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92 changes: 92 additions & 0 deletions machine_learning/loss_functions.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
return np.mean(absolute_percentage_diff)


def perplexity_loss(
y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
"""
Calculate the perplexity for the y_true and y_pred.

Compute the Perplexity which useful in predicting language model
accuracy in Natural Language Processing (NLP.)
Perplexity is measure of how certain the model in its predictions.

Perplexity Loss = exp(-1/N (Σ ln(p(x)))

Reference:
https://en.wikipedia.org/wiki/Perplexity

Args:
y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
epsilon: Small floating point number to avoid getting inf for log(0)

Returns:
Perplexity loss between y_true and y_pred.

>>> y_true = np.array([[1, 4], [2, 3]])
>>> y_pred = np.array(
... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
... [0.24, 0.19, 0.09, 0.18, 0.27]],
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12]]]
... )
>>> perplexity_loss(y_true, y_pred)
5.0247347775367945
>>> y_true = np.array([[1, 4], [2, 3]])
>>> y_pred = np.array(
... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
... [0.24, 0.19, 0.09, 0.18, 0.27],
... [0.30, 0.10, 0.20, 0.15, 0.25]],
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12],
... [0.30, 0.10, 0.20, 0.15, 0.25]],]
... )
>>> perplexity_loss(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: Sentence length of y_true and y_pred must be equal.
>>> y_true = np.array([[1, 4], [2, 11]])
>>> y_pred = np.array(
... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
... [0.24, 0.19, 0.09, 0.18, 0.27]],
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12]]]
... )
>>> perplexity_loss(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: Label value must not be greater than vocabulary size.
>>> y_true = np.array([[1, 4]])
>>> y_pred = np.array(
... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
... [0.24, 0.19, 0.09, 0.18, 0.27]],
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12]]]
... )
>>> perplexity_loss(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: Batch size of y_true and y_pred must be equal.
"""

vocab_size = y_pred.shape[2]

if y_true.shape[0] != y_pred.shape[0]:
raise ValueError("Batch size of y_true and y_pred must be equal.")
if y_true.shape[1] != y_pred.shape[1]:
raise ValueError("Sentence length of y_true and y_pred must be equal.")
if np.max(y_true) > vocab_size:
raise ValueError("Label value must not be greater than vocabulary size.")

# Matrix to select prediction value only for true class
filter_matrix = np.array(
[[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
)

# Getting the matrix containing prediction for only true class
true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

# Calculating perplexity for each sentence
perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

return np.mean(perp_losses)


if __name__ == "__main__":
import doctest

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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};
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codeBlock.parentElement.appendChild(btn);
});
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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" + '
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
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Add perplexity loss algorithm#11028

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#11028
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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; }
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.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('^' + ".*" + '
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
Skip to content
Add perplexity loss algorithm#11028

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 Merged

tianyizheng02
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Oct 27, 2023
 Merged


Add perplexity loss algorithm
#11028
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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; });
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document.head.appendChild(style);
function highlight(node) {
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if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
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highlight(document.body);
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
Skip to content
Add perplexity loss algorithm#11028

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 Merged

tianyizheng02
merged 1 commit into

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Oct 27, 2023
 Merged


Add perplexity loss algorithm
#11028
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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;
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return url;
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if (clean !== a.href) a.href = clean;
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if (node.tagName === 'A') cleanLinks();
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Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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('^' + ".*" + '
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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('^' + ".*" + '
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
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Add perplexity loss algorithm#11028

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 Merged

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#11028
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

Expand Down

, '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); }
})();
})();
Add perplexity loss algorithm by PoojanSmart · Pull Request #11028 · TheAlgorithms/Python · GitHub
Skip to content
Add perplexity loss algorithm#11028

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tianyizheng02
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Oct 27, 2023
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Add perplexity loss algorithm
#11028
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92 changes: 92 additions & 0 deletions
machine_learning/loss_functions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,6 +379,98 @@ def mean_absolute_percentage_error(
 return np.mean(absolute_percentage_diff)


def perplexity_loss(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
 """
 Calculate the perplexity for the y_true and y_pred.

 Compute the Perplexity which useful in predicting language model
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.

 Perplexity Loss = exp(-1/N (Σ ln(p(x)))

 Reference:
 https://en.wikipedia.org/wiki/Perplexity

 Args:
 y_true: Actual label encoded sentences of shape (batch_size, sentence_length)
 y_pred: Predicted sentences of shape (batch_size, sentence_length, vocab_size)
 epsilon: Small floating point number to avoid getting inf for log(0)

 Returns:
 Perplexity loss between y_true and y_pred.

 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 5.0247347775367945
 >>> y_true = np.array([[1, 4], [2, 3]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12],
 ... [0.30, 0.10, 0.20, 0.15, 0.25]],]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Sentence length of y_true and y_pred must be equal.
 >>> y_true = np.array([[1, 4], [2, 11]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Label value must not be greater than vocabulary size.
 >>> y_true = np.array([[1, 4]])
 >>> y_pred = np.array(
 ... [[[0.28, 0.19, 0.21 , 0.15, 0.15],
 ... [0.24, 0.19, 0.09, 0.18, 0.27]],
 ... [[0.03, 0.26, 0.21, 0.18, 0.30],
 ... [0.28, 0.10, 0.33, 0.15, 0.12]]]
 ... )
 >>> perplexity_loss(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Batch size of y_true and y_pred must be equal.
 """

 vocab_size = y_pred.shape[2]

 if y_true.shape[0] != y_pred.shape[0]:
 raise ValueError("Batch size of y_true and y_pred must be equal.")
 if y_true.shape[1] != y_pred.shape[1]:
 raise ValueError("Sentence length of y_true and y_pred must be equal.")
 if np.max(y_true) > vocab_size:
 raise ValueError("Label value must not be greater than vocabulary size.")

 # Matrix to select prediction value only for true class
 filter_matrix = np.array(
 [[list(np.eye(vocab_size)[word]) for word in sentence] for sentence in y_true]
 )

 # Getting the matrix containing prediction for only true class
 true_class_pred = np.sum(y_pred * filter_matrix, axis=2).clip(epsilon, 1)

 # Calculating perplexity for each sentence
 perp_losses = np.exp(np.negative(np.mean(np.log(true_class_pred), axis=1)))

 return np.mean(perp_losses)


if __name__ == "__main__":
 import doctest

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