Add automatic differentiation algorithm by PoojanSmart · Pull Request #10977 · TheAlgorithms/Python

PoojanSmart · 2023-10-26T02:21:02Z

Describe your change:

Adding implementation of automatic differentiation algorithm which is useful for calculating gradients automatically. This algorithm is widely used in machine learning and deep learning models for optimization.

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:

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Python:

Formatted string literals (f-strings)

Type hints

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unittest

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algorithms-keeper · 2023-10-26T02:21:09Z

machine_learning/auto-diff.py

+    will be calculated.
+    """
+
+    def __init__(self, value):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: value

algorithms-keeper · 2023-10-26T02:21:09Z

machine_learning/auto-diff.py

+                )
+        return result
+
+    def add_param_to(self, param_to: Operation):


Please provide return type hint for the function: add_param_to. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:09Z

machine_learning/auto-diff.py

+    def add_param_to(self, param_to: Operation):
+        self.param_to.append(param_to)
+
+    def add_result_of(self, result_of: Operation):


Please provide return type hint for the function: add_result_of. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:09Z

machine_learning/auto-diff.py

+    objects and pointer to resulting Variable from the operation.
+    """
+
+    def __init__(


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+        self.output: Variable | None = None
+        self.other_params = {} if other_params is None else other_params
+
+    def add_params(self, params: list[Variable]):


Please provide return type hint for the function: add_params. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+    based on the computation graph.
+    """
+
+    def __new__(cls):


Please provide return type hint for the function: __new__. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+            cls.instance = super().__new__(cls)
+        return cls.instance
+
+    def __init__(self):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+    def __init__(self):
+        self.enabled = False
+
+    def __enter__(self):


Please provide return type hint for the function: __enter__. If the function does not return a value, please provide the type hint as: def function() -> None:

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+        self.enabled = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, tb):


Please provide return type hint for the function: __exit__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: exc_type

Please provide type hint for the parameter: exc_value

Please provide type hint for the parameter: tb

algorithms-keeper · 2023-10-26T02:21:10Z

machine_learning/auto-diff.py

+    def __exit__(self, exc_type, exc_value, tb):
+        self.enabled = False
+
+    def add_operation(


Please provide return type hint for the function: add_operation. If the function does not return a value, please provide the type hint as: def function() -> None:

imSanko

Have you checked with Pre --commits

PoojanSmart · 2023-10-26T04:12:05Z

I am getting conflicting suggestions from mypy and ruff:

From mypy:

machine_learning\automatic_differentiation.py:45: error: Module "typing" has no attribute "Self"  [attr-defined]
machine_learning\automatic_differentiation.py:45: note: Use `from typing_extensions import Self` instead

And If I change typing to typing_extensions then ruff shows:

machine_learning\automatic_differentiation.py:45:1: UP035 [*] Import from `typing` instead: `Self`
   |
43 | from enum import Enum
44 | from types import TracebackType
45 | from typing_extensions import Self
   | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ UP035
46 |
47 | import numpy as np
   |
   = help: Import from `typing`

PoojanSmart · 2023-10-26T04:15:00Z

Apart from that I am not getting these ones in my local:

machine_learning/automatic_differentiation.py:195: error: Access to generic instance variables via class is ambiguous [misc] machine_learning/automatic_differentiation.py:195: error: Incompatible return value type (got "GradientTracker", expected "Self") [return-value]

@imSanko / @cclauss can you please help?

cclauss · 2023-10-26T05:24:49Z

machine_learning/automatic_differentiation.py

+
+from enum import Enum
+from types import TracebackType
+from typing import Self


Ruff was upgraded for Py3.12 but mypy has not yet been upgraded.

Suggested change

from typing import Self

from typing_extensions import Self # noqa: UP035

algorithms-keeper

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Contributing guidelines

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Type hints

doctest

unittest

pytest

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machine_learning/automatic_differentiation.py

algorithms-keeper · 2023-10-26T05:51:52Z

machine_learning/automatic_differentiation.py

+    def add_output(self, output: Variable) -> None:
+        self.output = output
+
+    def __eq__(self, value) -> bool:


Please provide type hint for the parameter: value

algorithms-keeper

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machine_learning/automatic_differentiation.py

algorithms-keeper · 2023-10-26T05:58:21Z

machine_learning/automatic_differentiation.py

+    def add_output(self, output: Variable) -> None:
+        self.output = output
+
+    def __eq__(self, value) -> bool:


Please provide type hint for the parameter: value

algorithms-keeper

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Repository:

Contributing guidelines

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Python:

Formatted string literals (f-strings)

Type hints

doctest

unittest

pytest

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machine_learning/automatic_differentiation.py

algorithms-keeper · 2023-10-26T06:05:39Z

machine_learning/automatic_differentiation.py

+    def add_output(self, output: Variable) -> None:
+        self.output = output
+
+    def __eq__(self, value) -> bool:


Please provide type hint for the parameter: value

for more information, see https://pre-commit.ci

cclauss

This looks really cool. I hope the comments help.

cclauss · 2023-10-26T12:54:24Z

machine_learning/automatic_differentiation.py

+...     c = a + b
+...     d = a * b
+...     e = c / d
+>>> print(tracker.gradient(e, a))


Let's try to avoid print() in doctests.

Suggested change

>>> print(tracker.gradient(e, a))

>>> tracker.gradient(e, a)

Repeat below.

cclauss · 2023-10-26T12:54:51Z

machine_learning/automatic_differentiation.py

+
+Reference: https://en.wikipedia.org/wiki/Automatic_differentiation
+
+Author: Poojan smart


Your choice...

Suggested change

Author: Poojan smart

Author: Poojan Smart

cclauss · 2023-10-26T12:55:52Z

machine_learning/automatic_differentiation.py

+>>> print(tracker.gradient(e, m))
+None


Suggested change

>>> print(tracker.gradient(e, m))

None

>>> tracker.gradient(e, m) is None

True

cclauss · 2023-10-26T12:59:03Z

machine_learning/automatic_differentiation.py

+Email: smrtpoojan@gmail.com
+
+Examples:
+


In general, it is better to put doctests into the Class docstring or function/method docstring so that if someone copies the Class, function, or method then the tests come along for the ride.

cclauss · 2023-10-26T13:03:57Z

machine_learning/automatic_differentiation.py

+    Class represents n-dimensional object which is used to wrap
+    numpy array on which operations will be performed and gradient
+    will be calculated.


We get 88 characters per line max so let's use them to get to fewer lines.

Suggested change

Class represents n-dimensional object which is used to wrap

numpy array on which operations will be performed and gradient

will be calculated.

Class represents n-dimensional object which is used to wrap numpy array on which

operations will be performed and the gradient will be calculated.

cclauss · 2023-10-26T13:22:58Z

machine_learning/automatic_differentiation.py

+                derivative = np.ones_like(params[0].numpy(), dtype=np.float64)
+            else:
+                derivative = -np.ones_like(params[1].numpy(), dtype=np.float64)
+        elif operation == OpType.MUL:
+            derivative = (


Suggested change

derivative = np.ones_like(params[0].numpy(), dtype=np.float64)

else:

derivative = -np.ones_like(params[1].numpy(), dtype=np.float64)

elif operation == OpType.MUL:

derivative = (

return np.ones_like(params[0].numpy(), dtype=np.float64)

else:

return -np.ones_like(params[1].numpy(), dtype=np.float64)

if operation == OpType.MUL:

return (

cclauss · 2023-10-26T13:23:50Z

machine_learning/automatic_differentiation.py

+            derivative = (
+                params[1].numpy().T if params[0] == param else params[0].numpy().T
+            )
+        elif operation == OpType.DIV:


Suggested change

elif operation == OpType.DIV:

if operation == OpType.DIV:

cclauss · 2023-10-26T13:24:30Z

machine_learning/automatic_differentiation.py

+                derivative = 1 / params[1].numpy()
+            else:
+                derivative = -params[0].numpy() / (params[1].numpy() ** 2)
+        elif operation == OpType.MATMUL:
+            derivative = (


Suggested change

derivative = 1 / params[1].numpy()

else:

derivative = -params[0].numpy() / (params[1].numpy() ** 2)

elif operation == OpType.MATMUL:

derivative = (

return 1 / params[1].numpy()

else:

return -params[0].numpy() / (params[1].numpy() ** 2)

if operation == OpType.MATMUL:

return (

cclauss · 2023-10-26T13:25:10Z

machine_learning/automatic_differentiation.py

+        elif operation == OpType.POWER:
+            power = operation.other_params["power"]
+            derivative = power * (params[0].numpy() ** (power - 1))


Suggested change

elif operation == OpType.POWER:

power = operation.other_params["power"]

derivative = power * (params[0].numpy() ** (power - 1))

if operation == OpType.POWER:

power = operation.other_params["power"]

return power * (params[0].numpy() ** (power - 1))

cclauss · 2023-10-26T13:26:09Z

machine_learning/automatic_differentiation.py

+        elif operation == OpType.POWER:
+            power = operation.other_params["power"]
+            derivative = power * (params[0].numpy() ** (power - 1))
+        return derivative


Do we want to raise ValueError("Invalid operation") here?

…o auto-diff

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…o auto-diff

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Repository:

Contributing guidelines

Project Euler solution guidelines

Python:

Formatted string literals (f-strings)

Type hints

doctest

unittest

pytest

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algorithms-keeper · 2023-10-27T02:48:55Z

machine_learning/automatic_differentiation.py

+        self.op_type = op_type
+        self.other_params = {} if other_params is None else other_params
+
+    def add_params(self, params: list[Variable]) -> None:


As there is no test file in this pull request nor any test function or class in the file machine_learning/automatic_differentiation.py, please provide doctest for the function add_params

algorithms-keeper · 2023-10-27T02:48:55Z

machine_learning/automatic_differentiation.py

+    def add_params(self, params: list[Variable]) -> None:
+        self.params = params
+
+    def add_output(self, output: Variable) -> None:


As there is no test file in this pull request nor any test function or class in the file machine_learning/automatic_differentiation.py, please provide doctest for the function add_output

algorithms-keeper · 2023-10-27T02:48:55Z

machine_learning/automatic_differentiation.py

+    def add_output(self, output: Variable) -> None:
+        self.output = output
+
+    def __eq__(self, value) -> bool:


As there is no test file in this pull request nor any test function or class in the file machine_learning/automatic_differentiation.py, please provide doctest for the function __eq__

Please provide type hint for the parameter: value

cclauss

Awesome contribution! Thanks for doing this.

Added automatic differentiation algorithm

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sorted requirement.text dependency

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PoojanSmart and others added 4 commits October 27, 2023 08:08

Merge branch 'TheAlgorithms:master' into auto-diff

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refactoring

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Merge branch 'auto-diff' of https://github.com/PoojanSmart/Python int…

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resolved ruff

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isidroas mentioned this pull request Jan 25, 2025

dh isidroas/Python#1

Closed

14 tasks

	from typing import Self
	from typing_extensions import Self # noqa: UP035


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

		Author: Poojan smart

		Email: smrtpoojan@gmail.com

		Examples:

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PoojanSmart commented Oct 26, 2023

Describe your change:

Checklist:

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