Implemented - The name server does not support the requested kind of query. 5 - Refused - The name server refuses to perform the specified operation for policy reasons.

Name: DnsServerFailedError
Code: -802
Description: DNS_SERVER_FAILED
Type: dns

const err = new chromiumNetErrors.DnsServerFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-802);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_SERVER_FAILED');
const err = new Err();

DnsTimedOutError

DNS transaction timed out.

Name: DnsTimedOutError
Code: -803
Description: DNS_TIMED_OUT
Type: dns

const err = new chromiumNetErrors.DnsTimedOutError();
// or
const Err = chromiumNetErrors.getErrorByCode(-803);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_TIMED_OUT');
const err = new Err();

DnsCacheMissError

The entry was not found in cache or other local sources, for lookups where only local sources were queried. TODO(ericorth): Consider renaming to DNS_LOCAL_MISS or something like that as the cache is not necessarily queried either.

Name: DnsCacheMissError
Code: -804
Description: DNS_CACHE_MISS
Type: dns

const err = new chromiumNetErrors.DnsCacheMissError();
// or
const Err = chromiumNetErrors.getErrorByCode(-804);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_CACHE_MISS');
const err = new Err();

DnsSearchEmptyError

Suffix search list rules prevent resolution of the given host name.

Name: DnsSearchEmptyError
Code: -805
Description: DNS_SEARCH_EMPTY
Type: dns

const err = new chromiumNetErrors.DnsSearchEmptyError();
// or
const Err = chromiumNetErrors.getErrorByCode(-805);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_SEARCH_EMPTY');
const err = new Err();

DnsSortError

Failed to sort addresses according to RFC3484.

Name: DnsSortError
Code: -806
Description: DNS_SORT_ERROR
Type: dns

const err = new chromiumNetErrors.DnsSortError();
// or
const Err = chromiumNetErrors.getErrorByCode(-806);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_SORT_ERROR');
const err = new Err();

DnsSecureResolverHostnameResolutionFailedError

Failed to resolve the hostname of a DNS-over-HTTPS server.

Name: DnsSecureResolverHostnameResolutionFailedError
Code: -808
Description: DNS_SECURE_RESOLVER_HOSTNAME_RESOLUTION_FAILED
Type: dns

const err = new chromiumNetErrors.DnsSecureResolverHostnameResolutionFailedError();
// or
const Err = chromiumNetErrors.getErrorByCode(-808);
const err = new Err();
// or
const Err = chromiumNetErrors.getErrorByDescription('DNS_SECURE_RESOLVER_HOSTNAME_RESOLUTION_FAILED');
const err = new Err();

Author: Maxkueng
Source Code: https://github.com/maxkueng/chromium-net-errors
License: MIT license

#electron #node #error

Daron Moore

1641276000

DeltaPy⁠⁠ — Tabular Data Augmentation & Feature Engineering

Finance Quant Machine Learning

ML-Quant.com - Automated Research Repository

Introduction

Tabular augmentation is a new experimental space that makes use of novel and traditional data generation and synthesisation techniques to improve model prediction success. It is in essence a process of modular feature engineering and observation engineering while emphasising the order of augmentation to achieve the best predicted outcome from a given information set. DeltaPy was created with finance applications in mind, but it can be broadly applied to any data-rich environment.

To take full advantage of tabular augmentation for time-series you would perform the techniques in the following order: (1) transforming, (2) interacting, (3) mapping, (4) extracting, and (5) synthesising. What follows is a practical example of how the above methodology can be used. The purpose here is to establish a framework for table augmentation and to point and guide the user to existing packages.

For most the Colab Notebook format might be preferred. I have enabled comments if you want to ask question or address any issues you uncover. For anything pressing use the issues tab. Also have a look at the SSRN report for a more succinct insights.

Data augmentation can be defined as any method that could increase the size or improve the quality of a dataset by generating new features or instances without the collection of additional data-points. Data augmentation is of particular importance in image classification tasks where additional data can be created by cropping, padding, or flipping existing images.

Tabular cross-sectional and time-series prediction tasks can also benefit from augmentation. Here we divide tabular augmentation into columnular and row-wise methods. Row-wise methods are further divided into extraction and data synthesisation techniques, whereas columnular methods are divided into transformation, interaction, and mapping methods.

See the Skeleton Example, for a combination of multiple methods that lead to a halfing of the mean squared error.

Installation & Citation

pip install deltapy

@software{deltapy,
  title = {{DeltaPy}: Tabular Data Augmentation},
  author = {Snow, Derek},
  url = {https://github.com/firmai/deltapy/},
  version = {0.1.0},
  date = {2020-04-11},
}

 Snow, Derek, DeltaPy: A Framework for Tabular Data Augmentation in Python (April 22, 2020). Available at SSRN: https://ssrn.com/abstract=3582219

Function Glossary

Transformation

df_out = transform.robust_scaler(df.copy(), drop=["Close_1"]); df_out.head()
df_out = transform.standard_scaler(df.copy(), drop=["Close"]); df_out.head()           
df_out = transform.fast_fracdiff(df.copy(), ["Close","Open"],0.5); df_out.head()
df_out = transform.windsorization(df.copy(),"Close",para,strategy='both'); df_out.head()
df_out = transform.operations(df.copy(),["Close"]); df_out.head()
df_out = transform.triple_exponential_smoothing(df.copy(),["Close"], 12, .2,.2,.2,0); 
df_out = transform.naive_dec(df.copy(), ["Close","Open"]); df_out.head()
df_out = transform.bkb(df.copy(), ["Close"]); df_out.head()
df_out = transform.butter_lowpass_filter(df.copy(),["Close"],4); df_out.head()
df_out = transform.instantaneous_phases(df.copy(), ["Close"]); df_out.head()
df_out = transform.kalman_feat(df.copy(), ["Close"]); df_out.head()
df_out = transform.perd_feat(df.copy(),["Close"]); df_out.head()
df_out = transform.fft_feat(df.copy(), ["Close"]); df_out.head()
df_out = transform.harmonicradar_cw(df.copy(), ["Close"],0.3,0.2); df_out.head()
df_out = transform.saw(df.copy(),["Close","Open"]); df_out.head()
df_out = transform.modify(df.copy(),["Close"]); df_out.head()
df_out = transform.multiple_rolling(df, columns=["Close"]); df_out.head()
df_out = transform.multiple_lags(df, start=1, end=3, columns=["Close"]); df_out.head()
df_out  = transform.prophet_feat(df.copy().reset_index(),["Close","Open"],"Date", "D"); df_out.head()

Interaction

df_out = interact.lowess(df.copy(), ["Open","Volume"], df["Close"], f=0.25, iter=3); df_out.head()
df_out = interact.autoregression(df.copy()); df_out.head()
df_out = interact.muldiv(df.copy(), ["Close","Open"]); df_out.head()
df_out = interact.decision_tree_disc(df.copy(), ["Close"]); df_out.head()
df_out = interact.quantile_normalize(df.copy(), drop=["Close"]); df_out.head()
df_out = interact.tech(df.copy()); df_out.head()
df_out = interact.genetic_feat(df.copy()); df_out.head()

Mapping

df_out = mapper.pca_feature(df.copy(),variance_or_components=0.80,drop_cols=["Close_1"]); df_out.head()
df_out = mapper.cross_lag(df.copy()); df_out.head()
df_out = mapper.a_chi(df.copy()); df_out.head()
df_out = mapper.encoder_dataset(df.copy(), ["Close_1"], 15); df_out.head()
df_out = mapper.lle_feat(df.copy(),["Close_1"],4); df_out.head()
df_out = mapper.feature_agg(df.copy(),["Close_1"],4 ); df_out.head()
df_out = mapper.neigh_feat(df.copy(),["Close_1"],4 ); df_out.head()

Extraction

extract.abs_energy(df["Close"])
extract.cid_ce(df["Close"], True)
extract.mean_abs_change(df["Close"])
extract.mean_second_derivative_central(df["Close"])
extract.variance_larger_than_standard_deviation(df["Close"])
extract.var_index(df["Close"].values,var_index_param)
extract.symmetry_looking(df["Close"])
extract.has_duplicate_max(df["Close"])
extract.partial_autocorrelation(df["Close"])
extract.augmented_dickey_fuller(df["Close"])
extract.gskew(df["Close"])
extract.stetson_mean(df["Close"])
extract.length(df["Close"])
extract.count_above_mean(df["Close"])
extract.longest_strike_below_mean(df["Close"])
extract.wozniak(df["Close"])
extract.last_location_of_maximum(df["Close"])
extract.fft_coefficient(df["Close"])
extract.ar_coefficient(df["Close"])
extract.index_mass_quantile(df["Close"])
extract.number_cwt_peaks(df["Close"])
extract.spkt_welch_density(df["Close"])
extract.linear_trend_timewise(df["Close"])
extract.c3(df["Close"])
extract.binned_entropy(df["Close"])
extract.svd_entropy(df["Close"].values)
extract.hjorth_complexity(df["Close"])
extract.max_langevin_fixed_point(df["Close"])
extract.percent_amplitude(df["Close"])
extract.cad_prob(df["Close"])
extract.zero_crossing_derivative(df["Close"])
extract.detrended_fluctuation_analysis(df["Close"])
extract.fisher_information(df["Close"])
extract.higuchi_fractal_dimension(df["Close"])
extract.petrosian_fractal_dimension(df["Close"])
extract.hurst_exponent(df["Close"])
extract.largest_lyauponov_exponent(df["Close"])
extract.whelch_method(df["Close"])
extract.find_freq(df["Close"])
extract.flux_perc(df["Close"])
extract.range_cum_s(df["Close"])
extract.structure_func(df["Close"])
extract.kurtosis(df["Close"])
extract.stetson_k(df["Close"])

Test sets should ideally not be preprocessed with the training data, as in such a way one could be peaking ahead in the training data. The preprocessing parameters should be identified on the test set and then applied on the test set, i.e., the test set should not have an impact on the transformation applied. As an example, you would learn the parameters of PCA decomposition on the training set and then apply the parameters to both the train and the test set.

The benefit of pipelines become clear when one wants to apply multiple augmentation methods. It makes it easy to learn the parameters and then apply them widely. For the most part, this notebook does not concern itself with 'peaking ahead' or pipelines, for some functions, one might have to restructure to code and make use of open source packages to create your preferred solution.

Documentation by Example

Notebook Dependencies

pip install deltapy

pip install pykalman
pip install tsaug
pip install ta
pip install tsaug
pip install pandasvault
pip install gplearn
pip install ta
pip install seasonal
pip install pandasvault

Data and Package Load

import pandas as pd
import numpy as np
from deltapy import transform, interact, mapper, extract 
import warnings
warnings.filterwarnings('ignore')

def data_copy():
  df = pd.read_csv("https://github.com/firmai/random-assets-two/raw/master/numpy/tsla.csv")
  df["Close_1"] = df["Close"].shift(-1)
  df = df.dropna()
  df["Date"] = pd.to_datetime(df["Date"])
  df = df.set_index("Date")
  return df
df = data_copy(); df.head()

Some of these categories are fluid and some techniques could fit into multiple buckets. This is an attempt to find an exhaustive number of techniques, but not an exhaustive list of implementations of the techniques. For example, there are thousands of ways to smooth a time-series, but we have only includes 1-2 techniques of interest under each category.

(1) Transformation:

Scaling/Normalisation
Standardisation
Differencing
Capping
Operations
Smoothing
Decomposing
Filtering
Spectral Analysis
Waveforms
Modifications
Rolling
Lagging
Forecast Model

(2) Interaction:

Regressions
Operators
Discretising
Normalising
Distance
Speciality
Genetic

(3) Mapping:

Eigen Decomposition
Cross Decomposition
Kernel Approximation
Autoencoder
Manifold Learning
Clustering
Neighbouring

(4) Extraction:

Energy
Distance
Differencing
Derivative
Volatility
Shape
Occurrence
Autocorrelation
Stochasticity
Averages
Size
Count
Streaks
Location
Model Coefficients
Quantile
Peaks
Density
Linearity
Non-linearity
Entropy
Fixed Points
Amplitude
Probability
Crossings
Fluctuation
Information
Fractals
Exponent
Spectral Analysis
Percentile
Range
Structural
Distribution

(1) Transformation

Here transformation is any method that includes only one feature as an input to produce a new feature/s. Transformations can be applied to cross-section and time-series data. Some transformations are exclusive to time-series data (smoothing, filtering), but a handful of functions apply to both.

Where the time series methods has a centred mean, or are forward-looking, there is a need to recalculate the outputed time series on a running basis to ensure that information of the future does not leak into the model. The last value of this recalculated series or an extracted feature from this series can then be used as a running value that is only backward looking, satisfying the no 'peaking' ahead rule.

There are some packaged in Python that dynamically create time series and extracts their features, but none that incoropates the dynamic creation of a time series in combination with a wide application of prespecified list of extractions. Because this technique is expensive, we have a preference for models that only take historical data into account.

In this section we will include a list of all types of transformations, those that only use present information (operations), those that incorporate all values (interpolation methods), those that only include past values (smoothing functions), and those that incorporate a subset window of lagging and leading values (select filters). Only those that use historical values or are turned into prediction methods can be used out of the box. The entire time series can be used in the model development process for historical value methods, and only the forecasted values can be used for prediction models.

Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. When using an interpolation method, you are taking future information into account e.g, cubic spline. You can use interpolation methods to forecast into the future (extrapolation), and then use those forecasts in a training set. Or you could recalculate the interpolation for each time step and then extract features out of that series (extraction method). Interpolation and other forward-looking methods can be used if they are turned into prediction problems, then the forecasted values can be trained and tested on, and the fitted data can be diregarded. In the list presented below the first five methods can be used for cross-section and time series data, after that the time-series only methods follow.

(1) Scaling/Normalisation

There are a multitude of scaling methods available. Scaling generally gets applied to the entire dataset and is especially necessary for certain algorithms. K-means make use of euclidean distance hence the need for scaling. For PCA because we are trying to identify the feature with maximus variance we also need scaling. Similarly, we need scaled features for gradient descent. Any algorithm that is not based on a distance measure is not affected by feature scaling. Some of the methods include range scalers like minimum-maximum scaler, maximum absolute scaler or even standardisation methods like the standard scaler can be used for scaling. The example used here is robust scaler. Normalisation is a good technique when you don't know the distribution of the data. Scaling looks into the future, so parameters have to be training on a training set and applied to a test set.

(i) Robust Scaler

Scaling according to the interquartile range, making it robust to outliers.

def robust_scaler(df, drop=None,quantile_range=(25, 75) ):
    if drop:
      keep = df[drop]
      df = df.drop(drop, axis=1)
    center = np.median(df, axis=0)
    quantiles = np.percentile(df, quantile_range, axis=0)
    scale = quantiles[1] - quantiles[0]
    df = (df - center) / scale
    if drop:
      df = pd.concat((keep,df),axis=1)
    return df

df_out = transform.robust_scaler(df.copy(), drop=["Close_1"]); df_out.head()

(2) Standardisation

When using a standardisation method, it is often more effective when the attribute itself if Gaussian. It is also useful to apply the technique when the model you want to use makes assumptions of Gaussian distributions like linear regression, logistic regression, and linear discriminant analysis. For most applications, standardisation is recommended.

(i) Standard Scaler

Standardize features by removing the mean and scaling to unit variance

def standard_scaler(df,drop ):
    if drop:
      keep = df[drop]
      df = df.drop(drop, axis=1)
    mean = np.mean(df, axis=0)
    scale = np.std(df, axis=0)
    df = (df - mean) / scale  
    if drop:
      df = pd.concat((keep,df),axis=1)
    return df


df_out = transform.standard_scaler(df.copy(), drop=["Close"]); df_out.head()

(3) Differencing

Computing the differences between consecutive observation, normally used to obtain a stationary time series.

(i) Fractional Differencing

Fractional differencing, allows us to achieve stationarity while maintaining the maximum amount of memory compared to integer differencing.

import pylab as pl

def fast_fracdiff(x, cols, d):
    for col in cols:
      T = len(x[col])
      np2 = int(2 ** np.ceil(np.log2(2 * T - 1)))
      k = np.arange(1, T)
      b = (1,) + tuple(np.cumprod((k - d - 1) / k))
      z = (0,) * (np2 - T)
      z1 = b + z
      z2 = tuple(x[col]) + z
      dx = pl.ifft(pl.fft(z1) * pl.fft(z2))
      x[col+"_frac"] = np.real(dx[0:T])
    return x 
  
df_out = transform.fast_fracdiff(df.copy(), ["Close","Open"],0.5); df_out.head()

(4) Capping

Any method that provides sets a floor and a cap to a feature's value. Capping can affect the distribution of data, so it should not be exagerated. One can cap values by using the average, by using the max and min values, or by an arbitrary extreme value.

(i) Winzorisation

The transformation of features by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers by replacing it with a certain percentile value.

def outlier_detect(data,col,threshold=1,method="IQR"):
  
    if method == "IQR":
      IQR = data[col].quantile(0.75) - data[col].quantile(0.25)
      Lower_fence = data[col].quantile(0.25) - (IQR * threshold)
      Upper_fence = data[col].quantile(0.75) + (IQR * threshold)
    if method == "STD":
      Upper_fence = data[col].mean() + threshold * data[col].std()
      Lower_fence = data[col].mean() - threshold * data[col].std()   
    if method == "OWN":
      Upper_fence = data[col].mean() + threshold * data[col].std()
      Lower_fence = data[col].mean() - threshold * data[col].std() 
    if method =="MAD":
      median = data[col].median()
      median_absolute_deviation = np.median([np.abs(y - median) for y in data[col]])
      modified_z_scores = pd.Series([0.6745 * (y - median) / median_absolute_deviation for y in data[col]])
      outlier_index = np.abs(modified_z_scores) > threshold
      print('Num of outlier detected:',outlier_index.value_counts()[1])
      print('Proportion of outlier detected',outlier_index.value_counts()[1]/len(outlier_index))
      return outlier_index, (median_absolute_deviation, median_absolute_deviation)

    para = (Upper_fence, Lower_fence)
    tmp = pd.concat([data[col]>Upper_fence,data[col]<Lower_fence],axis=1)
    outlier_index = tmp.any(axis=1)
    print('Num of outlier detected:',outlier_index.value_counts()[1])
    print('Proportion of outlier detected',outlier_index.value_counts()[1]/len(outlier_index))
    
    return outlier_index, para

def windsorization(data,col,para,strategy='both'):
    """
    top-coding & bottom coding (capping the maximum of a distribution at an arbitrarily set value,vice versa)
    """

    data_copy = data.copy(deep=True)  
    if strategy == 'both':
        data_copy.loc[data_copy[col]>para[0],col] = para[0]
        data_copy.loc[data_copy[col]<para[1],col] = para[1]
    elif strategy == 'top':
        data_copy.loc[data_copy[col]>para[0],col] = para[0]
    elif strategy == 'bottom':
        data_copy.loc[data_copy[col]<para[1],col] = para[1]  
    return data_copy

_, para = transform.outlier_detect(df, "Close")
df_out = transform.windsorization(df.copy(),"Close",para,strategy='both'); df_out.head()

(5) Operations

Operations here are treated like traditional transformations. It is the replacement of a variable by a function of that variable. In a stronger sense, a transformation is a replacement that changes the shape of a distribution or relationship.

(i) Power, Log, Recipricol, Square Root

def operations(df,features):
  df_new = df[features]
  df_new = df_new - df_new.min()

  sqr_name = [str(fa)+"_POWER_2" for fa in df_new.columns]
  log_p_name = [str(fa)+"_LOG_p_one_abs" for fa in df_new.columns]
  rec_p_name = [str(fa)+"_RECIP_p_one" for fa in df_new.columns]
  sqrt_name = [str(fa)+"_SQRT_p_one" for fa in df_new.columns]

  df_sqr = pd.DataFrame(np.power(df_new.values, 2),columns=sqr_name, index=df.index)
  df_log = pd.DataFrame(np.log(df_new.add(1).abs().values),columns=log_p_name, index=df.index)
  df_rec = pd.DataFrame(np.reciprocal(df_new.add(1).values),columns=rec_p_name, index=df.index)
  df_sqrt = pd.DataFrame(np.sqrt(df_new.abs().add(1).values),columns=sqrt_name, index=df.index)

  dfs = [df, df_sqr, df_log, df_rec, df_sqrt]

  df=  pd.concat(dfs, axis=1)

  return df

df_out = transform.operations(df.copy(),["Close"]); df_out.head()

(6) Smoothing

Here we maintain that any method that has a component of historical averaging is a smoothing method such as a simple moving average and single, double and tripple exponential smoothing methods. These forms of non-causal filters are also popular in signal processing and are called filters, where exponential smoothing is called an IIR filter and a moving average a FIR filter with equal weighting factors.

(i) Tripple Exponential Smoothing (Holt-Winters Exponential Smoothing)

The Holt-Winters seasonal method comprises the forecast equation and three smoothing equations — one for the level $ℓt$, one for the trend &bt&, and one for the seasonal component $st$. This particular version is performed by looking at the last 12 periods. For that reason, the first 12 records should be disregarded because they can't make use of the required window size for a fair calculation. The calculation is such that values are still provided for those periods based on whatever data might be available.

def initial_trend(series, slen):
    sum = 0.0
    for i in range(slen):
        sum += float(series[i+slen] - series[i]) / slen
    return sum / slen

def initial_seasonal_components(series, slen):
    seasonals = {}
    season_averages = []
    n_seasons = int(len(series)/slen)
    # compute season averages
    for j in range(n_seasons):
        season_averages.append(sum(series[slen*j:slen*j+slen])/float(slen))
    # compute initial values
    for i in range(slen):
        sum_of_vals_over_avg = 0.0
        for j in range(n_seasons):
            sum_of_vals_over_avg += series[slen*j+i]-season_averages[j]
        seasonals[i] = sum_of_vals_over_avg/n_seasons
    return seasonals

def triple_exponential_smoothing(df,cols, slen, alpha, beta, gamma, n_preds):
    for col in cols:
      result = []
      seasonals = initial_seasonal_components(df[col], slen)
      for i in range(len(df[col])+n_preds):
          if i == 0: # initial values
              smooth = df[col][0]
              trend = initial_trend(df[col], slen)
              result.append(df[col][0])
              continue
          if i >= len(df[col]): # we are forecasting
              m = i - len(df[col]) + 1
              result.append((smooth + m*trend) + seasonals[i%slen])
          else:
              val = df[col][i]
              last_smooth, smooth = smooth, alpha*(val-seasonals[i%slen]) + (1-alpha)*(smooth+trend)
              trend = beta * (smooth-last_smooth) + (1-beta)*trend
              seasonals[i%slen] = gamma*(val-smooth) + (1-gamma)*seasonals[i%slen]
              result.append(smooth+trend+seasonals[i%slen])
      df[col+"_TES"] = result
    #print(seasonals)
    return df

df_out= transform.triple_exponential_smoothing(df.copy(),["Close"], 12, .2,.2,.2,0); df_out.head()

(7) Decomposing

Decomposition procedures are used in time series to describe the trend and seasonal factors in a time series. More extensive decompositions might also include long-run cycles, holiday effects, day of week effects and so on. Here, we’ll only consider trend and seasonal decompositions. A naive decomposition makes use of moving averages, other decomposition methods are available that make use of LOESS.

(i) Naive Decomposition

The base trend takes historical information into account and established moving averages; it does not have to be linear. To estimate the seasonal component for each season, simply average the detrended values for that season. If the seasonal variation looks constant, we should use the additive model. If the magnitude is increasing as a function of time, we will use multiplicative. Here because it is predictive in nature we are using a one sided moving average, as opposed to a two-sided centred average.

import statsmodels.api as sm

def naive_dec(df, columns, freq=2):
  for col in columns:
    decomposition = sm.tsa.seasonal_decompose(df[col], model='additive', freq = freq, two_sided=False)
    df[col+"_NDDT" ] = decomposition.trend
    df[col+"_NDDT"] = decomposition.seasonal
    df[col+"_NDDT"] = decomposition.resid
  return df

df_out = transform.naive_dec(df.copy(), ["Close","Open"]); df_out.head()

(8) Filtering

It is often useful to either low-pass filter (smooth) time series in order to reveal low-frequency features and trends, or to high-pass filter (detrend) time series in order to isolate high frequency transients (e.g. storms). Low pass filters use historical values, high-pass filters detrends with low-pass filters, so also indirectly uses historical values.

There are a few filters available, closely associated with decompositions and smoothing functions. The Hodrick-Prescott filter separates a time-series $yt$ into a trend $τt$ and a cyclical component $ζt$. The Christiano-Fitzgerald filter is a generalization of Baxter-King filter and can be seen as weighted moving average.

(i) Baxter-King Bandpass

The Baxter-King filter is intended to explicitly deal with the periodicity of the business cycle. By applying their band-pass filter to a series, they produce a new series that does not contain fluctuations at higher or lower than those of the business cycle. The parameters are arbitrarily chosen. This method uses a centred moving average that has to be changed to a lagged moving average before it can be used as an input feature. The maximum period of oscillation should be used as the point to truncate the dataset, as that part of the time series does not incorporate all the required datapoints.

import statsmodels.api as sm

def bkb(df, cols):
  for col in cols:
    df[col+"_BPF"] = sm.tsa.filters.bkfilter(df[[col]].values, 2, 10, len(df)-1)
  return df

df_out = transform.bkb(df.copy(), ["Close"]); df_out.head()

(ii) Butter Lowpass (IIR Filter Design)

The Butterworth filter is a type of signal processing filter designed to have a frequency response as flat as possible in the passban. Like other filtersm the first few values have to be disregarded for accurate downstream prediction. Instead of disregarding these values on a per case basis, they can be diregarded in one chunk once the database of transformed features have been developed.

from scipy import signal, integrate
def butter_lowpass(cutoff, fs=20, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = signal.butter(order, normal_cutoff, btype='low', analog=False)
    return b, a
    
def butter_lowpass_filter(df,cols, cutoff, fs=20, order=5):
    b, a = butter_lowpass(cutoff, fs, order=order)
    for col in cols:
      df[col+"_BUTTER"] = signal.lfilter(b, a, df[col])
    return df

df_out = transform.butter_lowpass_filter(df.copy(),["Close"],4); df_out.head()

(iii) Hilbert Transform Angle

The Hilbert transform is a time-domain to time-domain transformation which shifts the phase of a signal by 90 degrees. It is also a centred measure and would be difficult to use in a time series prediction setting, unless it is recalculated on a per step basis or transformed to be based on historical values only.

from scipy import signal
import numpy as np

def instantaneous_phases(df,cols):
    for col in cols:
      df[col+"_HILLB"] = np.unwrap(np.angle(signal.hilbert(df[col], axis=0)), axis=0)
    return df

df_out = transform.instantaneous_phases(df.copy(), ["Close"]); df_out.head()

(iiiv) Unscented Kalman Filter

The Kalman filter is better suited for estimating things that change over time. The most tangible example is tracking moving objects. A Kalman filter will be very close to the actual trajectory because it says the most recent measurement is more important than the older ones. The Unscented Kalman Filter (UKF) is a model based-techniques that recursively estimates the states (and with some modifications also parameters) of a nonlinear, dynamic, discrete-time system. The UKF is based on the typical prediction-correction style methods. The Kalman Smoother incorporates future values, the Filter doesn't and can be used for online prediction. The normal Kalman filter is a forward filter in the sense that it makes forecast of the current state using only current and past observations, whereas the smoother is based on computing a suitable linear combination of two filters, which are ran in forward and backward directions.

from pykalman import UnscentedKalmanFilter

def kalman_feat(df, cols):
  for col in cols:
    ukf = UnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, observation_covariance=0.1)
    (filtered_state_means, filtered_state_covariances) = ukf.filter(df[col])
    (smoothed_state_means, smoothed_state_covariances) = ukf.smooth(df[col])
    df[col+"_UKFSMOOTH"] = smoothed_state_means.flatten()
    df[col+"_UKFFILTER"] = filtered_state_means.flatten()
  return df 

df_out = transform.kalman_feat(df.copy(), ["Close"]); df_out.head()

(9) Spectral Analysis

There are a range of functions for spectral analysis. You can use periodograms and the welch method to estimate the power spectral density. You can also use the welch method to estimate the cross power spectral density. Other techniques include spectograms, Lomb-Scargle periodograms and, short time fourier transform.

(i) Periodogram

This returns an array of sample frequencies and the power spectrum of x, or the power spectral density of x.

from scipy import signal
def perd_feat(df, cols):
  for col in cols:
    sig = signal.periodogram(df[col],fs=1, return_onesided=False)
    df[col+"_FREQ"] = sig[0]
    df[col+"_POWER"] = sig[1]
  return df

df_out = transform.perd_feat(df.copy(),["Close"]); df_out.head()

(ii) Fast Fourier Transform

The FFT, or fast fourier transform is an algorithm that essentially uses convolution techniques to efficiently find the magnitude and location of the tones that make up the signal of interest. We can often play with the FFT spectrum, by adding and removing successive tones (which is akin to selectively filtering particular tones that make up the signal), in order to obtain a smoothed version of the underlying signal. This takes the entire signal into account, and as a result has to be recalculated on a running basis to avoid peaking into the future.

def fft_feat(df, cols):
  for col in cols:
    fft_df = np.fft.fft(np.asarray(df[col].tolist()))
    fft_df = pd.DataFrame({'fft':fft_df})
    df[col+'_FFTABS'] = fft_df['fft'].apply(lambda x: np.abs(x)).values
    df[col+'_FFTANGLE'] = fft_df['fft'].apply(lambda x: np.angle(x)).values
  return df 

df_out = transform.fft_feat(df.copy(), ["Close"]); df_out.head()

(10) Waveforms

The waveform of a signal is the shape of its graph as a function of time.

(i) Continuous Wave Radar

from scipy import signal
def harmonicradar_cw(df, cols, fs,fc):
    for col in cols:
      ttxt = f'CW: {fc} Hz'
      #%% input
      t = df[col]
      tx = np.sin(2*np.pi*fc*t)
      _,Pxx = signal.welch(tx,fs)
      #%% diode
      d = (signal.square(2*np.pi*fc*t))
      d[d<0] = 0.
      #%% output of diode
      rx = tx * d
      df[col+"_HARRAD"] = rx.values
    return df

df_out = transform.harmonicradar_cw(df.copy(), ["Close"],0.3,0.2); df_out.head()

(ii) Saw Tooth

Return a periodic sawtooth or triangle waveform.

def saw(df, cols):
  for col in cols:
    df[col+" SAW"] = signal.sawtooth(df[col])
  return df

df_out = transform.saw(df.copy(),["Close","Open"]); df_out.head()

(9) Modifications

A range of modification usually applied ot images, these values would have to be recalculate for each time-series.

(i) Various Techniques

from tsaug import *
def modify(df, cols):
  for col in cols:
    series = df[col].values
    df[col+"_magnify"], _ = magnify(series, series)
    df[col+"_affine"], _ = affine(series, series)
    df[col+"_crop"], _ = crop(series, series)
    df[col+"_cross_sum"], _ = cross_sum(series, series)
    df[col+"_resample"], _ = resample(series, series)
    df[col+"_trend"], _ = trend(series, series)

    df[col+"_random_affine"], _ = random_time_warp(series, series)
    df[col+"_random_crop"], _ = random_crop(series, series)
    df[col+"_random_cross_sum"], _ = random_cross_sum(series, series)
    df[col+"_random_sidetrack"], _ = random_sidetrack(series, series)
    df[col+"_random_time_warp"], _ = random_time_warp(series, series)
    df[col+"_random_magnify"], _ = random_magnify(series, series)
    df[col+"_random_jitter"], _ = random_jitter(series, series)
    df[col+"_random_trend"], _ = random_trend(series, series)
  return df

df_out = transform.modify(df.copy(),["Close"]); df_out.head()

(11) Rolling

Features that are calculated on a rolling basis over fixed window size.

(i) Mean, Standard Deviation

def multiple_rolling(df, windows = [1,2], functions=["mean","std"], columns=None):
  windows = [1+a for a in windows]
  if not columns:
    columns = df.columns.to_list()
  rolling_dfs = (df[columns].rolling(i)                                    # 1. Create window
                  .agg(functions)                                # 1. Aggregate
                  .rename({col: '{0}_{1:d}'.format(col, i)
                                for col in columns}, axis=1)  # 2. Rename columns
                for i in windows)                                # For each window
  df_out = pd.concat((df, *rolling_dfs), axis=1)
  da = df_out.iloc[:,len(df.columns):]
  da = [col[0] + "_" + col[1] for col in  da.columns.to_list()]
  df_out.columns = df.columns.to_list() + da 

  return  df_out                      # 3. Concatenate dataframes

df_out = transform.multiple_rolling(df, columns=["Close"]); df_out.head()

(12) Lagging

Lagged values from existing features.

(i) Single Steps

def multiple_lags(df, start=1, end=3,columns=None):
  if not columns:
    columns = df.columns.to_list()
  lags = range(start, end+1)  # Just two lags for demonstration.

  df = df.assign(**{
      '{}_t_{}'.format(col, t): df[col].shift(t)
      for t in lags
      for col in columns
  })
  return df

df_out = transform.multiple_lags(df, start=1, end=3, columns=["Close"]); df_out.head()

(13) Forecast Model

There are a range of time series model that can be implemented like AR, MA, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA, VARMAX, SES, and HWES. The models can be divided into autoregressive models and smoothing models. In an autoregression model, we forecast the variable of interest using a linear combination of past values of the variable. Each method might requre specific tuning and parameters to suit your prediction task. You need to drop a certain amount of historical data that you use during the fitting stage. Models that take seasonality into account need more training data.

(i) Prophet

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality. You can apply additive models to your training data but also interactive models like deep learning models. The problem is that because these models have learned from future observations, there would this be a need to recalculate the time series on a running basis, or to only include the predicted as opposed to fitted values in future training and test sets. In this example, I train on 150 data points to illustrate how the remaining or so 100 datapoints can be used in a new prediction problem. You can plot with df["PROPHET"].plot() to see the effect.

You can apply additive models to your training data but also interactive models like deep learning models. The problem is that these models have learned from future observations, there would this be a need to recalculate the time series on a running basis, or to only include the predicted as opposed to fitted values in future training and test sets.

from fbprophet import Prophet

def prophet_feat(df, cols,date, freq,train_size=150):
  def prophet_dataframe(df): 
    df.columns = ['ds','y']
    return df

  def original_dataframe(df, freq, name):
    prophet_pred = pd.DataFrame({"Date" : df['ds'], name : df["yhat"]})
    prophet_pred = prophet_pred.set_index("Date")
    #prophet_pred.index.freq = pd.tseries.frequencies.to_offset(freq)
    return prophet_pred[name].values

  for col in cols:
    model = Prophet(daily_seasonality=True)
    fb = model.fit(prophet_dataframe(df[[date, col]].head(train_size)))
    forecast_len = len(df) - train_size
    future = model.make_future_dataframe(periods=forecast_len,freq=freq)
    future_pred = model.predict(future)
    df[col+"_PROPHET"] = list(original_dataframe(future_pred,freq,col))
  return df

df_out  = transform.prophet_feat(df.copy().reset_index(),["Close","Open"],"Date", "D"); df_out.head()

(2) Interaction

Interactions are defined as methods that require more than one feature to create an additional feature. Here we include normalising and discretising techniques that are non-feature specific. Almost all of these method can be applied to cross-section method. The only methods that are time specific is the technical features in the speciality section and the autoregression model.

(1) Regression

Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables.

(i) Lowess Smoother

The lowess smoother is a robust locally weighted regression. The function fits a nonparametric regression curve to a scatterplot.

from math import ceil
import numpy as np
from scipy import linalg
import math

def lowess(df, cols, y, f=2. / 3., iter=3):
    for col in cols:
      n = len(df[col])
      r = int(ceil(f * n))
      h = [np.sort(np.abs(df[col] - df[col][i]))[r] for i in range(n)]
      w = np.clip(np.abs((df[col][:, None] - df[col][None, :]) / h), 0.0, 1.0)
      w = (1 - w ** 3) ** 3
      yest = np.zeros(n)
      delta = np.ones(n)
      for iteration in range(iter):
          for i in range(n):
              weights = delta * w[:, i]
              b = np.array([np.sum(weights * y), np.sum(weights * y * df[col])])
              A = np.array([[np.sum(weights), np.sum(weights * df[col])],
                            [np.sum(weights * df[col]), np.sum(weights * df[col] * df[col])]])
              beta = linalg.solve(A, b)
              yest[i] = beta[0] + beta[1] * df[col][i]

          residuals = y - yest
          s = np.median(np.abs(residuals))
          delta = np.clip(residuals / (6.0 * s), -1, 1)
          delta = (1 - delta ** 2) ** 2
      df[col+"_LOWESS"] = yest

    return df

df_out = interact.lowess(df.copy(), ["Open","Volume"], df["Close"], f=0.25, iter=3); df_out.head()

Autoregression

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step

from statsmodels.tsa.ar_model import AR
from timeit import default_timer as timer
def autoregression(df, drop=None, settings={"autoreg_lag":4}):

    autoreg_lag = settings["autoreg_lag"]
    if drop:
      keep = df[drop]
      df = df.drop([drop],axis=1).values

    n_channels = df.shape[0]
    t = timer()
    channels_regg = np.zeros((n_channels, autoreg_lag + 1))
    for i in range(0, n_channels):
        fitted_model = AR(df.values[i, :]).fit(autoreg_lag)
        # TODO: This is not the same as Matlab's for some reasons!
        # kk = ARMAResults(fitted_model)
        # autore_vals, dummy1, dummy2 = arburg(x[i, :], autoreg_lag) # This looks like Matlab's but slow
        channels_regg[i, 0: len(fitted_model.params)] = np.real(fitted_model.params)

    for i in range(channels_regg.shape[1]):
      df["LAG_"+str(i+1)] = channels_regg[:,i]
    
    if drop:
      df = pd.concat((keep,df),axis=1)

    t = timer() - t
    return df

df_out = interact.autoregression(df.copy()); df_out.head()

(2) Operator

Looking at interaction between different features. Here the methods employed are multiplication and division.

(i) Multiplication and Division

def muldiv(df, feature_list):
  for feat in feature_list:
    for feat_two in feature_list:
      if feat==feat_two:
        continue
      else:
       df[feat+"/"+feat_two] = df[feat]/(df[feat_two]-df[feat_two].min()) #zero division guard
       df[feat+"_X_"+feat_two] = df[feat]*(df[feat_two])

  return df

df_out = interact.muldiv(df.copy(), ["Close","Open"]); df_out.head()

(3) Discretising

In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes

(i) Decision Tree Discretiser

The first method that will be applies here is a supersived discretiser. Discretisation with Decision Trees consists of using a decision tree to identify the optimal splitting points that would determine the bins or contiguous intervals.

from sklearn.tree import DecisionTreeRegressor

def decision_tree_disc(df, cols, depth=4 ):
  for col in cols:
    df[col +"_m1"] = df[col].shift(1)
    df = df.iloc[1:,:]
    tree_model = DecisionTreeRegressor(max_depth=depth,random_state=0)
    tree_model.fit(df[col +"_m1"].to_frame(), df[col])
    df[col+"_Disc"] = tree_model.predict(df[col +"_m1"].to_frame())
  return df

df_out = interact.decision_tree_disc(df.copy(), ["Close"]); df_out.head()

(4) Normalising

Normalising normally pertains to the scaling of data. There are many method available, interacting normalising methods makes use of all the feature's attributes to do the scaling.

(i) Quantile Normalisation

In statistics, quantile normalization is a technique for making two distributions identical in statistical properties.

import numpy as np
import pandas as pd

def quantile_normalize(df, drop):

    if drop:
      keep = df[drop]
      df = df.drop(drop,axis=1)

    #compute rank
    dic = {}
    for col in df:
      dic.update({col : sorted(df[col])})
    sorted_df = pd.DataFrame(dic)
    rank = sorted_df.mean(axis = 1).tolist()
    #sort
    for col in df:
        t = np.searchsorted(np.sort(df[col]), df[col])
        df[col] = [rank[i] for i in t]
    
    if drop:
      df = pd.concat((keep,df),axis=1)
    return df

df_out = interact.quantile_normalize(df.copy(), drop=["Close"]); df_out.head()

(5) Distance

There are multiple types of distance functions like Euclidean, Mahalanobis, and Minkowski distance. Here we are using a contrived example in a location based haversine distance.

(i) Haversine Distance

The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere.

from math import sin, cos, sqrt, atan2, radians
def haversine_distance(row, lon="Open", lat="Close"):
    c_lat,c_long = radians(52.5200), radians(13.4050)
    R = 6373.0
    long = radians(row['Open'])
    lat = radians(row['Close'])
    
    dlon = long - c_long
    dlat = lat - c_lat
    a = sin(dlat / 2)**2 + cos(lat) * cos(c_lat) * sin(dlon / 2)**2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    
    return R * c

df_out['distance_central'] = df.apply(interact.haversine_distance,axis=1); df_out.head()

(6) Speciality

(i) Technical Features

Technical indicators are heuristic or mathematical calculations based on the price, volume, or open interest of a security or contract used by traders who follow technical analysis. By analyzing historical data, technical analysts use indicators to predict future price movements.

import ta

def tech(df):
  return ta.add_all_ta_features(df, open="Open", high="High", low="Low", close="Close", volume="Volume")
  
df_out = interact.tech(df.copy()); df_out.head()

(7) Genetic

Genetic programming has shown promise in constructing feature by osing original features to form high-level ones that can help algorithms achieve better performance.

(i) Symbolic Transformer

A symbolic transformer is a supervised transformer that begins by building a population of naive random formulas to represent a relationship.

df.head()

from gplearn.genetic import SymbolicTransformer

def genetic_feat(df, num_gen=20, num_comp=10):
  function_set = ['add', 'sub', 'mul', 'div',
                  'sqrt', 'log', 'abs', 'neg', 'inv','tan']

  gp = SymbolicTransformer(generations=num_gen, population_size=200,
                          hall_of_fame=100, n_components=num_comp,
                          function_set=function_set,
                          parsimony_coefficient=0.0005,
                          max_samples=0.9, verbose=1,
                          random_state=0, n_jobs=6)

  gen_feats = gp.fit_transform(df.drop("Close_1", axis=1), df["Close_1"]); df.iloc[:,:8]
  gen_feats = pd.DataFrame(gen_feats, columns=["gen_"+str(a) for a in range(gen_feats.shape[1])])
  gen_feats.index = df.index
  return pd.concat((df,gen_feats),axis=1)

df_out = interact.genetic_feat(df.copy()); df_out.head()

(3) Mapping

Methods that help with the summarisation of features by remapping them to achieve some aim like the maximisation of variability or class separability. These methods tend to be unsupervised, but can also take an supervised form.

(1) Eigen Decomposition

Eigendecomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Some examples are LDA and PCA.

(i) Principal Component Analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

def pca_feature(df, memory_issues=False,mem_iss_component=False,variance_or_components=0.80,n_components=5 ,drop_cols=None, non_linear=True):
    
  if non_linear:
    pca = KernelPCA(n_components = n_components, kernel='rbf', fit_inverse_transform=True, random_state = 33, remove_zero_eig= True)
  else:
    if memory_issues:
      if not mem_iss_component:
        raise ValueError("If you have memory issues, you have to preselect mem_iss_component")
      pca = IncrementalPCA(mem_iss_component)
    else:
      if variance_or_components>1:
        pca = PCA(n_components=variance_or_components) 
      else: # automated selection based on variance
        pca = PCA(n_components=variance_or_components,svd_solver="full") 
  if drop_cols:
    X_pca = pca.fit_transform(df.drop(drop_cols,axis=1))
    return pd.concat((df[drop_cols],pd.DataFrame(X_pca, columns=["PCA_"+str(i+1) for i in range(X_pca.shape[1])],index=df.index)),axis=1)

  else:
    X_pca = pca.fit_transform(df)
    return pd.DataFrame(X_pca, columns=["PCA_"+str(i+1) for i in range(X_pca.shape[1])],index=df.index)


  return df

df_out = mapper.pca_feature(df.copy(), variance_or_components=0.9, n_components=8,non_linear=False)

(2) Cross Decomposition

These families of algorithms are useful to find linear relations between two multivariate datasets.

(1) Canonical Correlation Analysis

Canonical-correlation analysis (CCA) is a way of inferring information from cross-covariance matrices.

from sklearn.cross_decomposition import CCA

def cross_lag(df, drop=None, lags=1, components=4 ):

  if drop:
    keep = df[drop]
    df = df.drop([drop],axis=1)

  df_2 = df.shift(lags)
  df = df.iloc[lags:,:]
  df_2 = df_2.dropna().reset_index(drop=True)

  cca = CCA(n_components=components)
  cca.fit(df_2, df)

  X_c, df_2 = cca.transform(df_2, df)
  df_2 = pd.DataFrame(df_2, index=df.index)
  df_2 = df.add_prefix('crd_')

  if drop:
    df = pd.concat([keep,df,df_2],axis=1)
  else:
    df = pd.concat([df,df_2],axis=1)
  return df

df_out = mapper.cross_lag(df.copy()); df_out.head()

(3) Kernel Approximation

Functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines.

(i) Additive Chi2 Kernel

Computes the additive chi-squared kernel between observations in X and Y The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative.

from sklearn.kernel_approximation import AdditiveChi2Sampler

def a_chi(df, drop=None, lags=1, sample_steps=2 ):

  if drop:
    keep = df[drop]
    df = df.drop([drop],axis=1)

  df_2 = df.shift(lags)
  df = df.iloc[lags:,:]
  df_2 = df_2.dropna().reset_index(drop=True)

  chi2sampler = AdditiveChi2Sampler(sample_steps=sample_steps)

  df_2 = chi2sampler.fit_transform(df_2, df["Close"])

  df_2 = pd.DataFrame(df_2, index=df.index)
  df_2 = df.add_prefix('achi_')

  if drop:
    df = pd.concat([keep,df,df_2],axis=1)
  else:
    df = pd.concat([df,df_2],axis=1)
  return df

df_out = mapper.a_chi(df.copy()); df_out.head()

(4) Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore noise.

(i) Feed Forward

The simplest form of an autoencoder is a feedforward, non-recurrent neural network similar to single layer perceptrons that participate in multilayer perceptrons

from sklearn.preprocessing import minmax_scale
import tensorflow as tf
import numpy as np

def encoder_dataset(df, drop=None, dimesions=20):

  if drop:
    train_scaled = minmax_scale(df.drop(drop,axis=1).values, axis = 0)
  else:
    train_scaled = minmax_scale(df.values, axis = 0)

  # define the number of encoding dimensions
  encoding_dim = dimesions
  # define the number of features
  ncol = train_scaled.shape[1]
  input_dim = tf.keras.Input(shape = (ncol, ))

  # Encoder Layers
  encoded1 = tf.keras.layers.Dense(3000, activation = 'relu')(input_dim)
  encoded2 = tf.keras.layers.Dense(2750, activation = 'relu')(encoded1)
  encoded3 = tf.keras.layers.Dense(2500, activation = 'relu')(encoded2)
  encoded4 = tf.keras.layers.Dense(750, activation = 'relu')(encoded3)
  encoded5 = tf.keras.layers.Dense(500, activation = 'relu')(encoded4)
  encoded6 = tf.keras.layers.Dense(250, activation = 'relu')(encoded5)
  encoded7 = tf.keras.layers.Dense(encoding_dim, activation = 'relu')(encoded6)

  encoder = tf.keras.Model(inputs = input_dim, outputs = encoded7)
  encoded_input = tf.keras.Input(shape = (encoding_dim, ))

  encoded_train = pd.DataFrame(encoder.predict(train_scaled),index=df.index)
  encoded_train = encoded_train.add_prefix('encoded_')
  if drop:
    encoded_train = pd.concat((df[drop],encoded_train),axis=1)

  return encoded_train

df_out = mapper.encoder_dataset(df.copy(), ["Close_1"], 15); df_out.head()

df_out.head()

(5) Manifold Learning

Manifold Learning can be thought of as an attempt to generalize linear frameworks like PCA to be sensitive to non-linear structure in data.

(i) Local Linear Embedding

Locally Linear Embedding is a method of non-linear dimensionality reduction. It tries to reduce these n-Dimensions while trying to preserve the geometric features of the original non-linear feature structure.

from sklearn.manifold import LocallyLinearEmbedding

def lle_feat(df, drop=None, components=4):

  if drop:
    keep = df[drop]
    df = df.drop(drop, axis=1)

  embedding = LocallyLinearEmbedding(n_components=components)
  em = embedding.fit_transform(df)
  df = pd.DataFrame(em,index=df.index)
  df = df.add_prefix('lle_')
  if drop:
    df = pd.concat((keep,df),axis=1)
  return df

df_out = mapper.lle_feat(df.copy(),["Close_1"],4); df_out.head()

(6) Clustering

Most clustering techniques start with a bottom up approach: each observation starts in its own cluster, and clusters are successively merged together with some measure. Although these clustering techniques are typically used for observations, it can also be used for feature dimensionality reduction; especially hierarchical clustering techniques.

(i) Feature Agglomeration

Feature agglomerative uses clustering to group together features that look very similar, thus decreasing the number of features.

import numpy as np
from sklearn import datasets, cluster

def feature_agg(df, drop=None, components=4):

  if drop:
    keep = df[drop]
    df = df.drop(drop, axis=1)

  components = min(df.shape[1]-1,components)
  agglo = cluster.FeatureAgglomeration(n_clusters=components)
  agglo.fit(df)
  df = pd.DataFrame(agglo.transform(df),index=df.index)
  df = df.add_prefix('feagg_')

  if drop:
    return pd.concat((keep,df),axis=1)
  else:
    return df


df_out = mapper.feature_agg(df.copy(),["Close_1"],4 ); df_out.head()

(7) Neigbouring

Neighbouring points can be calculated using distance metrics like Hamming, Manhattan, Minkowski distance. The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these.

(i) Nearest Neighbours

Unsupervised learner for implementing neighbor searches.

from sklearn.neighbors import NearestNeighbors

def neigh_feat(df, drop, neighbors=6):
  
  if drop:
    keep = df[drop]
    df = df.drop(drop, axis=1)

  components = min(df.shape[0]-1,neighbors)
  neigh = NearestNeighbors(n_neighbors=neighbors)
  neigh.fit(df)
  neigh = neigh.kneighbors()[0]
  df = pd.DataFrame(neigh, index=df.index)
  df = df.add_prefix('neigh_')

  if drop:
    return pd.concat((keep,df),axis=1)
  else:
    return df

  return df

df_out = mapper.neigh_feat(df.copy(),["Close_1"],4 ); df_out.head()

(4) Extraction

When working with extraction, you have decide the size of the time series history to take into account when calculating a collection of walk-forward feature values. To facilitate our extraction, we use an excellent package called TSfresh, and also some of their default features. For completeness, we also include 12 or so custom features to be added to the extraction pipeline.

The time series methods in the transformation section and the interaction section are similar to the methods we will uncover in the extraction section, however, for transformation and interaction methods the output is an entire new time series, whereas extraction methods takes as input multiple constructed time series and extracts a singular value from each time series to reconstruct an entirely new time series.

Some methods naturally fit better in one format over another, e.g., lags are too expensive for extraction; time series decomposition only has to be performed once, because it has a low level of 'leakage' so is better suited to transformation; and forecast methods attempt to predict multiple future training samples, so won't work with extraction that only delivers one value per time series. Furthermore all non time-series (cross-sectional) transformation and extraction techniques can not make use of extraction as it is solely a time-series method.

Lastly, when we want to double apply specific functions we can apply it as a transformation/interaction then all the extraction methods can be applied to this feature as well. For example, if we calculate a smoothing function (transformation) then all other extraction functions (median, entropy, linearity etc.) can now be applied to that smoothing function, including the application of the smoothing function itself, e.g., a double smooth, double lag, double filter etc. So separating these methods out give us great flexibility.

Decorator

def set_property(key, value):
    """
    This method returns a decorator that sets the property key of the function to value
    """
    def decorate_func(func):
        setattr(func, key, value)
        if func.__doc__ and key == "fctype":
            func.__doc__ = func.__doc__ + "\n\n    *This function is of type: " + value + "*\n"
        return func
    return decorate_func

(1) Energy

You can calculate the linear, non-linear and absolute energy of a time series. In signal processing, the energy $E_S$ of a continuous-time signal $x(t)$ is defined as the area under the squared magnitude of the considered signal. Mathematically, $E_{s}=\langle x(t), x(t)\rangle=\int_{-\infty}^{\infty}|x(t)|^{2} d t$

(i) Absolute Energy

Returns the absolute energy of the time series which is the sum over the squared values

#-> In Package
def abs_energy(x):

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    return np.dot(x, x)

extract.abs_energy(df["Close"])

(2) Distance

Here we widely define distance measures as those that take a difference between attributes or series of datapoints.

(i) Complexity-Invariant Distance

This function calculator is an estimate for a time series complexity.

#-> In Package
def cid_ce(x, normalize):

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    if normalize:
        s = np.std(x)
        if s!=0:
            x = (x - np.mean(x))/s
        else:
            return 0.0

    x = np.diff(x)
    return np.sqrt(np.dot(x, x))

extract.cid_ce(df["Close"], True)

(3) Differencing

Many alternatives to differencing exists, one can for example take the difference of every other value, take the squared difference, take the fractional difference, or like our example, take the mean absolute difference.

(i) Mean Absolute Change

Returns the mean over the absolute differences between subsequent time series values.

#-> In Package
def mean_abs_change(x):
    return np.mean(np.abs(np.diff(x)))

extract.mean_abs_change(df["Close"])

(4) Derivative

Features where the emphasis is on the rate of change.

(i) Mean Central Second Derivative

Returns the mean value of a central approximation of the second derivative

#-> In Package
def _roll(a, shift):
    if not isinstance(a, np.ndarray):
        a = np.asarray(a)
    idx = shift % len(a)
    return np.concatenate([a[-idx:], a[:-idx]])

def mean_second_derivative_central(x):

    diff = (_roll(x, 1) - 2 * np.array(x) + _roll(x, -1)) / 2.0
    return np.mean(diff[1:-1])

extract.mean_second_derivative_central(df["Close"])

(5) Volatility

Volatility is a statistical measure of the dispersion of a time-series.

(i) Variance Larger than Standard Deviation

#-> In Package
def variance_larger_than_standard_deviation(x):

    y = np.var(x)
    return y > np.sqrt(y)

extract.variance_larger_than_standard_deviation(df["Close"])

(ii) Variability Index

Variability Index is a way to measure how smooth or 'variable' a time series is.

var_index_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

@set_property("fctype", "combiner")
@set_property("custom", True)
def var_index(time,param=var_index_param):
    final = []
    keys = []
    for key, magnitude in param.items():
      w = 1.0 / np.power(np.subtract(time[1:], time[:-1]), 2)
      w_mean = np.mean(w)

      N = len(time)
      sigma2 = np.var(magnitude)

      S1 = sum(w * (magnitude[1:] - magnitude[:-1]) ** 2)
      S2 = sum(w)

      eta_e = (w_mean * np.power(time[N - 1] -
                time[0], 2) * S1 / (sigma2 * S2 * N ** 2))
      final.append(eta_e)
      keys.append(key)
    return {"Interact__{}".format(k): eta_e for eta_e, k in zip(final,keys) }

extract.var_index(df["Close"].values,var_index_param)

(6) Shape

Features that emphasises a particular shape not ordinarily considered as a distribution statistic. Extends to derivations of the original time series too For example a feature looking at the sinusoidal shape of an autocorrelation plot.

(i) Symmetrical

Boolean variable denoting if the distribution of x looks symmetric.

#-> In Package
def symmetry_looking(x, param=[{"r": 0.2}]):

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    mean_median_difference = np.abs(np.mean(x) - np.median(x))
    max_min_difference = np.max(x) - np.min(x)
    return [("r_{}".format(r["r"]), mean_median_difference < (r["r"] * max_min_difference))
            for r in param]
            
extract.symmetry_looking(df["Close"])

(7) Occurrence

Looking at the occurrence, and reoccurence of defined values.

(i) Has Duplicate Max

#-> In Package
def has_duplicate_max(x):
    """
    Checks if the maximum value of x is observed more than once

    :param x: the time series to calculate the feature of
    :type x: numpy.ndarray
    :return: the value of this feature
    :return type: bool
    """
    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    return np.sum(x == np.max(x)) >= 2

extract.has_duplicate_max(df["Close"])

(8) Autocorrelation

Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.

(i) Partial Autocorrelation

Partial autocorrelation is a summary of the relationship between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed.

#-> In Package
from statsmodels.tsa.stattools import acf, adfuller, pacf

def partial_autocorrelation(x, param=[{"lag": 1}]):

    # Check the difference between demanded lags by param and possible lags to calculate (depends on len(x))
    max_demanded_lag = max([lag["lag"] for lag in param])
    n = len(x)

    # Check if list is too short to make calculations
    if n <= 1:
        pacf_coeffs = [np.nan] * (max_demanded_lag + 1)
    else:
        if (n <= max_demanded_lag):
            max_lag = n - 1
        else:
            max_lag = max_demanded_lag
        pacf_coeffs = list(pacf(x, method="ld", nlags=max_lag))
        pacf_coeffs = pacf_coeffs + [np.nan] * max(0, (max_demanded_lag - max_lag))

    return [("lag_{}".format(lag["lag"]), pacf_coeffs[lag["lag"]]) for lag in param]

extract.partial_autocorrelation(df["Close"])

(9) Stochasticity

Stochastic refers to a randomly determined process. Any features trying to capture stochasticity by degree or type are included under this branch.

(i) Augmented Dickey Fuller

The Augmented Dickey-Fuller test is a hypothesis test which checks whether a unit root is present in a time series sample.

#-> In Package
def augmented_dickey_fuller(x, param=[{"attr": "teststat"}]):

    res = None
    try:
        res = adfuller(x)
    except LinAlgError:
        res = np.NaN, np.NaN, np.NaN
    except ValueError: # occurs if sample size is too small
        res = np.NaN, np.NaN, np.NaN
    except MissingDataError: # is thrown for e.g. inf or nan in the data
        res = np.NaN, np.NaN, np.NaN

    return [('attr_"{}"'.format(config["attr"]),
                  res[0] if config["attr"] == "teststat"
             else res[1] if config["attr"] == "pvalue"
             else res[2] if config["attr"] == "usedlag" else np.NaN)
            for config in param]

extract.augmented_dickey_fuller(df["Close"])

(10) Averages

(i) Median of Magnitudes Skew

@set_property("fctype", "simple")
@set_property("custom", True)
def gskew(x):
    interpolation="nearest"
    median_mag = np.median(x)
    F_3_value = np.percentile(x, 3, interpolation=interpolation)
    F_97_value = np.percentile(x, 97, interpolation=interpolation)

    skew = (np.median(x[x <= F_3_value]) +
            np.median(x[x >= F_97_value]) - 2 * median_mag)

    return skew

extract.gskew(df["Close"])

(ii) Stetson Mean

An iteratively weighted mean used in the Stetson variability index

stestson_param = {"weight":100., "alpha":2., "beta":2., "tol":1.e-6, "nmax":20}

@set_property("fctype", "combiner")
@set_property("custom", True)
def stetson_mean(x, param=stestson_param):
    
    weight= stestson_param["weight"]
    alpha= stestson_param["alpha"]
    beta = stestson_param["beta"]
    tol= stestson_param["tol"]
    nmax= stestson_param["nmax"]
    
    
    mu = np.median(x)
    for i in range(nmax):
        resid = x - mu
        resid_err = np.abs(resid) * np.sqrt(weight)
        weight1 = weight / (1. + (resid_err / alpha)**beta)
        weight1 /= weight1.mean()
        diff = np.mean(x * weight1) - mu
        mu += diff
        if (np.abs(diff) < tol*np.abs(mu) or np.abs(diff) < tol):
            break

    return mu

extract.stetson_mean(df["Close"])

(11) Size

(i) Lenght

#-> In Package
def length(x):
    return len(x)
    
extract.length(df["Close"])

(12) Count

(i) Count Above Mean

Returns the number of values in x that are higher than the mean of x

#-> In Package
def count_above_mean(x):
    m = np.mean(x)
    return np.where(x > m)[0].size

extract.count_above_mean(df["Close"])

(13) Streaks

(i) Longest Strike Below Mean

Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x

#-> In Package
import itertools
def get_length_sequences_where(x):

    if len(x) == 0:
        return [0]
    else:
        res = [len(list(group)) for value, group in itertools.groupby(x) if value == 1]
        return res if len(res) > 0 else [0]

def longest_strike_below_mean(x):

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    return np.max(get_length_sequences_where(x <= np.mean(x))) if x.size > 0 else 0

extract.longest_strike_below_mean(df["Close"])

(ii) Wozniak

This is an astronomical feature, we count the number of three consecutive data points that are brighter or fainter than $2σ$ and normalize the number by $N−2$

woz_param = [{"consecutiveStar": n} for n in [2, 4]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def wozniak(magnitude, param=woz_param):

    iters = []
    for consecutiveStar in [stars["consecutiveStar"] for stars in param]:
      N = len(magnitude)
      if N < consecutiveStar:
          return 0
      sigma = np.std(magnitude)
      m = np.mean(magnitude)
      count = 0

      for i in range(N - consecutiveStar + 1):
          flag = 0
          for j in range(consecutiveStar):
              if(magnitude[i + j] > m + 2 * sigma or
                  magnitude[i + j] < m - 2 * sigma):
                  flag = 1
              else:
                  flag = 0
                  break
          if flag:
              count = count + 1
      iters.append(count * 1.0 / (N - consecutiveStar + 1))

    return [("consecutiveStar_{}".format(config["consecutiveStar"]), iters[en] )  for en, config in enumerate(param)]

extract.wozniak(df["Close"])

(14) Location

(i) Last location of Maximum

Returns the relative last location of the maximum value of x. last_location_of_minimum(x),

#-> In Package
def last_location_of_maximum(x):

    x = np.asarray(x)
    return 1.0 - np.argmax(x[::-1]) / len(x) if len(x) > 0 else np.NaN

extract.last_location_of_maximum(df["Close"])

(15) Model Coefficients

Any coefficient that are obtained from a model that might help in the prediction problem. For example here we might include coefficients of polynomial $h(x)$, which has been fitted to the deterministic dynamics of Langevin model.

(i) FFT Coefficient

Calculates the fourier coefficients of the one-dimensional discrete Fourier Transform for real input.

#-> In Package
def fft_coefficient(x, param = [{"coeff": 10, "attr": "real"}]):

    assert min([config["coeff"] for config in param]) >= 0, "Coefficients must be positive or zero."
    assert set([config["attr"] for config in param]) <= set(["imag", "real", "abs", "angle"]), \
        'Attribute must be "real", "imag", "angle" or "abs"'

    fft = np.fft.rfft(x)

    def complex_agg(x, agg):
        if agg == "real":
            return x.real
        elif agg == "imag":
            return x.imag
        elif agg == "abs":
            return np.abs(x)
        elif agg == "angle":
            return np.angle(x, deg=True)

    res = [complex_agg(fft[config["coeff"]], config["attr"]) if config["coeff"] < len(fft)
           else np.NaN for config in param]
    index = [('coeff_{}__attr_"{}"'.format(config["coeff"], config["attr"]),res[0]) for config in param]
    return index

extract.fft_coefficient(df["Close"])

(ii) AR Coefficient

This feature calculator fits the unconditional maximum likelihood of an autoregressive AR(k) process.

#-> In Package
from statsmodels.tsa.ar_model import AR

def ar_coefficient(x, param=[{"coeff": 5, "k": 5}]):

    calculated_ar_params = {}

    x_as_list = list(x)
    calculated_AR = AR(x_as_list)

    res = {}

    for parameter_combination in param:
        k = parameter_combination["k"]
        p = parameter_combination["coeff"]

        column_name = "k_{}__coeff_{}".format(k, p)

        if k not in calculated_ar_params:
            try:
                calculated_ar_params[k] = calculated_AR.fit(maxlag=k, solver="mle").params
            except (LinAlgError, ValueError):
                calculated_ar_params[k] = [np.NaN]*k

        mod = calculated_ar_params[k]

        if p <= k:
            try:
                res[column_name] = mod[p]
            except IndexError:
                res[column_name] = 0
        else:
            res[column_name] = np.NaN

    return [(key, value) for key, value in res.items()]

extract.ar_coefficient(df["Close"])

(16) Quantiles

This includes finding normal quantile values in the series, but also quantile derived measures like change quantiles and index max quantiles.

(i) Index Mass Quantile

The relative index $i$ where $q%$ of the mass of the time series $x$ lie left of $i$ .

#-> In Package
def index_mass_quantile(x, param=[{"q": 0.3}]):

    x = np.asarray(x)
    abs_x = np.abs(x)
    s = sum(abs_x)

    if s == 0:
        # all values in x are zero or it has length 0
        return [("q_{}".format(config["q"]), np.NaN) for config in param]
    else:
        # at least one value is not zero
        mass_centralized = np.cumsum(abs_x) / s
        return [("q_{}".format(config["q"]), (np.argmax(mass_centralized >= config["q"])+1)/len(x)) for config in param]

extract.index_mass_quantile(df["Close"])

(17) Peaks

(i) Number of CWT Peaks

This feature calculator searches for different peaks in x.

from scipy.signal import cwt, find_peaks_cwt, ricker, welch

cwt_param = [ka for ka in [2,6,9]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def number_cwt_peaks(x, param=cwt_param):

    return [("CWTPeak_{}".format(n), len(find_peaks_cwt(vector=x, widths=np.array(list(range(1, n + 1))), wavelet=ricker))) for n in param]

extract.number_cwt_peaks(df["Close"])

(18) Density

The density, and more specifically the power spectral density of the signal describes the power present in the signal as a function of frequency, per unit frequency.

(i) Cross Power Spectral Density

This feature calculator estimates the cross power spectral density of the time series $x$ at different frequencies.

#-> In Package
def spkt_welch_density(x, param=[{"coeff": 5}]):
    freq, pxx = welch(x, nperseg=min(len(x), 256))
    coeff = [config["coeff"] for config in param]
    indices = ["coeff_{}".format(i) for i in coeff]

    if len(pxx) <= np.max(coeff):  # There are fewer data points in the time series than requested coefficients

        # filter coefficients that are not contained in pxx
        reduced_coeff = [coefficient for coefficient in coeff if len(pxx) > coefficient]
        not_calculated_coefficients = [coefficient for coefficient in coeff
                                       if coefficient not in reduced_coeff]

        # Fill up the rest of the requested coefficients with np.NaNs
        return zip(indices, list(pxx[reduced_coeff]) + [np.NaN] * len(not_calculated_coefficients))
    else:
        return pxx[coeff].ravel()[0]

extract.spkt_welch_density(df["Close"])

(19) Linearity

Any measure of linearity that might make use of something like the linear least-squares regression for the values of the time series. This can be against the time series minus one and many other alternatives.

(i) Linear Trend Time Wise

Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one.

from scipy.stats import linregress

#-> In Package
def linear_trend_timewise(x, param= [{"attr": "pvalue"}]):

    ix = x.index

    # Get differences between each timestamp and the first timestamp in seconds.
    # Then convert to hours and reshape for linear regression
    times_seconds = (ix - ix[0]).total_seconds()
    times_hours = np.asarray(times_seconds / float(3600))

    linReg = linregress(times_hours, x.values)

    return [("attr_\"{}\"".format(config["attr"]), getattr(linReg, config["attr"]))
            for config in param]

extract.linear_trend_timewise(df["Close"])

(20) Non-Linearity

(i) Schreiber Non-Linearity

#-> In Package
def c3(x, lag=3):
    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    n = x.size
    if 2 * lag >= n:
        return 0
    else:
        return np.mean((_roll(x, 2 * -lag) * _roll(x, -lag) * x)[0:(n - 2 * lag)])

extract.c3(df["Close"])

(21) Entropy

Any feature looking at the complexity of a time series. This is typically used in medical signal disciplines (EEG, EMG). There are multiple types of measures like spectral entropy, permutation entropy, sample entropy, approximate entropy, Lempel-Ziv complexity and other. This includes entropy measures and there derivations.

(i) Binned Entropy

Bins the values of x into max_bins equidistant bins.

#-> In Package
def binned_entropy(x, max_bins=10):
    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    hist, bin_edges = np.histogram(x, bins=max_bins)
    probs = hist / x.size
    return - np.sum(p * np.math.log(p) for p in probs if p != 0)

extract.binned_entropy(df["Close"])

(ii) SVD Entropy

SVD entropy is an indicator of the number of eigenvectors that are needed for an adequate explanation of the data set.

svd_param = [{"Tau": ta, "DE": de}
                      for ta in [4] 
                      for de in [3,6]]
                      
def _embed_seq(X,Tau,D):
  N =len(X)
  if D * Tau > N:
      print("Cannot build such a matrix, because D * Tau > N")
      exit()
  if Tau<1:
      print("Tau has to be at least 1")
      exit()
  Y= np.zeros((N - (D - 1) * Tau, D))

  for i in range(0, N - (D - 1) * Tau):
      for j in range(0, D):
          Y[i][j] = X[i + j * Tau]
  return Y                     

@set_property("fctype", "combiner")
@set_property("custom", True)
def svd_entropy(epochs, param=svd_param):
    axis=0
    
    final = []
    for par in param:

      def svd_entropy_1d(X, Tau, DE):
          Y = _embed_seq(X, Tau, DE)
          W = np.linalg.svd(Y, compute_uv=0)
          W /= sum(W)  # normalize singular values
          return -1 * np.sum(W * np.log(W))

      Tau = par["Tau"]
      DE = par["DE"]

      final.append(np.apply_along_axis(svd_entropy_1d, axis, epochs, Tau, DE).ravel()[0])


    return [("Tau_\"{}\"__De_{}\"".format(par["Tau"], par["DE"]), final[en]) for en, par in enumerate(param)]

extract.svd_entropy(df["Close"].values)

(iii) Hjort

The Complexity parameter represents the change in frequency. The parameter compares the signal's similarity to a pure sine wave, where the value converges to 1 if the signal is more similar.

def _hjorth_mobility(epochs):
    diff = np.diff(epochs, axis=0)
    sigma0 = np.std(epochs, axis=0)
    sigma1 = np.std(diff, axis=0)
    return np.divide(sigma1, sigma0)

@set_property("fctype", "simple")
@set_property("custom", True)
def hjorth_complexity(epochs):
    diff1 = np.diff(epochs, axis=0)
    diff2 = np.diff(diff1, axis=0)
    sigma1 = np.std(diff1, axis=0)
    sigma2 = np.std(diff2, axis=0)
    return np.divide(np.divide(sigma2, sigma1), _hjorth_mobility(epochs))

extract.hjorth_complexity(df["Close"])

(22) Fixed Points

Fixed points and equilibria as identified from fitted models.

(i) Langevin Fixed Points

Largest fixed point of dynamics $max\ {h(x)=0}$ estimated from polynomial $h(x)$ which has been fitted to the deterministic dynamics of Langevin model

#-> In Package
def _estimate_friedrich_coefficients(x, m, r):
    assert m > 0, "Order of polynomial need to be positive integer, found {}".format(m)
    df = pd.DataFrame({'signal': x[:-1], 'delta': np.diff(x)})
    try:
        df['quantiles'] = pd.qcut(df.signal, r)
    except ValueError:
        return [np.NaN] * (m + 1)

    quantiles = df.groupby('quantiles')

    result = pd.DataFrame({'x_mean': quantiles.signal.mean(), 'y_mean': quantiles.delta.mean()})
    result.dropna(inplace=True)

    try:
        return np.polyfit(result.x_mean, result.y_mean, deg=m)
    except (np.linalg.LinAlgError, ValueError):
        return [np.NaN] * (m + 1)


def max_langevin_fixed_point(x, r=3, m=30):
    coeff = _estimate_friedrich_coefficients(x, m, r)

    try:
        max_fixed_point = np.max(np.real(np.roots(coeff)))
    except (np.linalg.LinAlgError, ValueError):
        return np.nan

    return max_fixed_point

extract.max_langevin_fixed_point(df["Close"])

(23) Amplitude

Features derived from peaked values in either the positive or negative direction.

(i) Willison Amplitude

This feature is defined as the amount of times that the change in the signal amplitude exceeds a threshold.

will_param = [ka for ka in [0.2,3]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def willison_amplitude(X, param=will_param):
  return [("Thresh_{}".format(n),np.sum(np.abs(np.diff(X)) >= n)) for n in param]

extract.willison_amplitude(df["Close"])

(ii) Percent Amplitude

Returns the largest distance from the median value, measured as a percentage of the median

perc_param = [{"base":ba, "exponent":exp} for ba in [3,5] for exp in [-0.1,-0.2]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def percent_amplitude(x, param =perc_param):
    final = []
    for par in param:
      linear_scale_data = par["base"] ** (par["exponent"] * x)
      y_max = np.max(linear_scale_data)
      y_min = np.min(linear_scale_data)
      y_med = np.median(linear_scale_data)
      final.append(max(abs((y_max - y_med) / y_med), abs((y_med - y_min) / y_med)))

    return [("Base_{}__Exp{}".format(pa["base"],pa["exponent"]),fin) for fin, pa in zip(final,param)]

extract.percent_amplitude(df["Close"])

(24) Probability

(i) Cadence Probability

Given the observed distribution of time lags cads, compute the probability that the next observation occurs within time minutes of an arbitrary epoch.

#-> fixes required
import scipy.stats as stats

cad_param = [0.1,1000, -234]

@set_property("fctype", "combiner")
@set_property("custom", True)
def cad_prob(cads, param=cad_param):
    return [("time_{}".format(time), stats.percentileofscore(cads, float(time) / (24.0 * 60.0)) / 100.0) for time in param]
    
extract.cad_prob(df["Close"])

(25) Crossings

Calculates the crossing of the series with other defined values or series.

(i) Zero Crossing Derivative

The positioning of the edge point is located at the zero crossing of the first derivative of the filter.

zero_param = [0.01, 8]

@set_property("fctype", "combiner")
@set_property("custom", True)
def zero_crossing_derivative(epochs, param=zero_param):
    diff = np.diff(epochs)
    norm = diff-diff.mean()
    return [("e_{}".format(e), np.apply_along_axis(lambda epoch: np.sum(((epoch[:-5] <= e) & (epoch[5:] > e))), 0, norm).ravel()[0]) for e in param]

extract.zero_crossing_derivative(df["Close"])

(26) Fluctuations

These features are again from medical signal sciences, but under this category we would include values such as fluctuation based entropy measures, fluctuation of correlation dynamics, and co-fluctuations.

(i) Detrended Fluctuation Analysis (DFA)

DFA Calculate the Hurst exponent using DFA analysis.

from scipy.stats import kurtosis as _kurt
from scipy.stats import skew as _skew
import numpy as np

@set_property("fctype", "simple")
@set_property("custom", True)
def detrended_fluctuation_analysis(epochs):
    def dfa_1d(X, Ave=None, L=None):
        X = np.array(X)

        if Ave is None:
            Ave = np.mean(X)

        Y = np.cumsum(X)
        Y -= Ave

        if L is None:
            L = np.floor(len(X) * 1 / (
                    2 ** np.array(list(range(1, int(np.log2(len(X))) - 4))))
                            )
            
        F = np.zeros(len(L))  # F(n) of different given box length n

        for i in range(0, len(L)):
            n = int(L[i])  # for each box length L[i]
            if n == 0:
                print("time series is too short while the box length is too big")
                print("abort")
                exit()
            for j in range(0, len(X), n):  # for each box
                if j + n < len(X):
                    c = list(range(j, j + n))
                    # coordinates of time in the box
                    c = np.vstack([c, np.ones(n)]).T
                    # the value of data in the box
                    y = Y[j:j + n]
                    # add residue in this box
                    F[i] += np.linalg.lstsq(c, y, rcond=None)[1]
            F[i] /= ((len(X) / n) * n)
        F = np.sqrt(F)

        stacked = np.vstack([np.log(L), np.ones(len(L))])
        stacked_t = stacked.T
        Alpha = np.linalg.lstsq(stacked_t, np.log(F), rcond=None)

        return Alpha[0][0]

    return np.apply_along_axis(dfa_1d, 0, epochs).ravel()[0]

extract.detrended_fluctuation_analysis(df["Close"])

(27) Information

Closely related to entropy and complexity measures. Any measure that attempts to measure the amount of information from an observable variable is included here.

(i) Fisher Information

Fisher information is a statistical information concept distinct from, and earlier than, Shannon information in communication theory.

def _embed_seq(X, Tau, D):

    shape = (X.size - Tau * (D - 1), D)
    strides = (X.itemsize, Tau * X.itemsize)
    return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)

fisher_param = [{"Tau":ta, "DE":de} for ta in [3,15] for de in [10,5]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def fisher_information(epochs, param=fisher_param):
    def fisher_info_1d(a, tau, de):
        # taken from pyeeg improvements

        mat = _embed_seq(a, tau, de)
        W = np.linalg.svd(mat, compute_uv=False)
        W /= sum(W)  # normalize singular values
        FI_v = (W[1:] - W[:-1]) ** 2 / W[:-1]
        return np.sum(FI_v)

    return [("Tau_{}__DE_{}".format(par["Tau"], par["DE"]),np.apply_along_axis(fisher_info_1d, 0, epochs, par["Tau"], par["DE"]).ravel()[0]) for par in param]

extract.fisher_information(df["Close"])

(28) Fractals

In mathematics, more specifically in fractal geometry, a fractal dimension is a ratio providing a statistical index of complexity comparing how detail in a pattern (strictly speaking, a fractal pattern) changes with the scale at which it is measured.

(i) Highuchi Fractal

Compute a Higuchi Fractal Dimension of a time series

hig_para = [{"Kmax": 3},{"Kmax": 5}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def higuchi_fractal_dimension(epochs, param=hig_para):
    def hfd_1d(X, Kmax):
        
        L = []
        x = []
        N = len(X)
        for k in range(1, Kmax):
            Lk = []
            for m in range(0, k):
                Lmk = 0
                for i in range(1, int(np.floor((N - m) / k))):
                    Lmk += abs(X[m + i * k] - X[m + i * k - k])
                Lmk = Lmk * (N - 1) / np.floor((N - m) / float(k)) / k
                Lk.append(Lmk)
            L.append(np.log(np.mean(Lk)))
            x.append([np.log(float(1) / k), 1])

        (p, r1, r2, s) = np.linalg.lstsq(x, L, rcond=None)
        return p[0]
    
    return [("Kmax_{}".format(config["Kmax"]), np.apply_along_axis(hfd_1d, 0, epochs, config["Kmax"]).ravel()[0] ) for  config in param]
    
extract.higuchi_fractal_dimension(df["Close"])

(ii) Petrosian Fractal

Compute a Petrosian Fractal Dimension of a time series.

@set_property("fctype", "simple")
@set_property("custom", True)
def petrosian_fractal_dimension(epochs):
    def pfd_1d(X, D=None):
        # taken from pyeeg
        """Compute Petrosian Fractal Dimension of a time series from either two
        cases below:
            1. X, the time series of type list (default)
            2. D, the first order differential sequence of X (if D is provided,
               recommended to speed up)
        In case 1, D is computed using Numpy's difference function.
        To speed up, it is recommended to compute D before calling this function
        because D may also be used by other functions whereas computing it here
        again will slow down.
        """
        if D is None:
            D = np.diff(X)
            D = D.tolist()
        N_delta = 0  # number of sign changes in derivative of the signal
        for i in range(1, len(D)):
            if D[i] * D[i - 1] < 0:
                N_delta += 1
        n = len(X)
        return np.log10(n) / (np.log10(n) + np.log10(n / n + 0.4 * N_delta))
    return np.apply_along_axis(pfd_1d, 0, epochs).ravel()[0]

extract.petrosian_fractal_dimension(df["Close"])

(29) Exponent

(i) Hurst Exponent

The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases.

@set_property("fctype", "simple")
@set_property("custom", True)
def hurst_exponent(epochs):
    def hurst_1d(X):

        X = np.array(X)
        N = X.size
        T = np.arange(1, N + 1)
        Y = np.cumsum(X)
        Ave_T = Y / T

        S_T = np.zeros(N)
        R_T = np.zeros(N)
        for i in range(N):
            S_T[i] = np.std(X[:i + 1])
            X_T = Y - T * Ave_T[i]
            R_T[i] = np.ptp(X_T[:i + 1])

        for i in range(1, len(S_T)):
            if np.diff(S_T)[i - 1] != 0:
                break
        for j in range(1, len(R_T)):
            if np.diff(R_T)[j - 1] != 0:
                break
        k = max(i, j)
        assert k < 10, "rethink it!"

        R_S = R_T[k:] / S_T[k:]
        R_S = np.log(R_S)

        n = np.log(T)[k:]
        A = np.column_stack((n, np.ones(n.size)))
        [m, c] = np.linalg.lstsq(A, R_S, rcond=None)[0]
        H = m
        return H
    return np.apply_along_axis(hurst_1d, 0, epochs).ravel()[0]

extract.hurst_exponent(df["Close"])

(ii) Largest Lyauponov Exponent

In mathematics the Lyapunov exponent or Lyapunov characteristic exponent of a dynamical system is a quantity that characterizes the rate of separation of infinitesimally close trajectories.

def _embed_seq(X, Tau, D):
    shape = (X.size - Tau * (D - 1), D)
    strides = (X.itemsize, Tau * X.itemsize)
    return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)

lyaup_param = [{"Tau":4, "n":3, "T":10, "fs":9},{"Tau":8, "n":7, "T":15, "fs":6}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def largest_lyauponov_exponent(epochs, param=lyaup_param):
    def LLE_1d(x, tau, n, T, fs):

        Em = _embed_seq(x, tau, n)
        M = len(Em)
        A = np.tile(Em, (len(Em), 1, 1))
        B = np.transpose(A, [1, 0, 2])
        square_dists = (A - B) ** 2  # square_dists[i,j,k] = (Em[i][k]-Em[j][k])^2
        D = np.sqrt(square_dists[:, :, :].sum(axis=2))  # D[i,j] = ||Em[i]-Em[j]||_2

        # Exclude elements within T of the diagonal
        band = np.tri(D.shape[0], k=T) - np.tri(D.shape[0], k=-T - 1)
        band[band == 1] = np.inf
        neighbors = (D + band).argmin(axis=0)  # nearest neighbors more than T steps away

        # in_bounds[i,j] = (i+j <= M-1 and i+neighbors[j] <= M-1)
        inc = np.tile(np.arange(M), (M, 1))
        row_inds = (np.tile(np.arange(M), (M, 1)).T + inc)
        col_inds = (np.tile(neighbors, (M, 1)) + inc.T)
        in_bounds = np.logical_and(row_inds <= M - 1, col_inds <= M - 1)
        # Uncomment for old (miscounted) version
        # in_bounds = numpy.logical_and(row_inds < M - 1, col_inds < M - 1)
        row_inds[~in_bounds] = 0
        col_inds[~in_bounds] = 0

        # neighbor_dists[i,j] = ||Em[i+j]-Em[i+neighbors[j]]||_2
        neighbor_dists = np.ma.MaskedArray(D[row_inds, col_inds], ~in_bounds)
        J = (~neighbor_dists.mask).sum(axis=1)  # number of in-bounds indices by row
        # Set invalid (zero) values to 1; log(1) = 0 so sum is unchanged

        neighbor_dists[neighbor_dists == 0] = 1

        # !!! this fixes the divide by zero in log error !!!
        neighbor_dists.data[neighbor_dists.data == 0] = 1

        d_ij = np.sum(np.log(neighbor_dists.data), axis=1)
        mean_d = d_ij[J > 0] / J[J > 0]

        x = np.arange(len(mean_d))
        X = np.vstack((x, np.ones(len(mean_d)))).T
        [m, c] = np.linalg.lstsq(X, mean_d, rcond=None)[0]
        Lexp = fs * m
        return Lexp

    return [("Tau_{}__n_{}__T_{}__fs_{}".format(par["Tau"], par["n"], par["T"], par["fs"]), np.apply_along_axis(LLE_1d, 0, epochs, par["Tau"], par["n"], par["T"], par["fs"]).ravel()[0]) for par in param]
  
extract.largest_lyauponov_exponent(df["Close"])

(30) Spectral Analysis

Spectral analysis is analysis in terms of a spectrum of frequencies or related quantities such as energies, eigenvalues, etc.

(i) Whelch Method

The Whelch Method is an approach for spectral density estimation. It is used in physics, engineering, and applied mathematics for estimating the power of a signal at different frequencies.

from scipy import signal, integrate

whelch_param = [100,200]

@set_property("fctype", "combiner")
@set_property("custom", True)
def whelch_method(data, param=whelch_param):

  final = []
  for Fs in param:
    f, pxx = signal.welch(data, fs=Fs, nperseg=1024)
    d = {'psd': pxx, 'freqs': f}
    df = pd.DataFrame(data=d)
    dfs = df.sort_values(['psd'], ascending=False)
    rows = dfs.iloc[:10]
    final.append(rows['freqs'].mean())
  
  return [("Fs_{}".format(pa),fin) for pa, fin in zip(param,final)]

extract.whelch_method(df["Close"])

#-> Basically same as above
freq_param = [{"fs":50, "sel":15},{"fs":200, "sel":20}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def find_freq(serie, param=freq_param):

    final = []
    for par in param:
      fft0 = np.fft.rfft(serie*np.hanning(len(serie)))
      freqs = np.fft.rfftfreq(len(serie), d=1.0/par["fs"])
      fftmod = np.array([np.sqrt(fft0[i].real**2 + fft0[i].imag**2) for i in range(0, len(fft0))])
      d = {'fft': fftmod, 'freq': freqs}
      df = pd.DataFrame(d)
      hop = df.sort_values(['fft'], ascending=False)
      rows = hop.iloc[:par["sel"]]
      final.append(rows['freq'].mean())

    return [("Fs_{}__sel{}".format(pa["fs"],pa["sel"]),fin) for pa, fin in zip(param,final)]

extract.find_freq(df["Close"])

(31) Percentile

(i) Flux Percentile

Flux (or radiant flux) is the total amount of energy that crosses a unit area per unit time. Flux is an astronomical value, measured in joules per square metre per second (joules/m2/s), or watts per square metre. Here we provide the ratio of flux percentiles.

#-> In Package

import math
def flux_perc(magnitude):
    sorted_data = np.sort(magnitude)
    lc_length = len(sorted_data)

    F_60_index = int(math.ceil(0.60 * lc_length))
    F_40_index = int(math.ceil(0.40 * lc_length))
    F_5_index = int(math.ceil(0.05 * lc_length))
    F_95_index = int(math.ceil(0.95 * lc_length))

    F_40_60 = sorted_data[F_60_index] - sorted_data[F_40_index]
    F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
    F_mid20 = F_40_60 / F_5_95

    return {"FluxPercentileRatioMid20": F_mid20}

extract.flux_perc(df["Close"])

(32) Range

(i) Range of Cummulative Sum

@set_property("fctype", "simple")
@set_property("custom", True)
def range_cum_s(magnitude):
    sigma = np.std(magnitude)
    N = len(magnitude)
    m = np.mean(magnitude)
    s = np.cumsum(magnitude - m) * 1.0 / (N * sigma)
    R = np.max(s) - np.min(s)
    return {"Rcs": R}

extract.range_cum_s(df["Close"])

(33) Structural

Structural features, potential placeholders for future research.

(i) Structure Function

The structure function of rotation measures (RMs) contains information on electron density and magnetic field fluctuations when used i astronomy. It becomes a custom feature when used with your own unique time series data.

from scipy.interpolate import interp1d

struct_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

@set_property("fctype", "combiner")
@set_property("custom", True)
def structure_func(time, param=struct_param):

      dict_final = {}
      for key, magnitude in param.items():
        dict_final[key] = []
        Nsf, Np = 100, 100
        sf1, sf2, sf3 = np.zeros(Nsf), np.zeros(Nsf), np.zeros(Nsf)
        f = interp1d(time, magnitude)

        time_int = np.linspace(np.min(time), np.max(time), Np)
        mag_int = f(time_int)

        for tau in np.arange(1, Nsf):
            sf1[tau - 1] = np.mean(
                np.power(np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 1.0))
            sf2[tau - 1] = np.mean(
                np.abs(np.power(
                    np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 2.0)))
            sf3[tau - 1] = np.mean(
                np.abs(np.power(
                    np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 3.0)))
        sf1_log = np.log10(np.trim_zeros(sf1))
        sf2_log = np.log10(np.trim_zeros(sf2))
        sf3_log = np.log10(np.trim_zeros(sf3))

        if len(sf1_log) and len(sf2_log):
            m_21, b_21 = np.polyfit(sf1_log, sf2_log, 1)
        else:

            m_21 = np.nan

        if len(sf1_log) and len(sf3_log):
            m_31, b_31 = np.polyfit(sf1_log, sf3_log, 1)
        else:

            m_31 = np.nan

        if len(sf2_log) and len(sf3_log):
            m_32, b_32 = np.polyfit(sf2_log, sf3_log, 1)
        else:

            m_32 = np.nan
        dict_final[key].append(m_21)
        dict_final[key].append(m_31)
        dict_final[key].append(m_32)

      return [("StructureFunction_{}__m_{}".format(key, name), li)  for key, lis in dict_final.items() for name, li in zip([21,31,32], lis)]

struct_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

extract.structure_func(df["Close"],struct_param)

(34) Distribution

(i) Kurtosis

#-> In Package
def kurtosis(x):

    if not isinstance(x, pd.Series):
        x = pd.Series(x)
    return pd.Series.kurtosis(x)

extract.kurtosis(df["Close"])

(ii) Stetson Kurtosis

@set_property("fctype", "simple")
@set_property("custom", True)
def stetson_k(x):
    """A robust kurtosis statistic."""
    n = len(x)
    x0 = stetson_mean(x, 1./20**2)
    delta_x = np.sqrt(n / (n - 1.)) * (x - x0) / 20
    ta = 1. / 0.798 * np.mean(np.abs(delta_x)) / np.sqrt(np.mean(delta_x**2))
    return ta
  
extract.stetson_k(df["Close"])

(5) Synthesise

Time-Series synthesisation (TSS) happens before the feature extraction step and Cross Sectional Synthesisation (CSS) happens after the feature extraction step. Currently I will only include a CSS package, in the future, I would further work on developing out this section. This area still has a lot of performance and stability issues. In the future it might be a more viable candidate to improve prediction.

from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error

def model(df_final):
  model = LGBMRegressor()
  test =  df_final.head(int(len(df_final)*0.4))
  train = df_final[~df_final.isin(test)].dropna()
  model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
  preds = model.predict(test.drop(["Close_1"],axis=1))
  test =  df_final.head(int(len(df_final)*0.4))
  train = df_final[~df_final.isin(test)].dropna()
  model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
  val = mean_squared_error(test["Close_1"],preds); 
  return val

pip install ctgan

from ctgan import CTGANSynthesizer

#discrete_columns = [""]
ctgan = CTGANSynthesizer()
ctgan.fit(df,epochs=10) #15

Random Benchmark

np.random.seed(1)
df_in = df.copy()
df_in["Close_1"] = np.random.permutation(df_in["Close_1"].values)
model(df_in)

Generated Performance

df_gen = ctgan.sample(len(df_in)*100)
model(df_gen)

As expected a cross-sectional technique, does not work well on time-series data, in the future, other methods will be investigated.

(6) Skeleton Example

Here I will perform tabular agumenting methods on a small dataset single digit features and around 250 instances. This is not necessarily the best sized dataset to highlight the performance of tabular augmentation as some method like extraction would be overkill as it would lead to dimensionality problems. It is also good to know that there are close to infinite number of ways to perform these augmentation methods. In the future, automated augmentation methods can guide the experiment process.

The approach taken in this skeleton is to develop running models that are tested after each augmentation to highlight what methods might work well on this particular dataset. The metric we will use is mean squared error. In this implementation we do not have special hold-out sets.

The above framework of implementation will be consulted, but one still have to be strategic as to when you apply what function, and you have to make sure that you are processing your data with appropriate techniques (drop null values, fill null values) at the appropriate time.

Validation

Develop Model and Define Metric

from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error

def model(df_final):
  model = LGBMRegressor()
  test =  df_final.head(int(len(df_final)*0.4))
  train = df_final[~df_final.isin(test)].dropna()
  model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
  preds = model.predict(test.drop(["Close_1"],axis=1))
  test =  df_final.head(int(len(df_final)*0.4))
  train = df_final[~df_final.isin(test)].dropna()
  model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
  val = mean_squared_error(test["Close_1"],preds); 
  return val

Reload Data

df = data_copy()

model(df)

302.61676570345287

(1) (7) (i) Transformation - Decomposition - Naive

## If Inferred Seasonality is Too Large Default to Five
seasons = transform.infer_seasonality(df["Close"],index=0) 
df_out = transform.naive_dec(df.copy(), ["Close","Open"], freq=5)
model(df_out) #improvement

274.34477082783525

(1) (8) (i) Transformation - Filter - Baxter-King-Bandpass

df_out = transform.bkb(df_out, ["Close","Low"])
df_best = df_out.copy()
model(df_out) #improvement

267.1826850968307

(1) (3) (i) Transformation - Differentiation - Fractional

df_out = transform.fast_fracdiff(df_out, ["Close_BPF"],0.5)
model(df_out) #null

267.7083192402742

(1) (1) (i) Transformation - Scaling - Robust Scaler

df_out = df_out.dropna()
df_out = transform.robust_scaler(df_out, drop=["Close_1"])
model(df_out) #noisy

270.96980399571214

(2) (2) (i) Interactions - Operator - Multiplication/Division

df_out.head()

	Close_1	High	Low	Open	Close	Volume	Adj Close	Close_NDDT	Close_NDDS	Close_NDDR	Open_NDDT	Open_NDDS	Open_NDDR	Close_BPF	Low_BPF	Close_BPF_frac
Date
2019-01-08	338.529999	1.018413	0.964048	1.096600	1.001175	-0.162616	1.001175	0.832297	0.834964	1.335433	0.758743	0.691596	2.259884	-2.534142	-2.249135	-3.593612
2019-01-09	344.970001	1.012068	1.023302	1.011466	1.042689	-0.501798	1.042689	0.908963	-0.165036	1.111346	0.835786	0.333361	1.129783	-3.081959	-2.776302	-2.523465
2019-01-10	347.260010	1.035581	1.027563	0.996969	1.126762	-0.367576	1.126762	1.029347	2.120026	0.853697	0.907588	0.000000	0.533777	-2.052768	-2.543449	-0.747382
2019-01-11	334.399994	1.073153	1.120506	1.098313	1.156658	-0.586571	1.156658	1.109144	-5.156051	0.591990	1.002162	-0.666639	0.608516	-0.694642	-0.831670	0.414063
2019-01-14	344.429993	0.999627	1.056991	1.102135	0.988773	-0.541752	0.988773	1.107633	0.000000	-0.660350	1.056302	-0.915491	0.263025	-0.645590	-0.116166	-0.118012

df_out = interact.muldiv(df_out, ["Close","Open_NDDS","Low_BPF"]) 
model(df_out) #noisy

285.6420643864313

df_r = df_out.copy()

(2) (6) (i) Interactions - Speciality - Technical

import ta
df = interact.tech(df)
df_out = pd.merge(df_out,  df.iloc[:,7:], left_index=True, right_index=True, how="left")

Clean Dataframe and Metric

"""Droping column where missing values are above a threshold"""
df_out = df_out.dropna(thresh = len(df_out)*0.95, axis = "columns") 
df_out = df_out.dropna()
df_out = df_out.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
close = df_out["Close"].copy()
df_d = df_out.copy()
model(df_out) #improve

592.52971755184

(3) (1) (i) Mapping - Eigen Decomposition - PCA

from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA

df_out = transform.robust_scaler(df_out, drop=["Close_1"])

df_out = df_out.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
df_out = mapper.pca_feature(df_out, drop_cols=["Close_1"], variance_or_components=0.9, n_components=8,non_linear=False)

model(df_out) #noisy but not too bad given the 10 fold dimensionality reduction

687.158330455884

(4) Extracting

Here at first, I show the functions that have been added to the DeltaPy fork of tsfresh. You have to add your own personal adjustments based on the features you would like to construct. I am using self-developed features, but you can also use TSFresh's community functions.

The following files have been appropriately ammended (Get in contact for advice)

(4) (10) (i) Extracting - Averages - GSkew

extract.gskew(df_out["PCA_1"])

-0.7903067336449059

(4) (21) (ii) Extracting - Entropy - SVD Entropy

svd_param = [{"Tau": ta, "DE": de}
                      for ta in [4] 
                      for de in [3,6]]

extract.svd_entropy(df_out["PCA_1"],svd_param)

[('Tau_"4"__De_3"', 0.7234823323374294),
 ('Tau_"4"__De_6"', 1.3014347840145244)]

(4) (13) (ii) Extracting - Streaks - Wozniak

woz_param = [{"consecutiveStar": n} for n in [2, 4]]

extract.wozniak(df_out["PCA_1"],woz_param)

[('consecutiveStar_2', 0.012658227848101266), ('consecutiveStar_4', 0.0)]

(4) (28) (i) Extracting - Fractal - Higuchi

hig_param = [{"Kmax": 3},{"Kmax": 5}]

extract.higuchi_fractal_dimension(df_out["PCA_1"],hig_param)

[('Kmax_3', 0.577913816027104), ('Kmax_5', 0.8176960510304725)]

(4) (5) (ii) Extracting - Volatility - Variability Index

var_index_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

extract.var_index(df["Close"].values,var_index_param)

{'Interact__Open': 0.00396022538846289,
 'Interact__Volume': 0.20550155114176533}

Time Series Extraction

pip install git+git://github.com/firmai/tsfresh.git

#Construct the preferred input dataframe.
from tsfresh.utilities.dataframe_functions import roll_time_series
df_out["ID"] = 0
periods = 30
df_out = df_out.reset_index()
df_ts = roll_time_series(df_out,"ID","Date",None,1,periods)
counts = df_ts['ID'].value_counts()
df_ts = df_ts[df_ts['ID'].isin(counts[counts > periods].index)]

#Perform extraction
from tsfresh.feature_extraction import extract_features, CustomFCParameters
settings_dict = CustomFCParameters()
settings_dict["var_index"] = {"PCA_1":None, "PCA_2": None}
df_feat = extract_features(df_ts.drop(["Close_1"],axis=1),default_fc_parameters=settings_dict,column_id="ID",column_sort="Date")

Feature Extraction: 100%|██████████| 5/5 [00:10<00:00,  2.14s/it]

# Cleaning operations
import pandasvault as pv
df_feat2 = df_feat.copy()
df_feat = df_feat.dropna(thresh = len(df_feat)*0.50, axis = "columns")
df_feat_cons = pv.constant_feature_detect(data=df_feat,threshold=0.9)
df_feat = df_feat.drop(df_feat_cons, axis=1)
df_feat = df_feat.ffill()
df_feat = pd.merge(df_feat,df[["Close_1"]],left_index=True,right_index=True,how="left")
print(df_feat.shape)
model(df_feat) #noisy

7  variables are found to be almost constant
(208, 48)
2064.7813982935995

from tsfresh import select_features
from tsfresh.utilities.dataframe_functions import impute

impute(df_feat)
df_feat_2 = select_features(df_feat.drop(["Close_1"],axis=1),df_feat["Close_1"],fdr_level=0.05)
df_feat_2["Close_1"] = df_feat["Close_1"]
model(df_feat_2) #improvement (b/ not an augmentation method)

1577.5273071299482

(3) (6) (i) Feature Agglomoration; (1)(2)(i) Standard Scaler.

Like in this step, after (1), (2), (3), (4) and (5), you can often circle back to the initial steps to normalise the data and dimensionally reduce the data for the final model.

import numpy as np
from sklearn import datasets, cluster

def feature_agg(df, drop, components):
  components = min(df.shape[1]-1,components)
  agglo = cluster.FeatureAgglomeration(n_clusters=components,)
  df = df.drop(drop,axis=1)
  agglo.fit(df)
  df = pd.DataFrame(agglo.transform(df))
  df = df.add_prefix('fe_agg_')

  return df

df_final = transform.standard_scaler(df_feat_2, drop=["Close_1"])
df_final = mapper.feature_agg(df_final,["Close_1"],4)
df_final.index = df_feat.index
df_final["Close_1"] = df_feat["Close_1"]
model(df_final) #noisy

1949.89085894338

Final Model After Applying 13 Arbitrary Augmentation Techniques

model(df_final) #improvement

1949.89085894338

Original Model Before Augmentation

df_org = df.iloc[:,:7][df.index.isin(df_final.index)]
model(df_org)

389.783990984133

Best Model After Developing 8 Augmenting Features

df_best = df_best.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
model(df_best)

267.1826850968307

Commentary

There are countless ways in which the current model can be improved, this can take on an automated process where all techniques are tested against a hold out set, for example, we can perform the operation below, and even though it improves the score here, there is a need for more robust tests. The skeleton example above is not meant to highlight the performance of the package. It simply serves as an example of how one can go about applying augmentation methods.

Quite naturally this example suffers from dimensionality issues with array shapes reaching (208, 48), furthermore you would need a sample that is at least 50-100 times larger before machine learning methods start to make sense.

Nonetheless, in this example, Transformation, Interactions and Mappings (applied to extraction output) performed fairly well. Extraction augmentation was overkill, but created a reasonable model when dimensionally reduced. A better selection of one of the 50+ augmentation methods and the order of augmentation could further help improve the outcome if robustly tested against development sets.

[1] DeltaPy Development

Author: firmai
Source Code: https://github.com/firmai/deltapy

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1663559281

To-do List App with HTML, CSS and JavaScript

Learn how to create a to-do list app with local storage using HTML, CSS and JavaScript. Build a Todo list application with HTML, CSS and JavaScript. Learn the basics to JavaScript along with some more advanced features such as LocalStorage for saving data to the browser.

HTML:

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>To Do List With Local Storage</title>
    <!-- Font Awesome Icons -->
    <link
      rel="stylesheet"
      href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.2.0/css/all.min.css"
    />
    <!-- Google Fonts -->
    <link
      href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500&display=swap"
      rel="stylesheet"
    />
    <!-- Stylesheet -->
    <link rel="stylesheet" href="style.css" />
  </head>
  <body>
    <div class="container">
      <div id="new-task">
        <input type="text" placeholder="Enter The Task Here..." />
        <button id="push">Add</button>
      </div>
      <div id="tasks"></div>
    </div>
    <!-- Script -->
    <script src="script.js"></script>
  </body>
</html>

CSS:

* {
  padding: 0;
  margin: 0;
  box-sizing: border-box;
}
body {
  background-color: #0b87ff;
}
.container {
  width: 90%;
  max-width: 34em;
  position: absolute;
  transform: translate(-50%, -50%);
  top: 50%;
  left: 50%;
}
#new-task {
  position: relative;
  background-color: #ffffff;
  padding: 1.8em 1.25em;
  border-radius: 0.3em;
  box-shadow: 0 1.25em 1.8em rgba(1, 24, 48, 0.15);
  display: grid;
  grid-template-columns: 9fr 3fr;
  gap: 1em;
}
#new-task input {
  font-family: "Poppins", sans-serif;
  font-size: 1em;
  border: none;
  border-bottom: 2px solid #d1d3d4;
  padding: 0.8em 0.5em;
  color: #111111;
  font-weight: 500;
}
#new-task input:focus {
  outline: none;
  border-color: #0b87ff;
}
#new-task button {
  font-family: "Poppins", sans-serif;
  font-weight: 500;
  font-size: 1em;
  background-color: #0b87ff;
  color: #ffffff;
  outline: none;
  border: none;
  border-radius: 0.3em;
  cursor: pointer;
}
#tasks {
  background-color: #ffffff;
  position: relative;
  padding: 1.8em 1.25em;
  margin-top: 3.8em;
  width: 100%;
  box-shadow: 0 1.25em 1.8em rgba(1, 24, 48, 0.15);
  border-radius: 0.6em;
}
.task {
  background-color: #ffffff;
  padding: 0.3em 0.6em;
  margin-top: 0.6em;
  display: flex;
  align-items: center;
  border-bottom: 2px solid #d1d3d4;
  cursor: pointer;
}
.task span {
  font-family: "Poppins", sans-serif;
  font-size: 0.9em;
  font-weight: 400;
}
.task button {
  color: #ffffff;
  padding: 0.8em 0;
  width: 2.8em;
  border-radius: 0.3em;
  border: none;
  outline: none;
  cursor: pointer;
}
.delete {
  background-color: #fb3b3b;
}
.edit {
  background-color: #0b87ff;
  margin-left: auto;
  margin-right: 3em;
}
.completed {
  text-decoration: line-through;
}

Javascript:

//Initial References
const newTaskInput = document.querySelector("#new-task input");
const tasksDiv = document.querySelector("#tasks");
let deleteTasks, editTasks, tasks;
let updateNote = "";
let count;

//Function on window load
window.onload = () => {
  updateNote = "";
  count = Object.keys(localStorage).length;
  displayTasks();
};

//Function to Display The Tasks
const displayTasks = () => {
  if (Object.keys(localStorage).length > 0) {
    tasksDiv.style.display = "inline-block";
  } else {
    tasksDiv.style.display = "none";
  }

  //Clear the tasks
  tasksDiv.innerHTML = "";

  //Fetch All The Keys in local storage
  let tasks = Object.keys(localStorage);
  tasks = tasks.sort();

  for (let key of tasks) {
    let classValue = "";

    //Get all values
    let value = localStorage.getItem(key);
    let taskInnerDiv = document.createElement("div");
    taskInnerDiv.classList.add("task");
    taskInnerDiv.setAttribute("id", key);
    taskInnerDiv.innerHTML = `<span id="taskname">${key.split("_")[1]}</span>`;
    //localstorage would store boolean as string so we parse it to boolean back
    let editButton = document.createElement("button");
    editButton.classList.add("edit");
    editButton.innerHTML = `<i class="fa-solid fa-pen-to-square"></i>`;
    if (!JSON.parse(value)) {
      editButton.style.visibility = "visible";
    } else {
      editButton.style.visibility = "hidden";
      taskInnerDiv.classList.add("completed");
    }
    taskInnerDiv.appendChild(editButton);
    taskInnerDiv.innerHTML += `<button class="delete"><i class="fa-solid fa-trash"></i></button>`;
    tasksDiv.appendChild(taskInnerDiv);
  }

  //tasks completed
  tasks = document.querySelectorAll(".task");
  tasks.forEach((element, index) => {
    element.onclick = () => {
      //local storage update
      if (element.classList.contains("completed")) {
        updateStorage(element.id.split("_")[0], element.innerText, false);
      } else {
        updateStorage(element.id.split("_")[0], element.innerText, true);
      }
    };
  });

  //Edit Tasks
  editTasks = document.getElementsByClassName("edit");
  Array.from(editTasks).forEach((element, index) => {
    element.addEventListener("click", (e) => {
      //Stop propogation to outer elements (if removed when we click delete eventually rhw click will move to parent)
      e.stopPropagation();
      //disable other edit buttons when one task is being edited
      disableButtons(true);
      //update input value and remove div
      let parent = element.parentElement;
      newTaskInput.value = parent.querySelector("#taskname").innerText;
      //set updateNote to the task that is being edited
      updateNote = parent.id;
      //remove task
      parent.remove();
    });
  });

  //Delete Tasks
  deleteTasks = document.getElementsByClassName("delete");
  Array.from(deleteTasks).forEach((element, index) => {
    element.addEventListener("click", (e) => {
      e.stopPropagation();
      //Delete from local storage and remove div
      let parent = element.parentElement;
      removeTask(parent.id);
      parent.remove();
      count -= 1;
    });
  });
};

//Disable Edit Button
const disableButtons = (bool) => {
  let editButtons = document.getElementsByClassName("edit");
  Array.from(editButtons).forEach((element) => {
    element.disabled = bool;
  });
};

//Remove Task from local storage
const removeTask = (taskValue) => {
  localStorage.removeItem(taskValue);
  displayTasks();
};

//Add tasks to local storage
const updateStorage = (index, taskValue, completed) => {
  localStorage.setItem(`${index}_${taskValue}`, completed);
  displayTasks();
};

//Function To Add New Task
document.querySelector("#push").addEventListener("click", () => {
  //Enable the edit button
  disableButtons(false);
  if (newTaskInput.value.length == 0) {
    alert("Please Enter A Task");
  } else {
    //Store locally and display from local storage
    if (updateNote == "") {
      //new task
      updateStorage(count, newTaskInput.value, false);
    } else {
      //update task
      let existingCount = updateNote.split("_")[0];
      removeTask(updateNote);
      updateStorage(existingCount, newTaskInput.value, false);
      updateNote = "";
    }
    count += 1;
    newTaskInput.value = "";
  }
});

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Jack Salvator

1608113009

New Angular 7 Features With Example - Info Stans

What is new in New Angular 7? New Angular 7 features have turned out as a powerful release that really brought advancement in the application development structure.

Here, we have listed new Angular 7 features with examples and write the difference between Angular 6 and Angular 7.

Bundle Budget
Virtual Scrolling
Error Handling
Documentation Updates
Application Performance
Native Script
CLI Prompts
Component in angular 7
Drag and Drop
Angular Do-Bootstrap

Read more: Angular 7 Features With Example

#angular 7 features #what’s new angular 7 #new angular 7 features #angular 7 features with examples