Do... something? about machine_learning/forecasting #8780

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Do... something? about machine_learning/forecasting#8780
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enhancementThis PR modified some existing files

Description

@tianyizheng02
tianyizheng02
opened on May 27, 2023

Feature description

In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.

  • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.

  • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:

    this is code for forecasting
    but i modified it and used it for safety checker of data

    ... but modified from where? No sources were ever referenced.

  • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?

  • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.

My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      Do... something? about machine_learning/forecasting #8780
      Open
      Open
      Do... something? about machine_learning/forecasting#8780
      Labels
      enhancementThis PR modified some existing files
      Description
      @tianyizheng02
      tianyizheng02
      opened on May 27, 2023
      Feature description
      In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
      • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
      • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
        this is code for forecasting
        
but i modified it and used it for safety checker of data
        ... but modified from where? No sources were ever referenced.
      • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
      • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
      My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          Do... something? about machine_learning/forecasting #8780
          Open
          Open
          Do... something? about machine_learning/forecasting#8780
          Labels
          enhancementThis PR modified some existing files
          Description
          @tianyizheng02
          tianyizheng02
          opened on May 27, 2023
          Feature description
          In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
          • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
          • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
            this is code for forecasting
            
but i modified it and used it for safety checker of data
            ... but modified from where? No sources were ever referenced.
          • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
          • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
          My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              Do... something? about machine_learning/forecasting #8780
              Open
              Open
              Do... something? about machine_learning/forecasting#8780
              Labels
              enhancementThis PR modified some existing files
              Description
              @tianyizheng02
              tianyizheng02
              opened on May 27, 2023
              Feature description
              In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
              • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
              • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
                this is code for forecasting
                
but i modified it and used it for safety checker of data
                ... but modified from where? No sources were ever referenced.
              • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
              • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
              My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
              Metadata
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  Do... something? about machine_learning/forecasting #8780
                  Open
                  Open
                  Do... something? about machine_learning/forecasting#8780
                  Labels
                  enhancementThis PR modified some existing files
                  Description
                  @tianyizheng02
                  tianyizheng02
                  opened on May 27, 2023
                  Feature description
                  In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
                  • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
                  • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
                    this is code for forecasting
                    
but i modified it and used it for safety checker of data
                    ... but modified from where? No sources were ever referenced.
                  • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
                  • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
                  My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
                  Metadata
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      Do... something? about machine_learning/forecasting #8780
                      Open
                      Open
                      Do... something? about machine_learning/forecasting#8780
                      Labels
                      enhancementThis PR modified some existing files
                      Description
                      @tianyizheng02
                      tianyizheng02
                      opened on May 27, 2023
                      Feature description
                      In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
                      • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
                      • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
                        this is code for forecasting
                        
but i modified it and used it for safety checker of data
                        ... but modified from where? No sources were ever referenced.
                      • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
                      • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
                      My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
                      Metadata
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          Do... something? about machine_learning/forecasting #8780
                          Open
                          Open
                          Do... something? about machine_learning/forecasting#8780
                          Labels
                          enhancementThis PR modified some existing files
                          Description
                          @tianyizheng02
                          tianyizheng02
                          opened on May 27, 2023
                          Feature description
                          In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
                          • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
                          • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
                            this is code for forecasting
                            
but i modified it and used it for safety checker of data
                            ... but modified from where? No sources were ever referenced.
                          • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
                          • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
                          My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
                          Metadata
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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                              Do... something? about machine_learning/forecasting #8780
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                              Do... something? about machine_learning/forecasting#8780
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                              enhancementThis PR modified some existing files
                              Description
                              @tianyizheng02
                              tianyizheng02
                              opened on May 27, 2023
                              Feature description
                              In reference to #7305, it's clear to me that @nandiya isn't coming back to fix the warnings in machine_learning/forecasting/run.py. With that said, I want to discuss some more fundamental issues that I have with the file and get other opinions on what to do about them.
                              • At the risk of sounding harsh, I think some of the code was haphazardly written. The warnings mentioned in [PYTEST WARNING] Machine learning forecasting #7305 are mostly due to the code providing far too few input observations (like, literally only 4 observations). Adding more observations doesn't necessarily resolve the warnings either, as there are other bugs in the code (passing bad arguments into functions and incorrectly reading a CSV file) that prevent the code from fully running. To me, it almost seems like the file was never run or tested independently, as these bugs and warnings would've been caught by just running the code.
                              • I suspect that the code could've been plagiarized (or at the very least uncredited). At the top of the file, it says this:
                                this is code for forecasting
                                
but i modified it and used it for safety checker of data
                                ... but modified from where? No sources were ever referenced.
                              • This file isn't an algorithm, much less a machine learning algorithm. Forecasting is a very general statistical task, and there's no single algorithm for it. In actuality, this file uses three different statistical methods (linear regression, SARIMAX, and SVR) to complete a forecasting task. Why not simply implement each of those algorithms in separate files?
                              • Related to the previous point, this file is clearly a how-to. The SARIMAX and SVR code relies entirely on pre-existing implementations in other packages.
                              My main question is what we should do about this file. Fixing the existing code would resolve the warnings brought up in #7305 and would address the first issue, but it doesn't really address any of the other issues. What do you all think we should do about this file?
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