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tianyizheng02 merged 3 commits into from
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Added Mean Squared Logarithmic Error (MSLE) Loss Function #10637

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Added Mean Squared Logarithmic Error (MSLE)
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Added Mean Squared Logarithmic Error (MSLE)
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55 changes: 55 additions & 0 deletions machine_learning/loss_functions/mean_squared_logarithmic_error.py
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@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

Parameters:
- y_true: The true values (ground truth).
- y_pred: The predicted values.

Returns:
- msle: The Mean Squared Logarithmic Error between y_true and y_pred.

Example usage:
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> mean_squared_logarithmic_error(true_values, predicted_values)
0.0030860877925181344
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
>>> mean_squared_logarithmic_error(true_labels, predicted_probs)
Traceback (most recent call last):
...
ValueError: Input arrays must have the same length.
"""
if len(y_true) != len(y_pred):
raise ValueError("Input arrays must have the same length.")

squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
import doctest

doctest.testmod()
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
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55 changes: 55 additions & 0 deletions
machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

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 Merged

tianyizheng02
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Oct 19, 2023
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Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
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Added Mean Squared Logarithmic Error (MSLE)


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ded7e63

Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
cb2a223

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55 changes: 55 additions & 0 deletions
machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

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 Merged

tianyizheng02
merged 3 commits into

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Oct 19, 2023
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Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
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Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
ded7e63

Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
cb2a223

[pre-commit.ci] auto fixes from pre-commit.com hooks


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55 changes: 55 additions & 0 deletions
machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

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 Merged

tianyizheng02
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Oct 19, 2023
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Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
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Added Mean Squared Logarithmic Error (MSLE)


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ded7e63

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ank426 Oct 17, 2023
cb2a223

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55 changes: 55 additions & 0 deletions
machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

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 Merged

tianyizheng02
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Oct 19, 2023
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Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
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Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
ded7e63

Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
cb2a223

[pre-commit.ci] auto fixes from pre-commit.com hooks


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machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

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Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
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Added Mean Squared Logarithmic Error (MSLE)


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Added Mean Squared Logarithmic Error (MSLE)


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machine_learning/loss_functions/mean_squared_logarithmic_error.py

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@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
Added Mean Squared Logarithmic Error (MSLE) Loss Function by ank426 · Pull Request #10637 · TheAlgorithms/Python · GitHub
Skip to content
Added Mean Squared Logarithmic Error (MSLE) Loss Function#10637

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 3 commits into

from

Oct 19, 2023
 Merged


Added Mean Squared Logarithmic Error (MSLE) Loss Function
#10637
 Changes from all commits
Commits

Show all changes


3 commits


Select commit
Hold shift + click to select a range
a64f174

Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
ded7e63

Added Mean Squared Logarithmic Error (MSLE)


ank426 Oct 17, 2023
cb2a223

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 17, 2023

File filter

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55 changes: 55 additions & 0 deletions
machine_learning/loss_functions/mean_squared_logarithmic_error.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,55 @@
"""
Mean Squared Logarithmic Error (MSLE) Loss Function

Description:
MSLE measures the mean squared logarithmic difference between
true values and predicted values, particularly useful when
dealing with regression problems involving skewed or large-value
targets. It is often used when the relative differences between
predicted and true values are more important than absolute
differences.

Formula:
MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2

Source:
(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
"""

import numpy as np


def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 """
 Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.

 Parameters:
 - y_true: The true values (ground truth).
 - y_pred: The predicted values.

 Returns:
 - msle: The Mean Squared Logarithmic Error between y_true and y_pred.

 Example usage:
 >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
 >>> mean_squared_logarithmic_error(true_values, predicted_values)
 0.0030860877925181344
 >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
 >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
 >>> mean_squared_logarithmic_error(true_labels, predicted_probs)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same length.
 """
 if len(y_true) != len(y_pred):
 raise ValueError("Input arrays must have the same length.")

 squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
 return np.mean(squared_logarithmic_errors)


if __name__ == "__main__":
 import doctest

 doctest.testmod()