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VotePredictor

Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.

System Description

All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.

  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot

In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.

To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).

Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.

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Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
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Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
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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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})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
Resources
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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('^' + ".*" + '

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
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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" + '

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
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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('^' + ".*" + '

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
Resources
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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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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
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var __m = "*";
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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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VotePredictor
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
System Description
All code is written for python 3.6 and is assumed to be executed in the top level directory of the git repository. Additionally, nltk and matplotlib are dependencies for this project. Generated plots will vary slightly from displayed plots because of randomization in the training and validation sets.
  1. Clone the git repo
  2. Unzip complete.zip and keep complete.json in the same directory as model.py
  3. Execute python model.py (this creates three files training_set.json, validation_set.json, and model.json)
  4. Execute python validate.py 1 100 1" to parameter sweep c from the values 1-100. (Usage for this file is python validate.py start numIter step where start is the first value of c tested, numIter is the number of different values of c tested, and step is the amount c is incremented every iteration)
  5. Execute python plotChyperparam.py
  6. Open plot.png to examine the plot
In order to test multiple values for k, the value was adjusted by hand in the source code. Additionally, other values of c can be tested by following the usage of validate.py.
To generate baseline information execute python baseline.py and all three baselines will generate their respective result text files. Like validate.py, baseline.py outputs text files of the form results##.txt, where the pounds indicate the c value that was validated for. These files contain the correct prediction probability for every congressman, the proportion of correctly predicted bills, and the average of congressmen success predictions (these last two are indicated in the last two lines of the file).
Histograms are generated using by executing python hist_gen.py file, where file is any text file generated by validation.py or baseline.py.
About
Using Naive Bayes to Predict Votes of Congressmen from Bill Texts. By David Gibson, Thomas Chang, Mustafa Bal.
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