Add perplexity loss algorithm

[PoojanSmart]

Describe your change:

• Perplexity loss function which is used in NLP (Natural language processing) for finding accuracy of the model based on how much certain the model is on its predictions.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[imSanko]

Did the code passed the pre commit tests ??

[PoojanSmart]

Once #10713 is merged. This issue should get resolved.
@cclauss please review and merge #10713.

One more thing, in my case when I run pre-commit run --all-files --show-diff-on-failure, I am not getting the same types of errors as the log from git actions.

[cclauss]

PEP8: Backslash line continuation should be avoided in Python.

Suggested change

	>>> 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]]]\
	)
	>>> 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]]],
	... )

[cclauss]

pre-commit autoupdate

[tianyizheng02]

All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

[tianyizheng02]

Please make epsilon an optional function parameter so that users can change its value

[tianyizheng02]

Suggested change

	# Getting the matrix containing prediction for only true class
	true_class_pred = np.sum(y_pred * filter_matrix, axis=2)
	# Calculating perplexity for each sentence
	perp_losses = np.exp(
	np.negative(np.mean(np.log(true_class_pred + epsilon), axis=1))
	)
	# Getting the matrix containing prediction for only true class
	# Clip values to avoid log(0)
	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)))

You can use .clip() to restrict the range of the array's values instead of adding epsilon to every entry. This way, only the problematic values get changed while OK values remain the same. Note that this may change the value of your doctests.