added ridge regression #12250
added ridge regression #12250
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require descriptive names
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| import pandas as pd | ||
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| class RidgeRegression: | ||
| def __init__(self, alpha=0.001, regularization_param=0.1, num_iterations=1000): |
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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: regularization_param
Please provide type hint for the parameter: num_iterations
| self.theta = None | ||
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| def feature_scaling(self, X): |
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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/model.py, please provide doctest for the function feature_scaling
Please provide descriptive name for the parameter: X
Please provide type hint for the parameter: X
| # avoid division by zero for constant features (std = 0) | ||
| std[std == 0] = 1 # set std=1 for constant features to avoid NaN | ||
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| X_scaled = (X - mean) / std |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled
| return X_scaled, mean, std | ||
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| def fit(self, X, y): |
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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/model.py, please provide doctest for the function fit
Please provide descriptive name for the parameter: X
Please provide type hint for the parameter: X
Please provide descriptive name for the parameter: y
Please provide type hint for the parameter: y
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| def fit(self, X, y): | ||
| X_scaled, mean, std = self.feature_scaling(X) |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled
| self.theta -= self.alpha * gradient # updating weights | ||
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| def predict(self, X): |
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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/model.py, please provide doctest for the function predict
Please provide descriptive name for the parameter: X
Please provide type hint for the parameter: X
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| def predict(self, X): | ||
| X_scaled, _, _ = self.feature_scaling(X) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled
| return X_scaled.dot(self.theta) | ||
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| def compute_cost(self, X, y): |
There was a problem hiding this comment.
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/model.py, please provide doctest for the function compute_cost
Please provide descriptive name for the parameter: X
Please provide type hint for the parameter: X
Please provide descriptive name for the parameter: y
Please provide type hint for the parameter: y
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| def compute_cost(self, X, y): | ||
| X_scaled, _, _ = self.feature_scaling(X) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled
| return cost | ||
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| def mean_absolute_error(self, y_true, y_pred): |
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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/model.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
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require descriptive names
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| @@ -0,0 +1,90 @@ | |||
| import numpy as np | |||
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An error occurred while parsing the file: machine_learning/ridge_regression/model.py
Traceback (most recent call last):
File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse
reports = lint_file(
^^^^^^^^^^
libcst._exceptions.ParserSyntaxError: Syntax Error @ 1:1.
tokenizer error: no matching outer block for dedent
import numpy as np
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algorithms-keeper
bot
added
require descriptive names
There was a problem hiding this comment.
Click here to look at the relevant links ⬇️
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algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| self.num_iterations: int = num_iterations | ||
| self.theta: np.ndarray = None | ||
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| def feature_scaling( |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function feature_scaling
| self.theta: np.ndarray = None | ||
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| def feature_scaling( | ||
| self, X: np.ndarray |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: X
| x_scaled = (x - mean) / std | ||
| return x_scaled, mean, std | ||
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| def fit(self, x: np.ndarray, y: np.ndarray) -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function fit
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
| ) / m | ||
| self.theta -= self.alpha * gradient # updating weights | ||
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| def predict(self, X: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function predict
Please provide descriptive name for the parameter: X
| self.theta -= self.alpha * gradient # updating weights | ||
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| def predict(self, X: np.ndarray) -> np.ndarray: | ||
| X_scaled, _, _ = self.feature_scaling(X) |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled
| X_scaled, _, _ = self.feature_scaling(X) | ||
| return X_scaled.dot(self.theta) | ||
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| def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.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
| ) * np.sum(self.theta**2) | ||
| return cost | ||
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| def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function mean_absolute_error
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