Fixes issue #12108: Added Ridge Regression to Machine Learning #12246

Harmanaya · 2024-10-22T17:56:00Z

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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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algorithms-keeper · 2024-10-22T17:56:06Z

machine_learning/ridge_regression.py

+
+
+class RidgeRegression:
+    def __init__(self, alpha=0.001, lambda_=0.1, iterations=1000):


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: alpha

Please provide type hint for the parameter: lambda_

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-22T17:56:06Z

machine_learning/ridge_regression.py

+        self.iterations = iterations
+        self.theta = None
+
+    def feature_scaling(self, X):


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

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

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

algorithms-keeper · 2024-10-22T17:56:06Z

machine_learning/ridge_regression.py

+        # Avoid division by zero for constant features (std = 0)
+        std[std == 0] = 1  # Set std=1 for constant features to avoid NaN
+
+        X_scaled = (X - mean) / std


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:06Z

machine_learning/ridge_regression.py

+        X_scaled = (X - mean) / std
+        return X_scaled, mean, std
+
+    def fit(self, X, y):


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

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

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: y

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+        :param X: Input features, shape (m, n)
+        :param y: Target values, shape (m,)
+        """
+        X_scaled, mean, std = self.feature_scaling(X)  # Normalize features


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+            gradient = (X_scaled.T.dot(error) + self.lambda_ * self.theta) / m
+            self.theta -= self.alpha * gradient  # Update weights
+
+    def predict(self, X):


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

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

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+        :param X: Input features, shape (m, n)
+        :return: Predicted values, shape (m,)
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data
+        return X_scaled.dot(self.theta)
+
+    def compute_cost(self, X, y):


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

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

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: y

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+        :param y: Target values, shape (m,)
+        :return: Computed cost
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

machine_learning/ridge_regression.py

+        ) * np.sum(self.theta**2)
+        return cost
+
+    def mean_absolute_error(self, y_true, y_pred):


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

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

Please provide type hint for the parameter: y_true

Please provide type hint for the parameter: y_pred

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

algorithms-keeper

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

Contributing guidelines

Project Euler solution guidelines

Python:

Formatted string literals (f-strings)

Type hints

doctest

unittest

pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

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algorithms-keeper · 2024-10-22T18:29:02Z

machine_learning/ridge_regression.py

+        scaled_features = (features - mean) / std
+        return scaled_features, mean, std
+
+    def fit(self, x: np.ndarray, y: np.ndarray) -> None:


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

Please provide descriptive name for the parameter: x

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T18:29:02Z

machine_learning/ridge_regression.py

+            gradient = (x_scaled.T.dot(error) + self.lambda_ * self.theta) / m
+            self.theta -= self.alpha * gradient  # Update weights
+
+    def predict(self, x: np.ndarray) -> np.ndarray:


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

Please provide descriptive name for the parameter: x

algorithms-keeper · 2024-10-22T18:29:02Z

machine_learning/ridge_regression.py

+        x_scaled, _, _ = self.feature_scaling(x)  # Scale features using training data
+        return x_scaled.dot(self.theta)
+
+    def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:


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

Please provide descriptive name for the parameter: x

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T18:29:02Z

machine_learning/ridge_regression.py

+        ) * np.sum(self.theta**2)
+        return cost
+
+    def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:


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

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

Harmanaya · 2024-10-22T19:23:38Z

@cclauss Could you please review this PR when you get a chance?

Fix issue TheAlgorithms#12108: Added Ridge Regression to Machine Lear…

2293d37

…ning

algorithms-keeper bot added require descriptive names This PR needs descriptive function and/or variable names require tests Tests [doctest/unittest/pytest] are required require type hints https://docs.python.org/3/library/typing.html labels Oct 22, 2024

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Harmanaya and others added 3 commits October 22, 2024 23:49

Added type hints and minor case improvements

e9ef03e

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Resolved ruff checks

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algorithms-keeper bot removed the require type hints https://docs.python.org/3/library/typing.html label Oct 22, 2024

algorithms-keeper bot reviewed Oct 22, 2024

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Added doctests

61945d0

algorithms-keeper bot removed require descriptive names This PR needs descriptive function and/or variable names require tests Tests [doctest/unittest/pytest] are required labels Oct 22, 2024

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

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algorithms-keeper bot added the tests are failing Do not merge until tests pass label Oct 22, 2024

Harmanaya and others added 5 commits October 23, 2024 00:14

Resolved ruff checks

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

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Resolved ruff checks

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

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algorithms-keeper bot removed the tests are failing Do not merge until tests pass label Oct 22, 2024



		class RidgeRegression:
		def __init__(self, alpha=0.001, lambda_=0.1, iterations=1000):

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Fixes issue #12108: Added Ridge Regression to Machine Learning #12246

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Fixes issue #12108: Added Ridge Regression to Machine Learning #12246

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Harmanaya commented Oct 22, 2024

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